{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T06:37:25Z","timestamp":1764225445065,"version":"build-2065373602"},"reference-count":47,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2018,5,8]],"date-time":"2018-05-08T00:00:00Z","timestamp":1525737600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Taxi behavior is a spatial\u2013temporal dynamic process involving discrete time dependent events, such as customer pick-up, customer drop-off, cruising, and parking. Simulation models, which are a simplification of a real-world system, can help understand the effects of change of such dynamic behavior. In this paper, agent-based modeling and simulation is proposed, that describes the dynamic action of an agent, i.e., taxi, governed by behavior rules and properties, which emulate the taxi behavior. Taxi behavior simulations are fundamentally done for optimizing the service level for both taxi drivers as well as passengers. Moreover, simulation techniques, as such, could be applied to another field of application as well, where obtaining real raw data are somewhat difficult due to privacy issues, such as human mobility data or call detail record data. This paper describes the development of an agent-based simulation model which is based on multiple input parameters (taxi stay point cluster; trip information (origin and destination); taxi demand information; free taxi movement; and network travel time) that were derived from taxi probe GPS data. As such, agent\u2019s parameters were mapped into grid network, and the road network, for which the grid network was used as a base for query\/search\/retrieval of taxi agent\u2019s parameters, while the actual movement of taxi agents was on the road network with routing and interpolation. The results obtained from the simulated taxi agent data and real taxi data showed a significant level of similarity of different taxi behavior, such as trip generation; trip time; trip distance as well as trip occupancy, based on its distribution. As for efficient data handling, a distributed computing platform for large-scale data was used for extracting taxi agent parameter from the probe data by utilizing both spatial and non-spatial indexing technique.<\/jats:p>","DOI":"10.3390\/ijgi7050177","type":"journal-article","created":{"date-parts":[[2018,5,8]],"date-time":"2018-05-08T12:15:03Z","timestamp":1525781703000},"page":"177","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Agent-Based Modeling of Taxi Behavior Simulation with Probe Vehicle Data"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6398-2756","authenticated-orcid":false,"given":"Saurav","family":"Ranjit","sequence":"first","affiliation":[{"name":"Institute of Industrial Science, The University of Tokyo, 4-6-1, Komaba, Meguro-ku, Tokyo 153-8505, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1454-1820","authenticated-orcid":false,"given":"Apichon","family":"Witayangkurn","sequence":"additional","affiliation":[{"name":"Center for Spatial Information Science, The University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa-shi, Chiba 277-8568, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masahiko","family":"Nagai","sequence":"additional","affiliation":[{"name":"Graduate School of Sciences and Technology for Innovation, Yamaguchi University, 2-16-1, Tokiwadai, Ube, Yamaguchi 755-8611, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ryosuke","family":"Shibasaki","sequence":"additional","affiliation":[{"name":"Center for Spatial Information Science, The University of Tokyo, 5-1-5, Kashiwanoha, Kashiwa-shi, Chiba 277-8568, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,5,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Baster, B., Duda, J., Maciol, A., and Rebiasz, B. (2013, January 11\u201313). Rule-Based Approach to Human-like Decision Simulating in Agent-Based Modeling and Simulation. Proceedings of the 2013 17th International Conference on System Theory, Control and Computing (ICSTCC) 2013, Sinaia, Romania.","DOI":"10.1109\/ICSTCC.2013.6689049"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"7280","DOI":"10.1073\/pnas.082080899","article-title":"Agent-Based Modeling: Methods and Techniques for Simulating Human Systems","volume":"99","author":"Bonabeau","year":"2002","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.trc.2015.10.004","article-title":"Optimizing the Locations of Electric Taxi Charging Stations: A Spatial\u2013temporal Demand Coverage Approach","volume":"65","author":"Tu","year":"2016","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1111\/j.1467-9671.2012.01330.x","article-title":"Trajectories of Moving Objects on a Network: Detection of Similarities, Visualization of Relations, and Classification of Trajectories","volume":"17","author":"Sadahiro","year":"2013","journal-title":"Trans. GIS"},{"key":"ref_5","first-page":"21","article-title":"Route Identification and Travel Time Prediction Using Probe-Car Data","volume":"2","author":"Miwa","year":"2004","journal-title":"Int. J. ITS Res."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Cheng, S.F., and Nguyen, T.D. (2011, January 22\u201327). TaxiSim: A Multiagent Simulation Platform for Evaluating Taxi Fleet Operations. Proceedings of the 2011 IEEE\/WIC\/ACM International Conferences on Web Intelligence and Intelligent Agent Technology, Lyon, France.","DOI":"10.1109\/WI-IAT.2011.138"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Bischoff, J., Maciejewski, M., and Sohr, A. (2015, January 3\u20135). Analysis of Berlin\u2019s Taxi Services by Exploring GPS Traces. Proceedings of the 2015 International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), Budapest, Hungary.","DOI":"10.1109\/MTITS.2015.7223258"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2390","DOI":"10.1109\/TKDE.2012.153","article-title":"T-Finder: A Recommender System for Finding Passengers and Vacant Taxis","volume":"25","author":"Yuan","year":"2013","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_9","first-page":"54","article-title":"On Predicting the Taxi-Passenger Demand: A Real-Time Approach","volume":"Volume 8154 LNAI","author":"Correia","year":"2013","journal-title":"Progress in Artificial Intelligence"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1007\/s12652-016-0366-3","article-title":"Large-Scale Microscopic Simulation of Taxi Services. Berlin and Barcelona Case Studies","volume":"7","author":"Maciejewski","year":"2016","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.cosrev.2017.03.001","article-title":"Agent Based Modelling and Simulation Tools: A Review of the State-of-Art Software","volume":"24","author":"Abar","year":"2017","journal-title":"Comput. Sci. Rev."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"902","DOI":"10.1016\/j.procs.2015.05.162","article-title":"Agent Based Modelling for Simulating Taxi Services","volume":"52","author":"Grau","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Raychaudhuri, S. (2008, January 7\u201310). Introduction to Monte Carlo Simulation. Proceedings of the 2008 WSC Winter Simulation Conference, Miami, FL, USA.","DOI":"10.1109\/WSC.2008.4736059"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Deng, Z., and Ji, M. (2011, January 24\u201326). Spatiotemporal Structure of Taxi Services in Shanghai: Using Exploratory Spatial Data Analysis. Proceedings of the 2011 19th International Conference on Geoinformatics, Shanghai, China.","DOI":"10.1109\/GeoInformatics.2011.5981129"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1002\/atr.5670390107","article-title":"Modeling the Bilateral Micro-Searching Behavior for Urban Taxi Services Using the Absorbing Markov Chain Approach","volume":"39","author":"Wong","year":"2005","journal-title":"J. Adv. Transp."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yuan, J., Zheng, Y., Zhang, L., Xie, X., and Sun, G. (2011, January 17\u201321). Where to Find My Next Passenger?. Proceedings of the 13th International Conference on Ubiquitous Computing, Beijing, China.","DOI":"10.1145\/2030112.2030128"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Li, B., Zhang, D., Sun, L., Chen, C., Li, S., Qi, G., and Yang, Q. (2011, January 21\u201325). Hunting or Waiting? Discovering Passenger-Finding Strategies from a Large-Scale Real-World Taxi Dataset. Proceedings of the 2011 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), Seattle, WA, USA.","DOI":"10.1109\/PERCOMW.2011.5766967"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1080\/12265934.2013.776292","article-title":"A Time-Dependent Logit-Based Taxi Customer-Search Model","volume":"17","author":"Szeto","year":"2013","journal-title":"Int. J. Urban Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.trc.2014.08.010","article-title":"A Cell-Based Logit-Opportunity Taxi Customer-Search Model","volume":"48","author":"Wong","year":"2014","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"2Wong, R.C.P., Szeto, W.Y., and Wong, S.C. (2015). Behavior of Taxi Customers in Hailing Vacant Taxis: A Nested Logit Model for Policy Analysis. J. Adv. Transp., 49, 867\u2013883.","DOI":"10.1002\/atr.1307"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"2Wong, R.C.P., Szeto, W.Y., and Wong, S.C. (2015). A Two-Stage Approach to Modeling Vacant Taxi Movements. Transp. Res. Procedia, 7, 254\u2013275.","DOI":"10.1016\/j.trpro.2015.06.014"},{"key":"ref_22","unstructured":"Chakka, V.P., Everspaugh, A.C., and Patel, J.M. (2003, January 5\u20138). Indexing Large Trajectory Data Sets with SETI. Proceedings of the CIDR Conference on Innovative Data Systems Research, Asilomar, CA, USA."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhang, Y., and Li, J. (2009, January 26\u201327). Research and Improvement of Search Engine Based on Lucene. Proceedings of the 2009 International Conference on Intelligent Human-Machine Systems and Cybernetics, Hangzhou, China.","DOI":"10.1109\/IHMSC.2009.191"},{"key":"ref_24","first-page":"17","article-title":"The Design of Large Scale Data Management for Spatial Analysis on Mobile Phone Dataset","volume":"13","author":"Witayangkurn","year":"2013","journal-title":"Asian J. Geoinform."},{"key":"ref_25","unstructured":"Ranjit, S., Nagai, M., Witayangkurn, A., and Shibasaki, R. (2017, January 11\u201314). Sensitivity Analysis of Map Matching Techniques of High Sampling Rate GPS Data Point of Probe Taxi on Dense Open Street Map Road Network of Bangkok in a Large-Scale Data Computing Platform. Proceedings of the 15th International Conference on Computers in Urban Planning and Urban Management, Adelaide, Australia."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"131","DOI":"10.3141\/2544-15","article-title":"Analysis of Grid Cell\u2013Based Taxi Ridership with Large-Scale GPS Data","volume":"2544","author":"Nam","year":"2016","journal-title":"Transp. Res. Rec. J. Transp. Res. Board"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2543581.2543584","article-title":"From Taxi GPS Traces to Social and Community Dynamics: A Survey","volume":"46","author":"Castro","year":"2013","journal-title":"ACM Comput. Surv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Li, Q., Zheng, Y., Xie, X., Chen, Y., Liu, W., and Ma, W.Y. (2008, January 5\u20137). Mining User Similarity Based on Location History. Proceedings of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Irvine, CA, USA.","DOI":"10.1145\/1463434.1463477"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2743025","article-title":"Trajectory Data Mining: An Overview","volume":"6","author":"Zheng","year":"2015","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_30","unstructured":"Ester, M., Kriegel, H., Sander, J., and Xu, X. (1996, January 2\u20134). A Density-Based Algorithm for Discovering Clusters a Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96), Portland, OR, USA."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Gan, J., and Tao, Y. (31\u20134, January 31). DBSCAN Revisited: Mis-Claim, Un-Fixability, and Approximation. Proceedings of the 2015 ACM SIGMOD IInternational Conference on Management of Data, Melbourne, Victoria, Australia.","DOI":"10.1145\/2723372.2737792"},{"key":"ref_32","unstructured":"Wong, D.W.S., and Huang, Q. (2016, January 5\u20138). Sensitivity of DBSCAN in Identifying Activity Zones Using Online Footprints. Proceedings of the Spatial Accuracy 2016, Montpellier, France."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Campello, R.J.G.B., Moulavi, D., and Sander, J. (2013). Density-Based Clustering Based on Hierarchical Density Estimates. Advances in Knowledge Discovery and Data Mining, Springer.","DOI":"10.1007\/978-3-642-37456-2_14"},{"key":"ref_34","unstructured":"Gonzales, E., Yang, C., Morgul, F., and Ozbay, K. (2014). Modeling Taxi Demand with GPS Data from Taxis and Transit, Mineta National Transit Research Consortium."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1016\/j.trc.2016.06.002","article-title":"Updating Origin-Destination Matrices with Aggregated Data of GPS Traces","volume":"69","author":"Ge","year":"2016","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.eswa.2015.08.048","article-title":"Time-Evolving O-D Matrix Estimation Using High-Speed GPS Data Streams","volume":"44","author":"Gama","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1109\/TBDATA.2016.2627224","article-title":"Taxi-Passenger-Demand Modeling Based on Big Data from a Roving Sensor Network","volume":"3","author":"Zhang","year":"2016","journal-title":"IEEE Trans. Big Data"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1016\/j.trc.2017.10.016","article-title":"Short-Term Forecasting of Passenger Demand under on-Demand Ride Services: A Spatio-Temporal Deep Learning Approach","volume":"85","author":"Ke","year":"2017","journal-title":"Transp. Res. Part C Emerg. Technol."},{"key":"ref_39","first-page":"156","article-title":"A Performance Evaluation Model for Taxi Cruising Path Recommendation System","volume":"Volume 10235 LNAI","author":"Kim","year":"2017","journal-title":"Advances in Knowledge Discovery and Data Mining"},{"key":"ref_40","unstructured":"Grau, J.M.S., Moreira-Matias, L., Saadallah, A., Tzenos, P., Aifadopoulou, G., Chaniotakis, E., and Romeu, M.A.E. (2018, January 7\u201311). Informed versus Non-Informed Taxi Drivers: Agent-Based Simulation Framework for Assessing Their Performance. Proceedings of the Transportation Research Board 97th Annual Meeting, Washington, DC, USA."},{"key":"ref_41","first-page":"21","article-title":"An Analysis of the Cost Efficiency of Probe Vehicle Data at Different Transmission Frequencies","volume":"4","author":"Liu","year":"2006","journal-title":"Int. J. ITS Res."},{"key":"ref_42","unstructured":"Liu, K., Yamamoto, T., and Morikawa, T. (2007, January 9\u201313). Comparison of Time\/space Polling Schemes for a Probe Vehicle System. Proceedings of the 14th World Conference on Intelligent Transport Systems, Beijing, China."},{"key":"ref_43","unstructured":"Wang, Y., Zhu, Y., He, Z., Yue, Y., and Li, Q. (2011). Challenges and Opportunities in Exploiting Large-Scale GPS Probe Data, HP Laboratories. Technical Report HPL-2011-109."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Helbing, D. (2012). Agent-Based Modeling. Social Self-Organization: Agent-Based Simulations and Experiments to Study Emergent Social Behavior, Springer.","DOI":"10.1007\/978-3-642-24004-1"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1109\/MPRV.2011.43","article-title":"PFlow: Reconstruction of People Flow by Recycling Large-Scale Fragmentary Social Survey Data","volume":"10","author":"Sekimoto","year":"2011","journal-title":"IEEE Pervasive Comput."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Kanasugi, H., Sekimoto, Y., Kurokawa, M., Watanabe, T., Muramatsu, S., and Shibasaki, R. (2013, January 18\u201322). Spatiotemporal Route Estimation Consistent with Human Mobility Using Cellular Network Data. Proceedings of the 2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), San Diego, CA, USA.","DOI":"10.1109\/PerComW.2013.6529493"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"3851","DOI":"10.1080\/03610928908830127","article-title":"The Overlapping Coefficient as a Measure of Agreement Between Probability Distributions and Point Estimation of the Overlap of Two Normal Densities","volume":"18","author":"Inman","year":"1989","journal-title":"Commun. Stat. Theory Methods"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/7\/5\/177\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:03:46Z","timestamp":1760195026000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/7\/5\/177"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,5,8]]},"references-count":47,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2018,5]]}},"alternative-id":["ijgi7050177"],"URL":"https:\/\/doi.org\/10.3390\/ijgi7050177","relation":{},"ISSN":["2220-9964"],"issn-type":[{"type":"electronic","value":"2220-9964"}],"subject":[],"published":{"date-parts":[[2018,5,8]]}}}