Colpaert, Pieter

D'haene, Karel

D'haene, Kurt

Van de Vyvere, Brecht

Verborgh, Ruben

English

Ghent University Academic Bibliography

Abstract.Predicting phase durations of traffic lights using liveOpen Traffic Lights dataBrecht Van de Vyvere, Karel D’haene, Kurt D’haene, Pieter Colpaert, RubenVerborghIDLab, Department of Electronics and Information Systems, Ghent University – imecDynamic traffic lights change their current phase duration according tothe situation on the intersection, such as crowdedness. In Flanders, only theminimum and maximum duration of the current phase is published. When routeplanners want to reuse this data they have to predict how long the current phasewill take in order to route over these traffic lights. We tested for a live OpenTraffic Lights dataset of Antwerp how frequency distributions of phase durations(i) can be used to predict the duration of the current phase and (ii) can begenerated client-side on-the-fly with a demonstrator. An overall mean averageerror (MAE) of 5.1 seconds is reached by using the median for predictions. Adistribution is created for every day with time slots of 20 minutes. This result isbetter than expected, because phase durations can range between a few secondsand over two minutes. When taking the remaining time until phase change intoaccount, we see a MAE around 10 seconds when the remaining time is less than aminute which we still deem valuable for route planning. Unfortunately, the MAEgrows linear for phases longer than a minute making our prediction methoduseless when this occurs. Based on these results, we wish to present twodiscussion points during the workshop.1. IntroductionThe city of Antwerp invested during 2018 in connecting the traffic lights of anintersection to the Internet. With the Open Traffic Lights project [1], the data aboutthe phase and timing of these traffic lights have been made freely available on theWeb as Linked Open Data. Typically, this is used for Green Light Optimal SpeedAdvisory (GLOSA) [2] systems to save fuel through speed advice or count-downtimer [3]. While GLOSA systems focus on the event of approaching an intersection,route planners have a more global view of the user journey where fuel savings can beone parameter.This is a print-version of an article first written for the Web. The Web-version is available athttps://brechtvdv.github.io/Article-Predicting-traffic-light-phases . Copyright © 2019 for this paper by its authors. Use permitted under Creative CommonsLicense Attribution 4.0 International (CC BY 4.0).Implementing live changing data like these in traditional origin-destination routeplanning APIs would require much more complex tasks such as permanent tracking ofthe user.Recently, work has been done on serverless route planning over public transporttimetables [4] and road networks [5] performing the route planning algorithm on theclient-side. This appproach makes the client free to choose how it runs its routeplanning queries while data owners are only reponsible for publishing its data in aninteroperable way. Such a client (cfr. Planner.js) can be extended to not only queryover road networks, but also take traffic lights data into account. In the Netherlandsand Belgium, dynamic traffic lights are installed which change their current phaseduration according to detectors (pedestrian counters, cameras etc.). This introducesnew challenges [6], such as forecasting how long the current phase will likely take. Inthe Netherlands, opposed to the standard SPAT, it is mandatory to calculate andpublish this on the server-side, while in Belgium only the minimum and maximumduration is expected to be published. In this article, we investigate how the currentphase duration of the traffic lights in Antwerp can be predicted and demonstrate thison the client-side.2. Related workThe Open Traffic Lights (OTL) [1] project proposes a strategy for publishing trafficlights data in a semantically and technically interoperable way. Global identifiers(URIs) are defined with the OTL ontology for a subset of the terms of the currentlyused data standards SPAT and MAP. By agreeing on the semantics of these terms andapplying RDF for describing facts, data publishers are not limited to the strict datastructure in JSON format of SPAT and MAP. These standards propose the use of roadlane connections instead of traffic lights to describe whether a road user can follow acertain direction. A traffic light represents multiple connections (e.g. you can turnright) that follow a certain signal phase and timing (SPAT) (e.g. you need to stop andwait for at least 5 seconds). Since most connections follow the same phase andtiming, signal groups are introduced which represent the signal phase and timing ofone or more connections. With MAP the geometry of the departure and arrival lane ofa connection can be described. OTL also defines how the data can be published astime-sorted Linked Data Fragments. This allows Web clients to not only retrieve thelatest value of a signal group, but also its historic values through pagination linkswhich can be used for predicting the signal phase duration of a signal group.Predicting the phase duration has been done in related work [7, 8] where tests havebeen run on traffic lights with a fixed cycle time. This means that a group of signalphases occur repeatedly between fixed time intervals. Ibrahim et al. [7] propose afrequency distribution of phase durations for every signal phase and fixed cycle time,because an intersection can have a different fixed cycle time during working daysthan in the weekend. Bodenheimer et al. [8] use graph transformations forrepresenting the phase changes. For the prediction of the duration, they use timeslotsof 15 minutes and different day classes (e.g. regular versus vacation day). The latterhas been extended in [9] for dynamic traffic lights and makes clear that predictionerrors can be decreased significantly by having access to all detector information.3. Predicting the phase durationTo be able to predict the phase duration of a dynamically changing traffic lightphase, there are two steps we take: first, we create frequency distributions [7] of phasedurations for every signal group and then we predict the duration for every signalphase and timing (SPAT) update by using a selector on the distribution that isapplicable for that update. Since traffic patterns can change depending on the time ofthe day or day of the week, we grouped signal phases by applying multiple strategies[9]. As a baseline, frequency distributions are created for every signal group andsignal phase. Then we grouped phases according to their type of day (weekday orweekend) and in time slots of every hour. The reasoning behind this is that a trafficlight interacts differently during the weekends and peak hours. As last strategy, wegrouped per day (Monday, Tuesday…) and in time slots of 20 minutes. To predict theduration of a phase from a received update, we tested a few basic selectors: median,mean and mode. These selectors only consider phase durations that take longer thanits current duration, because these are still historically possible to occur. We chosethese selectors instead of graph transformations [8], because we discovered thatcertain phase durations of our tested dataset occur more frequently than others, whichcreates peaks in its frequency distribution. This made us hypothesize that thisbehavior will benefit using a method like the mean. Finally, to express the predictionerror we calculate the mean absolute error (MEA) for every SPAT update  as follows[7] where  is the predicted duration and  the real duration of :  .4. ResultsWe used traffic lights data from the intersection in Antwerp which contains SPATupdates of 8 dynamically changing signal groups. To run reproducible tests, weharvested a dataset from 8th till 25th March 2019 from the OTL endpoint containing50951 historical fragments. The test cases are ran using 10-fold cross validation. First,we extracted the SPAT updates from the fragments and randomly divided these in 10groups. Then we replayed every update , who belongs to one group, and predicted itsid (i)p d(i) i MAE =∣d (i) −n1 ∑i=1np d(i)∣iduration  using the frequency distributions made from the other 9 groups andcompared it with its real duration . Only updates with unknown duration (minimumand maximum duration differ) are considered.Table 1 shows the MAE for every grouping strategy and method we applied. Wesee that fine-grained grouping of phases improves the MAE which acknowledgesrelated work [9]. Also, using the median returns lower prediction errors than the meanand mode. A very good result is considered around 2s according to Bodeheimer et al.[9], but for a basic algorithm, an input variance of approximately 133s and withoutusing external detector information an overall MAE of 5.1s is still lower thanexpected. Especially because this test also considers updates whose time till phasechange is very high (above 30s).To test this causality between the MAE of a signal group and its time until phasechange, we plotted this in Fig. 1 for one signal group showing signal phase “Stop andremain” (red line) and “Protected Movement Allowed” (green line). The other signalgroups can be found at https:/ / kridhaen.github.io/OpenTrafficLightsDistributionsVisualizer/ by pressing the arrowbutton to “Visualization of the prediction error for each time to phase change for largedataset”. With these graphs, a user can compare the predictability of certain signalgroup and phase combinations. The signal phase “Protected Movement Allowed”(green line) shows a prediction error of almost 0s which can be confirmed with itsfrequency distribution: its phase duration generally takes 15s and exceptionally 18s.With this knowledge, route planners can safely assume that this signal group will havea green time of at least 15s. For the signal phase “Stop and remain” (red line), we seethree findings. First, between 0s and 9s there is no prediction error, because theminimum duration equaled the maximum duration. Next, we see a flat line for phasedurations between 9s and 65s with a MAE around 10s. Although we don’t know theexact distribution of the errors, a route planner needs to be aware that the prediction ison average this much off so it must do a best-effort to minimize the consequences forthe driver. For example, when the phase is 60s before change: on the one hand adpdMethod No grouping (s) Per type of day and every hour (s) Per day and every 20 minutes (s)Median 6.8 5.5 5.1Mean 7.0 5.9 5.6Mode 7.6 6.2 6.0Table 1: An overall prediction error of 5.1s is good, because a prediction is made forevery SPAT update without using external datasets. This hints that some dynamictraffic lights of Antwerp probably follow a predictable pattern which is feasible forpath planning algorithms.prediction of 50s can lead to 10s of waiting before a red light, on the other hand aprediction of 70s means that it is already 10s green with 5s remaining. This raises anew challenge whether the driver will be able to pass the green light in those 5sdepending on the crowdedness. Lastly, above 65s the prediction error grows linear.Although these long phase durations occur exceptionally, otherwise the overall MAEwould be much higher, route planning during such a phase would return completelyunreliable results.5. DemonstratorThis Web application demonstrates the prediction of the current phase duration of alive traffic light in Antwerp. This gives implementers of route planning engines betterinsight how traffic lights data work and how its reusability increases by applyingprediction methods. The green, red and blue line on the chart shows respectively theminimum, maximum and predicted duration in seconds of the phase. On the client-side the latest data from the Open Traffic Lights API gets harvested and frequencydistributions are constructed with the same strategy as Section 4. This vizualizationshows us that the minimum and maximum duration can differ tens of seconds,especially in the beginning. Also, when the minimum and maximum duration align,and thus it becomes certain how long the phase take, the blue line gets mostlycorrected with a few seconds. This behavior hints to our result from the meanprediction error (Table 1). The source code is available at https:/ / codepen.io/kridhaen/pen/VJrezO/.1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 97 103109115121127133010203040506070Time before phase change (s)Mean Average Error (s)Fig. 1: Predicting the duration of signal phase “Protected Movement Allowed” (greenline) for this signal group is perfectly possible as its prediction error is almost 0s. Forsignal phase “Stop and remain” (red line), we deem a prediction error around 10s stillvaluable for route planning.6. DiscussionA route planner can predict a fixed phase duration, e.g. the phase will probably last20 seconds, with a frequency distribution and selector like the mean, but can itanticipate on the error this prediction brings? By transforming the frequencydistribution to a cumulative frequency distribution, routing with a fixed probability ispossible. For example, when 90% of historical phase durations happened before acertain duration, then a route planner can be 90% sure that the phase will changebefore that time. This fixed probability can be configured as a user preference andalso gives the possibility to cut off the exceptionally long phase durations from thetime until phase change curve (Fig. 1). Consequently, a route planner can retrieve themaximum MAE from the left-side part of the curve to have an indication of howmuch the predicted duration can be averagely off and anticipate with it, e.g. will thedriver still be on time for a green light or is there another route with less waiting timefor a red light? Also, the maximum MAE can be configured by the user to better fittheir expected routing accuracy. As a first discussion point (i), we would like todiscuss whether a user should be able to configure a minimum probability or leavethis responsibility to the data publisher, like in the Netherlands. In future work, wesuggest to also use a frequency distribution for the variance of prediction errorsinstead of only calculating the MAE value. Whether a predicted phase duration isusefull depends on several factors, such as the crowdedness in the streets. When it isvery crowded, we deem data about travel times between points more usefull thantraffic lights data, because the driver does not have the flexibility to time its arrival atan intersection. Finally, related work [9] has already demonstrated that having accessto all detector information lowers the prediction error significantly. This brings us toFig. 2: The blue line shows the predicted phase duration which gives a betterunderstanding how this relates to the minimum (green) and maximum (red) phaseduration and whether it was a good prediction in the end.our second discussion point (ii): what would enable a better description of how atraffic light is connected with other sensors in the broader context of a city (cfr.Digital Twin)? As a next step, we will publish a live vehicle counter dataset from theintersection in Antwerp as Linked Open Data and reuse this for lowering theprediction error.References1. Van de Vyvere, B., Colpaert, P., Mannens, E., Verborgh, R.: Open traffic lights: astrategy for publishing and preserving traffic lights data. In: Proceedings of the5th Workshop on Managing the Evolution and Preservation of the Data Web(2019).2. Zweck, M., Schuch, M.: Traffic light assistant: Applying cooperative ITS inEuropean cities and vehicles. In: 2013 International Conference on ConnectedVehicles and Expo (ICCVE). pp. 509–513 (2013).3. Barth, M., Mandava, S., Boriboonsomsin, K., Xia, H.: Dynamic ECO-driving forarterial corridors. In: 2011 IEEE Forum on Integrated and SustainableTransportation Systems, FISTS 2011. pp. 182–188 (2011).4. Colpaert, P., Verborgh, R., Mannens, E.: Public Transit Route Planning throughLightweight Linked Data Interfaces. In: Cabot, J., Virgilio, R. de, and Torlone, R.(eds.) Proceedings of the 17th International Conference on Web Engineering. pp.403–411. Springer (2017).5. Colpaert, P., Abelshausen, B., Rojas Meléndez, J., Delva, H., Verborgh, R.:Republishing Open Street Map’s roads as Linked Routable Tiles. In: Proceedingsof the 16th ESWC: Posters and Demos (2019).6. Amelink, M.: Signal phase and time (SPAT) and map data (MAP). Amsterdamgroup (2015).7. Ibrahim, S., Kalathil, D., Sanchez, R.O., Varaiya, P.: Estimating Phase Durationfor SPaT Messages. IEEE Transactions on Intelligent Transportation Systems. 20,2668–2676 (2019).8. Bodenheimer, R., Brauer, A., Eckhoff, D., German, R.: Enabling GLOSA foradaptive traffic lights. In: 2014 IEEE Vehicular Networking Conference (VNC).pp. 167–174 (2014).9. Bodenheimer, R., Eckhoff, D., German, R.: GLOSA for adaptive traffic lights:Methods and evaluation. In: 2015 7th International Workshop on ReliableNetworks Design and Modeling (RNDM). pp. 320–328 (2015).10. Protschky, V., Feit, S., Linnhoff-Popien, C.: Extensive Traffic Light Predictionunder Real-World Conditions. In: 2014 IEEE 80th Vehicular TechnologyConference (VTC2014-Fall). pp. 1–5 (2014).

Predicting phase durations of traffic lights using live Open Traffic Lights data

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Predicting phase durations of traffic lights using live Open Traffic Lights data

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