Eigner, György

Felde, Imre

Kovács, Levente

Mezei, Miklós

Varga, Árpád

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Overview of taxi database from viewpoint ofusability for traffic model development: a casestudy for BudapestA´rpa´d Varga∗, Gyo¨rgy Eigner†, Levente Kova´cs†, Imre Felde‡ and Miklo´s Mezei∗∗Kando´ Ka´lma´n Faculty of Electrical Engineering, O´buda University, Budapest, HungaryEmail: {varga.arpad,mezei.miklos}@kvk.uni-obuda.hu∗John von Neumann Faculty of Informatics, O´buda University, Budapest, HungaryEmail: felde.imre@nik.uni-obuda.hu†Research, Innovation and Service Center of O´buda University,Physiological Controls Group, O´buda University, Budapest, HungaryEmail: {eigner.gyorgy,kovacs.levente}@nik.uni-obuda.huAbstract—Forecasting and analyzing urban car traffic isan actual but still very complex problem. The modern carfleet handling IT systems designed for taxi and deliveryservice companies allows GPS coordinate data acquisitionfrom large amount of vehicles for optimizing the ride andfreight allocation. Since the database of these companiescontains movement patterns belonging to multitude ofvehicles, arise the question if these data, belonging tovehicles with special purpose, is suitable for representingthe whole car traffic. To make the first step to answer thisquestion, our case study utilizes the time-resolved GPScoordinate database of one of the largest taxi company inBudapest from year 2014.Index terms—Data mining, Taxi cab database, Datavisualization, Urban traffic modelingI. INTRODUCTIONDuring the XX. century the proportion of the urban popula-tion show unprecedented increase. Nowadays more the 54% oftotal human population live in cities or in urban regions [1].In case of Hungary, this proportion is 68%, and more than16% of the country’s population is concentrated surroundingsthe capital, Budapest [2]. The urban transportation networksare exposed due to the increased load. Thus, the optimization,design, and modeling of these systems became importanttopics. By the help of suitable arranged and organized urbantransportation network considerable cost reduction can beachieved. On the other side, since the majority of the vehiclesstill use fossil fuel, the emission of exhaust gases can bemoderated, which has positive feedback to the state of healthof the urban population also.To improve the design and optimization methods also re-quire the development and elaboration of sophisticated math-ematical models, which can describe and predict the maincharacteristics of the urban car traffic. In general, the trafficis characterized by two quantities, the car density ρ [vehi-cles/km], and the traffic flow q [vehicles/h], which can vary intime and space. For highway traffic, the connection betweenthese two quantities in a given road section is approximatelygiven by the ”fundamental relationship” [3]. For low ρ values,the q is linear function of ρ. For higher q values, the individualvehicles tend to prohibit the movement of each other, resultingfolding back and a decreasing segment in the fundamentalrelationship curve.This nonlinear characteristics – in case of existence ofseveral conditions – can cause the formation and propagationof congestion waves. For highway traffic, the evolution andpropagation of these waves can be described and predictmathematically well by using nonlinear partial differentialequations. Since the modern highways are densely equippedwith monitoring stations, capturing the type (automobile/truck)and speed of the passing vehicles, these models can bevalidated relatively easily [4].For urban traffic, both the model creation and its validationis far more complex task [5]. Unlike the highways, whichare dominated by long, straight sections with relatively fewjunctions, the urban roads are connected with numerous in-tersections, producing a complex network with ten thousandsof road sections and junctions [6]. The traffic flow capacityof the road sections and junctions varies between wide limits(small streets of the city center region and the suburbia vs.the slip road of the highways which connect the city withthe national road system). While the traffic flow and the maintravel paths show high level of predictable seasonality andperiodicity in various time scales (school starting period vs.the holiday period in summer, working day vs. the end of theweek, morning rush hours vs. the night traffic), unforeseenor proposed events, such as accidents, resurrections or largescale touristic (sport or cultural) programs can totally changethis temporal pattern. Moreover, since the urban roads areGy. Eigner was supported by the U´NKP-17-4/IV. New National Excellen-ce Program of the Ministry of Human Capacities.SISY 2017 • IEEE 15th International Symposium on Intelligent Systems and Informatics • September 14-16, 2017 • Subotica, Serbia978-1-5386-3855-2/17/$31.00 ©2017 IEEE 000105characterized by high level of connectivity, the effect of eventscan propagate across the elements of the urban traffic network,causing strong deviations from the overall seasonal-temporalpattern even far away from the initial place of the event.Among several issues of this kind of complex networkmodeling, also arises the question of validation: is it possibleto find a database, which contains real traffic data from allregions of the diverse urban road network? There are also existfix official installed traffic monitoring stations in the urbanregions, but they are concentrated in the main junctions andmay not represent the diversity of the urban roads. On the otherhand, the modern car fleet handling IT systems designed fortaxi and delivery service companies allow GPS coordinate dataacquisition from large amount of vehicles for optimizing theride and freight allocation [7]. Since the vehicles belonging tothese companies travel considerable amount of distance daily,and they are both rove the inner and outer regions of the city,it is reasonable to use this kind of data to validate varioustraffic models. Moreover, these companies are often handlingseveral hundred vehicles simultaneously, and each of them canbe considered as a passive ”probe”, which is moving togetherwith the surrounding traffic.In our paper, we make an attempt to visualize and sum-marize the main temporal and spatial properties of the taxiGPS coordinate database belonging to one of the largest taxicompany in Budapest, regarding to the year 2014.Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Jan20140100200300400500Figure 1: Variation of the number of active taxi cabs during the whole year of 2014.Mar. 3., Mon, 00:00Mar. 3., Mon, 12:00Mar. 4., Tue, 00:00Mar. 4., Tue, 12:00Mar. 5., Wed, 00:00Mar. 5., Wed, 12:00Mar. 6., Thu, 00:00Mar. 6., Thu, 12:00Mar. 7., Fri, 00:00Mar. 7., Fri, 12:00Mar. 8., Sat, 00:00Mar. 8., Sat, 12:00Mar. 9., Sun, 00:00Mar. 9., Sun, 12:0020140100200300400Figure 2: Variation of the number of active taxi cabs for a typical week of 2014.II. TEMPORAL OVERVIEW OF TAXI DATAEach taxi cab from the database is equipped with com-mercial tablet PC, which ensures the connection with thedispatcher center via mobile internet connection. The tabletsare also possess a built-in GPS receiver and the actual coordi-nates of the vehicle (geographical longitude and latitude) aredetermined from time to time, and sent to the dispatcher centertogether with the present state of the taxi (engaged, vacant, notin service, en route for an incoming carriage request). Theinstantaneous cab positions and states are used to allocatethe incoming carriage requests. Several possible routes arecalculated between the actual client calling places and thenearest vehicles, and the most preferable is chosen based onthe shortest access time. The planned route is also sent to thetablet of the chosen vehicle, helping the work of the taxi driver.The central distribution system is also capable to considerthe traffic situation and also try to uniform the load of theindividual vehicles. Eg., if two taxi cab have similar accesstime to the same customer, then that cab is sent to the locationof order, which has fewer carriage in the actual shift. Thelatitude, longitude coordinate logs, equipped with timestampand a unique vehicle identification number, are stored in acentral log file for later analysis and revision.Á. Varga et al. • Overview of Taxi Database from Viewpoint of Usability for Traffic Model Development: a Case Study for Budapest000106Jan Feb Mar Apr May Jun Jul Aug Sep Okt Nov Dec Jan1002003004005006007008000 50 100 150 200 250 300 350Figure 3: Variation of the number of hourly incoming coordinate logs for all taxi cabs during the whole year of 2014.Mar. 3., Mon, 00:00Mar. 3., Mon, 12:00Mar. 4., Tue, 00:00Mar. 4., Tue, 12:00Mar. 5., Wed, 00:00Mar. 5., Wed, 12:00Mar. 6., Thu, 00:00Mar. 6., Thu, 12:00Mar. 7., Fri, 00:00Mar. 7., Fri, 12:00Mar. 8., Sat, 00:00Mar. 8., Sat, 12:00Mar. 9., Sun, 00:00Mar. 9., Sun, 12:003003103203303403500 50 100 150 200 250 300 350Figure 4: Similar to Fig. 3. shown for a typical week in case of taxi cabs with ID 300-350. Hours are colored with white ifhas no incoming coordinate logs.In our case, the size of the central log file, concerningto the year 2014, is 36 GB and stored in plain text format.It contains more than 4.5 · 108 position logs belonging to801 individual cabs. The incoming latitude and longitudecoordinate pairs are denoted with a time stamp with 0.01 secprecision. To analyze the temporal distribution and continuityof the captured coordinates, the log file is parceled to 8760section based on the time stamp, distinguishing the hours inyear 2014. Then, the number of coordinate logs, belongingto different taxi cabs is counted in each hour. A taxi cab ina given hour is considered as an ‘active’ taxi cab, if thereexists at least 1 coordinate log belonging to its ID in thegiven interval, regardless its state (since the vacant, free, andofficially not in service vehicles can provide equally valuableinformation from viewpoint of the traffic monitoring).The variation of the number of active taxi cabs per hourduring the whole year and for a typical week is shown inFig. 1-2. The minimum, maximum, and mean hourly activevehicle numbers are 21, 461 and 207.83. The fact that thereis no hour passed without less than 21 active taxis, assumethat the continuous traffic monitoring can be realized based onthe taxi log data, since the presence of these vehicles in thetraffic is constant. However, the number of active cars showsstrong fluctuations, in accordance with the general travellinghabits. The peak of the active hourly vehicle number areusually reached between 8:00-11:00 or 14:00-16:00 duringand ordinary weekday, the minimum values are usually oc-curs in a period 2:00-3:00 at nighttime. Peaks belonging tothe weekends or holidays reach only about the half of theweekdays. During April and May a strong decrease can beSISY 2017 • IEEE 15th International Symposium on Intelligent Systems and Informatics • September 14-16, 2017 • Subotica, Serbia000107observed in the active car numbers, since about the half ofthe cabs were temporarily withdrawn from circulation due totechnical reasons.Figure 5: Scatter plot of the captured position logs (”street scatter”) from all vehicles during Jan. 7., Tuesday, 2014. Theposition logs belonging to cab with ID No. 118. are connected and colored by the time of recording, calculated from the timestamp of the first position sample.Another important question is the temporal density ofcoordinate logs incoming from the cabs, since it determinesthe temporal resolution of traffic report, based on taxi data.To get an overall view about the temporal log density, thehourly coordinate log numbers are counted for each individualtaxi cab. Then, the hourly captured log numbers are depictedas a waterfall diagram (Fig. 3-4.), where the horizontal axisrepresents the date, and the cab ID-s are indicated on thevertical axis. Therefore, each square or pixel in the diagramrepresents an hour in a year, belonging to a specific taxi,colored by the log number. In Fig. 3 there is an empty territorywithout coordinate logs between April and May for cabswith ID 410-801, because the withdrawn of these vehiclesfrom circulation were planned, since they had to preparedaccording to the requirements of the new taxi policy, whichwas introduced in 2013. The length of a typical shift fora taxi cab was 4-16 hours (of course, the drivers can bechanged in that period). The number of hourly incomingcoordinate samples shows high level of variability, but forthe majority of the investigated hourly periods is between180-360 log/hour, which means that the mean elapsed timebetween two coordinate sample is about 20-10 sec. It is alsointeresting, that the subtraction of about the half of the fleetresults remarkable growth in the hourly log numbers, whichindicates that the coordinate logging performance is sensitiveto the number of the actual running cabs. From Septemberthere is again a noticeable rise in the hourly log numbers,since the IT systems of the company is developed.III. SPACIAL OVERVIEW OF TAXI DATAThe precision of the positioning is approximately 5-10 m,and it is strongly depend on the surface conditions. Eg. intunnels and in the vicinity of hills and larger buildings thesignal of the GPS satellites are often overshadowed, resultinginaccurate position logs. To investigate the spatial distributionof the incoming coordinates in general, a scatter plot isdrawn based on the coordinate logs from all vehicles on atypical weekday (Jan. 7., Tuesday, Fig. 5.), where each pointrepresents an incoming coordinate sample. The structure of thedowntown streets of Budapest is clearly outlined by the scatter,even though the considered data are concerning only a one dayperiod. In the 2014 annual log file, several coordinates arelabeled as a potential street crossing (registered manually bythe taxi drivers), in Fig. 5. they are also depicted. The majorityof the potential junctions are true crossings, but some of themdepict false crossing points. To characterize the resolution ofmotion of an individual cab, the positions of a single vehicleare highlighted and consecutive positions of the chosen vehicleare connected. The movement of the vehicle can be followedeven through the street network of the downtown, mainlycharacterized by short street sections.Á. Varga et al. • Overview of Taxi Database from Viewpoint of Usability for Traffic Model Development: a Case Study for Budapest000108Figure 6: Taxi attendance histogram of Budapest corresponding to period Jul. 21. Mon.-Jul. 27. Sun. Empty cells are coloredwith white.On the scatter plot it is clearly visible, that some streets andstreet sections are covered by more position samples than theothers, indicating, that these routes are more often used by taxicabs. However, for longer time periods, the scatter diagram isnot suitable to visualize the ”attendance” frequency of differentstreets by taxis, since the density of scatter points becomeindistinguishable. To make the depiction more expressive,first, the territory of Budapest and the connecting suburbia (aquadratic region with dimensions of 45× 45 km), is parceledby a 8000 × 8000 quadratic mesh. The size of an individualmesh element is 5.625×5.625 m. Then, the number of positionsamples, falling in each quadratic cell, is counted, and coloredby the count number, resulting an attendance histogram. For1 week period (between Jul 21. Mon. – Jul. 27. Sun) for alltaxi it is shown in Fig 6-7. As it is expectable, the downtownterritories are far more attended in general, then the suburbia.At the same time, some suburban roads (typically: highwayintroductory sections, or road to the international airport) arealso characterized by higher attendance frequency level. Themost densely used road sections in the downtown area are theboulevards and the bridges. It can be also noticed, that somecolored parcel is located in the Danube between the bridges(naturally, they represent invalid coordinate samples), whichcan be explained by the inaccuracy and limitations of the GPSsystem, as it was mentioned before.IV. CONCLUSIONSeveral main issues of the urban traffic modeling are sum-marized and the operation of a coordinate data acquisitionsystem, belonging to a modern typical taxi company is pre-sented. Several possible graphical methods are displayed toinvestigate, understand and estimate the temporal and spatialqualities of such a complex and large-scale dataset. Sincethe taxi cab tracking data provide temporarily and spatiallyextensive picture about the movement of great amount ofvehicles, it is possible to use it as a base for validation of trafficmodels. This validation will be done in our future research.SISY 2017 • IEEE 15th International Symposium on Intelligent Systems and Informatics • September 14-16, 2017 • Subotica, Serbia000109Figure 7: Same attendance histogram as in Fig. 6, but for the downtown of BudapestACKNOWLEDGMENTA´. Varga, M. Mezei and Gy. Eigner acknowledge thesupport of the Robotics Special College of Obuda Universityand the Doctoral School of Applied Informatics and AppliedMathematics of O´buda University. The research was alsosupported by the Research and Innovation Center of O´budaUniversity.REFERENCES[1] United Nations, “World urbanization prospects: The 2014 revision, high-lights. department of economic and social affairs,” Population Division,United Nations, 2014.[2] Population Census 2011, 1st ed., Hungarian Central Statistical Office,2012, 1. Preliminary data.[3] M. Treiber and A. Kesting, “Validation of traffic flow models withrespect to the spatiotemporal evolution of congested traffic patterns,”Transportation research part C: emerging technologies, vol. 21, no. 1,pp. 31–41, 2012.[4] ——, “Evidence of convective instability in congested traffic flow: A sys-tematic empirical and theoretical investigation,” Transportation ResearchPart B: Methodological, vol. 45, no. 9, pp. 1362–1377, 2011.[5] A. Jamshidnejad, I. Papamichail, M. Papageorgiou, and B. De Schutter,“Sustainable Model-Predictive Control in Urban Traffic Networks: Effi-cient Solution Based on General Smoothening Methods,” IEEE Transac-tions on Control Systems Technology, 2017.[6] S. Zhao, P. Zhao, and Y. Cui, “A network centrality measure frameworkfor analyzing urban traffic flow: A case study of Wuhan, China,” PhysicaA: Statistical Mechanics and its Applications, vol. 478, pp. 143–157,2017.[7] N. Ferreira, J. Poco, H. T. Vo, J. Freire, and C. T. Silva, “Visualexploration of big spatio-temporal urban data: A study of new york citytaxi trips,” IEEE Transactions on Visualization and Computer Graphics,vol. 19, no. 12, pp. 2149–2158, 2013.Á. Varga et al. • Overview of Taxi Database from Viewpoint of Usability for Traffic Model Development: a Case Study for Budapest000110

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