Predicting Traffic Congestion in Presence of Planned Special Events

Simon KwoczekGroup ResearchVolkswagen AG

simon.kwoczek@volkswagen.de

Sergio Di MartinoGroup ResearchVolkswagen AG

sergio.di.martino@volkswagen.de

Wolfgang NejdlL3S Research CenterUniversity of Hanover

nejdl@l3s.de

Abstract

The recent availability of datasets on transportation net-works with high spatial and temporal resolution is enablingnew research activities in the fields of Territorial Intelli-gence and Smart Cities. Within these domains, in this paperwe focus on the problem of predicting traffic congestion inurban environments caused by attendees leaving a PlannedSpecial Events (PSE), such as a soccer game or a concert.The proposed approach consists of two steps. In the firstone, we use the K-Nearest Neighbor algorithm to predictcongestions within the vicinity of the venue (e.g. a Stadion)based on the knowledge from past observed events. In thesecond step, we identify the road segments that are likelyto show congestion due to PSEs and map our prediction tothese road segments. To visualize the traffic trends and con-gestion behavior we learned and to allow Domain Expertsto evaluate the situation we also provide a Google Earth-based GUI. The proposed solution has been experimentallyproven to outperform current state of the art solutions byabout 35% and thus it can successfully serve to reliably pre-dict congestions due to PSEs.Keywords: traffic prediction, planned special event, eventanalysis

1. Introduction

In times of ongoing urbanization and steady growth ofmobility demands, traffic congestions cost billions of dol-lars to the society every year [12]. Within the last years,thanks to the advances in sensor technologies (like Smart-phones, GPS handhelds, etc.) and storage capabilities,datasets about traffic with high spatial and temporal reso-lution have become available. This has led to advanced in-vestigations on the impact of different influencing factors,as traffic lights, daily rush hour, construction zones, etc,on traffic congestions (i.e. [2, 7, 9]). Most of these factorsare either recurring on a regular base (i.e. rush hour), ex-ist only once for limited time (i.e. construction zones) or

their occurrences are unpredictable (i.e. accidents). Currentstate of the art commercial solutions are able to work wellwith recurring traffic situations as it’s behavior can easilybe learned from historical data [18]. On the other hand,also non-periodic events with an expected large attendance(known also as Planned Special Events, or PSE as intro-duced in [6]), such as concerts, soccer games, etc., playa major role for delays in everyday transportation [9]. Asexample, the concert of Rihanna in Johannesburg (SouthAfrica) in October 2013 caused people to sit in traffic foras long as five hours, trying to reach the stadium. Simi-larly, the concert of Robbie Williams in London, in 2003,caused tailbacks up to 10 miles on the highway A1 towardsthe stadium. An interesting aspect is that the traffic due toPSEs has a quite typical behavior, having two subsequentwaves of congestion [10]. The first one is caused by peo-ple going to the event, while the second one is due to peo-ple leaving the venue, and may be even bigger that the firstwave. Very few research attention has been devoted to pre-dict the congestion due to a PSE. At the same time, eventhe most advanced available commercial systems are inca-pable of predicting this kind of non-recurring traffic aheadof time.

To address this open issue, in this paper we describe a so-lution we developed to predict the spatio-temporal impact ofthe second wave of traffic due to a PSE around its venue. Inparticular, the proposed approach is meant to be executedwhile the event is happening, and takes as input the cate-gory of the PSE, like Concert, Entertainment, etc., and theinformation on the first wave of traffic, coming from tra-ditional traffic providers. Then, using an adaptation of theK-Nearest Neighbors (K-NN) algorithm, we look for themost similar past PSEs (cases) among historic observations,in order to derive a prediction of the impact of the secondwave of traffic. Such a prediction is done in terms of av-erage delay over the road segments around the venue thathave been found to be highly correlated with the conges-tions due to PSEs. To graphically visualize the results, weprovide a Google Earth-based tool 1, recalling the one de-

1http://earth.google.com

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veloped in [4] or in [13]. Thanks to this tool, it is possiblefor Decision Makers to understand how the traffic situationis influenced by a PSE.

To assess the proposed approach, we used data abouttraffic and events from June to December 2013 in the in-ner city of Cologne, Germany. The traffic data has beenprovided by one of the most prominent traffic providers inEurope. It comes mainly from Floating Car Data, i.e. datacollected from GPS sensors in vehicles, and it covers mostof the streets within the inner city. As for the PSEs, we con-sidered all the 29 events hosted in the Cologne LANXESSarena, in the same temporal span. By performing a leave-one-out cross validation on the event dataset, we comparedour proposal with current state of the art, intended as realtime traffic informations, and with a baseline consistingsimply of replicating the impact of the first wave as pre-dicted second wave. Results show that our proposals out-performs both the alternative solutions, providing predic-tion that are better up to 41%.

The remainder of the paper is structured as follows: sec-tion 2 presents related work within the field of traffic pre-dictions and PSEs and some background terminology. Insection 3 we present the approach to predict the conges-tions caused by outbound traffic after an event, plus the GUIto visualize the results. In section 4 we describe the Re-search Questions and the evaluation protocol to assess theproposed approach. In section 5 the results of the empiricalassessment are presented and discussed. Finally in section 6conclusions are outlined, together with some future researchdirections.

2. Background and Definitions

Since years, traffic congestion predictions have beenwidely studied within the research communities of ITS,Smart Cities and Territorial Intelligence, leading to a richbody of literature on these topics. Indeed, by knowing inadvance the traffic patterns it is possible to optimize mo-bility. For instance, route calculation engines can computemore energy efficient routes, able also to save time for thedrivers.

In general, predicting traffic congestion in urban envi-ronments is a highly complex task. Early approaches fortraffic predictions used simulations and theoretical model-ing (e.g. [2, 3]). Nowadays thanks to the availability of newmassive datasets on traffic, several different statistic anddata driven approaches have been presented to the commu-nity. Examples include generalized linear regression ([21]),nonlinear time series ([8]), Kalman filters ([11]), supportvector regression ([20]), and various neural network models([11, 17, 19]). A combination of some of the latter kind ofapproaches is used also by current commercial navigationsolutions, able to predict recurring congestions by identi-

fying characteristic traffic flow patterns for street segmentsfrom historical data. On top of that, these commercial sys-tems can also optimize the route planning based on the real-time traffic situation [18]. In general, traffic congestion canbe divided into recurring congestions, usually caused due toa mobility demand that exceeds the capacity of the road net-work (e.g. due to rush hour), and congestions that are non-recurring (e.g. due to incidents or special events) [9]. Theeffects of nonrecurring traffic congestions and their predic-tion is a widely investigated topic within the research com-munity (e.g. [14–16]). Although these approaches showeda significant improvement in prediction they use data fromstationary loop sensors that are not always capable of re-flecting the traffic state in a granularity required for urbanscenarios. In addition, their focus lies on one-directionalstreet segments as highways whereby usually in the innercities the impact is a multidimensional problem, evolvingin a 2D, more complex route network.

Previous researches have highlighted that also PSEs are apossible influencing factors [7,9], since they may lead thou-sands (or even hundreds of thousands) of people to traveltowards the same destination in a very limited time interval,and then to leave the venue again in a very short time span.To the best of our knowledge, the only work available thathas its focus on the influence of PSEs on traffic is presentedin [10]. The authors report a generic overview about the in-fluence of PSEs on the road network, derived from an eventclassification defined by the Chinese State Council. Theyalso introduce management plans for the different types ofevents, but there is no quantifiable solution for the predic-tion of the traffic.

2.1. Definitions

A PSE may have an impact on the traffic behavior in aspecific region over time. Such an impact region can be de-fined as the list of congested segments of the road network.A congested segment is a piece of road network where thedifference between expected and actual traffic speed is big-ger than a certain threshold. The expected traffic velocity ona given segment can be obtained from a number of historicalobservations, coming from special sensors in the infrastruc-ture and/or Floating Car Data. In this way it is possible tolearn the typical traffic behavior for a given road segmenton a certain day of the week, at a given time. If a segmentis congested it takes more time to pass by it. This additionaltime is defined as delay time and is measured in seconds. Asan example, Figure 1 shows the summed up delay time in acircular impact region, with a radius of 500 meters aroundthe LANXESS Arena in Cologne, Germany for all Tues-days in our considered dataset, where no events happened.The red line presents the model that is derived from his-torical observations as the average delay time on all Tues-

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days. From this trend line it is possible to detect the rushhour behavior in the morning and in the evening. As statedabove, the graph only contains Tuesdays without events andthe generated trend line can therefore be seen as the regu-lar pattern around the venue on Tuesdays. As described inthe Introduction, a PSE leads to two waves of traffic. Anexample can be seen in figure 2. It shows the traffic behav-ior around the LANXESS Arena on Tuesday the 2nd of July2013, when Mark Knopfler gave a concert in the arena. Thefigure shows the difference between the observed delay timeat that day and the trend line for that day of the week. Fromthe figure we can clearly see the two peaks of congestion,between 18:00 and 20:00, and between 22:00 and 23:00.Since the concert started at 20:00 and ended around 22:00,the assumption arises that the two peaks are caused by peo-ple going to the event (inbound traffic) and leaving after itsend (outbound traffic). To quantify the impact of PSEs ontraffic, we focused on the traffic data happening before andafter the events. From the 29 events we analyzed, and forthis specific location, we found out that the inbound trafficis always contained in a time frame within two hours beforethe scheduled begin of the event. As for the outbound traf-fic, in our dataset the time frame starts two hour after the be-gin of the event and lasts for two hours. These time framesensure that the entire event-caused traffic is captured. Forboth time frames, we define a normalized timescale, usingthe difference to the planned begin of the event as scale. In

this way, we can compare traffic situations happening in thesame relative time corridor, for events that start at differenttimes. For both time frames we observe the average delaytime, defined as avgdelay (measured in seconds) as measurefor the impact of the PSE on traffic.

3. The Proposed Approach

In this section we define two different approaches topredict the outbound traffic, namely the Category-BasedModeling Approach (CBMA) and the Category SpecificInbound-Based Prediction Approach (CIPA). Afterwards,we introduce a tool to visualize the results of the predic-tions.

3.1. Impact of event category

The LANXESS arena hosts different events of differentcategories. During the time span of our study, we observed18 concerts, 6 entertainment events, 3 comedy events and 2sporting events. The naming of the categories is taken fromthe official arena schedule.Although most of the considered events started roughly atthe same time (between 19:00 and 21:00) the different cat-egories show significant differences in their impact on traf-fic. In total, 12 out of the 18 concerts showed significantinbound congestions and 11 of these events showed alsostrong outbound congestions. While comedy events did notaccount for any significant raise in congestions, entertain-ment events caused congestions in 2 out of 6 times for theinbound and 1 time in the outbound traffic period. The onlyevents that differed significantly in their start time were thesporting events (in the time span of our research the sport-ing events were two handball games). Both of them showeda comparable congestion behaviour for in- and outboundtraffic. To further illustrate the correlation between eventcategory and congestion behaviour, we report in figure 3the behavior for each category using the avgdelay observedas congestion measure. The boxplot highlights the differentbehavior of the different event categories.

These differences motivated us to develop a first ap-proach that focuses on the typical congestion behavior fordifferent categories that we named Category-Based Model-ing Approach (CBMA).

3.2. Category-Based Modeling Approach (CBMA)

The CBMA is aimed at detecting the specific congestionpatterns of each observed category and uses these patternsfor predictions. We learn the behavior of each category byaccumulating the congestion behavior during the outboundtraffic for all events of that category in our training set.From that, we consider the average of all observations and

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Figure 3. Outbound congestion behavior forthe different observed categories

use the generated trend line for predicting the outbound traf-fic congestion behavior for all events in the test set. Sincethe only information CMBA uses for predictions is the cat-egory of the events, it can be used without any knowledgeabout the traffic situation on the specific day of the targetedevent. Thus, it can be applied early ahead of time.

3.3. Impact of Inbound Traffic Congestion

While each category of events shows a different pattern,some events of the same category also vary a lot in their con-gestion behavior. For example, the Rihanna concert on the26th of June 2013 and the concert of Barbra Streisand on the12th of June 2013 are both of the same category (concert)and start almost at the same time. Thus, one would expectthat they have a similar impact on traffic. However whilethe outbound congestion after the Rihanna concert shows anavgdelay of around 4600 seconds, the outbound congestionafter the Barbra Streisand concert only shows an avgdelayof 2100 seconds. A likely explanation for the phenomenonlies in the different amount of people going to these twoconcerts. Although the actual number of attendees is notavailable, we were willing to explore if the inbound con-gestion of an event is a good indicator of how severe theoutbound congestion is going to be. In other words, we ex-pect the congestion during the inbound traffic to describethe ”hype” about the event by serving as an indicator abouthow many people are going to the event. This motivatedus to develop a second approach, named Category SpecificInbound-Based Prediction Approach (CIPA) that includesthis information into the prediction model.

3.4. Category Specific Inbound-Based Prediction

Approach (CIPA)

The CIPA is based on the idea that both category andinbound traffic are explanatory variables for the expectedoutbound traffic. One possible way to incorporate these in-

formation into the prediction process lies in applying Ma-chine Learning techniques on an adequate dataset of his-toric data to automatically learn the correlations betweenthese variables. For CIPA, the dataset contains informationabout the category of an event and the observed avgdelayduring the inbound traffic. We use these features as predic-tors of the avgdelay during the outbound traffic. In our case,we applied the K-Nearest Neighbor regression [1] MachineLearning technique. In order to do a prediction, this tech-nique aggregates the values the k ”closest” examples in thetraining set, where k is an input parameter to the algorithm.To compute the distance among observations we used theEuclidean distance function, while as aggregation formula,we used the mean of the observed values. Further detailson K-NN can be found in [1]. A drawback of this approachcompared to CBMA lies in the fact, that it can only be usedfor predictions on the event day, after the inbound traffic hasalready been observed.

3.5. Mapping Delay on the Road Network

In order to use the generated predictions in AdvancedDriver Information Systems, the resulting delay time needsto be mapped onto the street network. We identify road seg-ments that are likely of being congested due to outboundtraffic from historic data by selecting all segments that wereaffected in at least 1/3rd of all PSE caused congestions.Then we spread the predicted outbound delay time for anPSE over the segments, assuming a normal distribution.From the results, we calculate the delay per meter DMindex (in seconds) for that specific event, which gives anoverview about the severity of congestion in the area. Theindex is also used in the GUI we describe in the next sec-tion, to allow Domain Experts to get an overview about theexpected impact of the PSE.

3.6. The proposed interactive GUI

Once the results are produced by the one of the previ-ous prediction engines, they can feed a visualization layer.To this aim, we have developed a Graphical User Interface,shown in figure 4, that embeds Google Earth to render in3D the predicted spatio-temporal impact area, over a geo-referenced satellite image, optionally also enhanced by ad-ditional informative layers. The goal is to facilitate DomainExperts and Decision Makers to visually understand spatialrelationships among the datasets and their spatial context.The proposed GUI contains a main frame that holds the cur-rent representation of the predicted traffic severity level inGoogle Earth. On the left side, the user can pick a loca-tion from a list of available data. After selecting a location,the set of scheduled PSEs for the venue is shown in the leftpanel. If the information is available, the inbound traffic of

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the event will be represented in a graphic way at the bottomof the left panel. By selecting one of the developed algo-rithms (CBMA or CIPA) the prediction process starts andthe user gets a visual representation of the predicted con-gestion area as a new layer on the map. The severity levelcan be manually adjusted by the settings tab, allowing Do-main Experts to easily customize the representation of theresults.

4. Experiments

This section describes the setup of the experiments con-ducted to assess the validity of our proposals.

4.1. Dataset Description

The traffic information used in this research is collectedby one of the most prominent traffic providers all over Eu-rope. It covers the main road network within the inner cityof Cologne. The information is generated from a combi-nation of various different sources. These include Float-ing Car Data (originated from millions of vehicles equippedwith GPS sensors), GSM probe data and data from station-ary sensor (e.g. loop detectors, camera sensors) obtainedfrom local traffic management centers. More informationabout the different sources and their aggregation can befound in [18]. From these combined sources, an accuratedescription of detected traffic congestions can be derived.Specifically, for each detected congestion, the data containsthe resulting delay time on a segment of the road network(in seconds) and the congestion level, on a scale from 1 to 5,where 1 means no relevant traffic congestion, 4 is the high-est level and 5 means that the congestion level is unknown.We received data from June the 1st 2013 until December the31st, 2013. The dataset we used in this research accountsfor about 50 Gigabytes which leads to a favorable coverageof the area around the arena in Cologne. All measurementspresented in this section show the accumulated values ofall road segments within an area of 500 meters around thestadium. As for the events, we collected all the PSEs hap-pening in the LANXESS arena in the same time span cov-ered by the above described traffic information. In theseseven months, 29 events were scheduled. Regarding theseobserved events, 16 showed congestion due to inbound traf-fic. For these events, the observed an accumulated avgde-lay on the considered road segments varied from very minorwith 420 seconds (Andreas Gabalier, 17th of October 2013)to severe traffic congestion with accumulated 9422 seconds(Rihanna, 27th of June 2013), that is a delay of almost 3hours in the considered area. The outbound traffic causedcongestion in 17 of the 29 events whereas the avgdelay var-ied from 124 seconds (Cirque du Soleil, 25th of October2013) up to 4632 seconds (Rihanna, 26th of June 2013).

4.2. Baseline Approaches

As stated before, to the best of our knowledge, there isno research that has studied the impact of PSEs on traffic ina similar manner before. Therefore, we introduce two ap-proaches that serve as baseline for our proposed CBMA andCIPA, named the Zero approach and the Start approach.

Zero approach: In the Zero approach, we simply as-sume that there is no traffic in the area during outbound traf-fic. While this approach seems fairly simplistic, it actuallyreflects the current state of the art in navigation solutions,and is therefore suitable for comparing newly developed al-gorithms against it. As matter of fact, by relying just onhistoric data, current navigation solutions are unable to pre-dict congestions due to nonrecurring events.

Start approach: While the Zero approach simply as-sumes no additional traffic at all above the historic model,the Start approach goes one step further. It is based on theidea that the amount of people going to the event is the sameas the people leaving the event. Consequently, the outboundtraffic could be estimated as the same as the inbound traffic.Thus, we could use the exact information about the inboundavgdelay as a prediction for the outbound avgdelay.

4.3. Research Questions (RQ)

Given the above described approaches, the researchquestions underlying this paper can be formulated as fol-lows:

1. Are the predictions of expected delay due to outboundtraffic using CBMA better than those obtained with theZero and the Start approaches?

2. Are the predictions of expected delay due to outboundtraffic using CIPA better than those obtained with theZero and the Start approaches?

3. Are the predictions of expected delay due to outboundtraffic using CIPA better than those obtained withCBMA?

For these research questions, the performance of the dif-ferent approaches is measured using the root mean squareerror (RMSE) defined as:

RMSE =

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Figure 4. The Google Earth-based GUI for the visualization of the results

5. Results

To evaluate the validity of the proposed approaches, andthus to answer the Research Questions, we applied the foursolutions described in the previous section on the dataset of29 events by means of a cross-validation, that is splitting thedata set into training and validation sets. Training sets areused to build models, while validation sets are used to val-idate the obtained prediction models. In particular, we ex-ploited a leave-one-out cross validation, which means thatthe original data set is divided into n=29 different subsetsof training and validation sets, where each validation set hasjust one event.

As for the use of the K-Nearest Neighbors algorithm, oneof the issue is the optimal choice of the parameter k, that isthe number of closest training examples to be consideredin the feature space. This choice usually depends upon thedata. It is outside the scope of this research to investigatehyperparameter optimization techniques (e.g. as in [5]). Inour case we investigated the use of k=3 and k=5, obtain-ing very similar results. Consequently, in the following wereport only on the use of k=5.

Figure 5 shows the RSMEs of the predicted outboundtraffic avgdelay for each approach separately. The figureshows, for the entire dataset of PSEs, the prediction perfor-mance for each method in terms of the RMSE values ob-tained from the cross validation. The data shows, that thebaseline which uses the avgdelay from the inbound trafficas predictor has the highest RMSE of 2095 seconds. TheZero approach, which has no a-priori information about the

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Figure 5. RMSE of the four approaches

PSEs or the traffic information at all, shows an RMSE of1918 and therefore slightly outperforms the Start approach.

To answer the RQ1, we can see that the RMSE for theCBMA is 1364, outperforming both the Zero- and the Startapproach by far. Indeed, the proposed approach has a pre-dicted delay error that is 28% lower than current state of theart, and 35% lower than the Start approach. Consequently,we can positively answer the RQ1, since CMBA outper-forms the two baselines.

As for RQ2, the RMSE for the CIPA, which incorpo-rates the most information about the situation, is 1230. Thisreflects a reduction of the prediction error of around 41%percent compared to the Start approach and still 35% per-cent compared to the Zero approach. Consequently, we canpositively answer the RQ2, since CIPA outperforms the twobaselines.

As for the RQ3, we can see from 5 that CBMA and CIPAprovide quite similar results. This motivated us to furtherinvestigate the performances, splitting the results for each

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category of events. The results are shown in figure 6. Wefound that for very homogeneous categories (as for Sportand Comedy in our examples), the CIPA was outperformedby the CBMA. For instance, CBMA showed an RMSE of80 seconds for the Sport category while the RMSA for CIPAis 418 seconds. At the same time, for high fluctuating cat-egories (as for the categories Concert and Entertainment inour examples), the CIPA shows much better results of for in-stance an RMSE of 1441 seconds for the concert category,compared to 1621 using CBMA. Consequently we cannotprovide a definitive answer to RQ3, since CIPA and CBMAhave comparable performances.

5.1. Mapping results on the Road Network

We identified a set of 52 street segments that showedhigh correlation of the congestion with PSEs, which ac-count for a total distance of 2105 meters. For these links,calculating the delay per meter index DM shows the differ-ent severity levels of the events. As an example, the CIPApredicted a delay time of 2129 seconds during the MarkKnopfler concert, that, given the total length of the consid-ered road segments, leads to an severity index of around1s/m. In a future integration with a route calculation en-gine, this means that the travel time of each route passing inthe vicinity of the arena, in the considered timeframe, takesabout 100 additional seconds per 100 meters of the route.Consequently, the route calculation engine has now newtravel times for each segment, and can reroute the driveron the most efficient path.

6. Conclusion and Future Work

Improving mobility is a paramount issue due to thehuge environmental and social impact of vehicular traffic.Thanks to the recent availability of high quality spatio-temporal datasets coming from Floating Car Data and otherdata sources, new and unprecedented research opportuni-ties are arising in the direction of smarter solutions for mo-bility. Within this topic, to the best of our knowledge, a

very limited attention has been dedicated to the predictionof the impact of Planned Special Events, such as concertsor sport games, on urban traffic, even if some researcheshighlight that they are a significant fraction of the total traf-fic. PSEs have a very specific impact on mobility, inducingtwo waves of traffic. The first one is due to the incomingspectators while the second one is due to the people leavingthe event. In this paper we have proposed an approach topredict the impact of the second wave of traffic due a PSE,given information on it’s category and on the first wave oftraffic. To this aim, we have defined a two-step solution,where at first we created models able to predict the out-bound congestion based on historic data about past eventsand, in the second step, mapped these congestion predictionon to the route network by identifying the road segmentsthat are most likely of being congested due to PSEs. Theapproach is complemented by a GUI that allows DecisionMakers to visualize on an interactive 3D map the dynamicextension of the impact area and the severety of the expectedcongestions. An empirical assessment has been done usingfloating car data covering 7 months of mobility informationin the city of Cologne, Germany, and using all the PSEshosted in the LANXESS arena in the same period.The results show that our proposal can positively identifythe influence of the PSE on the traffic, being up to 35% bet-ter than actual state of the art solutions. Consequently itcan be a viable module to be integrate in any navigation de-vice, in order to reroute drivers away from the location ofthe event and optimize urban mobility.As future work, the next obvious step is to predict also thefirst wave of traffic, but our experiments showed that rely-ing only on the time schedule is not sufficient, since there isno way to predict the amount of people that will attend theevent. The idea we are starting to investigate is to use socialnetworks, such as Facebook or Twitter, to understand whatis the popularity of a specific event or artist, consideringalso the geographical and age distribution of the support-ers.

7. Acknowledgment

The research leading to these results has been partlyfunded by the European Communitys Seventh Frame-work Programme (FP7/2007-2013) under grant agreementn 610990 - Project COMPANION. The authors wish alsoto thank TomTom NV for providing the traffic data used inthis research.

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