A Participatory Sensing Approach for Personalized

Distance-to-empty Prediction and Green Telematics

Chien-Ming Tseng

†, Chi-Kin Chau

†, Sohan Dsouza

†and Erik Wilhelm

‡

†Masdar Institute of Science and Technology, UAE

‡Singapore University of Technology and Design, Singapore

{ctseng, ckchau, sdsouza}@masdar.ac.ae, erikwilhelm@sutd.edu.sg

ABSTRACTParticipatory sensing is an emerging concept that integratescrowd-sourced data collection and knowledge discovery ofcollective behavior. Capitalizing on the advent of abundantsensors and information collection systems in near-futurevehicles, we develop a participatory sensing based systemand its methodologies for driving energy e�ciency applica-tions. Distance-to-empty (DTE) is the distance an electricor internal-combustion engine (ICE) vehicle can reach beforeits energy/fuel is exhausted, which is determined by a varietyof uncertain factors, such as driving behavior, terrain, typesof road, tra�c, and vehicle specification. Green telematicsaims to optimize the route selection with lower energy con-sumption. In this paper, we explore an e↵ective approachthat integrates the vehicle data gathered from participatorysensing to provide more accurate personalized DTE predic-tion and green telematics. Our approach relies on extractingthe driver/vehicle/route dependent features and discoveringcorrelations from collective driving data. We also presentconcrete case studies of our results, such as (1) DTE predic-tion for EVs based on the data of ICE vehicles, (2) classifi-cation and recommendations of energy-e�cient driving be-havior, and (3) route-level energy consumption geo-fencingand planning.

Categories and Subject DescriptorsJ.0 [Computer Applications]: General; K.4 [ComputingMilieux]: Computers and Society

KeywordsParticipatory sensing; Distance-to-empty; Green telematics

1. INTRODUCTIONWhile the in-vehicle information systems are increasingly

sophisticated, the information presented in vehicles is notalways accurate. One of the major features is distance-to-

empty (DTE), which is the distance an electric or internal-combustion engine (ICE) vehicle can reach before its en-ergy/fuel is exhausted. DTE is determined by a variety of

Permission to make digital or hard copies of all or part of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor profit or commercial advantage and that copies bear this notice and the full cita-tion on the first page. Copyrights for components of this work owned by others thanACM must be honored. Abstracting with credit is permitted. To copy otherwise, or re-publish, to post on servers or to redistribute to lists, requires prior specific permissionand/or a fee. Request permissions from permissions@acm.org.e-Energy’15, July 14–17, 2015, Bangalore, India.Copyright is held by the owner/author(s). Publication rights licensed to ACM.ACM 978-1-4503-3609-3/15/07 ...$15.00.DOI: http://dx.doi.org/10.1145/2768510.2768530.

factors, such as driver behavior, terrain, types of road, andtra�c, as well as the vehicle specification (e.g., electric orgasoline power, tank capacity, engine load, vehicle weight).

The conventional approach of DTE prediction employedby car manufacturers is based on the projection of past long-term average vehicle energy e�ciency (i.e., the total con-sumed energy/fuel over the total travelled distance). If thereis perfect knowledge about the vehicle, driving behavior andthe route to travel, future energy e�ciency can be estimatedwith high accuracy. However, there are numerous uncertainfactors, which make accurate prediction challenging.

On the other hand, the option of green telematics is be-ing enabled by telematics providers, which aims to opti-mize the route selection decisions with lower fuel/energyconsumption [5]. Green telematics systems are critical tooptimal planning for recharging/refueling and fleet manage-ment. Like DTE prediction, green telematics is a↵ected bythe uncertain factors, such as driving behavior and routes.

These driving energy e�ciency applications can be en-hanced by exploring the historic data from other drivers.Participatory sensing is an emerging concept that integratescrowd-sourced data collection and knowledge discovery ofcollective behavior, enabling a variety of novel applicationsfor pervasive computing systems [3]. The vehicles are be-coming a vital platform for participatory sensing. First,there is a rise of advanced in-vehicle information systems,equipped with network connectivity and processing power,which can be turned into mobile sensing and informationcollection systems. Second, the wide availability of smart-phones carried by passengers can extend the computing andsensing abilities of vehicles. Third, there are abundant o↵-the-shelf and after-market products for vehicle diagnostictools to gather driving data and vehicle information. Aparticipatory sensing platform for vehicles has been criti-cal for a number of intelligent transportation applications(e.g., Google Map, Waze, real-time tra�c alerts). Waze isa participatory App for tra�c monitoring, which howeverdoes not have driving energy e�ciency features.

In this paper, we focus on the applications of participatorysensing related to driving energy e�ciency. In particular,there are a few example applications as follows.

1. Vehicle Variable Applications: Range anxiety is a crit-ical issue for EVs, and accurate DTE prediction ishighly desirable [12]. Since there are far many moreICE vehicles on the road than EVs, one can harnessthe data collected from ICE vehicles to improve theaccuracy of DTE prediction for EVs. In particular,the data of taxis and buses on regular routes can shedlight on the energy consumption of other vehicles.

47

2. Driver Variable Applications: With the diverse datacollected from various drivers, one can compare thedriving behavior among drivers. By identifying thedriver-specific characteristics from the data, one cananalyze the driving behavior and make energy e�-cient driving recommendations. The insights gatheredfrom driving behavior analytics are also relevant to safedriving and auto insurance, to the benefit of consumers(e.g., pay-as-you-drive plan [6]).

3. Route Variable Applications: Extensive green telemat-ics can be enabled to compare di↵erent route optionsaccording to energy/fuel consumption. Route-level en-ergy consumption geo-fencing can be constructed forEVs. Moreover, one can also optimize the route selec-tion in order to refuel at cheaper gas stations.

In spite of the promising potential, there are several chal-lenges of developing an e↵ective participatory sensing sys-tem for driving energy e�ciency applications:

1. Automation: With the availability of large amount ofthe data, an automated system is required to processand analyze data, with minimal human assistance.

2. Flexibility: The analytics synthesized from the datashould be reusable to diverse applications, without theneed for re-processing.

3. Errors and Noise: There exist considerable errors andnoise in the participatory sensing data (e.g., due tosynchronization, mechanic dumping, loss of data, dif-ferences in data sources). The system needs to resolvethe inconsistency and maximize the integrality.

4. Scalability and E�ciency: Many applications requirereal-time processing, and the system needs to deliverthe results e�ciently in a scalable manner.

In this paper, we explore an e↵ective approach that inte-grates the vehicle data gathered from participatory sensingto provide more accurate personalized applications. Ourapproach relies on extracting the driver/vehicle/route de-pendent features and discovering correlations from collec-tive driving data. Furthermore, we present concrete casestudies that utilize our results in diverse related applica-tions, such as (1) DTE prediction for EVs based on the dataof ICE vehicles, (2) classification and recommendations ofenergy-e�cient driving behavior, and (3) route-level energyconsumption geo-fencing and planning.

Outline: We present the related work in Sec. 2. Thesystem framework is presented in Sec. 3. The methodologyof energy consumption model and correlation discovery ispresented in Sec. 4. We performed empirical evaluation onour methodology on electric and ICE vehicles, with resultsdiscussed in Sec. 5. Finally, the case studies that utilize ourresults are discussed in Sec. 6.

2. RELATED WORKThe problem of estimating DTE has been the subject of

a significant number of academic publications, and is also afeature which vehicle manufacturers have been including inproduction vehicles for over two decades [2]. At its most ba-sic level, the DTE, or range remaining can be estimated byobserving the mean energy use over short and long distancesas the authors describe in [12]. The same authors proceed todescribe a system for estimating future travel profile using a

Monte Carlo approach, which is a critical step in determin-ing remaining energy. Estimates of stochastic e↵ects whichmay impact travel velocity and acceleration profiles can becrowd-sourced for identifying tra�c congestion [4].The second step is using an accurate vehicle model to

take the travel profile generated and turn it into future en-ergy demands. Such model-based estimation can be per-formed as described in [7] for EVs. It is also possible forfuel consumption data shared between vehicles to be usedwithout underlying physical profile and vehicle modeling topredict the energy consumption for a given route [5], al-though this approach is sensitive to much uncertainty andnecessitates rigorous machine learning approaches – some-thing which these authors have investigated for control ap-plications, but which can easily be applied to the problemat the center of this work [11]. Early e↵orts at using socialnetwork participation to identify areas of fuel use reductionhave been published [8]. Another participatory sensing sys-tem for improving fuel e�ciency has been proposed in [10],and a simpler model for predicting fuel consumption acrossa driver-route matrix was proposed in [14].

This work di↵erentiates from the previous work in severalaspects: (1) We explicitly consider the features related tothe driver, vehicle and route dependence in the energy con-sumption model. (2) We explore the correlations in drivingdata for personalized applications of individual drivers. (3)We present a unified study to diverse applications related todriving energy e�ciency.

3. SYSTEM FRAMEWORKWe developed a system framework (depicted in Fig. 1)

that consists of a number of components for data sensingand collection, data analytics, and information processing.

Add-on

Device

Smart

phone

In-vehicle

system

Driving Data

Repository

Driver

/Vehicle/Route

Features

Driving Model Idling Model

Correlation Discovery

Energy Consumption

Model

Driving

Coaching

Refueling

Planning

Green

Telematics

DTE

Prediction

Data Collection

Processing

Feature

Extraction

Applications

Participatory Sensing Vehicles

Figure 1: System framework.

3.1 Data CollectionThe data sensing and collection system supports a range

of methods. One method was based on a smartphone App.We developed an App for Android phone paired over a Blue-tooth connection with a standard ELM327 dongle (depictedin Fig. 2), which is widely available online and in automotiveelectronic stores. The dongle plugs into the OBD port, andthe ELM327 protocol is used by the App to query for dataon specific engine and other vehicle parameters. The OBD

48

data, supplemented with data from the phone’s own posi-tioning and accelerometer sensors, are accumulated withinthe device and, if configured and connected for this purpose,uploaded to a specific data server.

Another method is to rely on a tailor-made sensor-equippedelectronic unit that plugs directly into the OBD port to drawpower and query similar data, uploading the data over thecellular network. In future, when in-vehicle computers be-come more open to legal software customization, it wouldalso be possible to develop user-level programs that can beinstalled on these computers and query data through APIsand use either the onboard connectivity or a paired mobiledata device to support participatory sensing.

Figure 2: Android app and ELM327 dongle

3.2 Data Processing and AnalyticsThe data processing and analytics system receives data

from the participatory sensing devices, stores it in databases,and processes it with analytic models for the applications.In a companion project, we develop CloudThink platform [1]as an open system for storing the data uploaded over Inter-net connections from the various in-vehicle data-gatheringapplications and devices, with a data access server allowingsecure and traceable API service in multiple formats, and aweb server to permit data visualization.

4. METHODOLOGYIn this section, we present a unified energy/fuel consump-

tion model for both electric and ICE vehicles.

4.1 Basic ConceptsIn order to perform the participatory sensing and subse-

quent estimation of DTE, features of the model must bedefined. For this work, three categories of features impor-tant for the estimation will be described. The first relatesvehicle speed and idle duration to the typical tra�c condi-tions experienced on a road segment. The second enables thetype of driver to be defined and parameterized. The thirddescribes the physical characteristics of the vehicle which isthe subject of the identification. The following subsectionsprovide more details on how the model was constructed.

1. Route: The level of tra�c which is expected for a roadsegment depends on a plethora of factors, includingtime of day, day of year, special events, etc., and isgenerally di�cult to predict. For this work, the typeof routes was used as the primary explanatory variableand was divided into three categories: small public orprivate roads with urban tra�c, lower capacity“urban”highways, and higher capacity freeways.

2. Driver: The parameterization of the driver was en-abled through the black-box statistical approach cho-sen for the model described in the next section. Di↵er-ent drivers are expected to behave according to prefer-ences for stop/start accelerations, aggression in variousscenarios, propensity for hypermiling, etc.

3. Vehicle: The extrinsic parameters capturing vehiclecharacteristics are only the weight of the vehicle. Allother characteristics such as power, wheelbase, topspeed are parametrized implicitly in the regression co-e�cients identified during the modeling process. Thisparameter is selected to be universal to both electricand ICE vehicles.

4.2 Energy Consumption ModelA tuple of driver-vehicle-route combination is denoted by

(D,V,R). The driving data repository is consisted of thedata sets as a subset of {(D,V,R)}.

We divide a route R by a set of n segments1. The set ofsegments are denoted by {Ri}n

i=1.The total energy consumption E of driver D with vehicle

V on route R is given by:

ED,V,R =nX

i=1

(EdrvD,V,Ri + E

idlD,V,Ri) (1)

where EdrvD,V,Ri is the driving energy consumption and E

idlD,V,Ri

is the idling energy consumption of segment Ri. In this pa-per, we denote a symbol by ˆ for its estimated quantity. Forexample, ED,V,R denotes the estimated energy consumption.

4.2.1 Driving Energy Consumption Model

Considering a particular segment, the driving energy con-sumption of a moving vehicle E

drv has unit in liter or kWh.Here, we drop the subscript D,V,Ri for brevity.

While there are more sophisticated approaches of estimat-ing the driving energy consumption by detailed modelling ofvehicle mechanics, this paper utilizes a black-box approachwithout the detailed knowledge of vehicle mechanics. Thisapproach aims to maximize the applicability on a wide rangeof scenarios arising from participatory sensing.

In this paper, we estimate E

drv by a linear equation:

E

drv = ↵1v + ↵2v2 + ~↵3

~

d+ ~↵4~a+ ~↵5~g

+↵6`+ ↵7m+ c (2)

where

• v is the continuous average speed (i.e., the averagespeed without idling);

• ~

d = (⌧d

, µ

d

,�

d

) is the deceleration tuple, where

– ⌧

d

is the total duration of deceleration;

– µ

d

is the mean deceleration (i.e., the sum of de-celeration values divided by the deceleration du-ration);

– �

d

is the standard deviation of deceleration;

• ~a is the acceleration tuple (similar to the decelerationtuple);

1The length of each segment we choose currently is 100m.But we remark that it is possible to have dynamically vari-able lengths according to the road environments.

49

• ~g = (gx

, g

y

, g

z

) is the gyroscope tuple, where

– g

x

, g

y

, g

z

are the mean absolute measurement val-ues in x/y/z-axis along the moving vehicle;

• ` is the auxiliary load of idling, which the baselinereading when the vehicle is not moving;

• m is the vehicle weight;

• ↵1, ...,↵8, c are coe�cients.

Remarks:

1. The deceleration tuple is critical to capture the energyconsumption for EVs in the presence of regenerativebraking, by which the vehicle’s kinetic energy is con-verted to energy storage, during braking.

2. The deceleration duration ⌧

d

can capture the dynamicof deceleration. For example, when a driver graduallyreleases gas pedal from high speed, the mean deceler-ation µ

d

is small with a long deceleration duration, onthe other hand, when a driver decelerates his vehiclefor a short brake (e.g., avoiding speed camera), µ

d

isalso small with a short deceleration duration. In bothcases, the mean deceleration will be similar, but theduration of deceleration can distinguish the di↵erence.

3. The gyroscope tuple ~g can capture the curvature ofa route. Normally, travelling a curve route consumesmore energy than a straight route, in spite of equaldistance. The gyroscope provides important informa-tion about the environments. The gyroscope may beavailable in smartphones.

4. The auxiliary load of idling ` is the calculated engine

load when the vehicle is not moving, which is oftenavailable from On Board Diagnostic (OBD) port. Theauxiliary load of idling provides the baseline energyconsumption of a stationary vehicle, which is morea↵ected by factors such as in-vehicle air-conditioningand stereo systems.

4.2.2 Idling Energy Consumption Model

Similarly, we rely on a black-box approach to estimate theidling energy consumption. Considering a particular seg-ment, we estimate the idling energy consumption E

idl by alinear equation:

E

idl = �1⌧idl + �2` (3)

where

• ⌧

idl is the total idle duration,

• ` is the auxiliary load of idling.

Here, we drop the subscript D,V,Ri for brevity. The auxil-iary load of idling ` provides the baseline energy consump-tion of a stationary vehicle.

4.3 Estimation of CoefficientsThe coe�cients (↵1, ...,↵7), (�1,�2) and c in the linear

equations (Eqns. (2)-(3)) can be estimated by the standardregression method, if provided a su�ciently large data setof driving data (v, ~d,~a,~g,` ,m,⌧

idl) and energy consumptiondata (Edrv

, E

idl). We assume that each driver-vehicle pair

(D,V) has collected su�cient historic personal driving data,and the coe�cients can be estimated in a-priori manner.

One notable advantage of regression method is that it isless susceptible to random noise, which can arise from var-ious sources (e.g., due to synchronization, mechanic dump-ing, inaccurate measurements).

4.4 Dependence and FeaturesBy participatory sensing, drivers can share their driving

data (e.g., coe�cients of Eqns. (2)-(3)) with each other.Next, we explore an e↵ective approach that integrates theparticipatory sensing data to personalized applications.

In fact, the linear equations (Eqns. (2)-(3)) provide a con-venient way to extract the features that are related to driver,vehicle, and route dependence. In Table 1, we heuristicallyassign the dependence of each parameter, according to themajor e↵ects from the driver, vehicle or route.

For the coe�cients, it is assumed that their dependenceis complementary to that of the respective parameters. Forexample, the average speed v is more likely a↵ected by thedriver and route, while to a less extent by the type of vehicle.Hence, coe�cient ↵1 is considered to be vehicle-dependent,such that the product ↵1v will be specific to a particulartuple (D,V,Ri). For convenience, we assume that c is driver-dependent.

Parameters Driver- Vehicle- Route-dependent dependent dependent

v,

~

d,~a,~g X X` X Xm X⌧

idl X↵1, ..., ~↵5 X

↵6 X↵7 X Xc X�1 X X�2 X

Table 1: Dependence of parameters and coe�cients.

In light of dependence, we can specify each quantity bythe respective subscripts when referring to a particular tuple(D,V,Ri). For example, we write v(D,V) and ↵1(V).

We denote a matrix of energy consumptions byE = (ED,V,Ri).Each ED,V,Ri can be computed by a linear equation:

ED,V,Ri = (cV, cRi , cD,Ri , cD,V)·(xD,Ri ,xD,V,xV,xRi)T+c (4)

where

cV = (↵1(V),↵2(V), ~↵3(V), ~↵4(V), ~↵5(V)) (5)

xD,Ri = (vD,Ri , v2D,Ri ,

~

dD,Ri ,~aD,Ri ,~gD,Ri) (6)

cRi = (↵6(Ri),�2(Ri)) (7)

xD,V = (`D,V, `D,V) (8)

cD,Ri = (↵7(D,Ri)) (9)

xV = (mV) (10)

cD,V = (�1(D,V)) (11)

xRi = (⌧ idlRi ) (12)

To conveniently align with dependence, we refer the inputsas features, when they are grouped by the driver, vehicle or

50

route dependence. The features we refer to are as follows.

• Driver dependent features: xD,Ri , xD,V, cD,Ri , cD,V.

• Vehicle dependent features: cV, xD,V, xV, cD,V.

• Route dependent features: xD,Ri , cRi , cD,Ri , xRi .

A number of applications can be enabled by flexibly sub-stituting the proper features. We present several examplesas follows.

1. To predict ED,V,Ri by a di↵erent driver (D0,V,Ri), we

can use the following equation:

ED,V,Ri = (cV, cRi , cD0,Ri , cD0

,V) · (xD0,Ri ,xD0

,V,xV,xRi)T + c

0

(13)

2. To predict ED,V,Ri by a di↵erent vehicle (D,V0,Ri), we

can use the following equation:

ED,V,Ri = (cV, cRi , cD,Ri , cD,V0) · (xD,Ri ,xD,V0,xV,xRi)T + c

(14)

3. To predict ED,V,Ri by a di↵erent route (D,V,R0i), wecan use the following equation:

ED,V,Ri = (cV, cR0i , cD,R0i , cD,V) · (xD,R0i ,xD,V,xV,xR0i)T + c

(15)

4.5 Correlation Discovery in Driving Data SetLet all the data sets in the repository be {D,V,Ri}. Note

that not every tuple is present in the repository. Next, weseek to identify the correlations in {D,V,Ri}, such that onecan identify the most similar (D0

,V,Ri) to predict the energyconsumption of (D,V,Ri).In this paper, we characterize the correlation in the driv-

ing data between a pair (D,V) and (D0,V0) with the same

route using the concept of dynamic time warping (DTW)[13]. DTW is a widely used concept for determining thesimilarity among time series, and identifying the correspond-ing similar regions between two time series. DTW has beenused in many applications, such as speech recognition, ges-ture recognition, robotics and bioinformatics.

The basic idea of DTW is to determine an optimal align-ment between two time series. Consider two time seriesX = (x[t])nX

t=1 and Y = (y[t])nYt=1 of lengths n

X

and n

Y

respectively. A warp path is defined as W = (w[k])nWk=1,

where the k

th element is w

k

= (i, j), such that i is an in-dex from time series x[t] and j is an index from time se-ries y[t]. n

W

is the length of the warp path W , such thatmax(n

X

, n

Y

) n

W

< n

X

+ n

Y

.The warp path W is subject to the following constraints:

1. w[1] = (1, 1);

2. w[nW

] = (nX

, n

Y

);

3. if w[k] = (i, j) and w[k+1] = (i0, j0), then i i

0 i+1and j j

0 j + 1.

An optimal warp path (illustrated in Fig. 3) is the onewith the minimum distance dist(W ⇤), defined by:

dist(W ⇤) = arg minw

nWX

k=1

d(w[k]) (16)

where d(w[k]) is the distance of the coordinates (i, j) of thek

th element in W .

w[2]

w[3]

w[4]

w[5]w[6]

w[7]

w[8]

w[1]

Figure 3: An illustration of warp path

A simple approach to determine an optimal warp pathbetween two time series is to use dynamic programming.But there are other more e�cient algorithms with linearrunning time [13].

Let vD,V,Ri [t] be the time series of speed profile for tuple(D,V,Ri). For each pair of (D,V,Ri) and (D0

,V0,Ri), let

�

Ri

(D,V),(D0,V0) = dist(W ⇤) (17)

where W

⇤ is the minimum-distance warp path between thetime series vD,V,Ri [t] and vD0

,V0,Ri [t].

Let R(D,V) be a set of route segments that have speedprofiles recorded with (D,V). Namely, if Ri 2 R(D,V), thenthe speed profile vD,V,Ri [t] exists in the repository.Define a correlation metric between each pair of (D,V)

and (D0,V0) by the average minimum warp path distance

over all recorded segments:

�(D,V),(D0,V0) =

X

Ri2R(D,V)\R(D0,V0)

�

Ri

(D,V),(D0,V0)

|R(D,V) \ R(D0,V0)| (18)

Note that �(D,V),(D0,V0) = 1, if R(D,V) \ R(D0

,V0) = ?.In this paper, we will use �(D,V),(D0

,V0) to characterize thesimilarity between each pair of (D,V) and (D0

,V0). We findthe tuple (D0

,V0) with the smallest value �(D,V),(D0,V0) to

estimate energy consumption of (D,V). We also employk-nearest neighbors (k-NN) clustering to find the k mostsimilar speed profiles of (D,V).

Example 1. In Fig. 4, the speed profiles of three drivers

(D1,V1), (D2,V2), (D3,V3) on the same route R1are plotted.

Table 2 shows the minimum warp path distances between

driver (D1,V1) and other drivers. We observe that smaller

minimum warp path distance indeed shows closer similarity

in the speed profiles; (D1,V1) is more similar with (D2,V2)than (D3,V3).

Minimum warp path distance

�

R1

(D1,V1),(D2,V2)1.1385

�

R1

(D1,V1),(D3,V3)1.3883

Table 2: Minimum warp path distance

51

0 5 10 15 200

20

40

60

80

100

Distance (km)

Spee

d (k

m/h

)

(D1,V1)(D2,V2)(D3,V3)

Figure 4: Speed profiles of three drivers on the same route.

5. EXPERIMENTSWe performed experiments to empirically evaluate the

performance of our approach. The observations are reportedin this section.

5.1 Experiment Setup

5.1.1 Routes

We select a range of di↵erent roads, as depicted in Fig. 5and summarized in Table 3. R1 has light tra�c, consistingof both highway and suburban roads. R2 has heavier tra�cwith tra�c lights, and thus results in the much longer idletime seen in Table 3. R3 and R4 are similar in terms oftra�c, with no need to stop. However, R3 has a number oftra�c signs and speed bumps, which force drivers to slowdown, whereas R4 has no restriction.

Route Average Route Average Driving Average IdleLength (KM) Duration (sec) Duration (sec)

R1 21.49 1356.4 10.4R2 10.36 761.8 398.6R3 6.78 412 0R4 8.18 450.8 0

Table 3: The routes in the experiments.

5.1.2 Vehicles

Four vehicles are used in the experiments, as summarizedin Table 4, which consist of three ICE vehicles and one EV.The pictures of the vehicles are shown in Fig. 6.

Vehicle Maker Model Year Type DisplacementV1 Toyota Yaris 2014 ICE 1.5V2 Hyundai Veloster 2014 ICE 1.6V3 Nissan LEAF 2013 EV NAV4 BMW 650i 2014 ICE 5.0

Table 4: The vehicles in the experiments.

5.1.3 Data Collection

For ICE vehicles, we collected data through BluetoothELM327 dongles connected to the vehicles’ onboard diag-nostic (OBD) ports and paired with a smartphone App. TheOBD data collected include mass air-flow, manifold absolutepressure, intake air temperature and engines’ RPM, which

(a) Toyota Yaris (b) Hyundai Veloster

(c) Nissan LEAF (d) BMW 650i

Figure 6: The vehicles in the experiments.

are then utilized to compute the fuel consumption rate. Fur-thermore, the geo-location data, accelerometer and gyro-scope measurements from the smartphone are also recorded.For EV (i.e., Nissan LEAF), we rely on Android App called“LEAF Spy Pro” to collect the EV’s data. The sample rateof our smartphone App is 2 Hz, whereas the one for LEAFspy pro is 0.25 Hz.

5.2 Data SetsFig. 7 shows some sample driving data. Fig. 7a shows the

speed profiles of the EV and an ICE vehicle by the samedriver on the same route. We can observe that both ve-hicles stopped several times due to tra�c lights. Fig. 7bshows the energy consumption profiles of EV and ICE ve-hicles. Although the speed profiles are similar, there areremarkable di↵erences in the energy consumption profiles.Notably, the energy consumption level decreases when theEV slows down, because of the regenerative braking thatcan convert kinetic energy into stored energy in battery.

Figs. 7c-7d show the driving data collected from the ve-hicles. The gyroscope data is used to determine the vehiclemovement and orientation, which is gathered from smart-phone. However, the gyroscope data is required to be alignedwith vehicle’s moving direction in order to obtain the cor-rected orientation. We use an automatic alignment algo-rithm to infer the vehicle’s reference orientation [9, 15].

5.3 Estimation ErrorsTo evaluate the accuracy of estimation, we measure the

deviation error of the estimated energy consumption by theper segment error:

"

i =|(Edrv

D,V,Ri + E

idlD,V,Ri)� (Edrv

D,V,Ri + E

idlD,V,Ri)|

E

drvD,V,Ri + E

idlD,V,Ri

(19)

and the accumulative error:

"

acc =|ED,V,R � ED,V,R|

ED,V,R(20)

As an example, we plot the distribution of per-segmenterrors of one vehicle collected from three rounds of exper-iments in Fig. 8. It shows that the mean error is about3.5% and the distribution is close to a normal distribution.Since we are interested in the energy consumption of theoverall trip, the accumulative error is more relevant. Fig. 9

52

(a) Route R1. (b) Route R2.(c) Route R3. (d) Route R4.

Figure 5: The routes in the experiments.

0 0.5 1 1.5 2 2.50

50

100

Distance (km)

Spee

d (k

m/h

)

Electric VehicleICE Vehicle

(a) Speed profiles of EV and ICE vehicle.

0 0.5 1 1.5 2 2.50

0.1

0.2

0.3

0.4

Distance (km)

Ener

gy c

onsu

mpt

ion

(lite

r or k

Wh)

Electric VehicleICE Vehicle

Regenerativebraking

Regenerativebraking

(b) Energy/fuel consumption profiles of EV and ICE vehicle.

0 0.5 1 1.5 2 2.50

100

200

300

400

500

Distance (km)

Res

pect

ive

valu

e

Speed (km/h)RPM (*10)Aligned gyroscope (*10−3 rad/sec)Mass air flow (*0.5 g/second)

(c) Sample driving data of ICE vehicle.

0 0.5 1 1.5 2 2.50

20

40

60

80

100

120

Distance (km)

Res

pect

ive

valu

e

Speed (km/h)State of charge (SOC %)Pair voltage of the battery (*10 volt.)AC power (*10 Watts)

(d) Sample driving data of EV.

Figure 7: Sample driving data.

shows the accumulative error against travelled distance. Weobserve that the accumulative error fluctuates against trav-elled distance, but there is a general trend of decreasingerror. This is due to the fact that the positive and nega-tive deviations can o↵set each other over a longer distance.Therefore, the accumulative error reaches a lower value aftera longer distance.

−80 −60 −40 −20 0 20 40 60 800

0.02

0.04

0.06

0.08

0.1

Average error is 3.45 %

% segment errors

Segm

ent e

rror d

istri

butio

nFigure 8: Distribution of per segment errors.

0 5 10 15 200

0.1

0.2

0.3

0.4

Distance (km)

Accu

mul

ativ

e er

ror

Round 1Round 2Round 3

Figure 9: Accumulative error against travelled distance.

5.4 Energy Consumption Model ValidationWe first use the data sets of R1 and R2 to obtain the co-

e�cients of Eqns. (2)-(3). Next, we examine the accuracyof estimating the energy consumption using Eqns. (2)-(3)against the actual energy consumption. The respective ac-cumulative error averaged over three rounds of experimentsare shown in Table 5 and Fig. 10. We observe that the ac-cumulative errors range from 0.1% to around 5%, which issu�cient to practical DTE prediction. In Fig. 11, we useother drivers’ driving data to predict other drivers’ energyconsumption in the same route. The initial error is relativelylarge due to several reasons: 1) the prediction can overes-timate or underestimate the accurate DTE, which createsshort-term fluctuations. The prediction error will be o↵-set by the positive and negative deviations in a long term.Therefore, the accumulative error can reach a lower valueafter a longer distance. 2) For vehicle D3 (LEAF), the sam-ple rate is relative low, therefore, the initial error is larger

53

than others’, because the driving behavior cannot be fullycaptured when sample rate is low. We are working on in-creasing the sample rate of the LEAF which will improvethe initial error.

Route Vehicle Driver Average accumulative errorR1 V1 D1 3.6%R1 V2 D2 0.1%R1 V3 D2 3.2%R1 V3 D3 2.0%R1 V4 D4 0.1%R2 V1 D1 4.4%R2 V2 D2 3.7%R2 V3 D2 4.7%R2 V3 D3 5.4%R2 V4 D4 5.1%

Table 5: Estimation errors for route-vehicle-driver tuples.

5.5 Correlation DiscoveryNext, we obtain the correlation metrics �(D,V),(D0

,V0) in Ta-ble 6. The two top-most similar pairs are highlighted in boldin each row. For example, (D1,V1) has a small value with(D2,V2) and (D3,V3). We use k-nearest neighbor methodto choose the k most similar (D0

,V0) for each (D,V), anduse their respective driving data to estimate the energy con-sumption of (D,V).

�(D,V),(D0,V0)(D1,V1) (D2,V2) (D3,V3) (D2,V3) (D4,V4)

(D1,V1) 0 1.0799 1.0214 1.1659 1.2319(D2,V2) 1.0799 0 1.1022 0.9872 1.1895(D3,V3) 1.0214 1.1022 0 1.1423 1.4211(D2,V3) 1.1659 0.9872 1.1423 0 1.2169(D4,V4) 1.2319 1.1895 1.4211 1.2169 0

Table 6: Correlation metrics.

6. CASE STUDIESIn this section, we apply our results to some concrete case

studies: (1) DTE prediction, (2) characterization of drivingbehavior, (3) energy-e�cient driving recommendations, and(4) route-level energy consumption geo-fencing and refuelingplanning.

6.1 DTE PredictionBased on the correlation metrics, we can estimate the en-

ergy consumption using the driving data by the most similardriver-vehicle pairs. In particular, one can use the data ofICE vehicles (V1,V2,V4) to predict the energy consumptionof the EV (V3).

In the following, five scenarios are considered:

1. Predicting (D1,V1) using estimators (D2,V2),(D3,V3)

2. Predicting (D2,V2) using estimators (D1,V1),(D2,V3)

3. Predicting (D3,V3) using estimators (D1,V1),(D2,V2)

4. Predicting (D2,V3) using estimators (D2,V2),(D3,V3)

5. Predicting (D4,V4) using estimators (D2,V2),(D2,V3)

0 5 10 15 200

0.1

0.2

0.3

0.4

Distance (km)

Accu

mul

ativ

e er

ror

Round 1Round 2Round 3

(a) (D1,V1) on route R1

0 5 100

0.1

0.2

0.3

0.4

Distance (km)

Accu

mul

ativ

e er

ror

Round 1Round 2Round 3

(b) (D1,V1) on route R2

0 5 10 15 200

0.1

0.2

0.3

0.4

Distance (km)

Accu

mul

ativ

e er

ror

Round 1Round 2Round 3

(c) (D2,V2) on route R1

0 5 100

0.1

0.2

0.3

0.4

Distance (km)

Accu

mul

ativ

e er

ror

Round 1Round 2Round 3

(d) (D2,V2) on route R2

0 5 10 15 200

0.1

0.2

0.3

0.4

Distance (km)

Accu

mul

ativ

e er

ror

Round 1Round 2Round 3

(e) (D3,V3) on route R1

0 5 100

0.1

0.2

0.3

0.4

Distance (km)

Accu

mul

ativ

e er

ror

Round 1Round 2Round 3

(f) (D3,V3) on route R2

0 5 10 15 200

0.1

0.2

0.3

0.4

Distance (km)

Accu

mul

ativ

e er

ror

Round 1Round 2Round 3

(g) (D2,V3) on route R1

0 5 100

0.1

0.2

0.3

0.4

Distance (km)Ac

cum

ulat

ive

erro

r

Round 1Round 2Round 3

(h) (D2,V3) on route R2

0 5 10 15 200

0.1

0.2

0.3

0.4

Distance (km)

Accu

mul

ativ

e er

ror

Round 1Round 2Round 3

(i) (D4,V4) on route R1

0 5 100

0.1

0.2

0.3

0.4

Distance (km)

Accu

mul

ativ

e er

ror

Round 1Round 2Round 3

(j) (D4,V4) on route R2

Figure 10: Estimation errors against traveled distance.

The respective accumulative errors for route R1 are shownin Table 7, along with the prediction using its own data.Three rounds of data for each driver-vehicle pair are aver-aged and then used to predict others. For each driver-vehiclepair, we use the two most similar driver-vehicle pairs, as wellas its own data. The diagonal entries of the Table 7 arethe predictions based on the own data. Except driver (D4),the estimation errors are observed to be low. Driver (D4)drives much more aggressively compared to other drivers,and hence, its correlation metrics have larger values. There-fore, the estimation errors are larger for (D4) using otherdrivers.

54

0 5 10 15 200

0.1

0.2

0.3

0.4

Distance (km)

Accu

mul

ativ

e er

ror

Estimator (D1,V1)Estimator (D2,V2)Estimator (D3,V3)

(a) (D1,V1)

0 5 10 15 200

0.1

0.2

0.3

0.4

Distance (km)

Accu

mul

ativ

e er

ror

Estimator (D1,V1)Estimator (D2,V2)Estimator (D2,V3)

(b) (D2,V2)

0 5 10 15 200

0.1

0.2

0.3

0.4

Distance (km)

Accu

mul

ativ

e er

ror

Estimator (D1,V1)Estimator (D2,V2)Estimator (D3,V3)

(c) (D3,V3)

0 5 10 15 200

0.1

0.2

0.3

0.4

Distance (km)

Accu

mul

ativ

e er

ror

Estimator (D2,V2)Estimator (D3,V3)Estimator (D2,V3)

(d) (D2,V3)

0 5 10 15 200

0.1

0.2

0.3

0.4

Distance (km)

Accu

mul

ativ

e er

ror

Estimator (D2,V2)Estimator (D2,V3)Estimator (D4,V4)

(e) (D4,V4)

Figure 11: Estimation errors against traveled distance.

EstimatorError (D1,V1)(D2,V2)(D3,V3)(D2,V3)(D4,V4)

(D1,V1) 4.7% 5.8% 4.6% .. ..(D2,V2) 4.3% 1.4% .. 2.4% ..(D3,V3) 5.2% 7.4% 2.0% .. ..(D2,V3) .. 5.9% 6.7% 5.3% ..(D4,V4) .. 12.1% .. 10.3% 2.5%

Table 7: Estimation errors.

6.1.1 DTE Prediction for EV

In particular, we study the performance of DTE predictionfor EV (i.e., Nissan LEAF). The data collected from EVincludes:

1. State of charge (SOC), denoted by S, indicates howmuch electricity remains in the battery.

2. Initial capacity of the battery, denoted by BA

.

3. Battery pack voltage when driving, denoted by BV

.

The remaining energy (�Et) in battery at time t is given by:

�Et = St ⇥ BA

⇥ Bt

V

(21)

If the future average power intensity (P) is known, thenDTE is given by:

DTE =�Et

P(22)

We especially compare the DTE prediction based on ourenergy consumption model with the on board DTE meter onNissan LEAF, which is captured by a camera mounted overthe dashboard. Fig. 12 shows our estimated DTE and onboard DTE meter readings in the experiment. The referenceline in the Fig. 12 denotes the true DTE as the distancegoes. We observe that our approach gives a considerablymore accurate prediction than the onboard meter.

6.2 Classification of Driving BehaviorThe correlation metrics between every pair of drivers can

provide a distance matrix. One can apply standard clus-tering techniques on the set of driver-vehicle pairs in sucha metric space. The clustered data set can classify energy-e�cient driving behavior. Fig. 13 shows the clustered dataset based on Table 6. We observe that energy-e�cient driver-vehicle pairs (colored in green) tend to stay closer in thecorrelation metric space. Likewise, one can also obtain thecluster of energy-ine�cient driver-vehicle pairs, and a com-plete classification of driving behavior.

0 10 20 30 40

80

100

120

140

Distance (km)

DTE

(km

)

Estimated DTEDTE on Boardreference

Figure 12: Comparison between our estimated DTE andonboard DTE meter reading.

(D2,V3)

(D3,V3)

(D4,V4)

(D2,V2)

(D1,V1)1.02

1.14

1.10 1.42

1.23

1.220.99

1.191.171.08

Figure 13: Clustering of driver-vehicle pairs based on corre-lation metrics. Green nodes are the energy-e�cient driver,whereas the red node is energy-ine�cient driver.

6.3 Driving RecommendationsOur energy consumption model can be applied to pro-

vide recommendations of energy-e�cient driving. Assumingother parameters are constant (e.g., acceleration, decelera-tion, and gyroscope data), the energy consumption is onlydependent on the vehicle speed in our model. For exam-ple, Fig. 14 shows the energy intensity of several driver-vehicle pairs at di↵erent speeds. It is observed that there isa minimum point for ICE vehicles (namely, the least energy-consuming speed), and an energy intensity increasing withspeed for EV (because electric motors operate more e�-ciency at low speeds). For ICE vehicles, we can recommendthat the driver to maintain at the least energy-consumingspeed obtained from the model. For EV, the optimal speedwill be the smallest possible speed that can arrive at thedestination before a deadline.

6.4 Geo-fencing and PlanningGeo-fencing depicts the geographical range before the en-

ergy of vehicle is exhausted. Traditionally, geo-fencing isestimated only considering the geographical distance. Withthe information from our model, more detailed route-level

55

0 20 40 60 80 100 120 1400

0.05

0.1

0.15

0.2

Speed (km/h)

Ener

gy in

tens

ity (l

iter/k

m)

0 20 40 60 80 100 120 1400.06

0.08

0.1

0.12

0.14

Ener

gy in

tens

ity (k

Wh/

km)

(D1,V1)(D2,V2)(D4,V4)(D3,V3)

Figure 14: Energy intensity of vehicles at di↵erent speedaccording to our energy consumption model.

energy consumption geo-fencing can be constructed, for ex-ample, as seen in Fig. 15. The route-level energy consump-tion geo-fencing is constructed in the following manner. Wefirst obtain the map data from OpenStreetMap (OSM). Weemploy our model to estimate the energy consumption ateach point along a route. The least energy consumptionrequired to reach a particular point can be estimated byA⇤ algorithm, considering multiple route alternatives. Theleast energy consumption will be visualized on top of OSMdata to provide route-level energy consumption geo-fencing.A critical application energy consumption geo-fencing is toenable informed decisions for refueling.

Figure 15: Route-level energy consumption geo-fencing.

7. CONCLUSION AND FUTURE WORKAn e↵ective approach has been proposed to integrates the

vehicle data gathered from diverse drivers and vehicles forpersonalized applications of improving driving energy e�-ciency. Our system provides a unifying approach for bothICE vehicles and EVs. The advantages include identifyingthe features according to the driver, vehicle and route de-pendence. The processed data can flexibly support diverseapplications, such as DTE prediction, green telematics andenergy-e�cient route planning, and classification of energye�cient driving behavior.

Future work will include integration of extensive data fromlarge-scale datasets such as those to be available from Cloud-Think [1] as well as from expanded GIS databases(e.g. thegeometric of the roads). We are working to integrate ourmethodology to open platforms (e.g. OpenStreetMap). More-over, hybrid vehicles or plug-in hybrid vehicles are not con-sidered in this work, because of the lack of proper softwareto interoperate the proprietary data about battery state in

these vehicles. Proper software will be sought in future toallow further experiments on these vehicles.

8. ACKNOWLEDGEMENTWe would like to thank the reviewers and the shepherd

(Dr. Sarvapali D.(Gopal) Ramchurn) for helpful commentsand suggestions.

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