Modeling Microscopic Car-Following Strategy of Mixed Traffic to...

Research ArticleModeling Microscopic Car-Following Strategy ofMixed Traffic to Identify Optimal Platoon Configurations forMultiobjective Decision-Making

Mudasser Seraj 1 Jiangchen Li 1 and Zhijun Qiu 12

1Department of Civil and Environmental Engineering University of Alberta Edmonton Alberta Canada2Intelligent Transportation System Research Center Wuhan University of Technology Wuhan Hubei China

Correspondence should be addressed to Zhijun Qiu zhijunqiuualbertaca

Received 31 May 2018 Revised 16 August 2018 Accepted 10 September 2018 Published 27 September 2018

Guest Editor Guoyuan Wu

Copyright copy 2018 Mudasser Seraj et alThis is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Microscopic detail of complex vehicle interactions in mixed traffic involving manual driving system (MDS) and automateddriving system (ADS) is imperative in determining the extent of response by ADS vehicles in the connected automated vehicle(CAV) environment In this context this paper proposes a naıve microscopic car-following strategy for a mixed traffic streamin CAV settings and specified shifts in traffic mobility safety and environmental features Additionally this study explores theinfluences of platoon properties (ie intra-platoon headway inter-platoon headway and maximum platoon length) on trafficstream characteristics Different combinations of MDS and ADS vehicles are simulated in order to understand the variations ofimprovements induced by ADS vehicles in a traffic stream Simulation results reveal that grouping ADS vehicles at the front oftraffic stream to apply Cooperative Adaptive Cruise Control (CACC) based car-following model will generate maximummobilitybenefits for upstream vehicles Both mobility and environmental improvements can be realized by forming long closely spacedADS vehicles at the cost of reduced safety To achieve balanced mobility safety and environmental advantages from mixed trafficenvironment dynamically optimized platoon configurations should be determined at varying traffic conditions and ADS marketpenetrations

1 Introduction

Vehicles with diverse levels of integrated connectivity andautomated control systems are considered to be pushinga technological leap towards diminished trip delay fuelefficiency reduced emission and enhanced safety of roadtraffic Although a purely automated vehicle-based trafficstream could take decades to become a reality introducingand gradually increasing market shares of automated drivingsystem-based vehicles in traffic streams would enable us toperceive and harness the potential gains from these technolo-gies Varied perceptions of mixed traffic streams and theircollaborative motion dynamics hindered both researchersand practitioners from progressing with these technologiesFurthermore the ideal compositions of automated vehiclesin mixed traffic conditions remain unfamiliar to most Inresponse to these problems this study proposes a simple

yet effective car-following strategy for mixed traffic streamand measures the resulting impact on mobility safety andthe environment Additionally the car-following strategyinvolved platoon development in a connected automatedvehicle (CAV) environment and the study explores variousplatoon configurations to determine platoon parameters atdifferent traffic states to obtain utmost benefits

Numerous studies have been conducted by acclaimedresearchers and practitioners to interpret the complexdynamics of combined traffic movements [1ndash6] While thesestudies transcended in conceiving the levels of impact of auto-mated driving technologies through simplified to complexmacroscopic and mesoscopic modeling the motivation ofthe present was shaped by the need of modeling microscopiccar-following behavior in heterogeneous traffic in order tostudy macroscopic consequences from mobility safety andenvironmental perspectives With that intention this study

HindawiJournal of Advanced TransportationVolume 2018 Article ID 7835010 15 pageshttpsdoiorg10115520187835010

2 Journal of Advanced Transportation

gives insights into a wide variation of distinct forms ofimpact while simulating automated-control-enabled vehicleson ideal locations and distributions along traffic streamThese insights into mixed traffic movements and platooncharacteristics will motivate researchers to consider otherunattended aspects of mixed traffic dynamics (eg lane-changing gap acceptance and merging) in order to rectifyperceived benefits Similarly traffic operational authoritiescan take these lessons into account to impose different controlstrategies (eg dynamic aggregated controls for manuallydriven vehicles dynamic personalized controls on connectedvehicles) on traffic to attain maximum improvements withregard to reduced travel time collision rates greenhouse gasemissions etc

The rest of the paper is organized as followsThe next sec-tion summarizes the existing literature on car-followingmod-els and strategies for mixed traffic and also touches on studiesthat identify the different form of impact that automatedvehicles have on traffic The proposed car-following strategyis described in the following section The description ofsimulation procedures and the discussion on obtained resultsare covered respectively in two subsequent sections Thefollowing section proposed an approach to obtain dynamicoptimal platoon configuration for specific traffic state Thelast section provides the synopsis of findings of the study andalso gives recommendations for future research

2 Literature Review

As the primary aim of this study relates to car-followingstrategy for mixed traffic environment the literature relatedto car-following models for both forms (ie manual auto-mated) of driving system is explored here Numerous micro-scopic car-following models have been proposed to imitatedriving pattern of manual driving system [7ndash12] Among theproposed stimulus-response-based car-following models theintelligent driver model (IDM) is widely used in literatureto depict manual driving dynamics The ability of thismodel to define numerous microscopic (eg desired velocityand accelerationdeceleration limits) and macroscopic (egcapacity capacity drop and fundamental diagram) phe-nomena made it the prevalent model On the other handdue to rapid growth of CAV technology the longitudinalcontrol models for automated vehicles were also examined byresearchers [13ndash18] These studies provide us with structuresto work on car-following strategy in mixed traffic environ-ment and identify the extents of potential paradigm shifts

A clear distinction of the driving system is dictated bythe operational authority While the manual driving system(MDS) represents driving systems controlled by humans themotion dynamics of vehicles with the automated drivingsystem (ADS) are mandated by distinct levels of automationADS vehiclesrsquo longitudinal movements are commonly por-trayed with adaptive cruise control (ACC) and cooperativeadaptive cruise control (CACC) Many studies have analyzedthe contributions of longitudinal control system of ADSvehicles on traffic mobility [19ndash28] While mobility was themain focus of these studies the impact of traffic movementfrom safety and environmental perspectivewas often ignored

Yeo et al [29] proposed an integrated car-following and lanechangingmodel to performmicrosimulation of oversaturatedfreeway traffic The proposed algorithm considered complexdynamic interactions at a microscopic level to replicatevehicle movements However the aptitude of this model tocapture possible consequences was not tested Wang et al[30] proposed a car-following control for autonomous vehicleand identified the impact focusing mainly on traffic flowcharacteristics Liberis et al [31] took amacroscopic approachto identify traffic mobility parameters in a heterogeneoustraffic environment The authors used the market penetrationrate of connected vehicles to estimate traffic states Moreoverother researchers studied the impact of introducing ADSbased vehicles with conventional vehicles [32ndash36] on flowand mobility Reviews of these studies provide us with theopportunity to constructively examine the contributions ofearlier studies identify the necessities to improve currentknowledge and uncover the latent insights to progresspromptly with CA technology

While the mobility attributes of traffic flow were widelydiscussed in many studies the safety and environmentalaspects which are equally if not more important wererelatively unexplored by a majority of the studies The impactof automated vehicles on both safety and mobility was dis-cussed by Fernandes andNunes [37]They studied platooningof ADS vehicles with different communication schemes atvarious flow rates to improve roadway capacity Severalstudies assessed the safety aspects of CAV based trafficAccording to the National Highway Traffic Safety Admin-istration (NHTSA) a complete adaptation of CAV basedtraffic movements would annually prevent 439000ndash615000crashes [38] Li et al [39] evaluated the impact of CACCcontrol on reducing rear-end collisions on freeways Thestudy shows a reduction in safety improvements with increas-ing market share of ADS vehicles Rahman and Abdel-Aty[40] compared potential improvement in longitudinal safetydue to varying market penetration of connected vehiclesAccording to the analysis presented the managed-lane CACcontrol outperformed multilane control with regard to traf-fic safety The report of Zabat et al [41] stated that thepresence of boundary layer along closely spaced vehicleswould reduce aerodynamic drag resulting in reduced fuelconsumption and less emission Platoon-wide environment-friendly CACC system was studied by Wang et al [42] andtheir objective assessment attained 2 fuel saving with 17emission reductions Mamouei et al [43] argued that fuel-economy based ACC control model would not lead to highlyconservative driving dynamics of traffic

Although the reviewed studies had remarkable contri-butions that helped to clarify the roles and influences ofADS vehicles in traffic the inadequacy of multiobjectivedecision-making approach to address ADS vehiclesrsquo poten-tial has influenced this research further to investigate thecomplex interdependencies of mixed traffic This researchseeks to contribute on three research gaps identified fromthe literature These gaps are (i) the significance of ADSvehiclesrsquo position and distributions along traffic stream (ii)the variations of traffic flow attributes (ie mobility safetyand environmental) resulting from structural changes of

Journal of Advanced Transportation 3

Leading Vehicleamp Subject Vehicle

Combinations

ADS amp MDS

MDS amp MDS

MDS amp ADS

ADS amp ADS

IDM

IDM

CACC

ACC

Subject Vehiclersquos Car-Following Behavior

LeadingVehicle

SubjectVehicle

Figure 1 Proposed car-following strategy for mixed traffic

CACC platoons and (iii) adjusting platoon configurationsdynamically to obtain balanced benefits from consideredtraffic attributes

3 Proposed Car-Following Strategy forIndividual Vehicle

Interactions and behaviors of vehicles at microscopic levelshave macroscopic implications Factors like maximum accel-erations comfortable decelerations preferred timeheadwaysetc are directly linked to traffic mobility safety and envi-ronmental aspects Car-following models provide individualvehiclesrsquo acceleration from dynamic interactions with adja-cent vehicles control constraints to generate velocity andposition to determine vehicle trajectory The car-followingmodels of vehicles in mixed traffic were schemed hereto simulate real-traffic movements As mentioned earlierthe existence of two types of vehicle driving system wasconsidered for combined traffic The proposed driving strat-egy identified all potential combinations of leading vehicleand subject vehicle based on driving systems to determinesuitable car-following models

The proposed car-following mechanism presumed thatMDS vehicles would maintain conventional car-followingbehavior irrespective of the leading vehiclersquos driving system[Figure 1] In this regard intelligent driver model (IDM)[10] was chosen to represent manual driversrsquo car-followingbehavior Extensive applications of thismodel across differentstudies developed this model as a perfect example to simulateMDS vehiclesrsquo car-following behavior An enhanced versionof traditional IDM was used to determine a realistic longitu-dinal control decision of MDS vehicles (see (1)) Discretizedkinematic equations were used for all vehicles irrespectiveof the driving system to determine vehiclersquos velocity andposition (see (2) and (3))

V (119905 + Δ119905) = 119886[[1 minus (V (119905)

V0)4

minus (1199040 + max [0 V (119905) times 119879 + (V (119905) times ΔV (119905)) 2radic119886119887]119904 )

2]]

(1)

V (119905 + Δ119905) = V (119905) + 119886 (119905) times Δ119905 (2)

119901 (119905 + Δ119905) = 119901 (119905) + V (119905) times Δ119905 + 12119886 (119905) times Δ1199052 (3)

where V is acceleration of vehicle (ms2) v is velocity(ms) 119901 is vehicle position (m) 119886 is maximum acceleration(set as 3 ms2) V0 is desired velocity (35ms) 1199040 is leading gapat jam density (5 m) 119887 is desirable deceleration (-3 ms2) ΔVis velocity difference with leading vehicle and 119879 is preferredtime headway (25 sec)

To demonstrate the car-following mechanism of ADSvehicles both ACC and CACC based car-following wereimplemented A MDS based leading vehicle would promptADS vehicle to follow ACC with relatively high preferredtime headway Provided that the leading vehicle had ADSthe subject vehicle would choose CACC based car-followingWhether the subject vehicle would join the CACC platoondepends on the leading vehiclersquos platoon ID Platoon ID isan identification number assigned to an ADS vehicle thatrepresents its order of position in the platoon If an ADSvehicle is a part of a platoon it will have a fixed platoon IDotherwise its platoon ID will contain a platoon ID = 0 (zero)While travelling through roads the built-in communicationtechnology of ADS vehicles would enable them to identify theleading vehicles driving system as well as platoon ID If theplatoon ID of the leading vehicle was equal to the maximumplatoon length then the subject vehicle would form a newplatoonbymaintaining inter-platoon headway and as a leaderof the newplatoon In addition if the leading vehiclersquos platoonID was lower than maximum platoon length the subjectvehicle would join the platoon by maintaining intra-platoonheadway We adopted the ACC and CACC car-followingmodels developed in [17] The accelerations of the subject


vehicle were determined with respect to relative position andvelocity The following equation was used to determine theacceleration of the subject vehicle

V (119905 + Δ119905) = 1198961 (Δ119901 (119905) minus V (119905) times 119879 minus 1199040) + 1198962ΔV (119905) (4)

where 1198961 1198962 are control constants for relative distanceand speed respectively (1198961 1198962 gt 0) and Δ119901(119905) is positiondifference with leading vehicle The stability of the proposedACC system was proved in [17] Suitable 1198961 1198962 values werechosen according to [17] to implement realistic simula-tion accounting for the sensitivity of these factors Similarapproach of dual consensus was taken by Wang et al [18]where both position and velocity consensus were consideredto determine accelerationdeceleration decision While bothACC and CACC car-following models used (4) to determineacceleration values for ADS vehicles higher preferred timeheadways (119879 = 15 sec) distinguish ACC mode with CACCmode (119879 le 10 sec)

4 Simulation Procedures

Amicroscopic simulation structure was built onMATLAB toreplicate vehiclesrsquo motion on a two-lane directional highwayThe simulation environment was grounded on numericalanalysis-based car-following behavior All previously men-tioned motion dynamic equations were coded to follow pro-posed car-following strategy A stream of 20 vehicles follow-ing a controlled leading vehicle was simulated for numerousscenarios The time headways between the vehicles in trafficstream were manipulated to simulate distinct traffic flowrates In the simulation environment the acceleration of thefirst vehicle was controlled consciously to generate multipleshockwaves and to observe the reaction of the vehicles behindit Each simulation ran for 1000 time steps and 20 timesfor each scenario The preferred time headway (T) for MDSvehicles was considered as a normally distributed variablewith mean value of 25 sec and standard deviation of 05 secMultiple runs for each scenario were executed to ensure thatthe obtained outcome was free from anomaly The averagevalues of 20 runs were listed for analysis In the beginningof the simulation the first vehicle was travelling at 25 ms for210 time steps and then accelerated at 0167 ms3 rate for 60time steps followed by steady state (accelerationdecelerationrate = 0 ms3 velocity = 35 ms) for 120 time steps Finallythe controlled vehicle at front decelerated again at 0167ms3rate for 60 time steps to regain 25ms velocity and movedwith constant velocity for the remaining time steps Themaximum velocity was set to 35 ms The combinationsgenerated from the following variables sets were simulatedto represent various traffic states encountered in roadways aswell as to identify the variations on improvements obtainedby introducing the ADS vehicles in the connected automatedvehicle (CAV) environment

(a) Initial flow rate (vehhr) (i) 1400 (ii) 1800 (iii) 2400(b) ADS market share () (i) 25 (ii) 50 (iii) 75(c) Maximum platoon length (vehicle) (i) 3 (ii) 4 (iii) 5

(iv) 6

(d) Inter-platoon headway (sec) (i) 2 (ii) 4 (iii) 6 (iv) 8

(e) Intra-platoon headway (sec) (i) 05 (ii) 075 (iii) 10(iv) 125

The variables sets were restricted by the above values tolimit the analysis and discussions within manageable rangeswhile covering awide range of variations in traffic conditionsPlatoon parameters (ie maximum platoon length inter-platoon headway and intra-platoon headway) were variedwithin reasonable ranges to identify observable trends Twodistinct driving systems were simulated by assigning specificvalues of driving system (0 for MDS 1 for ADS) Thedriving system values assigned for vehicles were used toimplement the proposed car-following strategy on the CAVenvironment Assigned driving systemvalueswere also usefulto adopt proper sets of motion dynamic equations

5 Analysis Results and Findings

51 Impact of ADS Vehicles Location and Distribution Beforeanalyzing the mobility safety and environmental aspectsof ADS vehicles on traffic the influences of ADS vehicleslocation and distribution in traffic stream were explored Itwas hypothesized that the positions of ADS vehicles in trafficstream dictated their impacts on the remaining vehicles Toprove this hypothesis the proposed car-following strategywas simulated by allotting ADS vehicles at diverse com-binations of positions with gradually increasing the initialflow rate and ADS market share To clearly comprehend thesignificance of vehicle position more clearly and to reduce theintricacy of comprehension only two features were analyzedacceleration fluctuation of MDS vehicle in the vehicle groupand variations ofmaximum traffic flow at varying traffic stateSince numerous combinations of ADS vehiclesrsquo distributionare viable at different penetration rates of ADS vehicles only ahandful of combinations were selected to cover most possiblevariations

Initially these distributions were generated by placingADS vehicles as far apart as possible (-- Comb-1) inthe vehicle stream while maintaining target ADS marketshare Gradually ADS vehicles were grouped together indifferent combinations The purpose of placing ADS vehiclesin such an order was to visualize and measure the impactof ADS vehicles location and distribution along the vehiclestream The combinations are listed in Table 1 The firstcolumn of the table shows percentages of ADS vehicles inthe traffic stream The numbers in second column of Table 1identify the position ID of ADS vehicles in the traffic streamOther vehicles except the positions mentioned in the tablewere MDS vehicles The last column of the table providesdistinct combination name of each distribution of ADSvehicles These combinations were simulated on developedsimulation environment by virtually placing ADS vehiclesin the mentioned position IDs of the vehicle stream and byfollowing proposed car-following strategy for mixed trafficThe listed combinations in Table 1 were assumed to representvarying ranges of ADS vehicles distribution on vehicle groupAnalyzing these sets of vehicle location and distribution


Table 1 List of ADS vehicle combinations simulated for different market penetrations

ADSMarket Share Distribution of ADS vehicles (position) Combination Name

25

4 8 12 16 20 25 Comb-14 5 1011 16 25 Comb-25 6 7 13 14 25 Comb-39 10 1112 17 25 Comb-42 3 4 5 6 25 Comb-5

16 17 18 19 20 25 Comb-6

50

2 4 6 8 10 12 14 16 18 20 50 Comb -12 3 6 7 10 11 14 15 18 19 50 Comb -22 3 4 8 9 10 14 15 16 20 50 Comb -32 3 4 5 10 11 12 13 18 19 50 Comb -42 3 4 5 6 7 8 9 1011 50 Comb -5

752 3 4 6 7 8 10 11 12 14 15 16 18 19 20 75 Comb-12 3 4 5 6 910 11 12 13 16 17 18 19 20 75 Comb-22 3 4 5 6 9 10 11 12 13 16 17 18 19 20 75 Comb-3

provided the opportunity to shed light on resulting impactsdue to ADS vehiclesrsquo position on traffic stream

From the analysis the simulation outcomes of the ini-tial flow rate of 1800 vehhr with different ADS marketpenetration are provided in Figure 2 to demonstrate theinfluences of ADSvehicles position and distribution along thestream from both microscopic and macroscopic perspectiveFigure 2(a) represents the variations of maximum flow ratesresulting from the proposed car-following strategy at listedcombinations Figure 2(b) shows the average coefficient ofvariations (CoV) of acceleration of MDS vehicles in thesimulated vehicle stream Boxplots for a specific combinationwere plotted from the maximum flow rate and average CoVof acceleration data of simulated scenarios with varyingplatoon parameters as listed before Macroscopic analysison maximum flow rates at different ADS vehicle shares(Figure 2(a)) identified the pattern of gradual increment withincreasing ADS shares in the traffic Observations of differentcombinations revealed that combinations with scattered ADSvehicles lead to lower maximum flow rates in comparisonto combinations with grouped ADS vehicles Additionallygrouping ADS vehicles at the front of the vehicle stream(ie 25 Comb-6 50 Comb-5 and 75 Comb-3) resultedin 67-115 higher maximum flow rates in comparison tothe scattered distribution of ADS vehicles (ie 25 Comb-1 50 Comb-1 and 75 Comb-1) Analysis on microscopiccharacteristics ofMDSvehicleswas undertaken bymeasuringthe averageCoVof acceleration at differentmarket shares andcombinations of ADS vehicles The resulting analysis showeda gradual decreasing CoV of acceleration with increasingshares of ADS vehicles Similar to macroscopic analysis themaximum amount of decrease in CoV (169ndash663) wasobserved from combinations with ADS vehicles at the frontof the traffic stream grouped together Specific analysis onmaximum platoon lengthrsquos influence on acceleration fluctu-ations of MDS vehicles revealed that increasing maximumplatoon length reduced the average coefficient of variation ofacceleration for ADS vehicles Similar analysis on the othertwo platoon parameters (ie inter-platoon headway and

intra-platoon headway) demonstrated a reciprocal relationwith acceleration fluctuations (increasing inter- and intra-platoon headway increased the average CoV of acceleration)

The analysis of the remaining initial flow rates and ADSmarket share revealed that creating platoons of ADS vehiclesby positioning them at the front of traffic stream wouldbe beneficial to the rest of vehicles in the traffic streamFurthermore increasing market shares of ADS vehiclescould gradually reduce the acceleration fluctuation of MDSvehicles Finally increasing the flow rates could inverselyinfluence traffic flow improvements with a specific ADSlocation and distribution combination The notion of trafficflow improvements guided the authors in this study toexploremobility improvement potentials of the proposed car-following strategy by placing ADS vehicles at ideal positionsalong the traffic stream

52 Impact on Mobility Since creating platoons of ADSvehicles was found to be the most effective way of acquiringassociated benefits influences of ADS vehicles on trafficmobility were examined with respect to three key variablesof platooning intra-platoon headway inter-platoon headwayand maximum platoon length Combinations of these threevariables within listed sets were utilized to generate variousplatoon scenarios for simulation and analysis The impactof these platoon structures on mobility was measured andcompared with the help of two parameters Average TravelTime (ATT) (see (5)) and Average Travel Distance (ATD)(see (6)) Later case scores were computed by providingequal weights to ATT and ATD (see (7)) Different cases ofplatoon configurationswere simulated and evaluated throughcase scores Higher dispersion from base-case (0 ADSshare) scores indicated higher mobility improvements Theobjective of this analysis was to identify the optimal platoonconfiguration to improve mobility by increasing ATD andreducing ATTThe following equations were used to identifythe mobility gains

119860119879119879 = sum119869119895=1119860119879119879119895119869 = 119868sum119894=1

(119901119894119895 minus 119901119894119895minus1)V119894119895

(5)


ADS Share 25

2 3 4 5 61Combinations

1800

2000

2200

2400

2600

2800

3000

3200

Max

Flo

w R

ate (

veh

hr)

1800

2000

2200

2400

2600

2800

3000

3200ADS Share 50

2 3 4 51Combinations

1800

2000

2200

2400

2600

2800

3000

3200ADS Share 75

2 31Combinations

(a)

206

207

208

209

21

211

212

213

214

215

216

Aver

age

Coe

ffici

ent o

f Var

iatio

n

ADS Share 25


14

142

144

146

148

15

152

154

156ADS Share 50


08

0805

081

0815

082

0825

083

0835

084ADS Share 75

2 31Combinations

(b)

Figure 2 Influences of ADS vehiclesrsquo position on (a) maximum flow rate and (b) average coefficient of variation of accelerations

119860119879119863 = sum119869119869=1 119860119879119863119895119869 119860119879119863119895 = (119901119868119895 minus 1199011119895)119868

(6)

119878119888119900119903119890119862119886119904119890 119896 = sum119869119869=1 V1119895 (119860119879119863119895119862119886119904119890 119896)119869 minus (119860119879119879119862119886119904119890 119896) (7)

Here i is vehicle index (I = 21) j is time index (J= 1000) V119894119895 is velocity of vehicle i at time step j 119901119894119895 isposition of vehicle i at time step j and 119878119888119900119903119890119878119862119886119904119890 119896 is score

of case k Aforementioned (Section 4) platoon variables(ie maximum platoon length inter-platoon headway andintra-platoon headway) were explored to generate distinctplatoon scenarios The combinations of these parameter setsproduced 64 distinct platoon configuration cases that weresimulated for different traffic flows and ADSmarket shares todetect the capability ofmobility improvementsMoreover thelimits of mobility improvements due to variation of platoonconfigurations were also revealed in this analysis Obtainedmobility improvements from base cases at different trafficstates are presented in Figure 3(a) The three-quarter circlesshowed comparative mobility progresses at different flow


1400

1800

2400

Case 1

Case 2

Case 3Case 4Case 5

Case 6

Case 7

Case 8

Case 9

Case 10

Case 11Case 12

Case 13Case 14Case 15Case 16Case 17Case 18Case 19Case 20

Case 21Case 22Case 23Case 24Case 25

Case 26

Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6Ca

se 4

7

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54

Case 55Case 56 Case 57 Case 58 Case 59 Case 60 Case 61 Case 62 Case 63

Case 64

-5

0

5

10

MobilityImpaired

times 100

ADS FlowShare Rate Mobility Improved

755025

755025

755025

times10-3

(a)

CaseIntra

Platoon Headway

Inter Platoon Headway

Max Platoon Length

Mobility Improvement Case

Intra Platoon Headway


Max Platoon Length

Mobility Improvement

1 050 2 3 0200 33 050 2 5 02042 075 2 3 0196 34 075 2 5 02003 100 2 3 0193 35 100 2 5 01964 125 2 3 0190 36 125 2 5 01925 050 4 3 0189 37 050 4 5 01986 075 4 3 0186 38 075 4 5 01947 100 4 3 0182 39 100 4 5 01908 125 4 3 0179 40 125 4 5 01869 050 6 3 0178 41 050 6 5 0193

10 075 6 3 0175 42 075 6 5 018911 100 6 3 0172 43 100 6 5 018512 125 6 3 0168 44 125 6 5 018113 050 8 3 0168 45 050 8 5 018814 075 8 3 0164 46 075 8 5 018415 100 8 3 0161 47 100 8 5 018016 125 8 3 0158 48 125 8 5 017617 050 2 4 0202 49 050 2 6 020418 075 2 4 0198 50 075 2 6 020019 100 2 4 0194 51 100 2 6 019620 125 2 4 0191 52 125 2 6 019221 050 4 4 0194 53 050 4 6 019822 075 4 4 0190 54 075 4 6 019423 100 4 4 0186 55 100 4 6 019024 125 4 4 0183 56 125 4 6 018625 050 6 4 0186 57 050 6 6 019326 075 6 4 0182 58 075 6 6 018927 100 6 4 0178 59 100 6 6 018528 125 6 4 0175 60 125 6 6 018129 050 8 4 0178 61 050 8 6 018830 075 8 4 0174 62 075 8 6 018431 100 8 4 0170 63 100 8 6 018032 125 8 4 0167 64 125 8 6 0176

(b)

Figure 3 Variations of mobility benefits due to varying platoon configurations at (a) different flow rates and ADS shares and (b) specific flowrate (1800 vehhr) and ADS share (75)


rates and ADS market shares simulated for the analysis Thecolor bar in Figure 3(a) indicated the extent of generatedmobility score improvements Figure 3(b) reveals detailedanalysis for a specific flow rate and ADS share For clearunderstanding of the impact of platoon configurations at aspecific traffic state mobility improvements at initial flowrate of 1800 vehhr and 75 ADS share are provided inFigure 3(b) as an example As observed in Figure 3(b)sixty-four (64) separate platoon configurations are generatedfrom listed parameter set (Section 4) Parameters for eachcase are listed in the table in Figure 3(b) The mobilityimprovement column was calculated by comparing the basecase (flow rate = 1800 vehhr ADS share = 0) withthe corresponding cases and transforming the value into apercentage Negative percentages indicate impaired mobilityand positive percentages denote improved mobility resultingfrom a specific platoon configuration When inspectingFigure 3(b) it was found that maximum mobility benefits(0204 improvement on case score) could be obtained fromCase 33 (platoon configuration intra-platoon headway =050 sec inter-platoon headway = 2 sec and max platoonlength = 5) and Case 49 (platoon configuration intra-platoonheadway = 050 sec inter-platoon headway = 2 sec and maxplatoon length = 6) for that specific traffic state A decliningtrend ofmobility gainswas capturedwith increasing inter andintra-platoon headway Additionally increasing maximumplatoon length parameter showed expansion with regard tomobility which came to a halt at maximum platoon length =5

An exploration of Figure 3(a) revealed that increasingADS market could bring broader mobility enhancement athigher flow rates (yellow to green bands on 2400 vehhr flowrate) IncreasedADS share at lowflow rates had a diminishingeffect on mobility (light red to deep red bands on 1400vehhr flow rate) Another finding of this analysis was thatthe closely spaced ADS vehicles with long platoons wouldgenerate moremobility improvements Hence the maximummobility benefit was experienced in Case 33 and Case 49Although the analysis concluded that closely spaced longADS platoons could attain higher mobility benefits closeproximity of ADS platoons and long chain of ADS vehiclesin these platoon configurations would severely restrict merg-ing vehicles from neighboring lanes on-ramps side roadsetc

Analysis on platoon parameters at different traffic staterevealed that with other parameters being constant increas-ing platoon length resulted in improved mobility gainsSimilar investigation on inter-platoon headway presentedthat increase in inter-platoon headway would reduce traf-fic mobility if other two parameters remain constant ata specific traffic state Analysis of intra-platoon headwayscoincides with the insights of inter-platoon headway anal-ysis Therefore compactness of ADS vehicles would bringmore mobility benefits in roadway sections with minimalconflict points (eg spans between onoff-ramps on free-ways and sections between intersections in arterial) Thenotion of conflict points led to the next section of thisstudy examining the impact of ADS vehicles on trafficsafety

53 Impact on Safety Although Cases 33 and 49 werefound to be an obvious choice among 64 tested platoonconfiguration cases with respect to mobility enhancementsall aforementioned cases were examined again to identifythe potential impact on traffic safety Findings from ADSvehicles location and distribution influenced the simulationof safety improvements by placing a series of ADS vehiclesat the front of traffic stream to obtain optimal benefits Sinceno merging traffic was considered the safety enhancementswere examined as a measure of potentials to reduce rear-end collision risks Three safety surrogate measures wereconsidered in this regard time-to-collision (TTC) timeexposed time-to-collision (TET) and time integrated time-to-collision (TIT)

TTC TET and TIT introduced by Hayward Minder-houd and Bovy [44 45] were widely used by traffic safetyresearchers The time required for two successive vehicles inthe same lane to hit if they maintain their current velocityis represented by TTC Higher TTC would indicate safertraffic condition TTC can be used to evaluate safety of atraffic environment since lower TTC is indicative of potentialdangerous situation [46] Both TET andTIT are derived fromTTC to measure safety improvements from macroscopicstandpoint Since TET is the summation of instances whenTTC are lower than threshold value the lower TET value isexpected at safer traffic conditions TET value was measuredby (9) where TTC values for each vehicle at each time stamp(119879119879119862119894119895) were compared with the threshold TTC (119879119879119862lowast)value to calculate TET value for each scenario TIT measuresthe value of TTC lower than the threshold TTC Similar toTET a higher TIT value indicates higher safety concerns Thevalues of these parameters weremeasured using the followingequations

119879119879119862119894119895 = 119901119894minus1119895 minus 119901119894119895 minus 119871V119894119895 minus V119894minus1119895

119894119891 V119894119895 gt V119894minus1119895

119868119899119891 119894119891 V119894119895 le V119894minus1119895(8)

119879119864119879 = 119869sum119895=1

119879119864119879119895

119879119864119879119895 = 119868sum119894=1

120575119895Δ119895 120575119895 = 1 forall0 lt 119879119879119862119894119895 lt 119879119879119862lowast0 119890119897119904119890

(9)

119879119868119879 = 119869sum119895=1

119879119868119879119895

119879119868119879119895 = 119868sum119894=1

[ 1119879119879119862119894119895 minus1119879119879119862lowast] Δ119895

forall0 lt 119879119879119862119894119895 lt 119879119879119862lowast(10)

The threshold TTC values to measure TET and TITwere set as 25 sec similar to standard perception reactiontime Resulting changes with regard to safety are displayedin Figure 4 Figure 4(a) presents total TET and averageTIT values over the simulation period on base cases which


0

015

03

045

06

Aver

age T

IT

12 14 16 18 2 22 24 26 28 31Flow Rate (vehhr)

50

100

150

200

250

Tota

l TET

(a)

minus150

minus100

minus50

0

50

TET

Cha

nge

Flow Rate 1400 vehhr

minus150

minus100

minus50

0

50


minus150

minus100

minus50

0

50


50 7525ADS Share

50 7525ADS Share

50 7525ADS Share

(b)

1400

1800

2400

Case 1

Case 2

Case 3Case 4Case 5

Case 6

Case 7

Case 8

Case 9

Case 10Case 11

Case 12Case 13Case 14Case 15Case 16Case 17Case 18Case 19Case 20


Case 26

Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6

Case

47

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54

Case 55Case 56 Case 57 Case 58 Case 59 Case 60 Case 61 Case 62

Case 63Case 64

-02

-015

-01

-005

0

005

times 100

TIT Increased

TIT Reduced

ADS FlowShare Rate755025

755025

755025

(c)

1400

1800

2400

Case 1

Case 2

Case 3Case 4Case 5

Case 6

Case 7

Case 8

Case 9

Case 10

Case 11Case 12

Case 13Case 14Case 15Case 16Case 17Case 18Case 19Case 20Case 21Case 22Case 23Case 24Case 25

Case 26

Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6

Case

47

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54


Case 64

-08

-06

-04

-02

0

times 100

TIT Increased

TIT Reduced


755025

755025

(d)

Figure 4 (a) Base-case safety parameter values at varying flow rates (b) Changes in total TET (c) variations of averageTIT values consideringMDS vehicles only and (d) variations of average TIT values considering all vehicles due to varying platoon configurations at different flowrates and ADS shares


were utilized to measure safety improvements gained withthe introduction of ADS vehicles Figure 4(b) displays therange of changes on total TET values at different trafficstates with varying platoon structures Increasing ADS sharesshowed a gradual decline of total TET values The extent ofdeclination was much higher in higher flow rates Howeveran exception was observed at high flow rates and lower ADSshares (flow rate = 2400 vehhr ADS share = 25) wheretotal TET value increased from base traffic states Thereforeit can be stated that higher ADS share is required to bringnoticeable safety improvements with increasing flow ratesFigures 4(c) and 4(d) show analysis results of average TITchanges As shown in earlier figure (Figure 3(a)) both fac-tional circles revealed resulting improvements on averageTITparameters Figure 4(c) shows resulting safety improvementsof the vehicle stream for different platoon configurationsADS shares and flow rates by comparing with base averageTIT values This analysis considered average TIT values ofMDS vehicles only in the traffic stream Average TIT valuesof MDS vehicles in the CAV environment were comparedwith corresponding vehicles on base case for this analysisThe average TIT reduction of MDS vehicles was found tobe within the range of -2076ndash855 Additionally highersafety gains were achieved with shorter platoons includingADS vehicles sparsely spaced

On the other hand Figure 4(d) shows the analysis bycomparing average TIT values of all vehicles with base caseFor this analysis it was assumed that there was no collisionrisk for ADS vehicles (average TIT values = 0 for ADSvehicles) irrespective of platoon configurations Comparisonbetween Figures 4(c) and 4(d) shows significantly higherimprovements on average TIT values for all vehicles overMDS vehiclesThe range of average reduction is much higherin Figure 4(d) Detailed analysis of safety enhancement for aspecific traffic state provided further insights on the impactof platoon configurations For instance simulation resultsof 1800 vehhr flow rate with 75 ADS share traffic staterevealed that increasing ADS vehiclesrsquo stretch over the trafficstream resulted in greater safety benefits for the remainingvehicles Hence the maximum safety gain was attained fromCase 16 (-1023 reduction on average TIT of MDS vehicles)for this specific traffic state Although a similar pattern wasobserved for other traffic states unexpectedly high safetyconcerns were experienced for some cases (dark red strip inFigure 4(c) for 2400 vehhr with 25 ADS share) Moreovermaximum safety gains on MDS vehicles were obtained on50 ADS share at 1800 vehhr flow The findings from safetyimpact analysis have led us to conclude that increasing ADSvehicles with increasing flow rates would improve safety ofall vehicles if ADS vehicles form short sparse platoon in startof traffic stream Although rear-end collision risk for MDSvehicles would proportionately reduce with increasing ADSshare at comparatively high and low flow rate this correlationbetween safety gain and ADS share did not hold true for flowrates near capacity level

Exploring the evolution pattern of platoon parame-ters provided important insights into safety feature Whileother parameters (ie inter-platoon headway and max pla-toon length) remain the same continuous increment of

intra-platoon headway showed reduction on rear-end col-lision expectation Inter-platoon headway followed similarpattern to intra-platoon headway However range of safetyimprovement in both parameters depends on maximumplatoon length Magnitude of safety gains was much higherat small platoons (ie max platoon length = 3) than bigplatoons (ie max platoon length)

54 Impact on Environment Environmental implicationsof proposed car-following mechanism were measured withrespect to fuel consumption and emission reduction Whilenumerous models were available and utilized in the literature[47ndash49] the integrational simplicity of the VT-micro model[50ndash52] with car-following model persuaded us to implementthis model Output from car-following models can be directlyused on the VT-micro model as input to estimate environ-mental impact due to vehicle dynamics which makes thismodel a perfect candidate for this analysis According to theVT-micro model the fuel consumption of or emission rate ofith vehicle at time step j can be measured using the followingequations

ln (119872119874119864119894119895) =

3sum119897=0

3sum119898=0

119870119897119898 times V119897119894119895 times 119886119898119894119895 119894119891 119886 ge 03sum119897=0

3sum119898=0

1198701015840119897119898 times V119897119894119895 times 119886119898119894119895 119894119891 119886 lt 0 (11)

where 119872119874119864119894119895 is measure of effectiveness with respectto fuel consumptions CO2 emissions and NOx emissionsfor vehicle i at time j and 119870119897119898 are regression coefficients forMOEs at powers l andmThe values of regression coefficientsare obtained from [50] V119897119894119895 is velocity of vehicle i at time jwithpower l 119886119898119894119895 is acceleration of vehicle i at time jwith powerm

Analysis using the VT-micro model for the base case(0 ADS share) measured average fuel consumptions CO2emissions and NOx emissions of the vehicles in simulatedtraffic stream at different flow rates which is presented inFigure 5(a) Simulation results indicated that the lowest fuelconsumption CO2 emission andNOx emission at base trafficstate occurred at 1800 vehhr flow rate Therefore low flowrates do not necessarily ensure low environmental impactThe transformation in environmental impact resulting fromvarying shares of ADS vehicles is demonstrated in Figures5(b) 5(c) and 5(d) Gradual increments of ADS share showeda continuous reduction in fuel consumption However CO2and NOx emissions for the traffic stream followed a differenttrend As previous figures the fractional circles displayedchanges in average environmental parameters resulting fromvarying platoon structures and traffic states (ie flow ratesand ADS shares) For a specific traffic state (ie flow rateand ADS share) the effect on environment demonstratedsimilar patterns to the impact on mobility As an example wecan examine the platoon structures for 1800 vehhr flow ratewith 75 ADS share The observations of this specific trafficstate revealed that environmental benefits kept increasingwith the gradual compaction of ADS vehicle in traffic streamFor instance maximum reduction on fuel consumption wasobtained for Case 33 and Case 49 (platoon configuration


1400 1800 2400

Fuel ConsumptionLitre 100km

857 852 866

Kg 100km

1971 1964 199

g 100km 2803 2796 2733

Base Case (0 ADS Share)

Parameters UnitFlow Rate (vehhr)

2 Emission

Ｒ Emission

(a)

1400

1800

2400

Case 1

Case 2

Case 3Case 4Case 5Case 6

Case 7

Case 8

Case 9

Case 10

Case 11Case 12


Case 21Case 22Case 23Case 24Case 25Case 26

Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6Ca

se 4

7

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54

Case 55Case 56

Case 57 Case 58 Case 59 Case 60 Case 61 Case 62 Case 63Case 64

-004-0020002004006

times 100Average Fuel

Consumption Reduced


Average Fuel ConsumptionIncreased

(b)

1400

1800

2400

Case 1

Case 2


Case 7

Case 8

Case 9

Case 10

Case 11Case 12



Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6Ca

se 4

7

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54


Case 64-01-008-006-004-0020

times100

755025

755025755025

ADS FlowShare Rate CO2 Emission Increased

CO2 Emission Reduced

(c)

1400

1800

2400

Case 1

Case 2


Case 7

Case 8

Case 9

Case 10

Case 11Case 12



Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6Ca

se 4

7

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54


Case 64-008-006-004-0020

times 100

755025

ADS FlowShare Rate

755025

755025

NOx Emission Increased

NOx Emission Reduced

(d)

Figure 5 Observed variations of (a) environmental parameters at base case (b) fuel consumption (c) CO2 emission and (d) NOx emissionfor varying platoon structures at different traffic state

intra-platoon headway = 050 sec inter-platoon headway= 2 sec and max platoon length = 5 and 6 respectively)which reduced average fuel consumption by 487 from thebase case whereas Cases 48 and 64 (platoon configurationintra-platoon headway = 125 sec inter-platoon headway =8 sec and max platoon length = 5 and 6] reduced fuelconsumption by 142 and 144 respectively A similarpattern was observed for the other two parameters (ie CO2emission and NOx emission) for this traffic state Detailedanalysis of the environmental impact identified a propor-tional relation between fuel consumption reduction and ADSshare at all simulated traffic flow rates However the extent ofimprovements varied at different flow rates As for CO2 and

NOx emission the relation between emission reduction andADS share was found to be proportionate at lower flow rates(ie 1400 1800 vehhr) At high flow rate (ie 2400 vehhr)higher reduction was obtained at lowADS share Out analysisof the environmental impact of ADS vehicles and formedplatoons provided us with the insights of fuel consumptionCO2 emission and NOx emission characteristics in order tomake informed decision regarding platoon structures with anaim to attain optimal environmental benefits

Close inspection of platoon parameters revealed similarinclinations to mobility Unlike mobility gains the degree ofenvironmental gainswas significantly higher at larger platoonsizes (iemax platoon length= 56) in comparison to smaller


platoons (ie max platoon length = 34) Furthermore theincrements of intra and inter-platoon headway values showedsteady declination of environmental benefits at a specifictraffic state with other parameters being constant

6 Identification of Optimal PlatoonParameter Set

An analysis of proposed car-following strategy deliveredinsights regarding mobility safety and environmentalimprovement potentials due to presence of ADS vehicles atmixed traffic conditions One key finding of the analysis wasthat the expectation to obtain multiobjective improvements(ie mobility safety and environmental) from singleplatoon configuration was impractical Since mobilityand environmental developments maintained a reciprocalrelationship with safety enhancements a suboptimal platoonconfiguration could be determined to procure maximumgains from these three features Another compellingoutcome of prior analysis involved recognizing the fact thatboth traffic flow rates and ADS market shares impactedobtained benefits Hence achieving maximum mobilitysafety and environmental advantages from fixed suboptimalplatoon configuration at different flow rates was unrealisticTo this end it was necessary to present an approach thatidentified dynamic suboptimal platoon configurations formultiobjective decision-making purposes

Influenced byKhondaker andKattan [53] an analysis wasperformed to identify the suboptimal platoon configurationsto maximize mobility safety and environmental gains gener-ated by ADS vehicles Collective influences from these threefeatures were measured by placing different weights on themto get resulting variations on improvements (Figure 6(b))Four sets of multiobjective functions were investigated toobtain suitable platoon structure Sets for platoon variableswere chosen from earlier analyses to identify suboptimalconfigurations

The optimization of platoon variables for different mul-tiobjective function identified each featuresrsquo (ie mobilitysafety and environmental) individual and collective inclina-tions To obtain clear and precise insights of these trendsthe group of vehicles with 1800 vehhr flow rate and 75ADS market penetration is demonstrated in Figure 6 Theimprovements obtained due to ADS vehicles were scaledwithin [0 1] using extreme values from prior analysis ofall the features (Figure 6(a)) For mobility improvementsthe scenario scores were scaled within the above-mentionedrange Extreme average TIT values measured in safety impactanalysis were applied to measure safety scores of differentplatoon configurations Similarly environmental score wascalculated by assigning equal weights to three components ofenvironmental impact (ie fuel consumption CO2 emissionand NOx emission) while scaling them within the range of0 and 1 This action was performed due to variations ofunits in measures of effectiveness and to bring them in thesame scale for optimization Reviews of individual featuresidentified a gradual reduction of mobility and environmentalimprovements with an increase of intra and inter-platoon

headway However safety improvements showed oppositepattern Figure 6(b) shows the results of a set of objectivefunctions with predefined weight put on mobility safety andenvironmental aspect The goal of this analysis is to obtainsuboptimal platoon configurations for predefined objectivesets and also to identify the objective functionwithmaximumbenefits from the assorted weight sets Analysis of combinedimpacts identified that maximum benefits for all objectivefunctions were achieved with the platoon configuration ofintra-platoon headway = 050 sec inter-platoon headway = 2sec and maximum platoon length = 56 vehicles The objec-tive of this analysis was to present an approach to identifysuboptimal platoon configurations suitable for specific flowrates and ADS market share with specific motivation to assistin multiobjective decision-making

7 Conclusion and Future Extensions

The objective of the study was to obtain rationalized insighton mixed traffic movements and evaluate the impact thatADS vehicles will supposedly have on traffic While thepotential of connectivity and automated controls is astound-ing the extent of harnessing the benefits depends on dis-cerning their influences on traffic In this regard we haveproposed a naıve car-following mechanism for mixed trafficand analyzed their motion dynamics to determine the pos-sible improvements Initially the location and distributionsof ADS vehicles along the traffic stream were discovered tobe moving forward with established framework to obtainthe highest rewards The mobility safety and environmentalgains obtained from CAV traffic stream were examined forvarying traffic flow ADS market penetration and platoonconfigurations with the intention of determining the limitsof these potential improvements The final stage of this studywas the analysis to obtain optimal platoon configurations toachieve maximum collective improvements

The findings of the research show that to obtain max-imum mobility benefits close and compact platoons arefavorable in roadway sections without side frictions How-ever segments with on-ramps off-ramps side roads etcneed to be researched in the future to account for sidefrictions and their consequences on collectivemobility safetyand environmental gains Identifying suboptimal platoonconfigurations for varying flow rates and market sharesof ADS vehicles will assist traffic operation authorities topropose traffic state responsive dynamic platoon structuresUtilizing these platoon configurations will make the bestuse of ADS vehicles on prevailing traffic conditions toobtain maximum gains Future research based on this studywill account for vehicles with conflicting movements (ielane changing merging traffic from on-ramps divergingtraffic towards off-ramp etc) and propose potential improve-ments

Data Availability

The data used to support the findings of this study areavailable from the corresponding author upon request


[05023]

[05063]

[05024]

[05064]

[05025]

[05065]

[05026]

[05066]

[12586]

Cases[Intra-platoon Headway Inter-platoon Headway Max Platoon Length]

0

02

04

06

08

1Sc

ores

MobilitySafetyEnvironment

(a)

[05023]

[05063]

[05024]

[05064]

[05025]

[05065]

[05026]

[05066]

[12586]


ObjObj

ObjObj

02

04

06

08

1

Scor

es

Obj Mobility Safety Environment

033 033 033

05 025 025

025 05 025025 025 05

＜Ｄ1

＜Ｄ2

＜Ｄ3

＜Ｄ4

(b)

Figure 6 Observed variations of (a) individual features due to diverse platoon variables listed and (b) listed multiobjective function setsresulting from changing platoon variables


Disclosure

The contents of this paper reflect the views of the authorswho are responsible for the facts and the accuracy of thedata presented herein The contents do not necessarily reflectthe official views or policies of the City of Edmonton andTransport Canada This paper does not constitute a standardspecification or regulation

Conflicts of Interest

The authors declare that they have no conflicts of interest

Acknowledgments

This research work was jointly supported by the NaturalSciences and Engineering Research Council (NSERC) ofCanada City of Edmonton and Transport Canada

References

[1] S Fakharian ldquoEvaluation of Cooperative Adaptive Cruise Con-trol (CACC)Vehicles onManaged LanesUtilizing Macroscopicand Mesoscopic Simulationrdquo Transp Res Rec J Transp ResBoard vol no p 16 2016

[2] A Ghiasi O Hussain Z Qian and X P Li ldquoA mixed trafficcapacity analysis and lane management model for connectedautomated vehicles A Maekov chain methodrdquo TransportationResearch Part B Methodological vol 106 pp 266ndash292 2017

[3] Y Liu J Guo J Taplin and YWang ldquoCharacteristicAnalysis ofMixed Traffic Flow of Regular and Autonomous Vehicles UsingCellular Automatardquo Journal of Advanced Transportation vol2017 Article ID 8142074 10 pages 2017

[4] A Talebpour and H S Mahmassani ldquoInfluence of connectedand autonomous vehicles on traffic flow stability and through-putrdquo Transportation Research Part C Emerging Technologiesvol 71 pp 143ndash163 2016

[5] N Bekiaris-Liberis C Roncoli and M Papageorgiou ldquoHigh-way traffic state estimation per lane in the presence of connectedvehiclesrdquo Transportation Research Part B Methodological vol106 pp 1ndash28 2017

[6] D Chen S AhnMChitturi andDANoyce ldquoTowards vehicleautomation Roadway capacity formulation for traffic mixedwith regular and automated vehiclesrdquo Transportation ResearchPart B Methodological vol 100 pp 196ndash221 2017

[7] R E Chandler R Herman and E W Montroll ldquoTrafficdynamics studies in car followingrdquo Operations Research vol 6pp 165ndash184 1958

[8] W Helly ldquoSimulation of bottlenecks in single-lane traffic flowrdquoineory of traffic flow pp 207ndash238 Elsevier Amsterdam 1961

[9] M Bando K Hasebe A Nakayama A Shibata and YSugiyama ldquoDynamical model of traffic congestion and numeri-cal simulationrdquoPhysical ReviewE Statistical Nonlinear and SoMatter Physics vol 51 no 2 pp 1035ndash1042 1995

[10] M Treiber A Hennecke and D Helbing ldquoCongested trafficstates in empirical observations and microscopic simulationsrdquoPhysical Review E Statistical Nonlinear and SoMatter Physicsvol 62 no 2 pp 1805ndash1824 2000

[11] P G Gipps ldquoA behavioural car-following model for computersimulationrdquo Transportation Research Part B Methodologicalvol 15 no 2 pp 105ndash111 1981

[12] L Evans and R Rothery Experimental measurement of per-ceptual thresholds in car following Highway Research BoardWashington District of Columbia United States 1973

[13] L C Davis ldquoOptimality and oscillations near the edge ofstability in the dynamics of autonomous vehicle platoonsrdquoPhysica A Statistical Mechanics and its Applications vol 392 no17 pp 3755ndash3764 2013

[14] P Y Li andA Shrivastava ldquoTraffic flow stability induced by con-stant time headway policy for adaptive cruise control vehiclesrdquoTransportation Research Part C Emerging Technologies vol 10no 4 pp 275ndash301 2002

[15] G Orosz J Moehlis and F Bullo ldquoDelayed car-followingdynamics for human and robotic driversrdquo in Proceedings ofthe ASME 2011 International Design Engineering TechnicalConferences and Computers and Information in EngineeringConference IDETCCIE 2011 pp 529ndash538 USA August 2011

[16] L Xiao and F Gao ldquoPractical string stability of platoon of adap-tive cruise control vehiclesrdquo IEEE Transactions on IntelligentTransportation Systems vol 12 no 4 pp 1184ndash1194 2011

[17] S G Hu H Y Wen L Xu and H Fu ldquoStability of platoon ofadaptive cruise control vehicleswith time delayrdquoTransportationLetters pp 1ndash10 2017

[18] Z Wang G Wu and M Barth ldquoDeveloping a distributedconsensus-based Cooperative Adaptive Cruise Control(CACC) systemrdquo J Adv Transp vol 2017 2017

[19] M Fountoulakis N Bekiaris-Liberis C Roncoli IPapamichail and M Papageorgiou ldquoHighway traffic stateestimation with mixed connected and conventional vehiclesMicroscopic simulation-based testingrdquoTransportation ResearchPart C Emerging Technologies vol 78 pp 13ndash33 2017

[20] Q Xin N Yang R Fu S Yu and Z Shi ldquoImpacts analysis of carfollowing models considering variable vehicular gap policiesrdquoPhysica A Statistical Mechanics and its Applications vol 501 pp338ndash355 2018

[21] J Sun Z Zheng and J Sun ldquoStability analysis methods andtheir applicability to car-following models in conventionaland connected environmentsrdquo Transportation Research Part BMethodological vol 109 pp 212ndash237 2018

[22] B Van Arem C J G Van Driel and R Visser ldquoThe impactof cooperative adaptive cruise control on traffic-flow character-isticsrdquo IEEE Transactions on Intelligent Transportation Systemsvol 7 no 4 pp 429ndash436 2006

[23] G N Bifulco L Pariota F Simonelli and R D Pace ldquoDevel-opment and testing of a fully adaptive cruise control systemrdquoTransportation Research Part C Emerging Technologies vol 29pp 156ndash170 2013

[24] J Yi and R Horowitz ldquoMacroscopic traffic flow propagationstability for adaptive cruise controlled vehiclesrdquo TransportationResearch Part C Emerging Technologies vol 14 no 2 pp 81ndash952006

[25] I A Ntousakis I K Nikolos and M Papageorgiou ldquoOnMicroscopic Modelling of Adaptive Cruise Control SystemsrdquoTransportation Research Procedia vol 6 pp 111ndash127 2015

[26] A I Delis I K Nikolos and M Papageorgiou ldquoMacroscopictraffic flowmodeling with adaptive cruise control developmentand numerical solutionrdquo Computers ampMathematics with Appli-cations vol 70 no 8 pp 1921ndash1947 2015

[27] L Ye and T Yamamoto ldquoModeling connected and autonomousvehicles in heterogeneous traffic flowrdquo Physica A StatisticalMechanics and its Applications vol 490 pp 269ndash277 2018


[28] W-X Zhu andHM Zhang ldquoAnalysis ofmixed traffic flowwithhuman-driving and autonomous cars based on car-followingmodelrdquoPhysica A Statistical Mechanics and its Applications vol496 pp 274ndash285 2018

[29] H Yeo A Skabardonis J Halkias J Colyar and V AlexiadisldquoOversaturated freeway flow algorithm for use in Next Genera-tion Simulationrdquo Transportation Research Record no 2088 pp68ndash79 2008

[30] N Chen M Wang T Alkim and B van Arem ldquoA RobustLongitudinal Control Strategy of Platoons under Model Uncer-tainties and Time Delaysrdquo Journal of Advanced Transportationvol 2018 Article ID 9852721 13 pages 2018

[31] N Bekiaris-Liberis C Roncoli and M Papageorgiou ldquoHigh-way traffic state estimation with mixed connected and conven-tional vehiclesrdquo IEEE Transactions on Intelligent TransportationSystems vol 17 no 12 pp 3484ndash3497 2016

[32] V A C van den Berg and E T Verhoef ldquoAutonomous cars anddynamic bottleneck congestion the effects on capacity valueof time and preference heterogeneityrdquo Transportation ResearchPart B Methodological vol 94 pp 43ndash60 2016

[33] P Tientrakool Y-C Ho and N F Maxemchuk ldquoHighwaycapacity benefits from using vehicle-to-vehicle communicationand sensors for collision avoidancerdquo in Proceedings of the IEEE74th Vehicular Technology Conference VTC FallTheHilton SanFrancisco Union Square San Francisco USA September 2011

[34] J Vander Werf S Shladover M Miller and N KourjanskaialdquoEffects of Adaptive Cruise Control Systems onHighway TrafficFlow Capacityrdquo Transportation Research Record vol 1800 pp78ndash84 2002

[35] D Ni J Li S Andrews and H Wang ldquoPreliminary estimate ofhighway capacity benefit attainable with IntelliDrive technolo-giesrdquo in Proceedings of the 13th International IEEE Conferenceon Intelligent Transportation Systems ITSC 2010 pp 819ndash824Portugal September 2010

[36] T-H Chang and I-S Lai ldquoAnalysis of characteristics of mixedtraffic flow of autopilot vehicles and manual vehiclesrdquo Trans-portation Research Part C Emerging Technologies vol 5 no 6pp 333ndash348 1997

[37] P Fernandes and U Nunes ldquoPlatooning with IVC-enabledautonomous vehicles Strategies to mitigate communicationdelays improve safety and traffic flowrdquo IEEE Transactions onIntelligent Transportation Systems vol 13 no 1 pp 91ndash106 2012

[38] N H T S Administration ldquoFMVSSNo 150 Vehicle-To-VehicleCommunication Technology For Light Vehiclesrdquo Off RegulAnal Eval Natl Cent Stat Anal no 150 2016

[39] Y Li H Wang W Wang L Xing S Liu and X Wei ldquoEval-uation of the impacts of cooperative adaptive cruise control onreducing rear-end collision risks on freewaysrdquo AccidentAnalysisamp Prevention vol 98 pp 87ndash95 2017

[40] M S Rahman and M Abdel-Aty ldquoLongitudinal safety eval-uation of connected vehiclesrsquo platooning on expresswaysrdquoAccident Analysis amp Prevention vol 117 pp 381ndash391 2018

[41] M Zabat N Stabile S Farascaroli and F Browand ldquoTheAerodynamic Performance Of Platoons A Final Reportrdquo CalifPartners Adv Transit Highw 1995

[42] Z Wang G Wu P Hao K Boriboonsomsin and M BarthldquoDeveloping a platoon-wide Eco-Cooperative Adaptive CruiseControl (CACC) systemrdquo in Proceedings of the 28th IEEEIntelligent Vehicles Symposium IV 2017 pp 1256ndash1261 USAJune 2017

[43] M Mamouei I Kaparias and G Halikias ldquoA frameworkfor user- and system-oriented optimisation of fuel efficiency

and traffic flow in Adaptive Cruise Controlrdquo TransportationResearch Part C Emerging Technologies vol 92 pp 27ndash41 2018

[44] J C Hayward ldquoNear-Miss Determination Throughrdquo HighwRes Board pp 24ndash35 1971

[45] M M Minderhoud and P H L Bovy ldquoExtended time-to-collision measures for road traffic safety assessmentrdquo AccidentAnalysis amp Prevention vol 33 no 1 pp 89ndash97 2001

[46] K Vogel ldquoA comparison of headway and time to collision assafety indicatorsrdquoAccident Analysis amp Prevention vol 35 no 3pp 427ndash433 2003

[47] M Barth et al ldquoDevelopment of a Comprehensive ModalEmissions Modelrdquo Natl Coop Highw Res Progr 2000

[48] J Koupal H Michaels M Cumberworth C Bailey and DBrzezinski ldquoEPAs plan for MOVES a comprehensive mobilesource emissions modelrdquo in Proceedings of the 12th CRC On-Road Veh Emiss Work San Diego CA

[49] S Hausberger J Rodler P Sturm and M Rexeis ldquoEmissionfactors for heavy-duty vehicles and validation by tunnel mea-surementsrdquo Atmospheric Environment vol 37 no 37 pp 5237ndash5245 2003

[50] K AhnMicroscopic Fuel Consumption and Emission ModelingVirginia Tech University 1998

[51] K Ahn H Rakha A Trani and M van Aerde ldquoEstimatingvehicle fuel consumption and emissions based on instantaneousspeed and acceleration levelsrdquo Journal of Transportation Engi-neering vol 128 no 2 pp 182ndash190 2002

[52] T-Q Tang Z-Y Yi and Q-F Lin ldquoEffects of signal light on thefuel consumption and emissions under car-following modelrdquoPhysica A Statistical Mechanics and its Applications vol 469pp 200ndash205 2017

[53] B Khondaker and L Kattan ldquoVariable speed limit a micro-scopic analysis in a connected vehicle environmentrdquo Trans-portation Research Part C Emerging Technologies vol 58 pp146ndash159 2015

International Journal of

AerospaceEngineeringHindawiwwwhindawicom Volume 2018

RoboticsJournal of

Hindawiwwwhindawicom Volume 2018


Active and Passive Electronic Components

VLSI Design



Shock and Vibration


Civil EngineeringAdvances in

Acoustics and VibrationAdvances in



Electrical and Computer Engineering

Journal of

Advances inOptoElectronics

Hindawiwwwhindawicom

Volume 2018

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

The Scientific World Journal

Volume 2018

Control Scienceand Engineering

Journal of



Journal ofEngineeringVolume 2018

SensorsJournal of



RotatingMachinery


Modelling ampSimulationin EngineeringHindawiwwwhindawicom Volume 2018


Chemical EngineeringInternational Journal of Antennas and

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Navigation and Observation


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gives insights into a wide variation of distinct forms ofimpact while simulating automated-control-enabled vehicleson ideal locations and distributions along traffic streamThese insights into mixed traffic movements and platooncharacteristics will motivate researchers to consider otherunattended aspects of mixed traffic dynamics (eg lane-changing gap acceptance and merging) in order to rectifyperceived benefits Similarly traffic operational authoritiescan take these lessons into account to impose different controlstrategies (eg dynamic aggregated controls for manuallydriven vehicles dynamic personalized controls on connectedvehicles) on traffic to attain maximum improvements withregard to reduced travel time collision rates greenhouse gasemissions etc

The rest of the paper is organized as followsThe next sec-tion summarizes the existing literature on car-followingmod-els and strategies for mixed traffic and also touches on studiesthat identify the different form of impact that automatedvehicles have on traffic The proposed car-following strategyis described in the following section The description ofsimulation procedures and the discussion on obtained resultsare covered respectively in two subsequent sections Thefollowing section proposed an approach to obtain dynamicoptimal platoon configuration for specific traffic state Thelast section provides the synopsis of findings of the study andalso gives recommendations for future research

2 Literature Review

As the primary aim of this study relates to car-followingstrategy for mixed traffic environment the literature relatedto car-following models for both forms (ie manual auto-mated) of driving system is explored here Numerous micro-scopic car-following models have been proposed to imitatedriving pattern of manual driving system [7ndash12] Among theproposed stimulus-response-based car-following models theintelligent driver model (IDM) is widely used in literatureto depict manual driving dynamics The ability of thismodel to define numerous microscopic (eg desired velocityand accelerationdeceleration limits) and macroscopic (egcapacity capacity drop and fundamental diagram) phe-nomena made it the prevalent model On the other handdue to rapid growth of CAV technology the longitudinalcontrol models for automated vehicles were also examined byresearchers [13ndash18] These studies provide us with structuresto work on car-following strategy in mixed traffic environ-ment and identify the extents of potential paradigm shifts

A clear distinction of the driving system is dictated bythe operational authority While the manual driving system(MDS) represents driving systems controlled by humans themotion dynamics of vehicles with the automated drivingsystem (ADS) are mandated by distinct levels of automationADS vehiclesrsquo longitudinal movements are commonly por-trayed with adaptive cruise control (ACC) and cooperativeadaptive cruise control (CACC) Many studies have analyzedthe contributions of longitudinal control system of ADSvehicles on traffic mobility [19ndash28] While mobility was themain focus of these studies the impact of traffic movementfrom safety and environmental perspectivewas often ignored

Yeo et al [29] proposed an integrated car-following and lanechangingmodel to performmicrosimulation of oversaturatedfreeway traffic The proposed algorithm considered complexdynamic interactions at a microscopic level to replicatevehicle movements However the aptitude of this model tocapture possible consequences was not tested Wang et al[30] proposed a car-following control for autonomous vehicleand identified the impact focusing mainly on traffic flowcharacteristics Liberis et al [31] took amacroscopic approachto identify traffic mobility parameters in a heterogeneoustraffic environment The authors used the market penetrationrate of connected vehicles to estimate traffic states Moreoverother researchers studied the impact of introducing ADSbased vehicles with conventional vehicles [32ndash36] on flowand mobility Reviews of these studies provide us with theopportunity to constructively examine the contributions ofearlier studies identify the necessities to improve currentknowledge and uncover the latent insights to progresspromptly with CA technology

While the mobility attributes of traffic flow were widelydiscussed in many studies the safety and environmentalaspects which are equally if not more important wererelatively unexplored by a majority of the studies The impactof automated vehicles on both safety and mobility was dis-cussed by Fernandes andNunes [37]They studied platooningof ADS vehicles with different communication schemes atvarious flow rates to improve roadway capacity Severalstudies assessed the safety aspects of CAV based trafficAccording to the National Highway Traffic Safety Admin-istration (NHTSA) a complete adaptation of CAV basedtraffic movements would annually prevent 439000ndash615000crashes [38] Li et al [39] evaluated the impact of CACCcontrol on reducing rear-end collisions on freeways Thestudy shows a reduction in safety improvements with increas-ing market share of ADS vehicles Rahman and Abdel-Aty[40] compared potential improvement in longitudinal safetydue to varying market penetration of connected vehiclesAccording to the analysis presented the managed-lane CACcontrol outperformed multilane control with regard to traf-fic safety The report of Zabat et al [41] stated that thepresence of boundary layer along closely spaced vehicleswould reduce aerodynamic drag resulting in reduced fuelconsumption and less emission Platoon-wide environment-friendly CACC system was studied by Wang et al [42] andtheir objective assessment attained 2 fuel saving with 17emission reductions Mamouei et al [43] argued that fuel-economy based ACC control model would not lead to highlyconservative driving dynamics of traffic

Although the reviewed studies had remarkable contri-butions that helped to clarify the roles and influences ofADS vehicles in traffic the inadequacy of multiobjectivedecision-making approach to address ADS vehiclesrsquo poten-tial has influenced this research further to investigate thecomplex interdependencies of mixed traffic This researchseeks to contribute on three research gaps identified fromthe literature These gaps are (i) the significance of ADSvehiclesrsquo position and distributions along traffic stream (ii)the variations of traffic flow attributes (ie mobility safetyand environmental) resulting from structural changes of



Combinations

ADS amp MDS

MDS amp MDS

MDS amp ADS

ADS amp ADS

IDM

IDM

CACC

ACC


LeadingVehicle

SubjectVehicle






V (119905 + Δ119905) = 119886[[1 minus (V (119905)

V0)4


2]]

(1)

V (119905 + Δ119905) = V (119905) + 119886 (119905) times Δ119905 (2)

119901 (119905 + Δ119905) = 119901 (119905) + V (119905) times Δ119905 + 12119886 (119905) times Δ1199052 (3)










(iv) 6










25


16 17 18 19 20 25 Comb-6

50


752 3 4 6 7 8 10 11 12 14 15 16 18 19 20 75 Comb-12 3 4 5 6 910 11 12 13 16 17 18 19 20 75 Comb-22 3 4 5 6 9 10 11 12 13 16 17 18 19 20 75 Comb-3






119860119879119879 = sum119869119895=1119860119879119879119895119869 = 119868sum119894=1

(119901119894119895 minus 119901119894119895minus1)V119894119895

(5)


ADS Share 25


1800

2000

2200

2400

2600

2800

3000

3200

Max

Flo

w R

ate (

veh

hr)

1800

2000

2200

2400

2600

2800

3000

3200ADS Share 50


1800

2000

2200

2400

2600

2800

3000

3200ADS Share 75

2 31Combinations

(a)

206

207

208

209

21

211

212

213

214

215

216

Aver

age

Coe

ffici

ent o

f Var

iatio

n

ADS Share 25


14

142

144

146

148

15

152

154

156ADS Share 50


08

0805

081

0815

082

0825

083

0835

084ADS Share 75

2 31Combinations

(b)


119860119879119863 = sum119869119869=1 119860119879119863119895119869 119860119879119863119895 = (119901119868119895 minus 1199011119895)119868

(6)

119878119888119900119903119890119862119886119904119890 119896 = sum119869119869=1 V1119895 (119860119879119863119895119862119886119904119890 119896)119869 minus (119860119879119879119862119886119904119890 119896) (7)




1400

1800

2400

Case 1

Case 2

Case 3Case 4Case 5

Case 6

Case 7

Case 8

Case 9

Case 10

Case 11Case 12



Case 26

Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6Ca

se 4

7

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54


Case 64

-5

0

5

10

MobilityImpaired

times 100


755025

755025

755025

times10-3

(a)

CaseIntra

Platoon Headway


Max Platoon Length




Max Platoon Length


1 050 2 3 0200 33 050 2 5 02042 075 2 3 0196 34 075 2 5 02003 100 2 3 0193 35 100 2 5 01964 125 2 3 0190 36 125 2 5 01925 050 4 3 0189 37 050 4 5 01986 075 4 3 0186 38 075 4 5 01947 100 4 3 0182 39 100 4 5 01908 125 4 3 0179 40 125 4 5 01869 050 6 3 0178 41 050 6 5 0193

10 075 6 3 0175 42 075 6 5 018911 100 6 3 0172 43 100 6 5 018512 125 6 3 0168 44 125 6 5 018113 050 8 3 0168 45 050 8 5 018814 075 8 3 0164 46 075 8 5 018415 100 8 3 0161 47 100 8 5 018016 125 8 3 0158 48 125 8 5 017617 050 2 4 0202 49 050 2 6 020418 075 2 4 0198 50 075 2 6 020019 100 2 4 0194 51 100 2 6 019620 125 2 4 0191 52 125 2 6 019221 050 4 4 0194 53 050 4 6 019822 075 4 4 0190 54 075 4 6 019423 100 4 4 0186 55 100 4 6 019024 125 4 4 0183 56 125 4 6 018625 050 6 4 0186 57 050 6 6 019326 075 6 4 0182 58 075 6 6 018927 100 6 4 0178 59 100 6 6 018528 125 6 4 0175 60 125 6 6 018129 050 8 4 0178 61 050 8 6 018830 075 8 4 0174 62 075 8 6 018431 100 8 4 0170 63 100 8 6 018032 125 8 4 0167 64 125 8 6 0176

(b)









119894119891 V119894119895 gt V119894minus1119895

119868119899119891 119894119891 V119894119895 le V119894minus1119895(8)

119879119864119879 = 119869sum119895=1

119879119864119879119895

119879119864119879119895 = 119868sum119894=1


(9)

119879119868119879 = 119869sum119895=1

119879119868119879119895

119879119868119879119895 = 119868sum119894=1

[ 1119879119879119862119894119895 minus1119879119879119862lowast] Δ119895




0

015

03

045

06

Aver

age T

IT

12 14 16 18 2 22 24 26 28 31Flow Rate (vehhr)

50

100

150

200

250

Tota

l TET

(a)

minus150

minus100

minus50

0

50

TET

Cha

nge


minus150

minus100

minus50

0

50


minus150

minus100

minus50

0

50


50 7525ADS Share

50 7525ADS Share

50 7525ADS Share

(b)

1400

1800

2400

Case 1

Case 2

Case 3Case 4Case 5

Case 6

Case 7

Case 8

Case 9

Case 10Case 11



Case 26

Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6

Case

47

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54


Case 63Case 64

-02

-015

-01

-005

0

005

times 100

TIT Increased

TIT Reduced


755025

755025

(c)

1400

1800

2400

Case 1

Case 2

Case 3Case 4Case 5

Case 6

Case 7

Case 8

Case 9

Case 10

Case 11Case 12


Case 26

Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6

Case

47

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54


Case 64

-08

-06

-04

-02

0

times 100

TIT Increased

TIT Reduced


755025

755025

(d)








ln (119872119874119864119894119895) =

3sum119897=0

3sum119898=0


3sum119898=0

1198701015840119897119898 times V119897119894119895 times 119886119898119894119895 119894119891 119886 lt 0 (11)




1400 1800 2400


857 852 866

Kg 100km

1971 1964 199

g 100km 2803 2796 2733



2 Emission

Ｒ Emission

(a)

1400

1800

2400

Case 1

Case 2


Case 7

Case 8

Case 9

Case 10

Case 11Case 12



Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6Ca

se 4

7

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54

Case 55Case 56


-004-0020002004006


Consumption Reduced



(b)

1400

1800

2400

Case 1

Case 2


Case 7

Case 8

Case 9

Case 10

Case 11Case 12



Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6Ca

se 4

7

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54


Case 64-01-008-006-004-0020

times100

755025

755025755025



(c)

1400

1800

2400

Case 1

Case 2


Case 7

Case 8

Case 9

Case 10

Case 11Case 12



Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6Ca

se 4

7

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54


Case 64-008-006-004-0020

times 100

755025

ADS FlowShare Rate

755025

755025



(d)















Data Availability



[05023]

[05063]

[05024]

[05064]

[05025]

[05065]

[05026]

[05066]

[12586]


0

02

04

06

08

1Sc

ores


(a)

[05023]

[05063]

[05024]

[05064]

[05025]

[05065]

[05026]

[05066]

[12586]


ObjObj

ObjObj

02

04

06

08

1

Scor

es


033 033 033

05 025 025

025 05 025025 025 05

＜Ｄ1

＜Ｄ2

＜Ｄ3

＜Ｄ4

(b)



Disclosure




Acknowledgments


References


























































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia




Combinations

ADS amp MDS

MDS amp MDS

MDS amp ADS

ADS amp ADS

IDM

IDM

CACC

ACC


LeadingVehicle

SubjectVehicle






V (119905 + Δ119905) = 119886[[1 minus (V (119905)

V0)4


2]]

(1)

V (119905 + Δ119905) = V (119905) + 119886 (119905) times Δ119905 (2)

119901 (119905 + Δ119905) = 119901 (119905) + V (119905) times Δ119905 + 12119886 (119905) times Δ1199052 (3)










(iv) 6










25


16 17 18 19 20 25 Comb-6

50


752 3 4 6 7 8 10 11 12 14 15 16 18 19 20 75 Comb-12 3 4 5 6 910 11 12 13 16 17 18 19 20 75 Comb-22 3 4 5 6 9 10 11 12 13 16 17 18 19 20 75 Comb-3






119860119879119879 = sum119869119895=1119860119879119879119895119869 = 119868sum119894=1

(119901119894119895 minus 119901119894119895minus1)V119894119895

(5)


ADS Share 25


1800

2000

2200

2400

2600

2800

3000

3200

Max

Flo

w R

ate (

veh

hr)

1800

2000

2200

2400

2600

2800

3000

3200ADS Share 50


1800

2000

2200

2400

2600

2800

3000

3200ADS Share 75

2 31Combinations

(a)

206

207

208

209

21

211

212

213

214

215

216

Aver

age

Coe

ffici

ent o

f Var

iatio

n

ADS Share 25


14

142

144

146

148

15

152

154

156ADS Share 50


08

0805

081

0815

082

0825

083

0835

084ADS Share 75

2 31Combinations

(b)


119860119879119863 = sum119869119869=1 119860119879119863119895119869 119860119879119863119895 = (119901119868119895 minus 1199011119895)119868

(6)

119878119888119900119903119890119862119886119904119890 119896 = sum119869119869=1 V1119895 (119860119879119863119895119862119886119904119890 119896)119869 minus (119860119879119879119862119886119904119890 119896) (7)




1400

1800

2400

Case 1

Case 2

Case 3Case 4Case 5

Case 6

Case 7

Case 8

Case 9

Case 10

Case 11Case 12



Case 26

Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6Ca

se 4

7

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54


Case 64

-5

0

5

10

MobilityImpaired

times 100


755025

755025

755025

times10-3

(a)

CaseIntra

Platoon Headway


Max Platoon Length




Max Platoon Length


1 050 2 3 0200 33 050 2 5 02042 075 2 3 0196 34 075 2 5 02003 100 2 3 0193 35 100 2 5 01964 125 2 3 0190 36 125 2 5 01925 050 4 3 0189 37 050 4 5 01986 075 4 3 0186 38 075 4 5 01947 100 4 3 0182 39 100 4 5 01908 125 4 3 0179 40 125 4 5 01869 050 6 3 0178 41 050 6 5 0193

10 075 6 3 0175 42 075 6 5 018911 100 6 3 0172 43 100 6 5 018512 125 6 3 0168 44 125 6 5 018113 050 8 3 0168 45 050 8 5 018814 075 8 3 0164 46 075 8 5 018415 100 8 3 0161 47 100 8 5 018016 125 8 3 0158 48 125 8 5 017617 050 2 4 0202 49 050 2 6 020418 075 2 4 0198 50 075 2 6 020019 100 2 4 0194 51 100 2 6 019620 125 2 4 0191 52 125 2 6 019221 050 4 4 0194 53 050 4 6 019822 075 4 4 0190 54 075 4 6 019423 100 4 4 0186 55 100 4 6 019024 125 4 4 0183 56 125 4 6 018625 050 6 4 0186 57 050 6 6 019326 075 6 4 0182 58 075 6 6 018927 100 6 4 0178 59 100 6 6 018528 125 6 4 0175 60 125 6 6 018129 050 8 4 0178 61 050 8 6 018830 075 8 4 0174 62 075 8 6 018431 100 8 4 0170 63 100 8 6 018032 125 8 4 0167 64 125 8 6 0176

(b)









119894119891 V119894119895 gt V119894minus1119895

119868119899119891 119894119891 V119894119895 le V119894minus1119895(8)

119879119864119879 = 119869sum119895=1

119879119864119879119895

119879119864119879119895 = 119868sum119894=1


(9)

119879119868119879 = 119869sum119895=1

119879119868119879119895

119879119868119879119895 = 119868sum119894=1

[ 1119879119879119862119894119895 minus1119879119879119862lowast] Δ119895




0

015

03

045

06

Aver

age T

IT

12 14 16 18 2 22 24 26 28 31Flow Rate (vehhr)

50

100

150

200

250

Tota

l TET

(a)

minus150

minus100

minus50

0

50

TET

Cha

nge


minus150

minus100

minus50

0

50


minus150

minus100

minus50

0

50


50 7525ADS Share

50 7525ADS Share

50 7525ADS Share

(b)

1400

1800

2400

Case 1

Case 2

Case 3Case 4Case 5

Case 6

Case 7

Case 8

Case 9

Case 10Case 11



Case 26

Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6

Case

47

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54


Case 63Case 64

-02

-015

-01

-005

0

005

times 100

TIT Increased

TIT Reduced


755025

755025

(c)

1400

1800

2400

Case 1

Case 2

Case 3Case 4Case 5

Case 6

Case 7

Case 8

Case 9

Case 10

Case 11Case 12


Case 26

Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6

Case

47

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54


Case 64

-08

-06

-04

-02

0

times 100

TIT Increased

TIT Reduced


755025

755025

(d)








ln (119872119874119864119894119895) =

3sum119897=0

3sum119898=0


3sum119898=0

1198701015840119897119898 times V119897119894119895 times 119886119898119894119895 119894119891 119886 lt 0 (11)




1400 1800 2400


857 852 866

Kg 100km

1971 1964 199

g 100km 2803 2796 2733



2 Emission

Ｒ Emission

(a)

1400

1800

2400

Case 1

Case 2


Case 7

Case 8

Case 9

Case 10

Case 11Case 12



Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6Ca

se 4

7

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54

Case 55Case 56


-004-0020002004006


Consumption Reduced



(b)

1400

1800

2400

Case 1

Case 2


Case 7

Case 8

Case 9

Case 10

Case 11Case 12



Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6Ca

se 4

7

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54


Case 64-01-008-006-004-0020

times100

755025

755025755025



(c)

1400

1800

2400

Case 1

Case 2


Case 7

Case 8

Case 9

Case 10

Case 11Case 12



Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6Ca

se 4

7

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54


Case 64-008-006-004-0020

times 100

755025

ADS FlowShare Rate

755025

755025



(d)















Data Availability



[05023]

[05063]

[05024]

[05064]

[05025]

[05065]

[05026]

[05066]

[12586]


0

02

04

06

08

1Sc

ores


(a)

[05023]

[05063]

[05024]

[05064]

[05025]

[05065]

[05026]

[05066]

[12586]


ObjObj

ObjObj

02

04

06

08

1

Scor

es


033 033 033

05 025 025

025 05 025025 025 05

＜Ｄ1

＜Ｄ2

＜Ｄ3

＜Ｄ4

(b)



Disclosure




Acknowledgments


References


























































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia









(iv) 6










25


16 17 18 19 20 25 Comb-6

50


752 3 4 6 7 8 10 11 12 14 15 16 18 19 20 75 Comb-12 3 4 5 6 910 11 12 13 16 17 18 19 20 75 Comb-22 3 4 5 6 9 10 11 12 13 16 17 18 19 20 75 Comb-3






119860119879119879 = sum119869119895=1119860119879119879119895119869 = 119868sum119894=1

(119901119894119895 minus 119901119894119895minus1)V119894119895

(5)


ADS Share 25


1800

2000

2200

2400

2600

2800

3000

3200

Max

Flo

w R

ate (

veh

hr)

1800

2000

2200

2400

2600

2800

3000

3200ADS Share 50


1800

2000

2200

2400

2600

2800

3000

3200ADS Share 75

2 31Combinations

(a)

206

207

208

209

21

211

212

213

214

215

216

Aver

age

Coe

ffici

ent o

f Var

iatio

n

ADS Share 25


14

142

144

146

148

15

152

154

156ADS Share 50


08

0805

081

0815

082

0825

083

0835

084ADS Share 75

2 31Combinations

(b)


119860119879119863 = sum119869119869=1 119860119879119863119895119869 119860119879119863119895 = (119901119868119895 minus 1199011119895)119868

(6)

119878119888119900119903119890119862119886119904119890 119896 = sum119869119869=1 V1119895 (119860119879119863119895119862119886119904119890 119896)119869 minus (119860119879119879119862119886119904119890 119896) (7)




1400

1800

2400

Case 1

Case 2

Case 3Case 4Case 5

Case 6

Case 7

Case 8

Case 9

Case 10

Case 11Case 12



Case 26

Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6Ca

se 4

7

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54


Case 64

-5

0

5

10

MobilityImpaired

times 100


755025

755025

755025

times10-3

(a)

CaseIntra

Platoon Headway


Max Platoon Length




Max Platoon Length


1 050 2 3 0200 33 050 2 5 02042 075 2 3 0196 34 075 2 5 02003 100 2 3 0193 35 100 2 5 01964 125 2 3 0190 36 125 2 5 01925 050 4 3 0189 37 050 4 5 01986 075 4 3 0186 38 075 4 5 01947 100 4 3 0182 39 100 4 5 01908 125 4 3 0179 40 125 4 5 01869 050 6 3 0178 41 050 6 5 0193

10 075 6 3 0175 42 075 6 5 018911 100 6 3 0172 43 100 6 5 018512 125 6 3 0168 44 125 6 5 018113 050 8 3 0168 45 050 8 5 018814 075 8 3 0164 46 075 8 5 018415 100 8 3 0161 47 100 8 5 018016 125 8 3 0158 48 125 8 5 017617 050 2 4 0202 49 050 2 6 020418 075 2 4 0198 50 075 2 6 020019 100 2 4 0194 51 100 2 6 019620 125 2 4 0191 52 125 2 6 019221 050 4 4 0194 53 050 4 6 019822 075 4 4 0190 54 075 4 6 019423 100 4 4 0186 55 100 4 6 019024 125 4 4 0183 56 125 4 6 018625 050 6 4 0186 57 050 6 6 019326 075 6 4 0182 58 075 6 6 018927 100 6 4 0178 59 100 6 6 018528 125 6 4 0175 60 125 6 6 018129 050 8 4 0178 61 050 8 6 018830 075 8 4 0174 62 075 8 6 018431 100 8 4 0170 63 100 8 6 018032 125 8 4 0167 64 125 8 6 0176

(b)









119894119891 V119894119895 gt V119894minus1119895

119868119899119891 119894119891 V119894119895 le V119894minus1119895(8)

119879119864119879 = 119869sum119895=1

119879119864119879119895

119879119864119879119895 = 119868sum119894=1


(9)

119879119868119879 = 119869sum119895=1

119879119868119879119895

119879119868119879119895 = 119868sum119894=1

[ 1119879119879119862119894119895 minus1119879119879119862lowast] Δ119895




0

015

03

045

06

Aver

age T

IT

12 14 16 18 2 22 24 26 28 31Flow Rate (vehhr)

50

100

150

200

250

Tota

l TET

(a)

minus150

minus100

minus50

0

50

TET

Cha

nge


minus150

minus100

minus50

0

50


minus150

minus100

minus50

0

50


50 7525ADS Share

50 7525ADS Share

50 7525ADS Share

(b)

1400

1800

2400

Case 1

Case 2

Case 3Case 4Case 5

Case 6

Case 7

Case 8

Case 9

Case 10Case 11



Case 26

Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6

Case

47

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54


Case 63Case 64

-02

-015

-01

-005

0

005

times 100

TIT Increased

TIT Reduced


755025

755025

(c)

1400

1800

2400

Case 1

Case 2

Case 3Case 4Case 5

Case 6

Case 7

Case 8

Case 9

Case 10

Case 11Case 12


Case 26

Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6

Case

47

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54


Case 64

-08

-06

-04

-02

0

times 100

TIT Increased

TIT Reduced


755025

755025

(d)








ln (119872119874119864119894119895) =

3sum119897=0

3sum119898=0


3sum119898=0

1198701015840119897119898 times V119897119894119895 times 119886119898119894119895 119894119891 119886 lt 0 (11)




1400 1800 2400


857 852 866

Kg 100km

1971 1964 199

g 100km 2803 2796 2733



2 Emission

Ｒ Emission

(a)

1400

1800

2400

Case 1

Case 2


Case 7

Case 8

Case 9

Case 10

Case 11Case 12



Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6Ca

se 4

7

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54

Case 55Case 56


-004-0020002004006


Consumption Reduced



(b)

1400

1800

2400

Case 1

Case 2


Case 7

Case 8

Case 9

Case 10

Case 11Case 12



Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6Ca

se 4

7

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54


Case 64-01-008-006-004-0020

times100

755025

755025755025



(c)

1400

1800

2400

Case 1

Case 2


Case 7

Case 8

Case 9

Case 10

Case 11Case 12



Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6Ca

se 4

7

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54


Case 64-008-006-004-0020

times 100

755025

ADS FlowShare Rate

755025

755025



(d)















Data Availability



[05023]

[05063]

[05024]

[05064]

[05025]

[05065]

[05026]

[05066]

[12586]


0

02

04

06

08

1Sc

ores


(a)

[05023]

[05063]

[05024]

[05064]

[05025]

[05065]

[05026]

[05066]

[12586]


ObjObj

ObjObj

02

04

06

08

1

Scor

es


033 033 033

05 025 025

025 05 025025 025 05

＜Ｄ1

＜Ｄ2

＜Ｄ3

＜Ｄ4

(b)



Disclosure




Acknowledgments


References


























































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia





25


16 17 18 19 20 25 Comb-6

50


752 3 4 6 7 8 10 11 12 14 15 16 18 19 20 75 Comb-12 3 4 5 6 910 11 12 13 16 17 18 19 20 75 Comb-22 3 4 5 6 9 10 11 12 13 16 17 18 19 20 75 Comb-3






119860119879119879 = sum119869119895=1119860119879119879119895119869 = 119868sum119894=1

(119901119894119895 minus 119901119894119895minus1)V119894119895

(5)


ADS Share 25


1800

2000

2200

2400

2600

2800

3000

3200

Max

Flo

w R

ate (

veh

hr)

1800

2000

2200

2400

2600

2800

3000

3200ADS Share 50


1800

2000

2200

2400

2600

2800

3000

3200ADS Share 75

2 31Combinations

(a)

206

207

208

209

21

211

212

213

214

215

216

Aver

age

Coe

ffici

ent o

f Var

iatio

n

ADS Share 25


14

142

144

146

148

15

152

154

156ADS Share 50


08

0805

081

0815

082

0825

083

0835

084ADS Share 75

2 31Combinations

(b)


119860119879119863 = sum119869119869=1 119860119879119863119895119869 119860119879119863119895 = (119901119868119895 minus 1199011119895)119868

(6)

119878119888119900119903119890119862119886119904119890 119896 = sum119869119869=1 V1119895 (119860119879119863119895119862119886119904119890 119896)119869 minus (119860119879119879119862119886119904119890 119896) (7)




1400

1800

2400

Case 1

Case 2

Case 3Case 4Case 5

Case 6

Case 7

Case 8

Case 9

Case 10

Case 11Case 12



Case 26

Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6Ca

se 4

7

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54


Case 64

-5

0

5

10

MobilityImpaired

times 100


755025

755025

755025

times10-3

(a)

CaseIntra

Platoon Headway


Max Platoon Length




Max Platoon Length


1 050 2 3 0200 33 050 2 5 02042 075 2 3 0196 34 075 2 5 02003 100 2 3 0193 35 100 2 5 01964 125 2 3 0190 36 125 2 5 01925 050 4 3 0189 37 050 4 5 01986 075 4 3 0186 38 075 4 5 01947 100 4 3 0182 39 100 4 5 01908 125 4 3 0179 40 125 4 5 01869 050 6 3 0178 41 050 6 5 0193

10 075 6 3 0175 42 075 6 5 018911 100 6 3 0172 43 100 6 5 018512 125 6 3 0168 44 125 6 5 018113 050 8 3 0168 45 050 8 5 018814 075 8 3 0164 46 075 8 5 018415 100 8 3 0161 47 100 8 5 018016 125 8 3 0158 48 125 8 5 017617 050 2 4 0202 49 050 2 6 020418 075 2 4 0198 50 075 2 6 020019 100 2 4 0194 51 100 2 6 019620 125 2 4 0191 52 125 2 6 019221 050 4 4 0194 53 050 4 6 019822 075 4 4 0190 54 075 4 6 019423 100 4 4 0186 55 100 4 6 019024 125 4 4 0183 56 125 4 6 018625 050 6 4 0186 57 050 6 6 019326 075 6 4 0182 58 075 6 6 018927 100 6 4 0178 59 100 6 6 018528 125 6 4 0175 60 125 6 6 018129 050 8 4 0178 61 050 8 6 018830 075 8 4 0174 62 075 8 6 018431 100 8 4 0170 63 100 8 6 018032 125 8 4 0167 64 125 8 6 0176

(b)









119894119891 V119894119895 gt V119894minus1119895

119868119899119891 119894119891 V119894119895 le V119894minus1119895(8)

119879119864119879 = 119869sum119895=1

119879119864119879119895

119879119864119879119895 = 119868sum119894=1


(9)

119879119868119879 = 119869sum119895=1

119879119868119879119895

119879119868119879119895 = 119868sum119894=1

[ 1119879119879119862119894119895 minus1119879119879119862lowast] Δ119895




0

015

03

045

06

Aver

age T

IT

12 14 16 18 2 22 24 26 28 31Flow Rate (vehhr)

50

100

150

200

250

Tota

l TET

(a)

minus150

minus100

minus50

0

50

TET

Cha

nge


minus150

minus100

minus50

0

50


minus150

minus100

minus50

0

50


50 7525ADS Share

50 7525ADS Share

50 7525ADS Share

(b)

1400

1800

2400

Case 1

Case 2

Case 3Case 4Case 5

Case 6

Case 7

Case 8

Case 9

Case 10Case 11



Case 26

Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6

Case

47

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54


Case 63Case 64

-02

-015

-01

-005

0

005

times 100

TIT Increased

TIT Reduced


755025

755025

(c)

1400

1800

2400

Case 1

Case 2

Case 3Case 4Case 5

Case 6

Case 7

Case 8

Case 9

Case 10

Case 11Case 12


Case 26

Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6

Case

47

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54


Case 64

-08

-06

-04

-02

0

times 100

TIT Increased

TIT Reduced


755025

755025

(d)








ln (119872119874119864119894119895) =

3sum119897=0

3sum119898=0


3sum119898=0

1198701015840119897119898 times V119897119894119895 times 119886119898119894119895 119894119891 119886 lt 0 (11)




1400 1800 2400


857 852 866

Kg 100km

1971 1964 199

g 100km 2803 2796 2733



2 Emission

Ｒ Emission

(a)

1400

1800

2400

Case 1

Case 2


Case 7

Case 8

Case 9

Case 10

Case 11Case 12



Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6Ca

se 4

7

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54

Case 55Case 56


-004-0020002004006


Consumption Reduced



(b)

1400

1800

2400

Case 1

Case 2


Case 7

Case 8

Case 9

Case 10

Case 11Case 12



Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6Ca

se 4

7

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54


Case 64-01-008-006-004-0020

times100

755025

755025755025



(c)

1400

1800

2400

Case 1

Case 2


Case 7

Case 8

Case 9

Case 10

Case 11Case 12



Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6Ca

se 4

7

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54


Case 64-008-006-004-0020

times 100

755025

ADS FlowShare Rate

755025

755025



(d)















Data Availability



[05023]

[05063]

[05024]

[05064]

[05025]

[05065]

[05026]

[05066]

[12586]


0

02

04

06

08

1Sc

ores


(a)

[05023]

[05063]

[05024]

[05064]

[05025]

[05065]

[05026]

[05066]

[12586]


ObjObj

ObjObj

02

04

06

08

1

Scor

es


033 033 033

05 025 025

025 05 025025 025 05

＜Ｄ1

＜Ｄ2

＜Ｄ3

＜Ｄ4

(b)



Disclosure




Acknowledgments


References


























































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia



ADS Share 25


1800

2000

2200

2400

2600

2800

3000

3200

Max

Flo

w R

ate (

veh

hr)

1800

2000

2200

2400

2600

2800

3000

3200ADS Share 50


1800

2000

2200

2400

2600

2800

3000

3200ADS Share 75

2 31Combinations

(a)

206

207

208

209

21

211

212

213

214

215

216

Aver

age

Coe

ffici

ent o

f Var

iatio

n

ADS Share 25


14

142

144

146

148

15

152

154

156ADS Share 50


08

0805

081

0815

082

0825

083

0835

084ADS Share 75

2 31Combinations

(b)


119860119879119863 = sum119869119869=1 119860119879119863119895119869 119860119879119863119895 = (119901119868119895 minus 1199011119895)119868

(6)

119878119888119900119903119890119862119886119904119890 119896 = sum119869119869=1 V1119895 (119860119879119863119895119862119886119904119890 119896)119869 minus (119860119879119879119862119886119904119890 119896) (7)




1400

1800

2400

Case 1

Case 2

Case 3Case 4Case 5

Case 6

Case 7

Case 8

Case 9

Case 10

Case 11Case 12



Case 26

Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6Ca

se 4

7

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54


Case 64

-5

0

5

10

MobilityImpaired

times 100


755025

755025

755025

times10-3

(a)

CaseIntra

Platoon Headway


Max Platoon Length




Max Platoon Length


1 050 2 3 0200 33 050 2 5 02042 075 2 3 0196 34 075 2 5 02003 100 2 3 0193 35 100 2 5 01964 125 2 3 0190 36 125 2 5 01925 050 4 3 0189 37 050 4 5 01986 075 4 3 0186 38 075 4 5 01947 100 4 3 0182 39 100 4 5 01908 125 4 3 0179 40 125 4 5 01869 050 6 3 0178 41 050 6 5 0193

10 075 6 3 0175 42 075 6 5 018911 100 6 3 0172 43 100 6 5 018512 125 6 3 0168 44 125 6 5 018113 050 8 3 0168 45 050 8 5 018814 075 8 3 0164 46 075 8 5 018415 100 8 3 0161 47 100 8 5 018016 125 8 3 0158 48 125 8 5 017617 050 2 4 0202 49 050 2 6 020418 075 2 4 0198 50 075 2 6 020019 100 2 4 0194 51 100 2 6 019620 125 2 4 0191 52 125 2 6 019221 050 4 4 0194 53 050 4 6 019822 075 4 4 0190 54 075 4 6 019423 100 4 4 0186 55 100 4 6 019024 125 4 4 0183 56 125 4 6 018625 050 6 4 0186 57 050 6 6 019326 075 6 4 0182 58 075 6 6 018927 100 6 4 0178 59 100 6 6 018528 125 6 4 0175 60 125 6 6 018129 050 8 4 0178 61 050 8 6 018830 075 8 4 0174 62 075 8 6 018431 100 8 4 0170 63 100 8 6 018032 125 8 4 0167 64 125 8 6 0176

(b)









119894119891 V119894119895 gt V119894minus1119895

119868119899119891 119894119891 V119894119895 le V119894minus1119895(8)

119879119864119879 = 119869sum119895=1

119879119864119879119895

119879119864119879119895 = 119868sum119894=1


(9)

119879119868119879 = 119869sum119895=1

119879119868119879119895

119879119868119879119895 = 119868sum119894=1

[ 1119879119879119862119894119895 minus1119879119879119862lowast] Δ119895




0

015

03

045

06

Aver

age T

IT

12 14 16 18 2 22 24 26 28 31Flow Rate (vehhr)

50

100

150

200

250

Tota

l TET

(a)

minus150

minus100

minus50

0

50

TET

Cha

nge


minus150

minus100

minus50

0

50


minus150

minus100

minus50

0

50


50 7525ADS Share

50 7525ADS Share

50 7525ADS Share

(b)

1400

1800

2400

Case 1

Case 2

Case 3Case 4Case 5

Case 6

Case 7

Case 8

Case 9

Case 10Case 11



Case 26

Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6

Case

47

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54


Case 63Case 64

-02

-015

-01

-005

0

005

times 100

TIT Increased

TIT Reduced


755025

755025

(c)

1400

1800

2400

Case 1

Case 2

Case 3Case 4Case 5

Case 6

Case 7

Case 8

Case 9

Case 10

Case 11Case 12


Case 26

Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6

Case

47

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54


Case 64

-08

-06

-04

-02

0

times 100

TIT Increased

TIT Reduced


755025

755025

(d)








ln (119872119874119864119894119895) =

3sum119897=0

3sum119898=0


3sum119898=0

1198701015840119897119898 times V119897119894119895 times 119886119898119894119895 119894119891 119886 lt 0 (11)




1400 1800 2400


857 852 866

Kg 100km

1971 1964 199

g 100km 2803 2796 2733



2 Emission

Ｒ Emission

(a)

1400

1800

2400

Case 1

Case 2


Case 7

Case 8

Case 9

Case 10

Case 11Case 12



Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6Ca

se 4

7

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54

Case 55Case 56


-004-0020002004006


Consumption Reduced



(b)

1400

1800

2400

Case 1

Case 2


Case 7

Case 8

Case 9

Case 10

Case 11Case 12



Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6Ca

se 4

7

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54


Case 64-01-008-006-004-0020

times100

755025

755025755025



(c)

1400

1800

2400

Case 1

Case 2


Case 7

Case 8

Case 9

Case 10

Case 11Case 12



Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6Ca

se 4

7

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54


Case 64-008-006-004-0020

times 100

755025

ADS FlowShare Rate

755025

755025



(d)















Data Availability



[05023]

[05063]

[05024]

[05064]

[05025]

[05065]

[05026]

[05066]

[12586]


0

02

04

06

08

1Sc

ores


(a)

[05023]

[05063]

[05024]

[05064]

[05025]

[05065]

[05026]

[05066]

[12586]


ObjObj

ObjObj

02

04

06

08

1

Scor

es


033 033 033

05 025 025

025 05 025025 025 05

＜Ｄ1

＜Ｄ2

＜Ｄ3

＜Ｄ4

(b)



Disclosure




Acknowledgments


References


























































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia



1400

1800

2400

Case 1

Case 2

Case 3Case 4Case 5

Case 6

Case 7

Case 8

Case 9

Case 10

Case 11Case 12



Case 26

Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6Ca

se 4

7

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54


Case 64

-5

0

5

10

MobilityImpaired

times 100


755025

755025

755025

times10-3

(a)

CaseIntra

Platoon Headway


Max Platoon Length




Max Platoon Length


1 050 2 3 0200 33 050 2 5 02042 075 2 3 0196 34 075 2 5 02003 100 2 3 0193 35 100 2 5 01964 125 2 3 0190 36 125 2 5 01925 050 4 3 0189 37 050 4 5 01986 075 4 3 0186 38 075 4 5 01947 100 4 3 0182 39 100 4 5 01908 125 4 3 0179 40 125 4 5 01869 050 6 3 0178 41 050 6 5 0193

10 075 6 3 0175 42 075 6 5 018911 100 6 3 0172 43 100 6 5 018512 125 6 3 0168 44 125 6 5 018113 050 8 3 0168 45 050 8 5 018814 075 8 3 0164 46 075 8 5 018415 100 8 3 0161 47 100 8 5 018016 125 8 3 0158 48 125 8 5 017617 050 2 4 0202 49 050 2 6 020418 075 2 4 0198 50 075 2 6 020019 100 2 4 0194 51 100 2 6 019620 125 2 4 0191 52 125 2 6 019221 050 4 4 0194 53 050 4 6 019822 075 4 4 0190 54 075 4 6 019423 100 4 4 0186 55 100 4 6 019024 125 4 4 0183 56 125 4 6 018625 050 6 4 0186 57 050 6 6 019326 075 6 4 0182 58 075 6 6 018927 100 6 4 0178 59 100 6 6 018528 125 6 4 0175 60 125 6 6 018129 050 8 4 0178 61 050 8 6 018830 075 8 4 0174 62 075 8 6 018431 100 8 4 0170 63 100 8 6 018032 125 8 4 0167 64 125 8 6 0176

(b)









119894119891 V119894119895 gt V119894minus1119895

119868119899119891 119894119891 V119894119895 le V119894minus1119895(8)

119879119864119879 = 119869sum119895=1

119879119864119879119895

119879119864119879119895 = 119868sum119894=1


(9)

119879119868119879 = 119869sum119895=1

119879119868119879119895

119879119868119879119895 = 119868sum119894=1

[ 1119879119879119862119894119895 minus1119879119879119862lowast] Δ119895




0

015

03

045

06

Aver

age T

IT

12 14 16 18 2 22 24 26 28 31Flow Rate (vehhr)

50

100

150

200

250

Tota

l TET

(a)

minus150

minus100

minus50

0

50

TET

Cha

nge


minus150

minus100

minus50

0

50


minus150

minus100

minus50

0

50


50 7525ADS Share

50 7525ADS Share

50 7525ADS Share

(b)

1400

1800

2400

Case 1

Case 2

Case 3Case 4Case 5

Case 6

Case 7

Case 8

Case 9

Case 10Case 11



Case 26

Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6

Case

47

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54


Case 63Case 64

-02

-015

-01

-005

0

005

times 100

TIT Increased

TIT Reduced


755025

755025

(c)

1400

1800

2400

Case 1

Case 2

Case 3Case 4Case 5

Case 6

Case 7

Case 8

Case 9

Case 10

Case 11Case 12


Case 26

Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6

Case

47

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54


Case 64

-08

-06

-04

-02

0

times 100

TIT Increased

TIT Reduced


755025

755025

(d)








ln (119872119874119864119894119895) =

3sum119897=0

3sum119898=0


3sum119898=0

1198701015840119897119898 times V119897119894119895 times 119886119898119894119895 119894119891 119886 lt 0 (11)




1400 1800 2400


857 852 866

Kg 100km

1971 1964 199

g 100km 2803 2796 2733



2 Emission

Ｒ Emission

(a)

1400

1800

2400

Case 1

Case 2


Case 7

Case 8

Case 9

Case 10

Case 11Case 12



Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6Ca

se 4

7

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54

Case 55Case 56


-004-0020002004006


Consumption Reduced



(b)

1400

1800

2400

Case 1

Case 2


Case 7

Case 8

Case 9

Case 10

Case 11Case 12



Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6Ca

se 4

7

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54


Case 64-01-008-006-004-0020

times100

755025

755025755025



(c)

1400

1800

2400

Case 1

Case 2


Case 7

Case 8

Case 9

Case 10

Case 11Case 12



Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6Ca

se 4

7

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54


Case 64-008-006-004-0020

times 100

755025

ADS FlowShare Rate

755025

755025



(d)















Data Availability



[05023]

[05063]

[05024]

[05064]

[05025]

[05065]

[05026]

[05066]

[12586]


0

02

04

06

08

1Sc

ores


(a)

[05023]

[05063]

[05024]

[05064]

[05025]

[05065]

[05026]

[05066]

[12586]


ObjObj

ObjObj

02

04

06

08

1

Scor

es


033 033 033

05 025 025

025 05 025025 025 05

＜Ｄ1

＜Ｄ2

＜Ｄ3

＜Ｄ4

(b)



Disclosure




Acknowledgments


References


























































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia









119894119891 V119894119895 gt V119894minus1119895

119868119899119891 119894119891 V119894119895 le V119894minus1119895(8)

119879119864119879 = 119869sum119895=1

119879119864119879119895

119879119864119879119895 = 119868sum119894=1


(9)

119879119868119879 = 119869sum119895=1

119879119868119879119895

119879119868119879119895 = 119868sum119894=1

[ 1119879119879119862119894119895 minus1119879119879119862lowast] Δ119895




0

015

03

045

06

Aver

age T

IT

12 14 16 18 2 22 24 26 28 31Flow Rate (vehhr)

50

100

150

200

250

Tota

l TET

(a)

minus150

minus100

minus50

0

50

TET

Cha

nge


minus150

minus100

minus50

0

50


minus150

minus100

minus50

0

50


50 7525ADS Share

50 7525ADS Share

50 7525ADS Share

(b)

1400

1800

2400

Case 1

Case 2

Case 3Case 4Case 5

Case 6

Case 7

Case 8

Case 9

Case 10Case 11



Case 26

Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6

Case

47

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54


Case 63Case 64

-02

-015

-01

-005

0

005

times 100

TIT Increased

TIT Reduced


755025

755025

(c)

1400

1800

2400

Case 1

Case 2

Case 3Case 4Case 5

Case 6

Case 7

Case 8

Case 9

Case 10

Case 11Case 12


Case 26

Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6

Case

47

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54


Case 64

-08

-06

-04

-02

0

times 100

TIT Increased

TIT Reduced


755025

755025

(d)








ln (119872119874119864119894119895) =

3sum119897=0

3sum119898=0


3sum119898=0

1198701015840119897119898 times V119897119894119895 times 119886119898119894119895 119894119891 119886 lt 0 (11)




1400 1800 2400


857 852 866

Kg 100km

1971 1964 199

g 100km 2803 2796 2733



2 Emission

Ｒ Emission

(a)

1400

1800

2400

Case 1

Case 2


Case 7

Case 8

Case 9

Case 10

Case 11Case 12



Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6Ca

se 4

7

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54

Case 55Case 56


-004-0020002004006


Consumption Reduced



(b)

1400

1800

2400

Case 1

Case 2


Case 7

Case 8

Case 9

Case 10

Case 11Case 12



Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6Ca

se 4

7

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54


Case 64-01-008-006-004-0020

times100

755025

755025755025



(c)

1400

1800

2400

Case 1

Case 2


Case 7

Case 8

Case 9

Case 10

Case 11Case 12



Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6Ca

se 4

7

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54


Case 64-008-006-004-0020

times 100

755025

ADS FlowShare Rate

755025

755025



(d)















Data Availability



[05023]

[05063]

[05024]

[05064]

[05025]

[05065]

[05026]

[05066]

[12586]


0

02

04

06

08

1Sc

ores


(a)

[05023]

[05063]

[05024]

[05064]

[05025]

[05065]

[05026]

[05066]

[12586]


ObjObj

ObjObj

02

04

06

08

1

Scor

es


033 033 033

05 025 025

025 05 025025 025 05

＜Ｄ1

＜Ｄ2

＜Ｄ3

＜Ｄ4

(b)



Disclosure




Acknowledgments


References


























































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia



0

015

03

045

06

Aver

age T

IT

12 14 16 18 2 22 24 26 28 31Flow Rate (vehhr)

50

100

150

200

250

Tota

l TET

(a)

minus150

minus100

minus50

0

50

TET

Cha

nge


minus150

minus100

minus50

0

50


minus150

minus100

minus50

0

50


50 7525ADS Share

50 7525ADS Share

50 7525ADS Share

(b)

1400

1800

2400

Case 1

Case 2

Case 3Case 4Case 5

Case 6

Case 7

Case 8

Case 9

Case 10Case 11



Case 26

Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6

Case

47

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54


Case 63Case 64

-02

-015

-01

-005

0

005

times 100

TIT Increased

TIT Reduced


755025

755025

(c)

1400

1800

2400

Case 1

Case 2

Case 3Case 4Case 5

Case 6

Case 7

Case 8

Case 9

Case 10

Case 11Case 12


Case 26

Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6

Case

47

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54


Case 64

-08

-06

-04

-02

0

times 100

TIT Increased

TIT Reduced


755025

755025

(d)








ln (119872119874119864119894119895) =

3sum119897=0

3sum119898=0


3sum119898=0

1198701015840119897119898 times V119897119894119895 times 119886119898119894119895 119894119891 119886 lt 0 (11)




1400 1800 2400


857 852 866

Kg 100km

1971 1964 199

g 100km 2803 2796 2733



2 Emission

Ｒ Emission

(a)

1400

1800

2400

Case 1

Case 2


Case 7

Case 8

Case 9

Case 10

Case 11Case 12



Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6Ca

se 4

7

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54

Case 55Case 56


-004-0020002004006


Consumption Reduced



(b)

1400

1800

2400

Case 1

Case 2


Case 7

Case 8

Case 9

Case 10

Case 11Case 12



Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6Ca

se 4

7

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54


Case 64-01-008-006-004-0020

times100

755025

755025755025



(c)

1400

1800

2400

Case 1

Case 2


Case 7

Case 8

Case 9

Case 10

Case 11Case 12



Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6Ca

se 4

7

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54


Case 64-008-006-004-0020

times 100

755025

ADS FlowShare Rate

755025

755025



(d)















Data Availability



[05023]

[05063]

[05024]

[05064]

[05025]

[05065]

[05026]

[05066]

[12586]


0

02

04

06

08

1Sc

ores


(a)

[05023]

[05063]

[05024]

[05064]

[05025]

[05065]

[05026]

[05066]

[12586]


ObjObj

ObjObj

02

04

06

08

1

Scor

es


033 033 033

05 025 025

025 05 025025 025 05

＜Ｄ1

＜Ｄ2

＜Ｄ3

＜Ｄ4

(b)



Disclosure




Acknowledgments


References


























































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia








ln (119872119874119864119894119895) =

3sum119897=0

3sum119898=0


3sum119898=0

1198701015840119897119898 times V119897119894119895 times 119886119898119894119895 119894119891 119886 lt 0 (11)




1400 1800 2400


857 852 866

Kg 100km

1971 1964 199

g 100km 2803 2796 2733



2 Emission

Ｒ Emission

(a)

1400

1800

2400

Case 1

Case 2


Case 7

Case 8

Case 9

Case 10

Case 11Case 12



Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6Ca

se 4

7

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54

Case 55Case 56


-004-0020002004006


Consumption Reduced



(b)

1400

1800

2400

Case 1

Case 2


Case 7

Case 8

Case 9

Case 10

Case 11Case 12



Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6Ca

se 4

7

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54


Case 64-01-008-006-004-0020

times100

755025

755025755025



(c)

1400

1800

2400

Case 1

Case 2


Case 7

Case 8

Case 9

Case 10

Case 11Case 12



Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6Ca

se 4

7

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54


Case 64-008-006-004-0020

times 100

755025

ADS FlowShare Rate

755025

755025



(d)















Data Availability



[05023]

[05063]

[05024]

[05064]

[05025]

[05065]

[05026]

[05066]

[12586]


0

02

04

06

08

1Sc

ores


(a)

[05023]

[05063]

[05024]

[05064]

[05025]

[05065]

[05026]

[05066]

[12586]


ObjObj

ObjObj

02

04

06

08

1

Scor

es


033 033 033

05 025 025

025 05 025025 025 05

＜Ｄ1

＜Ｄ2

＜Ｄ3

＜Ｄ4

(b)



Disclosure




Acknowledgments


References


























































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia



1400 1800 2400


857 852 866

Kg 100km

1971 1964 199

g 100km 2803 2796 2733



2 Emission

Ｒ Emission

(a)

1400

1800

2400

Case 1

Case 2


Case 7

Case 8

Case 9

Case 10

Case 11Case 12



Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6Ca

se 4

7

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54

Case 55Case 56


-004-0020002004006


Consumption Reduced



(b)

1400

1800

2400

Case 1

Case 2


Case 7

Case 8

Case 9

Case 10

Case 11Case 12



Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6Ca

se 4

7

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54


Case 64-01-008-006-004-0020

times100

755025

755025755025



(c)

1400

1800

2400

Case 1

Case 2


Case 7

Case 8

Case 9

Case 10

Case 11Case 12



Case 27

Case 28

Case 29

Case 30

Case 31

Case 32

Case 33

Case 3

4Cas

e 35

Case

36Ca

se 37

Case

38Ca

se 3

9Ca

se 4

0C

ase 4

1C

ase 4

2C

ase 4

3C

ase 4

4Ca

se 4

5Ca

se 4

6Ca

se 4

7

Case

48

Case

49

Case 5

0

Case 5

1

Case 52

Case 53

Case 54


Case 64-008-006-004-0020

times 100

755025

ADS FlowShare Rate

755025

755025



(d)















Data Availability



[05023]

[05063]

[05024]

[05064]

[05025]

[05065]

[05026]

[05066]

[12586]


0

02

04

06

08

1Sc

ores


(a)

[05023]

[05063]

[05024]

[05064]

[05025]

[05065]

[05026]

[05066]

[12586]


ObjObj

ObjObj

02

04

06

08

1

Scor

es


033 033 033

05 025 025

025 05 025025 025 05

＜Ｄ1

＜Ｄ2

＜Ｄ3

＜Ｄ4

(b)



Disclosure




Acknowledgments


References


























































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia












Data Availability



[05023]

[05063]

[05024]

[05064]

[05025]

[05065]

[05026]

[05066]

[12586]


0

02

04

06

08

1Sc

ores


(a)

[05023]

[05063]

[05024]

[05064]

[05025]

[05065]

[05026]

[05066]

[12586]


ObjObj

ObjObj

02

04

06

08

1

Scor

es


033 033 033

05 025 025

025 05 025025 025 05

＜Ｄ1

＜Ｄ2

＜Ｄ3

＜Ｄ4

(b)



Disclosure




Acknowledgments


References


























































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia



[05023]

[05063]

[05024]

[05064]

[05025]

[05065]

[05026]

[05066]

[12586]


0

02

04

06

08

1Sc

ores


(a)

[05023]

[05063]

[05024]

[05064]

[05025]

[05065]

[05026]

[05066]

[12586]


ObjObj

ObjObj

02

04

06

08

1

Scor

es


033 033 033

05 025 025

025 05 025025 025 05

＜Ｄ1

＜Ｄ2

＜Ｄ3

＜Ｄ4

(b)



Disclosure




Acknowledgments


References


























































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia



Disclosure




Acknowledgments


References


























































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia
































RoboticsJournal of




VLSI Design



Shock and Vibration







Journal of



Volume 2018



Volume 2018


Journal of




SensorsJournal of



RotatingMachinery





Propagation






Hindawi


Advances in

Multimedia


Modeling Microscopic Car-Following Strategy of Mixed Traffic to...

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Transcript of Modeling Microscopic Car-Following Strategy of Mixed Traffic to...