Framework for Multi-Resolution

Analyses of Advanced Traffic

Management Strategiespresented by

Mohammed Hadi, Ph.D., PE

Florida International University

December 6, 2016

Needs for the Framework

• Performance measurement and estimation is increasingly needed

• Combinations of advanced modeling tools are needed for advanced strategies

– Congested conditions, ML, construction and incident management, ATDM, ICM, and automated and connected vehicles

• Tools and methods are becoming available, however integration of tools is needed for effective modeling

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Multi-Resolution Modeling

3

Objectives

• Investigate the ability of combinations of tools in estimating performance and analyzing congestion and advanced strategies

• Recommend a framework for use in support of agency analysis and modeling processes

• Apply and assess the utilization of tools in the modeling of use cases

4

Proposed Framework Components

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Discussed Aspects

• Tool assessment criteria

• O-D estimation tools and inputs

• ML modeling applications– Fixed VOT versus VOT distribution

– Incorporating reliability in the assignment

– Ability of modeling ML shifts due to pricing change

– Modeling CAV on ML

– Demonstration of MRM use

• Work zone MRM modeling applications

• Capacity and signal timing Improvements MRM applications - varying demand levels

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Criteria for Tool Assessment

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• General Criteria (Hardware, Software, Interface, and etc.)

• Shortest Path and Path Choice

• Assignment type

• En-route dynamic routing (e.g., in-vehicle dynamic navigation system, DMS)

• Specification of fine-grained assignment interval (e.g., 15-30 minutes)

• Allows fixing paths for parts of the demands

• Convergence criteria

• Outputting and using interval-based convergence gap

• Assignment of individual vehicles

• Assignment of multiple demand types

Criteria for Tool Assessment (Con’t)

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• Traffic Flow Model (TFM)

• Traffic flow model type

• Queuing and spillback

• Modeling of signalized arterials

• Modeling of freeways

• Modeling of alternative routes to facilities

• Automatic calculation of signal timing in dynamic traffic assignment

• Lane-by-lane simulation

• Merging/weaving simulation

• Modeling turn lane and bay Length

Criteria for Tool Assessment (Con’t)

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• ML Modeling

• Generalized cost in assignment

• Incorporation of willingness-to-pay (WTP) into assignment

• Link access restrictions/prohibitions by vehicle type

• Modeling managed lanes and reversed lanes

• Fixed and time-of-day pricing by user types

• Dynamic pricing

• Inhomogeneous VOT and VOR

• Advanced Vehicle Technology

• Capacity as a function of proportion of vehicle types

• Fixed and time-of-day pricing by different percentage of advanced vehicle technology

ODME - Factorized Matrix Start Initial

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Goodness-of-Fit Statistics

Initial Demand

Running DTA

without ODMERunning ODME Optimization

Macroscopic Model Mesoscopic Models Macroscopic Model Mesoscopic Models

MAE (veh/ln/15min) 108.13 101.56 - 123.48 80.74 68.21 - 112.51

MAPE (%) 18.69 16.11 - 20.61 13.32 12.52 - 15.35

RMSE (veh/ln/15min) 158.79 149.14 - 181.32 118.56 100.16 - 165.21

R squared 0.46 0.29 - 0.47 0.72 0.39 - 0.81

Improved Demand Estimation Utilizing

ODME Optimization (%)25.33 8.88 - 32.84

Impact of ODME – Fine-Tuned Initial

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Goodness-of-Fit Statistics

Initial Demand

Running DTA

without ODMERunning ODME Optimization

Macroscopic Model Mesoscopic Models Macroscopic Model Mesoscopic Models

MAE (veh/ln/15min) 62.13 62.19 - 67.79 59.71 55.18 - 63.44

MAPE (%) 11.09 10.98 - 11.21 10.9 10.62 - 11.02

RMSE (veh/ln/15min) 91.24 91.33 - 95.15 87.68 81.03 - 93.15

R squared 0.821 0.80 - 0.832 0.95 0.81 - 0.96

Improved Demand Estimation Utilizing

ODME Optimization (%)3.9 2.09 - 11.27

Impact of Using Partial Trips (PT)

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Using Partial Trips

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Facility Type Initial (Before ODME) Counts-Only Adding Partial Trip

Freeway 28.43% 28.11% 22.01%

Freeway-Ramp 39.42% 37.86% 28.09%

Arterials 44.93% 34.69% 4.65%

Total 44.80% 39.10% 22.93%

Observed Count volume From Initial(No ODME) Counts Only Adding Partial Trip

26-894 44.75% 35.3% 6.81%

1308-2620 26.55% 26.18% 21%

Total 44.80% 39.10% 22.93%

ML Modeling VOT Distribution

Goodness-of-Fit Statistics

Value of Time $ (VOT)

$12 $20 $30 $40 $50$40 Fixed

(without Distribution)

MAPE (%) 16.50 9.70 11.86 4.01 5.73 9.03

RMSE(veh/ln/15min) 73.94 41.76 52.11 18.11 26.60 40.34

Impact of VOR Use

Goodness-of-Fit Statistics ELToD Meso Macro

With

Consideration of

VOR

RMSE

(veh/ln/15min)12.00 8.23-9.18 10.77

MAPE (%) 2.29 1.89-1.96 2.27

Without

Consideration of

VOR

RMSE

(veh/ln/15min)54.30 40.34-46.22 37.03

MAPE (%) 13.36 9.03-11.29 8.68

𝑇𝑇𝐼𝑛% = 𝑒(𝑗𝑛 𝐿𝐻𝐿+𝑘𝑛𝑑𝑐𝑐𝑟𝑖𝑡 +𝑙𝑛𝑅0.05" ) (4.1)

Where, 𝑇𝑇𝐼𝑛% = 𝑛𝑡ℎ 𝑝𝑒𝑟𝑐𝑒𝑛𝑡𝑖𝑙𝑒 𝑇𝑇𝐼 𝑣𝑎𝑙𝑢𝑒

𝐿𝐻𝐿 = 𝑙𝑎𝑛𝑒 − ℎ𝑜𝑢𝑟 𝑙𝑜𝑠𝑡 𝑑𝑐𝑐𝑟𝑖𝑡 = 𝑐𝑟𝑖𝑡𝑖𝑐𝑎𝑙 𝑑𝑒𝑚𝑎𝑛𝑑 − 𝑐𝑎𝑝𝑎𝑐𝑖𝑡𝑦 𝑟𝑎𝑡𝑖𝑜

𝑅0.05" = ℎ𝑜𝑢𝑟𝑠 𝑜𝑓 𝑟𝑎𝑖𝑛𝑓𝑎𝑙 𝑒𝑥𝑐𝑒𝑒𝑖𝑛𝑔 0.05 𝑖𝑛𝑐ℎ𝑒𝑠

𝑗𝑛 , 𝑘𝑛 ,𝑙𝑛 = 𝑐𝑜𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑡𝑠 𝑓𝑜𝑟 𝑛𝑡ℎ 𝑝𝑒𝑟𝑐𝑒𝑛𝑡𝑖𝑙𝑒

U 𝑎1 𝑇𝑇 + 𝑎2 𝑇𝐶 + 𝑎3 𝑇𝑇80% + 𝑎4 𝑇𝑇95%

Can Models Predict ML Shifts

Goodness-of-Fit StatisticsFixed Pricing and Static

Assignment(ELTOD)

Dynamic pricing with Dynamic Assignment

(Avenue)

New Toll PolicyRMSE (veh/ln/15min) 51.42 25.15

MAPE (%) 12.22 5.87

Old Toll policyRMSE (veh/ln/15min) 67.39 31.04

MAPE (%) 13.48 5.90

Macro+Meso+Micro Modeling

• Waiting for I-95 Model from FDOT D6

Estimation of CV MP

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Per

cen

tage

of

CV

Year

Min MP

Max MP

MP Difference

Estimation of CV MP

Percentage of CACC Vehicles (%) Lane Capacity (veh/ln/hr)

0 2018

0 2092

40 2230

60 2500

80 2890

100 4000

Impact of CACC on ML Using DTA

0

5

10

15

20

25

30

35

40

45

50

0.8 0.9 1 1.1 1.2 1.3 1.4 1.5

Tra

vel D

ela

y(m

in)

Demand/Capacity Ratio

QDAT

Quickzone

Freeval

DTAlite

Queue theory

Vissim

Work Zone Modeling

Construction Diversion Analysis

• Demonstrate how day-to-day learning type assignment can be used in conditions, in which equilibrium cannot be assumed

• For work zones with different number of days utilizing DTALite learning

• Compare to user equilibrium and logit model

Diversion Analysis Results

Meso+Micro Modeling of WZ

0

10

20

30

40

50

60

I-595 I-95_NB Ramp I-95_SB Ramp

Without Diversion

With Diversion_long-term

With Diversion_short-term

ATM - Modeling Different Demands

Turning Movement in DTA

• Good estimation of link volume does not guarantee good turning movement volumes

• Turning movement volumes needed for many applications - need incorporating TM in calibration and updating zones and connecters

• Use TM counts, improve network connectors, and fine-tune demands to produce counts

Observed Count volume

PRMSE Without

Multilevel calibration

PRMSE With Multi

level Calibration

0 ~ 250 56.35% 26.96%

250 ~ 500 62.99% 26.55%

>500 82.90% 23.28%

Application of MRM to DTA

• Testing the benefits of MRM for updating capacity and signal control under different demands