Abstract
Taxi services , despite deemed as a convenient form of commuting , are challenged by many issues. The issues can be categorized based on the perspectives of drivers and passengers. Regarding the issue from the driver’s perspective, taxi drivers are working longer hours. However, the revenue generated does not justify their increased working hours, (i.e. working longer hours with less revenue), which further implies that drivers are not getting enough passengers. By contrast, from the perspective of passengers, the prime issue with taxi services is that passengers are rejected or denied service. In this research, we aim to establish a taxi behavior simulation model for an existing conventional taxi operation and introduce optimization for this type of taxi operation . The evaluation between the two models shows, that by introducing optimization to the usual taxi behavior such as in providing greater flexibility in selecting a passenger, an improved service can be achieved for both taxi drivers and passengers.
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References
Abar S, Theodoropoulos GK, Lemarinier P, O’Hare GMP (2017) Agent based modelling and simulation tools: a review of the state-of-art software. Comput Sci Rev 24:13–33. https://doi.org/10.1016/j.cosrev.2017.03.001
Baster B, Duda J, Maciol A, Rebiasz B (2013) Rule-based approach to human-like decision simulating in agent-based modeling and simulation. In: 17th International conference on system theory, control and computing, ICSTCC 2013; joint conference of SINTES 2013, SACCS 2013, SIMSIS 2013—Proceedings, pp 739–743. https://doi.org/10.1109/icstcc.2013.6689049
Bischoff J, Maciejewski M, Sohr A (2015) Analysis of Berlin’s taxi services by exploring GPS traces. In: International conference on models and technologies for intelligent transportation systems, MT-ITS 2015, (June), pp 209–215. https://doi.org/10.1109/mtits.2015.7223258
Bonabeau E (2002) Agent-based modeling: methods and techniques for simulating human systems. Proc Natl Acad Sci 99(suppl. 3):7280–7287. https://doi.org/10.1073/pnas.082080899
Castro PS, Zhang D, Li S (2012) Urban Traffic modelling and prediction using large scale taxi GPS traces. In: Kay J, Lukowicz P, Tokuda H, Olivier P, Krüger A (eds) Pervasive computing. Springer, Berlin, Heidelberg, pp 57–72. https://doi.org/10.1007/978-3-642-31205-2_4
Cheng SF, Nguyen TD (2011) TaxiSim: a multiagent simulation platform for evaluating taxi fleet operations. In: 2011 IEEE/WIC/ACM international conferences on web intelligence and intelligent agent technology, vol 2, pp 14–21. https://doi.org/10.1109/wi-iat.2011.138
Deng Z, Ji M (2011) Spatiotemporal structure of taxi services in Shanghai: using exploratory spatial data analysis. In: 19th International conference on geoinformatics, pp 1–5. https://doi.org/10.1109/geoinformatics.2011.5981129
Ester M, Kriegel H, Sander J, Xu X (1996) A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of 2nd international conference on knowledge discovery and data mining (KDD-96)
Gan J, Tao Y (2015) DBSCAN revisited: mis-claim, un-fixability, and approximation. In: Proceedings of the 2015 ACM SIGMOD international conference on management of data, pp 519–530. https://doi.org/10.1145/2723372.2737792
Gonzales E, Yang C, Morgul F, Ozbay K (2014) Modeling taxi demand with GPS data from taxis and transit. Retrieved from http://transweb.sjsu.edu/PDFs/research/1141-modeling-taxi-demand-gps-transit-data.pdf
Grau JMS, Romeu MAE (2015) Agent based modelling for simulating taxi services. Proc Comput Sci 52(1):902–907. https://doi.org/10.1016/j.procs.2015.05.162
Lindfield G, Penny J (2017) An introduction to optimization. Introduction to nature-inspired optimization. https://doi.org/10.1016/b978-0-12-803636-5.00001-3
Liu K, Yamamoto T, Morikawa T (2008) Study on the cost-effectiveness of a probe vehicle system at lower polling frequencies. Int J ITS Res 6(1):29–36. https://trid.trb.org/view/888064, http://www.its-jp.org/journal/papers/50.pdf
Maciejewski M, Salanova JM, Bischoff J, Estrada M (2016) Large-scale microscopic simulation of taxi services. Berlin and Barcelona case studies. J Ambient Intell Hum Comput 7(3):385–393. https://doi.org/10.1007/s12652-016-0366-3
Miwa T, Sakai T, Morikawa T (2004) Route identification and travel time prediction using probe-car data. Int J ITS Res 2(1):21–28
Moreira-Matias L, Gama J, Ferreira M, Mendes-Moreira J, Damas L (2013) On predicting the taxi-passenger demand: a real-time approach. In: Correia L, Reis LP, Cascalho J (eds) Progress in artificial intelligence, vol 8154 LNAI. Springer, Berlin, Heidelberg, pp 54–65. https://doi.org/10.1007/978-3-642-40669-0_6
Nagashima Y, Hattori O, Kobayashi M (2014) Improvement of traffic signal control using probe data. SEI Technical Rev (78): 44–47. https://global-sei.com/technology/tr/bn78/, https://global-sei.com/technology/tr/bn78/pdf/78-09.pdf
Nam D, Hyun K, Kim H, Ahn K, Jayakrishnan R (2016) Analysis of grid cell–based taxi ridership with large-scale GPS data. Transp Res Rec: J Transp Res Board 2544(March 2017):131–140. https://doi.org/10.3141/2544-15
Peungnumsai A, Witayangkurn A, Nagai M, Arai A, Ranjit S, Ghimire BR (2017) Bangkok taxi service behavior analysis using taxi probe data and questionnaire survey. In: Proceedings of the 4th multidisciplinary international social networks conference, pp 1–8. https://doi.org/10.1145/3092090.3092117
Peungnumsai A, Witayangkurn A, Nagai M, Miyazaki H (2018) A taxi zoning analysis using large-scale probe data: a case study for metropolitan Bangkok. Rev Socionetwork Strat. 12(1):21–45. https://link.springer.com/article/10.1007/s12626-018-0019-4
Ranjit S, Nagai M, Witayangkurn A, Shibasaki R (2017) 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. In: 15th International conference on computers in urban planning and urban management
Ranjit S, Witayangkurn A, Nagai M, Shibasaki R (2018) Agent-based modeling of taxi behavior simulation with probe vehicle data. ISPRS Int J Geo-Inf 7(5):177. https://doi.org/10.3390/ijgi7050177
Rothlauf F (2011) Optimization problems. In: Design of modern heuristics, pp 7–45. https://doi.org/10.1007/978-3-540-72962-4
Sadahiro Y, Lay R, Kobayashi T (2013) Trajectories of moving objects on a network: detection of similarities, visualization of relations, and classification of trajectories. Trans GIS 17(1):18–40. https://doi.org/10.1111/j.1467-9671.2012.01330.x
Tang J, Jiang H, Li Z, Li M, Liu F, Wang Y (2016) A two-layer model for taxi customer searching behaviors using gps trajectory data. IEEE Trans Intell Transp Syst 17(11):3318–3324. https://doi.org/10.1109/TITS.2016.2544140
Thairath (2017) Bangkok public transport complaints: taxis top list—refusing customers still champ! Retrieved from https://news.thaivisa.com/article/15623/bangkok-public-transport-complaints-taxis-top-list-refusing-customers-still-champ
Torrens PM (2010) Agent-based models and the spatial sciences. Geogr Compass 4(5):428–448. https://doi.org/10.1111/j.1749-8198.2009.00311.x
Wallsten S (2015) Has uber forced taxi drivers to step up their game? The Atlantic. Retrieved from https://www.theatlantic.com/business/archive/2015/07/uber-taxi-drivers-complaints-chicago-newyork/397931/
Witayangkurn A, Horanont T, Shibasaki R (2013) The design of large scale data management for spatial analysis on mobile phone dataset. Asian J Geoinf 13(3):17–24
Wong DWS, Huang Q (2016) Sensitivity of DBSCAN in identifying activity zones using online footprints. Proc Spatial Accuracy 2016:151–156
Yang Q, Gao Z, Kong X, Rahim A, Wang J, Xia F (2016) Taxi operation optimization based on big traffic data. In: Proceedings—2015 IEEE 12th international conference on ubiquitous intelligence and computing, 2015 IEEE 12th international conference on advanced and trusted computing, 2015 IEEE 15th international conference on scalable computing and communications, vol 20, pp 127–134. https://doi.org/10.1109/uic-atc-scalcom-cbdcom-iop.2015.42
Yao H, Wu F, Ke J, Tang X, Jia Y, Lu S et al (2018) Deep multi-view spatial-temporal network for taxi demand prediction. CoRR. https://arxiv.org/abs/1802.08714v2
Yuan NJ, Zheng Y, Zhang L, Xie X (2013) T-finder: a recommender system for finding passengers and vacant taxis. IEEE Trans Knowl Data Eng 25(10):2390–2403. https://doi.org/10.1109/TKDE.2012.153
Zhang S, Wang Z (2016) Inferring passenger denial behavior of taxi drivers from large-scale taxi traces. PLoS ONE 11(11):1–21. https://doi.org/10.1371/journal.pone.0165597
Zheng Z, Rasouli S, Timmermans H (2014) Evaluating the accuracy of GPS-based taxi trajectory records. Proc Environ Sci 22:186–198. https://doi.org/10.1016/j.proenv.2014.11.019
Acknowledgements
This research was facilitated and funded by Shibasaki Laboratory (http://shiba.iis.u-tokyo.ac.jp/) of The University of Tokyo. This research was partially supported by Toyota Tsusho Nexty Electronics (Thailand) Co., Ltd., by providing Taxi Probe Data for research purpose.
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Ranjit, S., Witayangkurn, A., Nagai, M., Shibasaki, R. (2019). Taxi Behavior Simulation and Improvement with Agent-Based Modeling. In: Geertman, S., Zhan, Q., Allan, A., Pettit, C. (eds) Computational Urban Planning and Management for Smart Cities. CUPUM 2019. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-030-19424-6_26
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