research article accurate real-time traffic speed estimation...

Research ArticleAccurate Real-Time Traffic Speed Estimation UsingInfrastructure-Free Vehicular Networks

Zongjian He Buyang Cao and Yan Liu

School of Software Engineering Tongji University Shanghai 201804 China

Correspondence should be addressed to Zongjian He hezongjiantongjieducn

Received 22 April 2015 Revised 8 July 2015 Accepted 12 July 2015

Academic Editor Ching-Hsien Hsu

Copyright copy 2015 Zongjian He et al This 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

Real-time traffic speed is indispensable for many ITS applications such as traffic-aware route planning and eco-driving advisorysystem Existing traffic speed estimation solutions assume vehicles travel along roads using constant speed However thisassumption does not hold due to traffic dynamicity and can potentially lead to inaccurate estimation in real world In this paper wepropose a novel in-network traffic speed estimation approach using infrastructure-free vehicular networks The proposed solutionutilizes macroscopic traffic flow model to estimate the traffic condition The selected model only relies on vehicle density whichis less likely to be affected by the traffic dynamicity In addition we also demonstrate an application of the proposed solution inreal-time route planning applications Extensive evaluations using both traffic trace based large scale simulation and testbed basedimplementation have been performedThe results show that our solution outperforms some existing ones in terms of accuracy andefficiency in traffic-aware route planning applications

1 Introduction

Nowadays public traffic is a serious problem in big cities Bil-lions of hours are wasted everyday on traffic congestions Inaddition emission caused by vehicles has become one of themajor sources of air pollution To tackle these problems peo-ple have developed many intelligent transportation system(ITS) applications to reduce vehicle traveling time by routeplanning [1] and to reduce emission by driving advisory [2]To develop such ITS applications real-time traffic informa-tion is a key prerequisiteWith accurate real-time traffic infor-mation as prior knowledge the performance of route plan-ning and emission reduction can be significantly improved

How to obtain real-time traffic information is one of themajor issues to be solved Conventional methods leveragetraffic monitoring infrastructures such as video surveillancecameras [3] inductive loops [4] or the combination of themto collect traffic data [5] The main problem of this approachis the low availability of these facilities due to the highdeployment and maintenance cost One alternative approachis to analyze historical traffic data [6] to find out some sta-tistical traffic patterns Unfortunately historical data can not

always reflect current situation since many emergent events(eg traffic accident) or temporary events (eg road surfacemaintenance) can lead to significant traffic flow variation

Recently using vehicular crowdsourcing to collect float-ing car data (FCD) for speed estimation [7] has attracted theattention of both academic and industrial bodies Comparedwith infrastructure based approaches the data collectioncost of crowdsourcing is significantly reduced by leveragingindividual usersrsquo contribution However the data transmis-sion cost is still considerably high due to the use of cellulardata to collect FCD for centralized data processing In manyITS applications (eg route planning) the real-time trafficinformation of a road segment is only interested by the nearbyvehicles In these scenarios an in-network traffic speedestimation solution is especially promising Moreover in-network processing can also reduce the cost of long distancedata transmission

With the rapid advancement of wireless communicationand embedded technology using vehicular ad hoc networks(VANET) to collect traffic information becomes popularrecently [8ndash11] Traffic information can be collected throughvehicle to infrastructure (V2I) or vehicle to vehicle (V2V)

Hindawi Publishing CorporationInternational Journal of Distributed Sensor NetworksVolume 2015 Article ID 530194 19 pageshttpdxdoiorg1011552015530194

2 International Journal of Distributed Sensor Networks

communication However it is not easy to collect floating cardata using VANETs Considering the unique characteristicsof VANETs and the application scenario several challengingissues are raised

(i) Network Disconnection Vehicle mobility is highlydynamic which can cause the network to be fre-quently disconnected When using VANET to collecttraffic information an interesting contradiction canbe found between traffic condition and networkcondition Good traffic condition (high vehicle speedlow traffic density) usually leads to poor networkcondition (high packet loss opportunistic network)and vice versa The solution must tolerate the poornetwork conditions

(ii) Vehicle Speed Variation Vehicles on roads do nottravel using constant speed The individual vehiclespeed can have remarkable change in a short periodFor example a red light is in front or a taxi stopsat the request of passengers The instant variation offloating car data can lead to inaccuracy to the trafficestimation algorithm A good analysis model must beable to eliminate this variation

(iii) Insufficient Traffic Sampling Due to the infrastruc-ture-less nature of VANETs it is not easy to monitoran area for a long period of time Moreover thefast-changing individual vehicle speed and fragilenetwork topology can potentially interfere with thedata collection Hence the data sampling is alwaysinsufficient The traffic estimation algorithm needs todeal with insufficient traffic sampling condition

To tackle these challenges some infrastructure basedsolution has been proposed These solutions utilize roadsideunit (eg Wi-Fi access points [12] cellular base stations[13] etc) and vehicle to infrastructure (V2I) communicationto monitor the traffic condition of a fixed area With thehelp of roadside infrastructure continuous sampling of anarea and caching collected data can easily be achievedHowever these solutions have the same deployment problemas those traffic facility based solutions Other researches relyon the assumption that vehicles travel with constant speed[8 14] Without considering vehicle speed variation somestraightforward traffic estimation algorithms like time meanspeed or space mean speed can be developed However thisassumption does not hold in real world and it can lead toinaccurate traffic speed estimation results

In this paper we propose a novel in-network trafficinformation collection and estimation solution We name itMICE or Model based Infrastructure-free traffic Collectionand Estimation In MICE traffic information is collectedusing infrastructure-free approach Then traffic condition isestimated using in-network data processing andmacroscopictraffic flow model The application scenario is depicted inFigure 1 When a vehicle wants to know the traffic conditionof nearby roads it sends a traffic collection request usingV2V communication to ambient vehicles on demand Thosevehicles that received the request send their information backto the original vehicle for traffic condition estimation

E

SN1 N2

N3

N5N4

Figure 1 Application scenario and system model using V2Vcommunication to collect real-time traffic information

The contributions of this work can be summarized asfollows

(1) We propose a floating car data collection protocolwhich leverages V2V communication It can collectfloating car data in segment level granularity and it isadaptive to different network conditions

(2) We adopt Underwood density-speed traffic flowmodel [15] developed by traffic scientists to predictthe traffic speedThismodel can eliminate the tempo-rary speed variations to improve the estimation accu-racy efficient enough for in-network data processing

(3) We apply the proposed solution to the shortest-time route planning application A heuristic basedpath finding algorithm is proposed to show how theproposed solution works in real world applications

(4) Extensive evaluations and analysis are conductedusing both large scale simulation and testbed imple-mentation Evaluation results show that our solutionoutperforms some static and dynamic route planningsolutions

The remainder of this paper is organized as followsrelated works are summarized in Section 2 The MICE prob-lem is formulated in Section 3 The solution to MICE ispresented in Section 4 in detail In Section 5 an applicationof the proposed solution is introduced The evaluationsand results will be analyzed in Section 6 Finally Section 7concludes this paper

2 Related Work

Several related works on using VANET to estimate trafficspeed can be found in literature We first introduce the workson traffic information collection followed by existing trafficspeed estimation solutions

21 Traffic Information Collection Solutions on traffic infor-mation collection can be classified by whether they rely

International Journal of Distributed Sensor Networks 3

on road side infrastructure Road side infrastructure cansimplify network protocol design since it is a convenient placeto store cache and analyze data Many infrastructure basedsolutions have been proposed [9 10 16 17]

PeerTIS and Kyun queue are two typical infrastructurebased solutions PeerTIS [13] firstly constructs a peer to peeroverlay using cellular networks among vehicles A trafficinformation dissemination protocol is developed to shareinformation among vehicles The drawbacks of using cellularnetworks as infrastructures are the high communication costKyun queue [18] proposed a novel solution to use RSSI tomonitor the road traffic congestion Specifically it monitorsthe length of the waiting queue in front of a road intersectionby observing the interference caused by stopping cars Theproposed solution is simple and yet effective However roadside wireless nodes still need to be deployed as infrastructuresin advance

Infrastructure-free solutions only use communicationamong vehicles and no extra infrastructure needs to bedeployed Therefore it is more flexible [8 19] MobSamplingGeocache and V2R2 are three typical solutions of this type

PGB [20] is an infrastructure-free data collection solu-tion The key idea is to allow receivers to determine forward-ing or dropping the packet With receiver-based decisionthe overhead of controlling message (eg RTSCST) canbe eliminated and the communication performance canbe improved The paper recommends using geographicalinformation or signal strength to group the receivers andonly the receivers that satisfy the predefined conditions canforward the data

MobSampling [21] uses infrastructure-free vehicular net-works to collection traffic information A role switchingalgorithm is developed to cache the data within certaingeographical regions When the vehicle with cached datamoves out of the region it is required to pass the datato another vehicle Geocache [22] is a pull-based geocastprotocol combined with caching mechanism to reduce thecommunication throughput Both MobSampling and Geo-cache work effectively under dense traffic Otherwise theywill fail if no nearby vehicle is available V2R2 [23] usesunicast along the shortest path and multicast for the returntrip to collect the traffic condition around navigation sourceand destination However V2R2 ignores the data collectionof roads with sparse traffic which may limit the applicationscenarios of the solution

Different from existing infrastructure-free solutions oursolution is designed for both dense and sparse traffic sce-narios Moreover our solution does not have the cost ofestablishing and maintaining in-network cache Thereforethere is no ldquowarm-uprdquo phase at the very beginning

22 Traffic Speed Estimation After floating car data is col-lected how to leverage these data to estimate traffic is anotherimportant issue The traffic estimation algorithm must bedesigned according to the properties of the data Generallyfloating data can be categorized as dense data and sparsedata depending on the sampling frequency Infrastructurebased data collections are useful to obtain dense data while

infrastructure-free data collections can usually get sparsedata

Li et al [14] proposed two traffic estimation algorithmslink-based algorithm (LBA) and vehicle-based algorithm(VBA) LBA estimates the road segment traffic only usingvehicles at the beginning and the end of the road segmentswhile VBA utilizes all vehiclesThe results are compared withthe ground truth captured by video surveillance Both LBAand VBA use vehicle speed to estimate trafficThe algorithmsare suitable with dense data

BeeJamA [24] is a heuristic solution for dense dataIt is derived from the honey bee behavior to avoid trafficcongestion A density-speed model is used to rate the con-gestion However this approach requires data to be collectedcontinuously from an area and therefore only works with thehelp of roadside infrastructure and V2I communication

Besides BeeJamA researchers have developed manyother heuristic models like neural networks [25] based andgenetic algorithm [26] based solutions for traffic estimationHowever these heuristic algorithms usually demand densedata as training set and also require centralized processingTherefore they are not suitable for the case of infrastructure-free VANET

When dealing with sparse floating car data differentanalysis models can be applied to estimate the traffic con-dition Some straightforward solutions use basic statisticanalysis techniques like mean vehicle speed [8] or mean passtime [17] Unfortunately these schemes only work under theassumption of constant vehicle speed In real world theymaynot be able to accurately reflect the real traffic condition (egdata are collected when the traffic light is red)

StreetSmart [19] is a congestion discovery solution usingV2V communication It uses distributed clustering to calcu-late traffic condition This approach is useful for long termtraffic monitoring since it needs to construct and maintain acluster structure Unfortunately it is not flexible enough foron-demand traffic estimation

VAN [8] has exactly the same functional requirementand the same traffic collection design concern with ourwork It uses average speed to estimate the traffic conditionand the candidate paths are bounded by packetsrsquo time-to-live attribute VAN can be considered as the state-of-the-artroute planning solution using infrastructure-free vehicularnetworks and we will compare our solution with VAN in ourevaluation

Different from the aforementioned solutions MICE doesnot require training dataset and only utilizes macroscopictraffic flowmodel to estimate traffic speedThis density-speedmodel can handle sparse data since it only relies on vehicledensity which varies slightly in a continuous time period

3 Problem Statement

31 Preliminaries In our research we assume vehicles havenavigation devices with GPS installed onboard The roadnetworks topology vehiclersquos current position velocity andglobal time can all be retrieved via GPS devices In additionwe further assume vehicles can communicate with each other


using V2V communication standard like DSRC to form thevehicular networks

Before we formulate the problem some definitions shallbe introduced first

Definition 1 (road segment) A road segment is a 5-tuple 119903 =119904(119909 119910) 119890(119909 119910) V

119898119897 119903 where 119904 and 119890 are the start and end

point with location (119909 119910) V119898is the speed limit of the road

segment 119897 is the number of lanes 119903 is the direction of the roadsegment and 119903 isin 119904 rarr 119890 119890 rarr 119904 The road segment lengthcan be approximately calculated by the Euclidean distance119903 = radic(119890 sdot 119909 minus 119904 sdot 119909)

2+ (119890 sdot 119910 minus 119904 sdot 119910)

2 For conveniencepurpose road segment can also be denoted using start andend point as ⟨se⟩

Definition 2 (vehicular network) Vehicular network is awireless ad hoc network formedby vehicles At a specific timeit can be defined as a graph VN = (119881 119862) where 119881 is thevehicle set and 119862 is network connections A vehicle V isin 119881

can be further defined as a 3-tuple id pos(119909 119910)997888rarr

vel whereid is the unified identifier of the vehicle like plate number posis current position and

997888rarr

vel is current velocity We denote themean network transmission range as tr which means thereexists a network connection between two vehicles if theirEuclidean distance is less than tr

Definition 3 (floating car data snapshot) A floating car datasnapshot is the traffic state of a road segment at a particularpoint of time It can be described as a vector of vehicles theirpositions and velocities The floating car data snapshot ofroad segment 119903 at time 119905 is denoted as119881119905

119903= (V1119905 V2119905 V119899119905)

Definition 4 (traffic condition) Traffic condition of a roadsegment can be defined as a series of floating car datasnapshots sampled for the road segment in consecutive timeperiods 119901 = [1199050 1199051 119905119899] It can be denoted as a group offloating car data snapshots 1198811199050

119903 1198811199051119903 119881

119905119899

119903

Alternatively it can also be transformed as a group ofvehicles that have appeared on the road segment during thetime period 119881119901

119903= V1 V2 V119899 and every vehicle has three

attributes id 119905119903 119904119903 where id is the same identifier as in

Definition 2 119905119903is the time duration which appears on road

segment 119903 and 119904119903is the length travels on road segment 119903

Obviously 119904119903le 119903 sdot 119897 and 119905

119903le 119905119899minus 1199050

With the latter representation we can use space meanspeed to denote the traffic condition as

V =sumVisin119881119901

119903

V sdot 119904119903

sumVisin119881119901119903

V sdot 119905119903

(1)

This definition is reasonable because when people talkabout traffic condition of an area they usually refer to theaverage condition during a time period and so does ourdefinition

Definition 5 (traffic density) Traffic density 119896 of a roadsegment is defined as the number of vehicles per unit lengthper lane The inverse of density 119889 = 1119896 is the mean headwaydistance between adjacent vehicles

Intuitively the traffic density and floating data snap-shot have some correlations Researchers have conductedextensive evaluations and built several models to investigatethe correlation Both urban driving scenario [27 28] andhighway driving scenario [29] have been studied Among allthe models Poisson arrival process has been widely adoptedbecause of the versatility and simplicity It has been usedto model the arrival of cars at toll gate traffic light waitingqueue and a road segment In our research we adopt thisresult Therefore given a traffic snapshot of a road seg-ment the corresponding traffic density can be calculated asfollows

Since we also assume vehicles follow Poisson arrivalprocess as a result the distances among consecutive vehicles119881119889

119903are exponentially distributed with pdf 119891(119909) = 120582119890

minus120582119909Under this distribution the traffic density can be expressedas

119896 =

1119864 (119881119889

119903)

=

11120582

= 120582 (2)

where 119864(119881119889119903) is the mathematical expectation of the distance

among vehicles on road segment 119903 Equation (2) shows thatthe traffic density is also exponentially distributed with meanvalue 119896 Moreover (2) also shows that the distribution onlyrelies on the number of vehicles on the road segment andtheir traveling speed will not affect the result However 119896 isan unknown variable for each road segment We will leavethis equation as an intermediate result to be used in latersections

The symbols and notations used in this paper are summa-rized in Summary of Notations

32 Problem Formulation With the above definitions andassumptions the objective of MICE is to (1) collect and then(2) estimate the traffic condition of a given road segment

The traffic information collection problem is a designissue We need to design a network data collection protocolfor VANET to collect traffic information In practice thetraffic density may vary from time to time Consequentlythe network connection is not always available when trafficdensity is low The design objective of the protocol is asfollows

(1) AdaptiveThe protocol should be adaptive to differenttraffic density Specifically it needs to handle networkdisconnections in sparse traffic condition

(2) Infrastructure-Free The protocol should not rely onany road side infrastructure or cellular station OnlyV2V networks should be used

(3) On-DemandThe protocol should not wait a period oftime to warm up before it can function well It mustwork on demand

(4) Real-Time The traffic information needs to be col-lected and estimated with low latency

Since we do not utilize infrastructure or cache the resultsof a data collection instance are a traffic snapshot whichcan be used to estimate the traffic speed The traffic speed


(a) Dense connection (b) Sparse connection

(c) Partial disconnection (d) Disconnection

Figure 2 Four VANET connection states depending on different vehicle density

estimation problem can be regarded as a data analysis issueIt can be formulated as follows

Given

(1) A single road segment 119903 = 119904(119909 119910) 119890(119909 119910) V119898119897 119903

(2) Space mean speed V in a period of time 119901 =

[1199050 1199051 119905119899](3) A traffic snapshot 119881119905

119903as specific time 119905 isin 119901

Objective Find a mapping function Vest = 119891(119881119905119903 ) to minimize

ΔV = (Vest minus V)2 (3)

Equation (3) shows that we want to minimize the errorbetween the estimated value and the calculated value (groundtruth) In our research we use traffic trace and our owntestbed In both cases the ground truth of the traffic speedcan be easily obtained

4 Solution

In this section we focus on how to collect and estimate trafficspeed for a single road segment In real world estimatingthe traffic condition of a single road segment does not seemnecessary since it can be directly observed by the driversmostof the times However the solution introduced in this sectioncan be easily extended to a road network composed by manyconnected road segments

41 Traffic Information Collection The traffic speed estima-tion request can be initiated by any vehicle that wants to knowthe surrounding information at any time After the request isinitiated the request packets are forwarded to nearby vehicleshop by hop To demonstrate how the protocol works we usea single road segment for instance

The traffic density changes from time to time causingVANET to be disconnected frequently Based on the connec-tion status of VANET we can define four different states asdepicted in Figure 2 They are as follows

(1) Dense Connection Vehicles on the same direction canbe connected directly

(2) Sparse Connection Vehicles on the same direction canonly be connected with the help of vehicles from theopposite side

(3) Partial Disconnection The vehicle does not have nexthop candidate but it is still connected with theprevious hop neighbor

(4) Total Disconnection The vehicle does not have anyneighbors it is completely disconnected

The design objective of the protocol is to be adaptivewith all these connection states Therefore different packetforwarding strategies shall be considered for different stateThe complete packet forwarding algorithm on the arrival of anew packet is shown in Algorithm 1

For dense connection packets will be forwarded greedilylike GPSR [30] to the vehicle in neighbor table which is theclosest to road segment end and has the same direction as theroad segment (line (4)) For sparse connection packets haveto be forwarded to a vehicle on the opposite direction (line(6)) Otherwise the vehicle will carry the received packet(lines (10) and (12)) for a while and try again The packetcarrying period is set to one second in our implementationIt is also possible that current vehicle is on the opposite direc-tion of the road segment In this case vehicle will never carrypacket since the longer time it carries the packet the furtherfrom the road segment end it will be In this case the vehicleat the opposite side will firstly try to forward the packetto vehicles with the same direction as the road (line (16))and then seek for another opposite directed vehicle whichis closer to the road segment end as an alternative (line (18))

When designing the packet forwarding protocol twowidely adopted strategies can be considered sender-baseddecision and receiver-based decision depending on whodetermines the next hop destination They have differ-ent strengths and weaknesses Generally solutions usingreceiver-based decision can reduce the overhead of control-lingmessage by eliminatingmultiple handshakes Differentlythose using sender-based decision are more flexible andadaptive to the environment because the sender usuallycollects information of all candidates and then makes deci-sion In our application scenario the protocol needs to beadaptive to different traffic conditions and flexibility is thekey requirementTherefore we decide to adopt sender-baseddecision


tr

a

c

s

b

d

e

CTSCTS

CTSCTS

tr

a

c

s

b

d

e

Forward

(a) Data collection (b) Packet forward

ffCTS

Road direction

Figure 3 The RTSCST scheme and forward strategy

Input The received packet 119901Input The neighbor table NTInput The current vehicle information viInput The current road segment 119903(1) procedure trafficcollection(119901 NT vi 119903)(2) if 119903 sdot 119903 = vi sdot

997888rarr

vel then same direction(3) if exist119881

119904sube NT st forallV isin 119881

119904 vi sdot

997888rarr

vel = 119903 sdot 119903 and V is closer to 119903 sdot 119890 then Dense connection(4) Greedy forward 119901 cup vi to 119881

119904

(5) else if exist119881119903sube NT st forallV isin 119881

119903 V is closer to 119903 sdot 119890 then Sparse connection

(6) Greedy forward 119901 cup vi to 119881119903

(7) Feedback speedestimation(119901 cup vi)(8) else if notexistV isin NT st V is closer to 119903 sdot 119890 and NT = then Partial disconnection(9) Feedback speedestimation(119901 cup vi)(10) Carry 119901(11) else NT = Disconnection(12) Carry 119901(13) end if(14) else 119903 =

997888rarrvi different direction(15) if exist119881


119904 vi sdot

997888rarr

vel = 119903 sdot 119903 then Forward to vehicles with same direction as road(16) Greedy forward 119901 to 119881

119904


119903 V is closer to 119903 sdot 119890 then Forward to vehicles with different direction as road

(18) Greedy forward 119901 to 119881119903

(19) Feedback speedestimation(119901)(20) end if(21) end if(22) end procedure

Algorithm 1 Packet forwarding algorithm on receiving a new packet

To implement the sender-based decision we design anRTSCTS scheme to collect traffic information aswell as buildneighbor table NT mentioned as an input of Algorithm 1This procedure can be illustrated as in Figure 3 When theforwarded packet is received by vehicle 119878 it will firstly broad-cast RTS with its own position information to all one-hopneighbor vehicles Then neighbors will respond by sendinga CTS packet together with the 3-tuple vehicle informationid pos(119909 119910)

997888rarr

vel All the received vehicle information willbe used to build the neighbor table but only vehicles withthe same directions as the road segment will be stored andestimated later because the opposite side traffic conditions

make no contribution to speed estimation In this examplevehicles 119886 119887 119888 119889 119891 will receive the broadcasted RTS from119904 and send CTS back 119904 will update its neighbor tableaccordingly and then the vehicle information from 119886 119889 willbe appended to local packet After applyingAlgorithm 1 119904willforward the packet to 119889 Vehicle 119888 will not be selected simplybecause its direction is opposite with the road even though itis closer to 119899

119890than 119889

The data collection result can be treated as a snapshotof floating car data After all vehicles on the road segmentare collected or one of the disconnection states occurs thecurrent traffic speed will be estimated Specifically speed


estimation algorithm is invoked if one of the followingconditions is satisfied

(1) Packet reaches a new road segment or destination(2) Sparse connection or partial connection occurs(3) The specified time interval has elapsed

Condition (2) has been shown in lines (7) and (9) ofAlgorithm 1 Condition (3) is used to handle the special casethat the traffic density is low but current vehicle moves slowly(eg stop for red light or temporary parking)

After the speed has been estimated the solution also usesgreedy forwarding algorithms to send the estimated speedback to the source vehicle When the source vehicle receivesthe estimated speed it will store the data locally for furtherusage It is possible to receive multiple feedbacks for a singleroad segment In this case the one with the latest time stampwill be kept

42 Traffic Speed Estimation The traffic speed estimationproblem has been formulated in Section 3 If we look atthe objective function in (3) it looks like an optimizationproblem However it can not be directly solved by any opti-mization methods because we are using sparse data (trafficsnapshot) to estimate the dense data (traffic condition) Andthe dense data is unknown but has correlations with thesparse data To estimate the speed we need tomodel the rela-tionship between the datawehave and the datawewant to get

In this research instead of training a model by ourselveswhich requires extensive dataset and computation we decideto utilize existing macroscopic traffic models developed intraffic engineering Macroscopic traffic model studies therelationship among density flow and speed In our researchwe select Underwood model [15] which is a typical density-speed model

The reasons we choose Underwood density-speed modelare twofold Firstly several properties can be obtained fromthe floating data snapshot like speed density vehicle posi-tion and so forth We need to select suitable features tobuild the model Many existing works selected speed [814 17] However we observe that the variance of speedwithin a period of time is significant To determine thevariance of the speed we analyze the traffic trace datafrom Cologne (details are introduced in Section 6) and theVMR (variance-to-mean ratio) of speed is 87 while theVMR of density is 45 Figure 5 shows the distribution ofspeed and density Therefore density is more stable thanspeed and can better reflect the traffic condition in a timeperiod Secondly many macroscopic density-speed modelshave been calibrated using site data for example GreenbergUnderwood and Drake [31] Different models have differentstrength and weakness Underwood is more accurate foruncongested condition comparedwith othermodels Judgingfrom Figure 5(b) the probability of uncongested condition ismuch higher than congested condition

The original Underwood model can be described as

Vest = V119891119890minus119896119896119888

(4)

20

15

10

5

00 01 02 03 04 05

Vehi

cle sp

eed

(ms

)

Vehicle density (vehiclem)

Underwood density-speed model

Figure 4 Underwood density-speed traffic model with V119891= 20

119896119888= 01

where V119891is the free flow speed and 119896

119888is the optimum density

That is the density corresponding to themaximum traffic flowand 119896 is current density Figure 4 shows a typical curve ofUnderwood model

When optimum traffic density 119896119888occurs the average

vehicle speed is V1198912 [15] We further assume adjacent vehi-

cles keep a safety distance which means if a vehicle appliesmaximumdeceleration and stopped a following vehiclemustbe able to stop before hitting the previous one We adopt thesafety distance defined in [32] as

119863safe = 119888119886 + 119888V + 119888119901 (5)

where and denote the acceleration and velocity ofvehicles respectively For simplicity 119888

119886is set to be 0 119888V is set

to be 2 seconds (two-second rule [33]) and 119888119901= 10m In this

case the optimum density 119896119888can be represented as

119896119888=

1119863safe (119888V = V

1198912)

=

1(V1198912) times 2 + 10

=

1V119891+ 10

(6)

Another issue is how to determine current density fromthe collected floating car data snapshot As described in (2)120582 is an unknown value Now we want to estimate 120582 from thecollected floating car data snapshot

From the floating car data snapshot we collected we canconstruct the vector of distances between adjacent vehiclesas 119881119889119903

= (1198831 1198832 119883119899minus1) where 119883119894 = V119894sdot pos V

(119894+1) sdotpos Applying maximum likelihood parameter estimationformula for exponential distribution 120582 can be estimated as

120582 =

1119883

(7)

Considering the traffic regulations we assume free flowspeed is the same asmaximumallowed speed that is V

119891= V119898

Combining (4) (6) and (7) together we get

Vest = V119898119890minus(V119898+10)119883

(8)


0 10 20 300

1

2

3

4

5

Vehicle speed (ms)

Tim

es

times105

(a) Speed distributionTi

mes

0 10 20 300

1

2

3

4

5

6

Number of vehicles per lane

times104

(b) Density distribution

Figure 5 The distribution of different vehicle properties

where119883 is the average vehicle distance per lane and

119883 =

sum119894lt119899minus1119894=0 119883

119894

(119899 minus 1)times 119903 sdot 119897 (9)

It is also possible to fail to collect traffic information fora road segment This is mainly caused by the sparse trafficIn this case we assume the average vehicle distance is largerthan tr and119883 = tr

The speed for a specific road segment can be estimatedusing (8) and (9)The only variables in these equations are thedistances among vehicles which can be obtained from trafficinformation collection results

5 MICE in Route Planning Application

The previous section introduced how to collect traffic dataand how to estimate traffic speed for a single road segmentin MICE In this section we apply the proposed solution to atypical and popular ITS application real-time route planningThis application utilizes the estimated traffic speed to find ashortest-time route for a vehicle To find the shortest-timeroute traffic condition of multiple road segments needs to becollected and estimated

However designing such an application is not trivial Tofind a global shortest-time path traffic information of allroads is prerequisite But considering a city with thousandsof roads in real world it is not feasible to collect informationof all roads since the search space is extremely huge To makethe solution practical we have to be tolerantwith a suboptimalsolution by collecting traffic information for part of the roadsand then make decision

Since the traffic information is unknown and shouldbe estimated in real-time the shortest-time route planning

problem is a typical stochastic shortest path problem withrecourse (SSPPR) in graph theory which explores adjacentnodes in a graph until finding the destination Before explor-ing a node theweight of the associated edges is unknownTheproblem has been proved to be NP-complete [34] and hencehas no polynomial time solution unless P = NP

Our objective here is not to propose a better approxima-tion algorithm for this difficult problem Instead we just wantto demonstrate the application of our proposed estimationsolution in a specific scenario To solve this problem wedesigned a heuristic solution according to two commonsenses (1) the longer the path is the longer the time it maytake (2) It makes no sense to choose another longer pathwhen shortest path is not congested We name the candidatepath selection algorithm Backoff-and-Fork or BnF in shortThe basic idea is derived from the two common senses For(1) we define a total length constraint which means the totallength of a candidate path can not exceed the threshold For(2) we define a speed threshold whichmeans if the estimatedspeed of the shortest path road is slower than the thresholdother nearby alternative paths will be searched

The complete BnF algorithm is shown in Algorithm 2At the beginning the algorithm will only choose the roadsegments on the shortest path as candidate paths (lines (1)ndash(3)) Then the data collection request packets are sent to allcandidate paths After that whenever a packet arrives at anew road segment it will collect traffic information of allneighbor road segments that have not been collected andthen estimate speed (lines (7)ndash(9)) If the estimated speedis good enough it will go on with the next road segmentsHowever if the speed is lower than the predefined threshold(line (10)) the algorithm will send a backoff packet to theprevious road segment (backoff)Then all the neighbor road


Input RN The road networkInput Navigation starting point 119899

119904 end point 119899

119890and current road intersection 119899

119888

Input 119891Threshold The speed thresholdInput Θ The length threshold

Initialize(1) for all sp isin shortestpath(RN 119899

119904 119899119890) do

(2) 119904119901IsCandidate = True(3) end for

On packet arrive at road segments(4) if 119899

119888== 119899119890then

(5) return(6) end if(7) for all 119903119904 isin 119899

119888NeighborRoad do

(8) if rsIsCandidate and not rsIsCollected then Collect data on road rs

(9) EstimatedSpeed = speedestimate(rs)(10) if EstimatedSpeed(119903119904 sdot V

119898) lt 119891Threshold then

Backoff and fork(11) for all 119903119901 isin 119899

119888NonCandidateNeighborRoad do

(12) Length = shortestpathlength(RN minus 119903119901 119903119901 sdot 119890 119899119890)

(13) if Length + CollectedLengh lt Θ then(14) for all r isin shortestpath(RN minus 119903119901 119903119901 sdot 119890 119899

119890) do

(15) 119903IsCandidate = True(16) end for(17) end if(18) end for(19) end if(20) end if(21) end for

Algorithm 2 Backoff-and-Fork candidate path selection

segments that have not been selected as candidate path willbe used as a starting point to build a new shortest path toend point (fork) and the newly built shortest path will beadded to the candidate path set if the length has not exceededthe threshold (lines (11)ndash(13)) To avoid overlapping betweenthe new and old shortest path the paths connecting existingcandidate paths are excluded when executing the shortestpath algorithm (line (12))

Figure 6 can be used to demonstrate how the BnF algo-rithm works The starting point and end point are 119878 and 119864respectively In the initial phase the shortest path (119878 119888 ℎ 119864)is selectedThen ⟨119878119888⟩ is collectedThe estimated speed of ⟨119878119888⟩is good so it continues with the next candidate path ⟨119888ℎ⟩At this time the estimated speed of ⟨119888ℎ⟩ is too slow and thealgorithm will back off to 119888 and fork to the neighbors 119889 and119891 The new shortest paths to 119864 are found to be (119889 119892 119890) and(119891 ℎ 119890) Then new candidate paths ⟨119888119889⟩ ⟨119889119892⟩ ⟨119892119864⟩ ⟨119888119891⟩and ⟨119891ℎ⟩ are added to the search list

The algorithm will terminate when the request packetsarrive at the navigation destination

6 Performance Evaluation

61 Experiment Setup In this section we evaluate the per-formance of MICE using both small-scaled testbed imple-mentation and traffic trace based large-scaled simulationWe

a

S

d

b

h

c

g

f

E

i

Normal pathCandidate path Congested path

Collected path Shortest path junction Normal junction

Figure 6 An example of Backoff-and-Fork algorithm

choose two evaluation methods because both of them haveadvantages and disadvantages The traffic trace provides arealistic evaluation scenario However some parameters likethe average vehicle density or speed are not configurableConsequently we can not use the trace to test the impact


(a) iTranSNet testbed and topology (b) Cologne road network topology

Figure 7 iTranSNet testbed and cologne topology

Predefined scenarios

(1) Deploy

(2) Execute

(3) Network and estimation performance

MICE algorithm

Evaluation results

iTranSNet

(a) Implementation

NS-3

SUMO

(1) Import

(2) Convert

(3) Import

Cologne trace file

NS trace file MICE algorithm

(4) Execute

(6) Path planning result

(5) Traffic collection performance

(7) Planningperformance

Evaluation results

(b) Simulation

Figure 8 Evaluation flow chart

of these parameters On the contrary the testbed is moreflexible but the road topology and traffic scale are verylimited Figures 7 and 8 show the testbed road networktopology and flow chart of the evaluation

We implemented the MICE on iTranSNet [35] TheiTranSNet is a testbed with several programmable mini carswhich is modified from toy cars by ourselves The movingspeed of the toy cars is very slow approximately 04msand we can control the speed with program The size ofiTranSNet platform is 3m times 6mThe road network topologyof iTranSNet is illustrated in Figure 7(a) The mini cars arecontrolled by onboard MICAz wireless sensor nodes TheMICAz sensor nodes can also communicate with each otherusing IEEE 802154 The MICE algorithm is implemented

on MICAz with TinyOS [36] In our implementation we setthe transmission power to the lowest level (approximately1m) to enable multihop among vehicles The positions of allmini cars can be determined by sensors on iTranSNet Thevehicles follow the traffic light control rules [37] We run theexperiments under several configurations dense traffic (25cars) sparse traffic (12 cars) and some intermediate valuesThe experiments are repeated 50 times with arbitrary routeplanning starting and end points

For simulation we use the traffic trace dataset fromCologne in Germany provided by [38] where detailedanalysis of the trace can also be found Traffic simulatorSUMO [39] is used to simulate the traffic mobility andnetwork simulator NS-3 [40] is used to simulate VANETs


0 500 1000 1500 20000

2000

4000

6000

8000

10000

12000

Total length (m)

Tota

l tim

e (s)

DenseSparse

(a) Latency

Total length (m)0 500 1000 1500 2000

0

05

1

15

2

Tota

l pac

kets

coun

t

DenseSparse

times104

(b) Number of packets

Figure 9 Network evaluation results using iTranSNet testbed

Table 1 NS-3 simulation parameters

Parameter ValueMACPHY standard IEEE 80211p SCHPropagation delay mode Constant speedMAC type Ad hoc Wi-Fi MACPropagation loss model Range propagation loss modelTransmission range 100mPackets carry duration 1 secondNeighbor wait duration 03 second

communication and run MICE algorithm Table 1 showsother parameter settings for NS-3 The simulation processworks as depicted by Figure 8(b) firstly a subset of the traffictrace file (geographical region [509222ndash509552 69077ndash69624]) is imported into SUMO to generate the trace filethat can be used by NS-3 After that NS-3 simulator willrun the MICE algorithm using the mobility trace and getthe route planning result as output Meanwhile the networkperformance data are generated Finally the route planningresults are imported into SUMO again to evaluate the routeplanning performance A Python script is used to automatethis process and the simulation is repeated 50 times witharbitrary starting and end points as well as starting time

62 Traffic Collection Evaluation To evaluate the perfor-mance of traffic collection protocol we are interested in thenetwork latency and the network overhead The networklatency is defined as 119879latency = 119879req minus 119879last feedback where 119879reqis the time stamp of the collection request which is sent and119879last feedback is the time stamp of the last feedback which is

received We use the time stamp of the last packet insteadof the first one because Algorithm 1 may send multiplefeedbacks the last one is the one we finally use and it usuallycontains more accurate estimation than the previous onesThe network overhead is represented by the total number ofpackets that have been sent

Figure 9 shows the traffic collection evaluation resultsTwo major factors are evaluated path length and trafficdensity The results (distance and time cost) of multipleevaluations are added together and plotted in Figure 9From Figure 9(a) we can see that the latency increases asthe path length And for the same length the latency forsparse traffic is usually larger than the case of dense trafficThis is caused by the frequent packet carrying in sparsetraffic condition Figure 9(b) shows that the total number ofpackets transmitted also increases as the path length For thesame length more packets are transmitted in dense trafficcondition This is because of the RTSCST scheme depictedin Figure 3 When the traffic is dense after an RTS is sentmore CTS replies are generated and transmitted over the air

Figure 10 illustrates the traffic collection evaluationresults using traffic traceWe run the experimentsmany timesand sum up the distance and time consumption As we cansee the increase trend of latency and the number of packetssent are similar as the results obtained by testbed Moreoverthe results are more convincing since the road conditionvehicle speed and distribution are more realistic The net-work latency of is less than 1minkm In real applicationscenario the delay is acceptable

63 Speed Estimation Evaluation To evaluate the perfor-mance of traffic speed estimation algorithm we consider


0 50 100 150 2000

5000

10000

15000

Tota

l tim

e (s)

Total length (km)

(a) Latency

0 50 100 150 2000

05

1

15

2

Total length (km)

Tota

l pac

ket c

ount

times104


Figure 10 Network evaluation results using traffic trace

0 005 01 015 02

Mea

n es

timat

ion

erro

r

0

2

4

6

8

10

12

14

MICEVAN

Density (vehicle(mlowastlane))

Figure 11 Traffic density and speed estimation accuracy

the impact of traffic density and time window length 119901 intraffic condition (Definition 4) Data of a single road segmentis collected and estimated The road segment is selectedrandomly and the experiments are repeated 50 times Wecompare our solution with VAN [8] which calculate themean speed of collected vehicles to represent the traffic speedThemean estimation error is calculated using (10)Thereforethe smaller the error is the better the method works

119864 =

119899

sum

119894=1

(V119894est minus V)2

119899

(10)

Figure 11 illustrates the relationship between traffic den-sity and the estimation error The unit of density is thenumber of vehicles per meter per lane From this figurewe can see that in most of the cases the estimation errorsof MICE are smaller than VAN which indicates that ourmethod outperforms VAN in traffic speed estimation Thekey insight is that compared with mean speed used by VAN

the density-speed model used by MICE can better reveal thetruth in most cases However the estimation error of MICEhas a remarkable increase trend when the traffic densityis high There are two reasons for this First Underwooddensity-speed model we select performs better in low densityscenario Second when vehicle density reaches 02 (headwaydistance is 5m) the traffic is highly congested and the vehiclesare almost stopped Then the variance is not significant themean speed method used by VAN can even better reflect theground truth

Figure 12 shows the relationship between estimation errorand time window sizeThe size of the time window affects thevalue of traffic condition mean speed V If the time windowis small enough V is close to instant speed In this case theestimation errors tend to be significant This trend can beobserved in Figure 12(a) For Figure 12(a) the vehicle densityis fixed at 005 vehicle per meter per lane For Figure 12(b)the density of traffic trace can not be controlled In most ofthe cases MICE performs better than VAN which further


0 50 100 150 2000

1

2

3

4

5

6

Time window size (s)

Erro

r

MICEVAN

(a) Testbed

0 50 100 150 2000

20

40

60

80

100


Erro

r

MICEVAN

(b) Trace

Figure 12 Time window size and speed estimation accuracy

Sparse Dense07

075

08

085

09

095

Density

()

Successful collection ratio

Figure 13 Collection ratio

validates that density-speed model is a better option thanmean speed Another observation is that the relationshipbetween time window size and the accuracy of the algorithmis not obviousThis result reveals the insight that in real casethe traffic condition is stable within certain time period (egwithin 200 seconds) It is also noticeable that the errors inFigure 12(b) are much larger than Figure 12(a)This is mainlybecause in real traffic traces the variation of car speed ismuch larger (0ndash80 kmh) than that in testbed (0ndash15 kmh)

64 Route Planning Application Finally we evaluate theperformance of different speed estimation solutions byputting them into route planning application We calculatethe shortest-time path using the estimated traffic speedfrom these solutions and then let the vehicles travel along

the shortest-time path in SUMO or iTransNet testbed toobtain the actual time consumption In this way we can findout the performance of these solutions in real applications

We firstly evaluate the successful collection ratio ofroad segments The collection ratio is defined as count(119862

119894)

count(119862) where count(119862) is the number of road segmentsin all candidate paths and count(119862

119894) is the number of road

segments that has been collected successfully The collectionfailure is usually caused by packets loss or extreme sparsetraffic In Figure 13 a steady increase for successful collectionratio can be found from about 75 to 95 Howeverthe increase trend is not obvious after the density reachesa specific threshold The reason for this is that after thethreshold is reached the VANETs approximately become afully connected graph Therefore the packet loss rate is then


05 1 15 2 25 3 35 40

100

200

300

400

500

Shortest path length (km)

Cand

idat

e pat

h le

ngth

(km

)

Candidate path searched

BnFEllipse

Figure 14 Candidate path

Dense Sparse0

02

04

06

08

()

All sameAll different

Dijkstra = MICEVAN = MICE

(a) Results using testbed

0

02

04

06

08

()

Same Diff SP = M ST = M VAN = M

(b) Results using trace

Figure 15 Overview of shortest-time path planning results

reduced dramaticallyThe average collection ratio using traceon NS-3 is above 90 since the trace is collected duringmorning rush hour

The performance of BnF algorithm is also evaluated Theevaluation is only performed with trace data since the resultsare not obvious on iTranSNet due to the small scale Wecompare our heuristic solution with a spatial constrainedbroadcast solution which collects traffic information of allroads within an ellipse with navigation starting point and endpoint as two foci of the ellipse In the evaluation we set thelength threshold Θ to 3 times of the shortest path length andthen increase the speed threshold119891Threshold gradually untilour solution and the ellipse-based solution have the same or

better resultwhen route planningThen the total length of thecandidate path is depicted in Figure 14 We can see that ourBnF algorithm can save up to 75 of the search paths As aresult the network communication overhead can be reducedsignificantly

Finally we evaluate the route planning performanceusing different route planning algorithms For route planningevaluation we are interested in the effectiveness of routeplanning that is howmuch time can be saved and the traveloverhead that is how many extra distances have to be trav-elled in order to bypass the congested road segments In theevaluation we compare MICE with two static route planningalgorithms shortest path Dijkstra algorithm (SP the weight


Overall efficiency

00

2

2

4

4

6

6 0 2 4 6

8

10

12Ti

me c

ost (

min

)

Shortest path distance (km)

SPVANMICE

0 2 4 6


SPVANMICE

SPVANMICE

2

0

minus2

minus4

minus6

minus8

Tim

e sav

ed (m

in)

Time saved


02

015

01

005

0

minus005

Distance increased

Incr

ease

d di

stan

ce (k

m)

Figure 16 Performance results for path planning application in dense traffic using iTranSNet testbed

of edges is 119903) and shortest-time Dijkstra algorithm (ST theweight of edges is 119903119903 sdotV

119898) and one dynamic route planning

algorithmVAN[8] which usesmean speed to estimate trafficOn iTranSNet due to hardware limitation the maximumallowed speed for all road segments is the same thus theshortest path and the shortest-time planning algorithm areidentical Therefore we ignore the results for shortest-timeplanning algorithm on iTranSNet

Given the same navigation starting and ending pointsdifferent route planning algorithms may generate either thesame or different route planning results Figure 15 showsthe similarity and differences of the algorithm output OniTanSNet testbed the route planning results for SP VANand MICE solutions are quite similar about 60 of the totaltries have the same result and only 5 of total tries have

completely different results This is caused by the small scaleFor traffic trace based simulation only 30 of the results areidentical It can also be observed that the similarity betweenMICE and VAN is higher than the two static ones

The overall efficiency charts in Figures 16 and 17 reveal thatour algorithm works better in dense traffic conditions com-pared with spare ones This conclusion does not contradictthe results from Figure 11 because the shortest-time routeplanning algorithms can bypass dense road segments andselect sparse ones automatically In sparse traffic scenariosthe performance of the three algorithms is quite similar due totraffic congestion althoughMICE indeed has some improve-ment In contrastMICEhas about 25 time saving comparedwith Dijkstra algorithm and about 15 time saving comparedwith VAN For the trace based simulation case in Figure 18


4

35

3

25

2

15

10 05 1 15 2

Tim

e cos

t (m

in)


SPVANMICE

005

0

minus005

minus01

minus015

minus02

Tim

e sav

ed (m

in)

02

015

01

005

0

Incr

ease

d di

stan

ce (k

m)

Distance increased

Time savedOverall efficiency

0 05 1 15 2


SPVANMICE

0 05 1 15 2


SPVANMICE

Figure 17 Performance results for path planning application in sparse traffic using iTranSNet testbed

MICE has about 15 time saving compared with Dijkstraalgorithm and about 5 time saving compared with VAN

The time saved charts in Figures 16 and 17 use the sameevaluation results as overall efficiency but use the time costfor the shortest path as baseline for better understanding Aninteresting observation can be found in which in the tracebased simulation sometimes the shortest-time algorithmspends even longer time than shortest path one The resultis consistent with our experiences However analyzing theresult is out of the scope of this paper

Finally the distance increased chart shows the traveloverhead to bypass the congested road It also uses shortestpath length as baseline It is observed that the travel overheadis controlled within 10 For the trace based simulation in

Figure 18 it is interesting to discover that the overhead ofMICE is only a little higher than the shortest-time algorithmand about 10 lower thanVAN In these figures the improve-ment of MICE is not that obvious As depicted in Figure 15about 75 of the path planning results for VAN and MICEare the same In order to reflect the real performance we didnot remove these duplicated results in the charts

7 Conclusion

In this paper we have proposed MICE a traffic estimationsolution by collecting and analyzing surrounding trafficinformation using VANETs in real-time The traffic collec-tion solution is infrastructure-free and scalable compared


12

10

8

6

6

4

4

2

200

Tim

e cos

t (m

in)


2

1

0

minus1

minus2

minus3

Tim

e sav

ed (m

in)

Overall efficiency Time saved

Incr

ease

d di

stan

ce (k

m)

15

1

05

0

Distance increased

SPST

VANMICE

6420


SPST

VANMICE

6420


SPST

VANMICE

Figure 18 Performance results for path planning application using traffic trace

with conventional infrastructure based ones We have alsoemployed a novel density-speed traffic model to estimatetraffic condition using the collected floating car data Thenwe have applied the proposed algorithm to a shortest-timeroute planning applications to demonstrate the performanceof the solution Extensive evaluations using both testbedbased implementation and trace based simulation have beenconductedThe results have shown that our solution can out-perform some existing ones in terms of network transmissionoverhead route planning effectiveness and traffic overhead

Summary of Notations

119903 or ⟨se⟩ A single road segment119903 The length of road segment 119903VN The vehicular networkstr Mean network transmission range for VN

119881119905

119903 The vector containing floating car data for road

segment 119903 aka snapshot119896 The traffic density for a single road segment119889 The mean headway distance of a road segmentVest The estimated traffic speed for specific road segment119863safe Minimum safety distance between adjacent vehicles119896119888 The optimum density or the maximum flow density

119881119875

119903 Vehicles appearing in road segment 119903 during time

period 119901119881119889

119903 The vector containing distance between adjacent

vehicles for road segment 119903

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper


Acknowledgment

This research is partially supported by Microsoft ResearchAsia Accelerating Urban Informatics with Azure Program

References

[1] Z He J Cao and T Li ldquoMICE A real-time traffic estimationbased vehicular path planning solution using VANETsrdquo inProceedings of the 1st International Conference on ConnectedVehicles and Expo (ICCVE rsquo12) pp 172ndash178 December 2012

[2] C Li and S Shimamoto ldquoAn open traffic light control model forreducing vehiclesrsquo CO

2emissions based on ETC vehiclesrdquo IEEE

Transactions on Vehicular Technology vol 61 no 1 pp 97ndash1102012

[3] B Tian Q Yao Y Gu K Wang and Y Li ldquoVideo processingtechniques for traffic flow monitoring a surveyrdquo in Proceedingsof the 14th IEEE International Intelligent Transportation SystemsConference (ITSC rsquo11) pp 1103ndash1108 October 2011

[4] K Singh and B Li ldquoEstimation of traffic densities for multilaneroadways using a markov model approachrdquo IEEE Transactionson Industrial Electronics vol 59 no 11 pp 4369ndash4376 2012

[5] Q-J Kong Z Li Y Chen and Y Liu ldquoAn approach to Urbantraffic state estimation by fusingmultisource informationrdquo IEEETransactions on Intelligent Transportation Systems vol 10 no 3pp 499ndash511 2009

[6] J Jeong S Guo YGu THe andDHCDu ldquoTrajectory-baseddata forwarding for light-traffic vehicular Ad Hoc networksrdquoIEEE Transactions on Parallel and Distributed Systems vol 22no 5 pp 743ndash757 2011

[7] R StanicaM Fiore and FMalandrino ldquoOffloading floating cardatardquo in Proceedings of the IEEE 14th International Symposiumon a World of Wireless Mobile and Multimedia Networks(WoWMoM rsquo13) pp 1ndash9 June 2013

[8] WChen S Zhu andD Li ldquoVAN vehicle-assisted shortest-timepath navigationrdquo in Proceedings of the 7th IEEE InternationalConference onMobile Adhoc and Sensor Systems (MASS rsquo10) pp442ndash451 November 2010

[9] K Collins and G-M Muntean ldquoA vehicle route managementsolution enabled by wireless vehicular networksrdquo in Proceedingsof the IEEE INFOCOMWorkshops pp 1ndash6 IEEE April 2008

[10] F Calabrese M Colonna P Lovisolo D Parata and C RattildquoReal-time urban monitoring using cell phones a case study inRomerdquo IEEE Transactions on Intelligent Transportation Systemsvol 12 no 1 pp 141ndash151 2011

[11] TNadeem SDashtinezhad C Liao and L Iftode ldquoTrafficviewtraffic data dissemination using car-to-car communicationrdquoACM SIGMOBILE Mobile Computing and CommunicationsReview vol 8 no 3 pp 6ndash19 2004

[12] C Lochert B Scheuermann C Wewetzer A Luebke andM Mauve ldquoData aggregation and roadside unit placement fora vanet traffic information systemrdquo in Proceedings of the 5thACM International Workshop on VehiculAr Inter-NETworking(VANET rsquo08) pp 58ndash65 ACM September 2008

[13] J Rybicki B Scheuermann M Koegel and M MauveldquoPeerTISmdasha peer-to-peer traffic information systemrdquo in Pro-ceedings of the 6th ACM International Workshop on VehiculArInter-NETworking (VANET rsquo09) pp 23ndash32 September 2009

[14] X Li W Shu M Li H-Y Huang P-E Luo and M-Y WuldquoPerformance evaluation of vehicle-based mobile sensor net-works for traffic monitoringrdquo IEEE Transactions on VehicularTechnology vol 58 no 4 pp 1647ndash1653 2009

[15] A May Traffic Flow Fundamentals Prentice Hall 1990[16] M H Arbabi and M C Weigle ldquoUsing vehicular networks

to collect common traffic datardquo in Proceedings of the 6thACM International Workshop on VehiculAr Inter-NETworking(VANET rsquo09) pp 117ndash118 ACM New York NY USA Septem-ber 2009

[17] Y Yu B Liu T Wu and M Liu ldquoFlow-based travel plan viaVANETrdquo International Journal of Digital Content Technologyand its Applications vol 5 no 6 pp 163ndash171 2011

[18] R Sen A Maurya B Raman et al ldquoKyun queue a sensornetwork system to monitor road traffic queuesrdquo in Proceedingsof the 10th ACM Conference on Embedded Network SensorSystems (SenSys rsquo12) pp 127ndash140 ACM Toronto CanadaNovember 2012

[19] S Dornbush and A Joshi ldquoStreetsmart traffic discovering anddisseminating automobile congestion using vanetrdquo in Proceed-ings of the Vehicular Technology Conference (VTC-Spring rsquo07)pp 11ndash15 April 2007

[20] V Naumov R Baumann and T Gross ldquoAn evaluation of inter-vehicle ad hoc networks based on realistic vehicular tracesrdquo inProceedings of the 7th ACM International Symposium on MobileAd Hoc Networking and Computing (MobiHoc rsquo06) pp 108ndash119May 2006

[21] L Garelli C Casetti C Chiasserini and M Fiore ldquoMobsam-pling V2v communications for traffic density estimationrdquo inProceedings of the 73rd IEEE Vehicular Technology Conference(VTC-Spring rsquo11) pp 1ndash5 2011

[22] A Lakas and M Shaqfa ldquoGeocache sharing and exchangingroad traffic information using peer-to-peer vehicular commu-nicationrdquo in Proceedings of the IEEE 73rd Vehicular TechnologyConference (VTC Spring rsquo11) pp 1ndash7 IEEE May 2011

[23] J-W Ding C-F Wang F-H Meng and T-Y Wu ldquoReal-timevehicle route guidance using vehicle-to-vehicle communica-tionrdquo IET Communications vol 4 no 7 pp 870ndash883 2010

[24] H F Wedde and S Senge ldquoBeejama a distributed self-adaptive vehicle routing guidance approachrdquo IEEE Transactionson Intelligent Transportation Systems vol 14 no 4 pp 1882ndash1895 2013

[25] V Hodge R Krishnan T Jackson J Austin and J PolakldquoShort-term traffic prediction using a binary neural networkrdquoin Proceedings of the 43rd Annual Universitiesrsquo Transport StudiesGroup Conference (UTSG rsquo11) Open University Milton KeynesUK 2011

[26] H Kanoh and K Hara ldquoHybrid genetic algorithm for dynamicmulti-objective route planning with predicted traffic in a real-world road networkrdquo in Proceedings of the 10th Annual Geneticand Evolutionary Computation Conference (GECCO rsquo08) pp657ndash664 ACM New York NY USA July 2008

[27] G Comert and M Cetin ldquoAnalytical evaluation of the errorin queue length estimation at traffic signals from probe vehicledatardquo IEEE Transactions on Intelligent Transportation Systemsvol 12 no 2 pp 563ndash573 2011

[28] G K S Mung A C K Poon andW H K Lam ldquoDistributionsof queue lengths at fixed time traffic signalsrdquo TransportationResearch Part BMethodological vol 30 no 6 pp 421ndash439 1996

[29] F Bai and B Krishnamachari ldquoSpatio-temporal variations ofvehicle traffic inVANETs facts and implicationsrdquo inProceedingsof the MobiHoc6th ACM International Workshop on VehiculArInter-NETworking (VANET rsquo09) pp 43ndash52 ACM September2009

[30] B Karp and H T Kung ldquoGpsr greedy perimeter statelessrouting for wireless networksrdquo in Proceedings of the 6th Annual


International Conference on Mobile Computing and Networking(MobiCom rsquo00) pp 243ndash254 ACM New York NY USA 2000

[31] S ArdekaniMGhandehari and SNepal ldquoMacroscopic speed-flow models for characterization of freeway and managedlanesrdquo Institutul Politehnic din Iasi Buletinul Sectia ConstructiiArhitectura vol 57 no 1 2011

[32] D N Godbole and J Lygeros ldquoLongitudinal control of the leadcar of a platoonrdquo IEEE Transactions on Vehicular Technologyvol 43 no 4 pp 1125ndash1135 1994

[33] Wikipedia ldquoTwo-second rulemdashwikipedia the free encyclope-diaonlinerdquo 2013 httpsenwikipediaorgwikiTwo-secondrule

[34] G Andreatta and L Romeo ldquoStochastic shortest paths withrecourserdquo Networks vol 18 no 3 pp 193ndash204 1988

[35] U Poly ldquoiTransnetrdquo 2010 httpimccomppolyueduhkisens-netdokuphpid=research

[36] P Levis S Madden J Polastre et al ldquoTinyos an operatingsystem for sensor networksrdquo in Ambient Intelligence W WeberJ Rabaey and E Aarts Eds pp 115ndash148 Springer BerlinGermany 2005

[37] B Zhou J Cao X Zeng and HWu ldquoAdaptive traffic light con-trol in wireless sensor network-based intelligent transportationsystemrdquo in Proceedings of the 72nd IEEE Vehicular TechnologyConference Fall (VTC rsquo10) pp 1ndash5 IEEE Ottawa CanadaSeptember 2010

[38] S Uppoor and M Fiore ldquoLarge-scale urban vehicular mobilityfor networking researchrdquo in Proceedings of the IEEE VehicularNetworking Conference (VNC rsquo11) pp 62ndash69 November 2011

[39] M Behrisch L Bieker J Erdmann andDKrajzewicz ldquoSumomdashsimulation of urbanmobility an overviewrdquo in Proceedings of the3rd International Conference on Advances in System Simulation(SIMUL rsquo11) Barcelona Spain October 2011

[40] T Henderson M Lacage G Riley C Dowell and J KopenaldquoNetwork simulations with the ns-3 simulatorrdquo in Proceedingsof the ACM SIGCOMM Conference on Data Communication(SIGCOMM rsquo08) Seattle Wash USA 2008

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Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

SensorsJournal of


Modelling amp Simulation in EngineeringHindawi Publishing Corporation httpwwwhindawicom Volume 2014


Chemical EngineeringInternational Journal of Antennas and

Propagation




Navigation and Observation



DistributedSensor Networks



communication However it is not easy to collect floating cardata using VANETs Considering the unique characteristicsof VANETs and the application scenario several challengingissues are raised

(i) Network Disconnection Vehicle mobility is highlydynamic which can cause the network to be fre-quently disconnected When using VANET to collecttraffic information an interesting contradiction canbe found between traffic condition and networkcondition Good traffic condition (high vehicle speedlow traffic density) usually leads to poor networkcondition (high packet loss opportunistic network)and vice versa The solution must tolerate the poornetwork conditions

(ii) Vehicle Speed Variation Vehicles on roads do nottravel using constant speed The individual vehiclespeed can have remarkable change in a short periodFor example a red light is in front or a taxi stopsat the request of passengers The instant variation offloating car data can lead to inaccuracy to the trafficestimation algorithm A good analysis model must beable to eliminate this variation

(iii) Insufficient Traffic Sampling Due to the infrastruc-ture-less nature of VANETs it is not easy to monitoran area for a long period of time Moreover thefast-changing individual vehicle speed and fragilenetwork topology can potentially interfere with thedata collection Hence the data sampling is alwaysinsufficient The traffic estimation algorithm needs todeal with insufficient traffic sampling condition

To tackle these challenges some infrastructure basedsolution has been proposed These solutions utilize roadsideunit (eg Wi-Fi access points [12] cellular base stations[13] etc) and vehicle to infrastructure (V2I) communicationto monitor the traffic condition of a fixed area With thehelp of roadside infrastructure continuous sampling of anarea and caching collected data can easily be achievedHowever these solutions have the same deployment problemas those traffic facility based solutions Other researches relyon the assumption that vehicles travel with constant speed[8 14] Without considering vehicle speed variation somestraightforward traffic estimation algorithms like time meanspeed or space mean speed can be developed However thisassumption does not hold in real world and it can lead toinaccurate traffic speed estimation results

In this paper we propose a novel in-network trafficinformation collection and estimation solution We name itMICE or Model based Infrastructure-free traffic Collectionand Estimation In MICE traffic information is collectedusing infrastructure-free approach Then traffic condition isestimated using in-network data processing andmacroscopictraffic flow model The application scenario is depicted inFigure 1 When a vehicle wants to know the traffic conditionof nearby roads it sends a traffic collection request usingV2V communication to ambient vehicles on demand Thosevehicles that received the request send their information backto the original vehicle for traffic condition estimation

E

SN1 N2

N3

N5N4

Figure 1 Application scenario and system model using V2Vcommunication to collect real-time traffic information

The contributions of this work can be summarized asfollows

(1) We propose a floating car data collection protocolwhich leverages V2V communication It can collectfloating car data in segment level granularity and it isadaptive to different network conditions

(2) We adopt Underwood density-speed traffic flowmodel [15] developed by traffic scientists to predictthe traffic speedThismodel can eliminate the tempo-rary speed variations to improve the estimation accu-racy efficient enough for in-network data processing

(3) We apply the proposed solution to the shortest-time route planning application A heuristic basedpath finding algorithm is proposed to show how theproposed solution works in real world applications

(4) Extensive evaluations and analysis are conductedusing both large scale simulation and testbed imple-mentation Evaluation results show that our solutionoutperforms some static and dynamic route planningsolutions

The remainder of this paper is organized as followsrelated works are summarized in Section 2 The MICE prob-lem is formulated in Section 3 The solution to MICE ispresented in Section 4 in detail In Section 5 an applicationof the proposed solution is introduced The evaluationsand results will be analyzed in Section 6 Finally Section 7concludes this paper

2 Related Work

Several related works on using VANET to estimate trafficspeed can be found in literature We first introduce the workson traffic information collection followed by existing trafficspeed estimation solutions

21 Traffic Information Collection Solutions on traffic infor-mation collection can be classified by whether they rely

















3 Problem Statement









2+ (119890 sdot 119910 minus 119904 sdot 119910)





997888rarr



119903= (V1119905 V2119905 V119899119905)


119903 1198811199051119903 119881

119905119899

119903







119903le 119905119899minus 1199050


V =sumVisin119881119901

119903

V sdot 119904119903

sumVisin119881119901119903

V sdot 119905119903

(1)







119896 =

1119864 (119881119889

119903)

=

11120582

= 120582 (2)
















Given








4 Solution












tr

a

c

s

b

d

e

CTSCTS

CTSCTS

tr

a

c

s

b

d

e

Forward


ffCTS

Road direction



997888rarr



119904 vi sdot

997888rarr


119904







119904 vi sdot

997888rarr


119904







997888rarr



119890than 119889











Vest = V119891119890minus119896119896119888

(4)

20

15

10

5

00 01 02 03 04 05

Vehi

cle sp

eed

(ms

)




119896119888= 01







119863safe = 119888119886 + 119888V + 119888119901 (5)





119896119888=

1119863safe (119888V = V

1198912)

=

1(V1198912) times 2 + 10

=

1V119891+ 10

(6)





120582 =

1119883

(7)


119891= V119898


Vest = V119898119890minus(V119898+10)119883

(8)


0 10 20 300

1

2

3

4

5

Vehicle speed (ms)

Tim

es

times105


mes

0 10 20 300

1

2

3

4

5

6


times104




119883 =

sum119894lt119899minus1119894=0 119883

119894

(119899 minus 1)times 119903 sdot 119897 (9)












119904 end point 119899


119888



119904 119899119890) do



119888== 119899119890then










119890) do








a

S

d

b

h

c

g

f

E

i









(1) Deploy

(2) Execute


MICE algorithm

Evaluation results

iTranSNet

(a) Implementation

NS-3

SUMO

(1) Import

(2) Convert

(3) Import

Cologne trace file


(4) Execute




Evaluation results

(b) Simulation







0 500 1000 1500 20000

2000

4000

6000

8000

10000

12000

Total length (m)

Tota

l tim

e (s)

DenseSparse

(a) Latency

Total length (m)0 500 1000 1500 2000

0

05

1

15

2

Tota

l pac

kets

coun

t

DenseSparse

times104












0 50 100 150 2000

5000

10000

15000

Tota

l tim

e (s)

Total length (km)

(a) Latency

0 50 100 150 2000

05

1

15

2

Total length (km)

Tota

l pac

ket c

ount

times104



0 005 01 015 02

Mea

n es

timat

ion

erro

r

0

2

4

6

8

10

12

14

MICEVAN




119864 =

119899

sum

119894=1


119899

(10)





0 50 100 150 2000

1

2

3

4

5

6


Erro

r

MICEVAN

(a) Testbed

0 50 100 150 2000

20

40

60

80

100


Erro

r

MICEVAN

(b) Trace


Sparse Dense07

075

08

085

09

095

Density

()







119894)





05 1 15 2 25 3 35 40

100

200

300

400

500


Cand

idat

e pat

h le

ngth

(km

)


BnFEllipse


Dense Sparse0

02

04

06

08

()




0

02

04

06

08

()









Overall efficiency

00

2

2

4

4

6

6 0 2 4 6

8

10

12Ti

me c

ost (

min

)


SPVANMICE

0 2 4 6


SPVANMICE

SPVANMICE

2

0

minus2

minus4

minus6

minus8

Tim

e sav

ed (m

in)

Time saved


02

015

01

005

0

minus005

Distance increased

Incr

ease

d di

stan

ce (k

m)









4

35

3

25

2

15

10 05 1 15 2

Tim

e cos

t (m

in)


SPVANMICE

005

0

minus005

minus01

minus015

minus02

Tim

e sav

ed (m

in)

02

015

01

005

0

Incr

ease

d di

stan

ce (k

m)

Distance increased


0 05 1 15 2


SPVANMICE

0 05 1 15 2


SPVANMICE






7 Conclusion



12

10

8

6

6

4

4

2

200

Tim

e cos

t (m

in)


2

1

0

minus1

minus2

minus3

Tim

e sav

ed (m

in)


Incr

ease

d di

stan

ce (k

m)

15

1

05

0

Distance increased

SPST

VANMICE

6420


SPST

VANMICE

6420


SPST

VANMICE





119881119905



119881119875


period 119901119881119889






Acknowledgment


References















































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation

























3 Problem Statement









2+ (119890 sdot 119910 minus 119904 sdot 119910)





997888rarr



119903= (V1119905 V2119905 V119899119905)


119903 1198811199051119903 119881

119905119899

119903







119903le 119905119899minus 1199050


V =sumVisin119881119901

119903

V sdot 119904119903

sumVisin119881119901119903

V sdot 119905119903

(1)







119896 =

1119864 (119881119889

119903)

=

11120582

= 120582 (2)
















Given








4 Solution












tr

a

c

s

b

d

e

CTSCTS

CTSCTS

tr

a

c

s

b

d

e

Forward


ffCTS

Road direction



997888rarr



119904 vi sdot

997888rarr


119904







119904 vi sdot

997888rarr


119904







997888rarr



119890than 119889











Vest = V119891119890minus119896119896119888

(4)

20

15

10

5

00 01 02 03 04 05

Vehi

cle sp

eed

(ms

)




119896119888= 01







119863safe = 119888119886 + 119888V + 119888119901 (5)





119896119888=

1119863safe (119888V = V

1198912)

=

1(V1198912) times 2 + 10

=

1V119891+ 10

(6)





120582 =

1119883

(7)


119891= V119898


Vest = V119898119890minus(V119898+10)119883

(8)


0 10 20 300

1

2

3

4

5

Vehicle speed (ms)

Tim

es

times105


mes

0 10 20 300

1

2

3

4

5

6


times104




119883 =

sum119894lt119899minus1119894=0 119883

119894

(119899 minus 1)times 119903 sdot 119897 (9)












119904 end point 119899


119888



119904 119899119890) do



119888== 119899119890then










119890) do








a

S

d

b

h

c

g

f

E

i









(1) Deploy

(2) Execute


MICE algorithm

Evaluation results

iTranSNet

(a) Implementation

NS-3

SUMO

(1) Import

(2) Convert

(3) Import

Cologne trace file


(4) Execute




Evaluation results

(b) Simulation







0 500 1000 1500 20000

2000

4000

6000

8000

10000

12000

Total length (m)

Tota

l tim

e (s)

DenseSparse

(a) Latency

Total length (m)0 500 1000 1500 2000

0

05

1

15

2

Tota

l pac

kets

coun

t

DenseSparse

times104












0 50 100 150 2000

5000

10000

15000

Tota

l tim

e (s)

Total length (km)

(a) Latency

0 50 100 150 2000

05

1

15

2

Total length (km)

Tota

l pac

ket c

ount

times104



0 005 01 015 02

Mea

n es

timat

ion

erro

r

0

2

4

6

8

10

12

14

MICEVAN




119864 =

119899

sum

119894=1


119899

(10)





0 50 100 150 2000

1

2

3

4

5

6


Erro

r

MICEVAN

(a) Testbed

0 50 100 150 2000

20

40

60

80

100


Erro

r

MICEVAN

(b) Trace


Sparse Dense07

075

08

085

09

095

Density

()







119894)





05 1 15 2 25 3 35 40

100

200

300

400

500


Cand

idat

e pat

h le

ngth

(km

)


BnFEllipse


Dense Sparse0

02

04

06

08

()




0

02

04

06

08

()









Overall efficiency

00

2

2

4

4

6

6 0 2 4 6

8

10

12Ti

me c

ost (

min

)


SPVANMICE

0 2 4 6


SPVANMICE

SPVANMICE

2

0

minus2

minus4

minus6

minus8

Tim

e sav

ed (m

in)

Time saved


02

015

01

005

0

minus005

Distance increased

Incr

ease

d di

stan

ce (k

m)









4

35

3

25

2

15

10 05 1 15 2

Tim

e cos

t (m

in)


SPVANMICE

005

0

minus005

minus01

minus015

minus02

Tim

e sav

ed (m

in)

02

015

01

005

0

Incr

ease

d di

stan

ce (k

m)

Distance increased


0 05 1 15 2


SPVANMICE

0 05 1 15 2


SPVANMICE






7 Conclusion



12

10

8

6

6

4

4

2

200

Tim

e cos

t (m

in)


2

1

0

minus1

minus2

minus3

Tim

e sav

ed (m

in)


Incr

ease

d di

stan

ce (k

m)

15

1

05

0

Distance increased

SPST

VANMICE

6420


SPST

VANMICE

6420


SPST

VANMICE





119881119905



119881119875


period 119901119881119889






Acknowledgment


References















































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation
















2+ (119890 sdot 119910 minus 119904 sdot 119910)





997888rarr



119903= (V1119905 V2119905 V119899119905)


119903 1198811199051119903 119881

119905119899

119903







119903le 119905119899minus 1199050


V =sumVisin119881119901

119903

V sdot 119904119903

sumVisin119881119901119903

V sdot 119905119903

(1)







119896 =

1119864 (119881119889

119903)

=

11120582

= 120582 (2)
















Given








4 Solution












tr

a

c

s

b

d

e

CTSCTS

CTSCTS

tr

a

c

s

b

d

e

Forward


ffCTS

Road direction



997888rarr



119904 vi sdot

997888rarr


119904







119904 vi sdot

997888rarr


119904







997888rarr



119890than 119889











Vest = V119891119890minus119896119896119888

(4)

20

15

10

5

00 01 02 03 04 05

Vehi

cle sp

eed

(ms

)




119896119888= 01







119863safe = 119888119886 + 119888V + 119888119901 (5)





119896119888=

1119863safe (119888V = V

1198912)

=

1(V1198912) times 2 + 10

=

1V119891+ 10

(6)





120582 =

1119883

(7)


119891= V119898


Vest = V119898119890minus(V119898+10)119883

(8)


0 10 20 300

1

2

3

4

5

Vehicle speed (ms)

Tim

es

times105


mes

0 10 20 300

1

2

3

4

5

6


times104




119883 =

sum119894lt119899minus1119894=0 119883

119894

(119899 minus 1)times 119903 sdot 119897 (9)












119904 end point 119899


119888



119904 119899119890) do



119888== 119899119890then










119890) do








a

S

d

b

h

c

g

f

E

i









(1) Deploy

(2) Execute


MICE algorithm

Evaluation results

iTranSNet

(a) Implementation

NS-3

SUMO

(1) Import

(2) Convert

(3) Import

Cologne trace file


(4) Execute




Evaluation results

(b) Simulation







0 500 1000 1500 20000

2000

4000

6000

8000

10000

12000

Total length (m)

Tota

l tim

e (s)

DenseSparse

(a) Latency

Total length (m)0 500 1000 1500 2000

0

05

1

15

2

Tota

l pac

kets

coun

t

DenseSparse

times104












0 50 100 150 2000

5000

10000

15000

Tota

l tim

e (s)

Total length (km)

(a) Latency

0 50 100 150 2000

05

1

15

2

Total length (km)

Tota

l pac

ket c

ount

times104



0 005 01 015 02

Mea

n es

timat

ion

erro

r

0

2

4

6

8

10

12

14

MICEVAN




119864 =

119899

sum

119894=1


119899

(10)





0 50 100 150 2000

1

2

3

4

5

6


Erro

r

MICEVAN

(a) Testbed

0 50 100 150 2000

20

40

60

80

100


Erro

r

MICEVAN

(b) Trace


Sparse Dense07

075

08

085

09

095

Density

()







119894)





05 1 15 2 25 3 35 40

100

200

300

400

500


Cand

idat

e pat

h le

ngth

(km

)


BnFEllipse


Dense Sparse0

02

04

06

08

()




0

02

04

06

08

()









Overall efficiency

00

2

2

4

4

6

6 0 2 4 6

8

10

12Ti

me c

ost (

min

)


SPVANMICE

0 2 4 6


SPVANMICE

SPVANMICE

2

0

minus2

minus4

minus6

minus8

Tim

e sav

ed (m

in)

Time saved


02

015

01

005

0

minus005

Distance increased

Incr

ease

d di

stan

ce (k

m)









4

35

3

25

2

15

10 05 1 15 2

Tim

e cos

t (m

in)


SPVANMICE

005

0

minus005

minus01

minus015

minus02

Tim

e sav

ed (m

in)

02

015

01

005

0

Incr

ease

d di

stan

ce (k

m)

Distance increased


0 05 1 15 2


SPVANMICE

0 05 1 15 2


SPVANMICE






7 Conclusion



12

10

8

6

6

4

4

2

200

Tim

e cos

t (m

in)


2

1

0

minus1

minus2

minus3

Tim

e sav

ed (m

in)


Incr

ease

d di

stan

ce (k

m)

15

1

05

0

Distance increased

SPST

VANMICE

6420


SPST

VANMICE

6420


SPST

VANMICE





119881119905



119881119875


period 119901119881119889






Acknowledgment


References















































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation














Given








4 Solution












tr

a

c

s

b

d

e

CTSCTS

CTSCTS

tr

a

c

s

b

d

e

Forward


ffCTS

Road direction



997888rarr



119904 vi sdot

997888rarr


119904







119904 vi sdot

997888rarr


119904







997888rarr



119890than 119889











Vest = V119891119890minus119896119896119888

(4)

20

15

10

5

00 01 02 03 04 05

Vehi

cle sp

eed

(ms

)




119896119888= 01







119863safe = 119888119886 + 119888V + 119888119901 (5)





119896119888=

1119863safe (119888V = V

1198912)

=

1(V1198912) times 2 + 10

=

1V119891+ 10

(6)





120582 =

1119883

(7)


119891= V119898


Vest = V119898119890minus(V119898+10)119883

(8)


0 10 20 300

1

2

3

4

5

Vehicle speed (ms)

Tim

es

times105


mes

0 10 20 300

1

2

3

4

5

6


times104




119883 =

sum119894lt119899minus1119894=0 119883

119894

(119899 minus 1)times 119903 sdot 119897 (9)












119904 end point 119899


119888



119904 119899119890) do



119888== 119899119890then










119890) do








a

S

d

b

h

c

g

f

E

i









(1) Deploy

(2) Execute


MICE algorithm

Evaluation results

iTranSNet

(a) Implementation

NS-3

SUMO

(1) Import

(2) Convert

(3) Import

Cologne trace file


(4) Execute




Evaluation results

(b) Simulation







0 500 1000 1500 20000

2000

4000

6000

8000

10000

12000

Total length (m)

Tota

l tim

e (s)

DenseSparse

(a) Latency

Total length (m)0 500 1000 1500 2000

0

05

1

15

2

Tota

l pac

kets

coun

t

DenseSparse

times104












0 50 100 150 2000

5000

10000

15000

Tota

l tim

e (s)

Total length (km)

(a) Latency

0 50 100 150 2000

05

1

15

2

Total length (km)

Tota

l pac

ket c

ount

times104



0 005 01 015 02

Mea

n es

timat

ion

erro

r

0

2

4

6

8

10

12

14

MICEVAN




119864 =

119899

sum

119894=1


119899

(10)





0 50 100 150 2000

1

2

3

4

5

6


Erro

r

MICEVAN

(a) Testbed

0 50 100 150 2000

20

40

60

80

100


Erro

r

MICEVAN

(b) Trace


Sparse Dense07

075

08

085

09

095

Density

()







119894)





05 1 15 2 25 3 35 40

100

200

300

400

500


Cand

idat

e pat

h le

ngth

(km

)


BnFEllipse


Dense Sparse0

02

04

06

08

()




0

02

04

06

08

()









Overall efficiency

00

2

2

4

4

6

6 0 2 4 6

8

10

12Ti

me c

ost (

min

)


SPVANMICE

0 2 4 6


SPVANMICE

SPVANMICE

2

0

minus2

minus4

minus6

minus8

Tim

e sav

ed (m

in)

Time saved


02

015

01

005

0

minus005

Distance increased

Incr

ease

d di

stan

ce (k

m)









4

35

3

25

2

15

10 05 1 15 2

Tim

e cos

t (m

in)


SPVANMICE

005

0

minus005

minus01

minus015

minus02

Tim

e sav

ed (m

in)

02

015

01

005

0

Incr

ease

d di

stan

ce (k

m)

Distance increased


0 05 1 15 2


SPVANMICE

0 05 1 15 2


SPVANMICE






7 Conclusion



12

10

8

6

6

4

4

2

200

Tim

e cos

t (m

in)


2

1

0

minus1

minus2

minus3

Tim

e sav

ed (m

in)


Incr

ease

d di

stan

ce (k

m)

15

1

05

0

Distance increased

SPST

VANMICE

6420


SPST

VANMICE

6420


SPST

VANMICE





119881119905



119881119875


period 119901119881119889






Acknowledgment


References















































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










tr

a

c

s

b

d

e

CTSCTS

CTSCTS

tr

a

c

s

b

d

e

Forward


ffCTS

Road direction



997888rarr



119904 vi sdot

997888rarr


119904







119904 vi sdot

997888rarr


119904







997888rarr



119890than 119889











Vest = V119891119890minus119896119896119888

(4)

20

15

10

5

00 01 02 03 04 05

Vehi

cle sp

eed

(ms

)




119896119888= 01







119863safe = 119888119886 + 119888V + 119888119901 (5)





119896119888=

1119863safe (119888V = V

1198912)

=

1(V1198912) times 2 + 10

=

1V119891+ 10

(6)





120582 =

1119883

(7)


119891= V119898


Vest = V119898119890minus(V119898+10)119883

(8)


0 10 20 300

1

2

3

4

5

Vehicle speed (ms)

Tim

es

times105


mes

0 10 20 300

1

2

3

4

5

6


times104




119883 =

sum119894lt119899minus1119894=0 119883

119894

(119899 minus 1)times 119903 sdot 119897 (9)












119904 end point 119899


119888



119904 119899119890) do



119888== 119899119890then










119890) do








a

S

d

b

h

c

g

f

E

i









(1) Deploy

(2) Execute


MICE algorithm

Evaluation results

iTranSNet

(a) Implementation

NS-3

SUMO

(1) Import

(2) Convert

(3) Import

Cologne trace file


(4) Execute




Evaluation results

(b) Simulation







0 500 1000 1500 20000

2000

4000

6000

8000

10000

12000

Total length (m)

Tota

l tim

e (s)

DenseSparse

(a) Latency

Total length (m)0 500 1000 1500 2000

0

05

1

15

2

Tota

l pac

kets

coun

t

DenseSparse

times104












0 50 100 150 2000

5000

10000

15000

Tota

l tim

e (s)

Total length (km)

(a) Latency

0 50 100 150 2000

05

1

15

2

Total length (km)

Tota

l pac

ket c

ount

times104



0 005 01 015 02

Mea

n es

timat

ion

erro

r

0

2

4

6

8

10

12

14

MICEVAN




119864 =

119899

sum

119894=1


119899

(10)





0 50 100 150 2000

1

2

3

4

5

6


Erro

r

MICEVAN

(a) Testbed

0 50 100 150 2000

20

40

60

80

100


Erro

r

MICEVAN

(b) Trace


Sparse Dense07

075

08

085

09

095

Density

()







119894)





05 1 15 2 25 3 35 40

100

200

300

400

500


Cand

idat

e pat

h le

ngth

(km

)


BnFEllipse


Dense Sparse0

02

04

06

08

()




0

02

04

06

08

()









Overall efficiency

00

2

2

4

4

6

6 0 2 4 6

8

10

12Ti

me c

ost (

min

)


SPVANMICE

0 2 4 6


SPVANMICE

SPVANMICE

2

0

minus2

minus4

minus6

minus8

Tim

e sav

ed (m

in)

Time saved


02

015

01

005

0

minus005

Distance increased

Incr

ease

d di

stan

ce (k

m)









4

35

3

25

2

15

10 05 1 15 2

Tim

e cos

t (m

in)


SPVANMICE

005

0

minus005

minus01

minus015

minus02

Tim

e sav

ed (m

in)

02

015

01

005

0

Incr

ease

d di

stan

ce (k

m)

Distance increased


0 05 1 15 2


SPVANMICE

0 05 1 15 2


SPVANMICE






7 Conclusion



12

10

8

6

6

4

4

2

200

Tim

e cos

t (m

in)


2

1

0

minus1

minus2

minus3

Tim

e sav

ed (m

in)


Incr

ease

d di

stan

ce (k

m)

15

1

05

0

Distance increased

SPST

VANMICE

6420


SPST

VANMICE

6420


SPST

VANMICE





119881119905



119881119875


period 119901119881119889






Acknowledgment


References















































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation


















Vest = V119891119890minus119896119896119888

(4)

20

15

10

5

00 01 02 03 04 05

Vehi

cle sp

eed

(ms

)




119896119888= 01







119863safe = 119888119886 + 119888V + 119888119901 (5)





119896119888=

1119863safe (119888V = V

1198912)

=

1(V1198912) times 2 + 10

=

1V119891+ 10

(6)





120582 =

1119883

(7)


119891= V119898


Vest = V119898119890minus(V119898+10)119883

(8)


0 10 20 300

1

2

3

4

5

Vehicle speed (ms)

Tim

es

times105


mes

0 10 20 300

1

2

3

4

5

6


times104




119883 =

sum119894lt119899minus1119894=0 119883

119894

(119899 minus 1)times 119903 sdot 119897 (9)












119904 end point 119899


119888



119904 119899119890) do



119888== 119899119890then










119890) do








a

S

d

b

h

c

g

f

E

i









(1) Deploy

(2) Execute


MICE algorithm

Evaluation results

iTranSNet

(a) Implementation

NS-3

SUMO

(1) Import

(2) Convert

(3) Import

Cologne trace file


(4) Execute




Evaluation results

(b) Simulation







0 500 1000 1500 20000

2000

4000

6000

8000

10000

12000

Total length (m)

Tota

l tim

e (s)

DenseSparse

(a) Latency

Total length (m)0 500 1000 1500 2000

0

05

1

15

2

Tota

l pac

kets

coun

t

DenseSparse

times104












0 50 100 150 2000

5000

10000

15000

Tota

l tim

e (s)

Total length (km)

(a) Latency

0 50 100 150 2000

05

1

15

2

Total length (km)

Tota

l pac

ket c

ount

times104



0 005 01 015 02

Mea

n es

timat

ion

erro

r

0

2

4

6

8

10

12

14

MICEVAN




119864 =

119899

sum

119894=1


119899

(10)





0 50 100 150 2000

1

2

3

4

5

6


Erro

r

MICEVAN

(a) Testbed

0 50 100 150 2000

20

40

60

80

100


Erro

r

MICEVAN

(b) Trace


Sparse Dense07

075

08

085

09

095

Density

()







119894)





05 1 15 2 25 3 35 40

100

200

300

400

500


Cand

idat

e pat

h le

ngth

(km

)


BnFEllipse


Dense Sparse0

02

04

06

08

()




0

02

04

06

08

()









Overall efficiency

00

2

2

4

4

6

6 0 2 4 6

8

10

12Ti

me c

ost (

min

)


SPVANMICE

0 2 4 6


SPVANMICE

SPVANMICE

2

0

minus2

minus4

minus6

minus8

Tim

e sav

ed (m

in)

Time saved


02

015

01

005

0

minus005

Distance increased

Incr

ease

d di

stan

ce (k

m)









4

35

3

25

2

15

10 05 1 15 2

Tim

e cos

t (m

in)


SPVANMICE

005

0

minus005

minus01

minus015

minus02

Tim

e sav

ed (m

in)

02

015

01

005

0

Incr

ease

d di

stan

ce (k

m)

Distance increased


0 05 1 15 2


SPVANMICE

0 05 1 15 2


SPVANMICE






7 Conclusion



12

10

8

6

6

4

4

2

200

Tim

e cos

t (m

in)


2

1

0

minus1

minus2

minus3

Tim

e sav

ed (m

in)


Incr

ease

d di

stan

ce (k

m)

15

1

05

0

Distance increased

SPST

VANMICE

6420


SPST

VANMICE

6420


SPST

VANMICE





119881119905



119881119875


period 119901119881119889






Acknowledgment


References















































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










0 10 20 300

1

2

3

4

5

Vehicle speed (ms)

Tim

es

times105


mes

0 10 20 300

1

2

3

4

5

6


times104




119883 =

sum119894lt119899minus1119894=0 119883

119894

(119899 minus 1)times 119903 sdot 119897 (9)












119904 end point 119899


119888



119904 119899119890) do



119888== 119899119890then










119890) do








a

S

d

b

h

c

g

f

E

i









(1) Deploy

(2) Execute


MICE algorithm

Evaluation results

iTranSNet

(a) Implementation

NS-3

SUMO

(1) Import

(2) Convert

(3) Import

Cologne trace file


(4) Execute




Evaluation results

(b) Simulation







0 500 1000 1500 20000

2000

4000

6000

8000

10000

12000

Total length (m)

Tota

l tim

e (s)

DenseSparse

(a) Latency

Total length (m)0 500 1000 1500 2000

0

05

1

15

2

Tota

l pac

kets

coun

t

DenseSparse

times104












0 50 100 150 2000

5000

10000

15000

Tota

l tim

e (s)

Total length (km)

(a) Latency

0 50 100 150 2000

05

1

15

2

Total length (km)

Tota

l pac

ket c

ount

times104



0 005 01 015 02

Mea

n es

timat

ion

erro

r

0

2

4

6

8

10

12

14

MICEVAN




119864 =

119899

sum

119894=1


119899

(10)





0 50 100 150 2000

1

2

3

4

5

6


Erro

r

MICEVAN

(a) Testbed

0 50 100 150 2000

20

40

60

80

100


Erro

r

MICEVAN

(b) Trace


Sparse Dense07

075

08

085

09

095

Density

()







119894)





05 1 15 2 25 3 35 40

100

200

300

400

500


Cand

idat

e pat

h le

ngth

(km

)


BnFEllipse


Dense Sparse0

02

04

06

08

()




0

02

04

06

08

()









Overall efficiency

00

2

2

4

4

6

6 0 2 4 6

8

10

12Ti

me c

ost (

min

)


SPVANMICE

0 2 4 6


SPVANMICE

SPVANMICE

2

0

minus2

minus4

minus6

minus8

Tim

e sav

ed (m

in)

Time saved


02

015

01

005

0

minus005

Distance increased

Incr

ease

d di

stan

ce (k

m)









4

35

3

25

2

15

10 05 1 15 2

Tim

e cos

t (m

in)


SPVANMICE

005

0

minus005

minus01

minus015

minus02

Tim

e sav

ed (m

in)

02

015

01

005

0

Incr

ease

d di

stan

ce (k

m)

Distance increased


0 05 1 15 2


SPVANMICE

0 05 1 15 2


SPVANMICE






7 Conclusion



12

10

8

6

6

4

4

2

200

Tim

e cos

t (m

in)


2

1

0

minus1

minus2

minus3

Tim

e sav

ed (m

in)


Incr

ease

d di

stan

ce (k

m)

15

1

05

0

Distance increased

SPST

VANMICE

6420


SPST

VANMICE

6420


SPST

VANMICE





119881119905



119881119875


period 119901119881119889






Acknowledgment


References















































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation











119904 end point 119899


119888



119904 119899119890) do



119888== 119899119890then










119890) do








a

S

d

b

h

c

g

f

E

i









(1) Deploy

(2) Execute


MICE algorithm

Evaluation results

iTranSNet

(a) Implementation

NS-3

SUMO

(1) Import

(2) Convert

(3) Import

Cologne trace file


(4) Execute




Evaluation results

(b) Simulation







0 500 1000 1500 20000

2000

4000

6000

8000

10000

12000

Total length (m)

Tota

l tim

e (s)

DenseSparse

(a) Latency

Total length (m)0 500 1000 1500 2000

0

05

1

15

2

Tota

l pac

kets

coun

t

DenseSparse

times104












0 50 100 150 2000

5000

10000

15000

Tota

l tim

e (s)

Total length (km)

(a) Latency

0 50 100 150 2000

05

1

15

2

Total length (km)

Tota

l pac

ket c

ount

times104



0 005 01 015 02

Mea

n es

timat

ion

erro

r

0

2

4

6

8

10

12

14

MICEVAN




119864 =

119899

sum

119894=1


119899

(10)





0 50 100 150 2000

1

2

3

4

5

6


Erro

r

MICEVAN

(a) Testbed

0 50 100 150 2000

20

40

60

80

100


Erro

r

MICEVAN

(b) Trace


Sparse Dense07

075

08

085

09

095

Density

()







119894)





05 1 15 2 25 3 35 40

100

200

300

400

500


Cand

idat

e pat

h le

ngth

(km

)


BnFEllipse


Dense Sparse0

02

04

06

08

()




0

02

04

06

08

()









Overall efficiency

00

2

2

4

4

6

6 0 2 4 6

8

10

12Ti

me c

ost (

min

)


SPVANMICE

0 2 4 6


SPVANMICE

SPVANMICE

2

0

minus2

minus4

minus6

minus8

Tim

e sav

ed (m

in)

Time saved


02

015

01

005

0

minus005

Distance increased

Incr

ease

d di

stan

ce (k

m)









4

35

3

25

2

15

10 05 1 15 2

Tim

e cos

t (m

in)


SPVANMICE

005

0

minus005

minus01

minus015

minus02

Tim

e sav

ed (m

in)

02

015

01

005

0

Incr

ease

d di

stan

ce (k

m)

Distance increased


0 05 1 15 2


SPVANMICE

0 05 1 15 2


SPVANMICE






7 Conclusion



12

10

8

6

6

4

4

2

200

Tim

e cos

t (m

in)


2

1

0

minus1

minus2

minus3

Tim

e sav

ed (m

in)


Incr

ease

d di

stan

ce (k

m)

15

1

05

0

Distance increased

SPST

VANMICE

6420


SPST

VANMICE

6420


SPST

VANMICE





119881119905



119881119875


period 119901119881119889






Acknowledgment


References















































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation













(1) Deploy

(2) Execute


MICE algorithm

Evaluation results

iTranSNet

(a) Implementation

NS-3

SUMO

(1) Import

(2) Convert

(3) Import

Cologne trace file


(4) Execute




Evaluation results

(b) Simulation







0 500 1000 1500 20000

2000

4000

6000

8000

10000

12000

Total length (m)

Tota

l tim

e (s)

DenseSparse

(a) Latency

Total length (m)0 500 1000 1500 2000

0

05

1

15

2

Tota

l pac

kets

coun

t

DenseSparse

times104












0 50 100 150 2000

5000

10000

15000

Tota

l tim

e (s)

Total length (km)

(a) Latency

0 50 100 150 2000

05

1

15

2

Total length (km)

Tota

l pac

ket c

ount

times104



0 005 01 015 02

Mea

n es

timat

ion

erro

r

0

2

4

6

8

10

12

14

MICEVAN




119864 =

119899

sum

119894=1


119899

(10)





0 50 100 150 2000

1

2

3

4

5

6


Erro

r

MICEVAN

(a) Testbed

0 50 100 150 2000

20

40

60

80

100


Erro

r

MICEVAN

(b) Trace


Sparse Dense07

075

08

085

09

095

Density

()







119894)





05 1 15 2 25 3 35 40

100

200

300

400

500


Cand

idat

e pat

h le

ngth

(km

)


BnFEllipse


Dense Sparse0

02

04

06

08

()




0

02

04

06

08

()









Overall efficiency

00

2

2

4

4

6

6 0 2 4 6

8

10

12Ti

me c

ost (

min

)


SPVANMICE

0 2 4 6


SPVANMICE

SPVANMICE

2

0

minus2

minus4

minus6

minus8

Tim

e sav

ed (m

in)

Time saved


02

015

01

005

0

minus005

Distance increased

Incr

ease

d di

stan

ce (k

m)









4

35

3

25

2

15

10 05 1 15 2

Tim

e cos

t (m

in)


SPVANMICE

005

0

minus005

minus01

minus015

minus02

Tim

e sav

ed (m

in)

02

015

01

005

0

Incr

ease

d di

stan

ce (k

m)

Distance increased


0 05 1 15 2


SPVANMICE

0 05 1 15 2


SPVANMICE






7 Conclusion



12

10

8

6

6

4

4

2

200

Tim

e cos

t (m

in)


2

1

0

minus1

minus2

minus3

Tim

e sav

ed (m

in)


Incr

ease

d di

stan

ce (k

m)

15

1

05

0

Distance increased

SPST

VANMICE

6420


SPST

VANMICE

6420


SPST

VANMICE





119881119905



119881119875


period 119901119881119889






Acknowledgment


References















































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










0 500 1000 1500 20000

2000

4000

6000

8000

10000

12000

Total length (m)

Tota

l tim

e (s)

DenseSparse

(a) Latency

Total length (m)0 500 1000 1500 2000

0

05

1

15

2

Tota

l pac

kets

coun

t

DenseSparse

times104












0 50 100 150 2000

5000

10000

15000

Tota

l tim

e (s)

Total length (km)

(a) Latency

0 50 100 150 2000

05

1

15

2

Total length (km)

Tota

l pac

ket c

ount

times104



0 005 01 015 02

Mea

n es

timat

ion

erro

r

0

2

4

6

8

10

12

14

MICEVAN




119864 =

119899

sum

119894=1


119899

(10)





0 50 100 150 2000

1

2

3

4

5

6


Erro

r

MICEVAN

(a) Testbed

0 50 100 150 2000

20

40

60

80

100


Erro

r

MICEVAN

(b) Trace


Sparse Dense07

075

08

085

09

095

Density

()







119894)





05 1 15 2 25 3 35 40

100

200

300

400

500


Cand

idat

e pat

h le

ngth

(km

)


BnFEllipse


Dense Sparse0

02

04

06

08

()




0

02

04

06

08

()









Overall efficiency

00

2

2

4

4

6

6 0 2 4 6

8

10

12Ti

me c

ost (

min

)


SPVANMICE

0 2 4 6


SPVANMICE

SPVANMICE

2

0

minus2

minus4

minus6

minus8

Tim

e sav

ed (m

in)

Time saved


02

015

01

005

0

minus005

Distance increased

Incr

ease

d di

stan

ce (k

m)









4

35

3

25

2

15

10 05 1 15 2

Tim

e cos

t (m

in)


SPVANMICE

005

0

minus005

minus01

minus015

minus02

Tim

e sav

ed (m

in)

02

015

01

005

0

Incr

ease

d di

stan

ce (k

m)

Distance increased


0 05 1 15 2


SPVANMICE

0 05 1 15 2


SPVANMICE






7 Conclusion



12

10

8

6

6

4

4

2

200

Tim

e cos

t (m

in)


2

1

0

minus1

minus2

minus3

Tim

e sav

ed (m

in)


Incr

ease

d di

stan

ce (k

m)

15

1

05

0

Distance increased

SPST

VANMICE

6420


SPST

VANMICE

6420


SPST

VANMICE





119881119905



119881119875


period 119901119881119889






Acknowledgment


References















































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










0 50 100 150 2000

5000

10000

15000

Tota

l tim

e (s)

Total length (km)

(a) Latency

0 50 100 150 2000

05

1

15

2

Total length (km)

Tota

l pac

ket c

ount

times104



0 005 01 015 02

Mea

n es

timat

ion

erro

r

0

2

4

6

8

10

12

14

MICEVAN




119864 =

119899

sum

119894=1


119899

(10)





0 50 100 150 2000

1

2

3

4

5

6


Erro

r

MICEVAN

(a) Testbed

0 50 100 150 2000

20

40

60

80

100


Erro

r

MICEVAN

(b) Trace


Sparse Dense07

075

08

085

09

095

Density

()







119894)





05 1 15 2 25 3 35 40

100

200

300

400

500


Cand

idat

e pat

h le

ngth

(km

)


BnFEllipse


Dense Sparse0

02

04

06

08

()




0

02

04

06

08

()









Overall efficiency

00

2

2

4

4

6

6 0 2 4 6

8

10

12Ti

me c

ost (

min

)


SPVANMICE

0 2 4 6


SPVANMICE

SPVANMICE

2

0

minus2

minus4

minus6

minus8

Tim

e sav

ed (m

in)

Time saved


02

015

01

005

0

minus005

Distance increased

Incr

ease

d di

stan

ce (k

m)









4

35

3

25

2

15

10 05 1 15 2

Tim

e cos

t (m

in)


SPVANMICE

005

0

minus005

minus01

minus015

minus02

Tim

e sav

ed (m

in)

02

015

01

005

0

Incr

ease

d di

stan

ce (k

m)

Distance increased


0 05 1 15 2


SPVANMICE

0 05 1 15 2


SPVANMICE






7 Conclusion



12

10

8

6

6

4

4

2

200

Tim

e cos

t (m

in)


2

1

0

minus1

minus2

minus3

Tim

e sav

ed (m

in)


Incr

ease

d di

stan

ce (k

m)

15

1

05

0

Distance increased

SPST

VANMICE

6420


SPST

VANMICE

6420


SPST

VANMICE





119881119905



119881119875


period 119901119881119889






Acknowledgment


References















































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










0 50 100 150 2000

1

2

3

4

5

6


Erro

r

MICEVAN

(a) Testbed

0 50 100 150 2000

20

40

60

80

100


Erro

r

MICEVAN

(b) Trace


Sparse Dense07

075

08

085

09

095

Density

()







119894)





05 1 15 2 25 3 35 40

100

200

300

400

500


Cand

idat

e pat

h le

ngth

(km

)


BnFEllipse


Dense Sparse0

02

04

06

08

()




0

02

04

06

08

()









Overall efficiency

00

2

2

4

4

6

6 0 2 4 6

8

10

12Ti

me c

ost (

min

)


SPVANMICE

0 2 4 6


SPVANMICE

SPVANMICE

2

0

minus2

minus4

minus6

minus8

Tim

e sav

ed (m

in)

Time saved


02

015

01

005

0

minus005

Distance increased

Incr

ease

d di

stan

ce (k

m)









4

35

3

25

2

15

10 05 1 15 2

Tim

e cos

t (m

in)


SPVANMICE

005

0

minus005

minus01

minus015

minus02

Tim

e sav

ed (m

in)

02

015

01

005

0

Incr

ease

d di

stan

ce (k

m)

Distance increased


0 05 1 15 2


SPVANMICE

0 05 1 15 2


SPVANMICE






7 Conclusion



12

10

8

6

6

4

4

2

200

Tim

e cos

t (m

in)


2

1

0

minus1

minus2

minus3

Tim

e sav

ed (m

in)


Incr

ease

d di

stan

ce (k

m)

15

1

05

0

Distance increased

SPST

VANMICE

6420


SPST

VANMICE

6420


SPST

VANMICE





119881119905



119881119875


period 119901119881119889






Acknowledgment


References















































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










05 1 15 2 25 3 35 40

100

200

300

400

500


Cand

idat

e pat

h le

ngth

(km

)


BnFEllipse


Dense Sparse0

02

04

06

08

()




0

02

04

06

08

()









Overall efficiency

00

2

2

4

4

6

6 0 2 4 6

8

10

12Ti

me c

ost (

min

)


SPVANMICE

0 2 4 6


SPVANMICE

SPVANMICE

2

0

minus2

minus4

minus6

minus8

Tim

e sav

ed (m

in)

Time saved


02

015

01

005

0

minus005

Distance increased

Incr

ease

d di

stan

ce (k

m)









4

35

3

25

2

15

10 05 1 15 2

Tim

e cos

t (m

in)


SPVANMICE

005

0

minus005

minus01

minus015

minus02

Tim

e sav

ed (m

in)

02

015

01

005

0

Incr

ease

d di

stan

ce (k

m)

Distance increased


0 05 1 15 2


SPVANMICE

0 05 1 15 2


SPVANMICE






7 Conclusion



12

10

8

6

6

4

4

2

200

Tim

e cos

t (m

in)


2

1

0

minus1

minus2

minus3

Tim

e sav

ed (m

in)


Incr

ease

d di

stan

ce (k

m)

15

1

05

0

Distance increased

SPST

VANMICE

6420


SPST

VANMICE

6420


SPST

VANMICE





119881119905



119881119875


period 119901119881119889






Acknowledgment


References















































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










Overall efficiency

00

2

2

4

4

6

6 0 2 4 6

8

10

12Ti

me c

ost (

min

)


SPVANMICE

0 2 4 6


SPVANMICE

SPVANMICE

2

0

minus2

minus4

minus6

minus8

Tim

e sav

ed (m

in)

Time saved


02

015

01

005

0

minus005

Distance increased

Incr

ease

d di

stan

ce (k

m)









4

35

3

25

2

15

10 05 1 15 2

Tim

e cos

t (m

in)


SPVANMICE

005

0

minus005

minus01

minus015

minus02

Tim

e sav

ed (m

in)

02

015

01

005

0

Incr

ease

d di

stan

ce (k

m)

Distance increased


0 05 1 15 2


SPVANMICE

0 05 1 15 2


SPVANMICE






7 Conclusion



12

10

8

6

6

4

4

2

200

Tim

e cos

t (m

in)


2

1

0

minus1

minus2

minus3

Tim

e sav

ed (m

in)


Incr

ease

d di

stan

ce (k

m)

15

1

05

0

Distance increased

SPST

VANMICE

6420


SPST

VANMICE

6420


SPST

VANMICE





119881119905



119881119875


period 119901119881119889






Acknowledgment


References















































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










4

35

3

25

2

15

10 05 1 15 2

Tim

e cos

t (m

in)


SPVANMICE

005

0

minus005

minus01

minus015

minus02

Tim

e sav

ed (m

in)

02

015

01

005

0

Incr

ease

d di

stan

ce (k

m)

Distance increased


0 05 1 15 2


SPVANMICE

0 05 1 15 2


SPVANMICE






7 Conclusion



12

10

8

6

6

4

4

2

200

Tim

e cos

t (m

in)


2

1

0

minus1

minus2

minus3

Tim

e sav

ed (m

in)


Incr

ease

d di

stan

ce (k

m)

15

1

05

0

Distance increased

SPST

VANMICE

6420


SPST

VANMICE

6420


SPST

VANMICE





119881119905



119881119875


period 119901119881119889






Acknowledgment


References















































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










12

10

8

6

6

4

4

2

200

Tim

e cos

t (m

in)


2

1

0

minus1

minus2

minus3

Tim

e sav

ed (m

in)


Incr

ease

d di

stan

ce (k

m)

15

1

05

0

Distance increased

SPST

VANMICE

6420


SPST

VANMICE

6420


SPST

VANMICE





119881119905



119881119875


period 119901119881119889






Acknowledgment


References















































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation










Acknowledgment


References















































RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation























RoboticsJournal of





Journal of



RotatingMachinery





VLSI Design



Shock and Vibration







Journal of



Volume 2014


SensorsJournal of





Propagation









research article accurate real-time traffic speed estimation...

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