Understanding Taxi Drivers’ Multi-day Cruising Patterns

  • Fang Zong Jilin University
  • Xiao Sun Jilin University
  • Huiyong Zhang Jilin University
  • Xiumei Zhu Jilin University
  • Wentian Qi Suzhou Institute of Construction and Communications
Keywords: taxi, multi-day, cruising pattern, GPS,


This study investigates taxi drivers’ multi-day cruising behaviours with GPS data collected in Shenzhen, China. By calculating the inter-daily variability of taxi drivers’ cruising behaviours, the multi-day cruising patterns are investigated. The impacts of learning feature and habitual feature on multi-day cruising behaviours are determined. The results prove that there is variability among taxis’ day-to-day cruising behaviours, and the day-of-week pattern is that taxi drivers tend to cruise a larger area on Friday, and a rather focused area on Monday. The findings also indicate that the impacts of learning feature and habitual feature are more obvious between weekend days than among weekdays. Moreover, learning feature between two sequent weeks is found to be greater than that within one week, while the habitual feature shows recession over time. By revealing taxis' day-to-day cruising pattern and the factors influencing it, the study results provide us with crucial information in predicting taxis' multi-day cruising locations, which can be applied to simulate taxis' multi-day cruising behaviour as well as to determine the traffic volume derived from taxis' cruising behaviour. This can help us in planning of transportation facilities, such as stop stations or parking lots for taxis. Moreover, the findings can be also employed in predicting taxis' adjustments of multi-day cruising locations under the impact of traffic management strategies.

Author Biographies

Fang Zong, Jilin University
Dr. Zong Fang, Associate Professor, College of Transportation, Jilin University
Xiao Sun, Jilin University
Prof. Xiao Sun, Applied Technology College, Jilin University
Huiyong Zhang, Jilin University
Dr. Huiyong Zhang, School of Management, Jilin University
Xiumei Zhu, Jilin University
Prof. Xiumei Zhu, School of Management, Jilin University
Wentian Qi, Suzhou Institute of Construction and Communications
MS. Wentian Qi, Suzhou Institute of Construction and Communications


Empty cabs waste fuel, cause pollution. Shanghai Daily 2011 Feb 16; Available from: http://www.china.org.cn/environment/2011-02/16/content_21932450.htm

Li JZ. Optimal incentive for transportation management under a symmetric information. Journal of Chongqing Jiao Tong University (Natural Science). 2007;26(5):117-121.

Castro PS, Zhang D, Li S. Urban traffic modelling and prediction using large scale taxi GPS traces. Proceedings of the 10th International Conference on Pervasive Computing; 2012 June 18-22; Newcastle, UK. Springer-Verlag Berlin Heidelberg; 2012. p. 57-72.

Liu L, Andris C, Biderman A, Ratti C. Uncovering taxi driver's mobility intelligence through his trace. Seventh Annual IEEE International Conference on Pervasive Computing and Communications; 2009 March 9-13; Galveston, Texas.

Kim H, Oh J-S, Jayakrishnan R. Effect of Taxi Information System on Efficiency and Quality of Taxi Services. Transportation Research Record: Journal of the Transportation Research Board. 2005;1903:96-104.

Xu HL, Zhou J, Xu W. A decision-making rule for modeling travelers’ route choice behavior based on cumulative prospect theory. Transportation Research Part C. 2011;19(2):218-228.

Li Q, Zeng Z, Zhang T, Li J, Wu Z. Path-finding through flexible hierarchical road networks: An experiential approach using taxi trajectory data. International Journal of Applied Earth Observation and Geoinformation. 2011;13(1):110-119.

Yu B, Yang ZZ, Chen K. Hybrid model for prediction of bus arrival times at next station. Journal of Advanced Transportation. 2010;44(3):193-204.

Yu B, Yang ZZ, Li S. Real-Time partway deadheading strategy based on transit service reliability assessment.

Transportation Research Part A. 2012;46(8):1265-1279.

Yu B, Yang ZZ, Yao BZ. A hybrid algorithm for vehicle routing problem with time windows. Expert Systems with Applications. 2011;38(1):435-441.

Yao BZ, Hu P, Lu XH, Gao JJ, Zhang MH. Transit network design based on travel time reliability. Transportation Research Part C. 2014;43:233-248.

Cascetta E. A stochastic process approach to the analysis of temporal dynamics in transportation networks. Transportation Research B. 1989;23(1):1-17.

Cascetta E, Cantarella GE. A day-to-day and within day dynamic stochastic assignment model. Transportation Research A. 1991;25(5):277-291.

Horowitz JL. The stability of stochastic equilibrium in a two-link transportation network. Transportation Research B. 1984;18(1):13-28.

Mahmassani H, Chang G, Herman R. Individual decisions and collective effects in a simulated traffic systems. Transportation Science. 1986;21(2):258-271.

Hanson S, Huff JO. Assessing day-to-day variability in complex travel patterns. Transportation Research Record: Journal of the Transportation Research Board. 1982;891:18-24.

Jha M, Madanat S, Peeta S. Perception updating and day-to-day travel choice dynamics in trac networks with information provision. Transportation Research Part C, 1998;6:189-212.

Lerman S, Manski C. A model of the effect of information difusion on travel. Transportation science. 1982;16(2):171-199.

Aarts H, Verplanken B, van Knippenberg A. Predicting behavior from actions in the past: Repeated decision-making or a matter of habit? Journal of Applied Social Psychology. 1998;28(15):1355-1374.

Bamberg S, Ajzen I, Schmidt P. Choice of travel mode in the theory of planned behavior: The roles of past behavior, habit, and reasoned action. Basic and Applied Social Psychology. 2003;25(3):175-187.

Ouellette JA, Wood W. Habit and intention in everyday life: the multiple processes by which past behavior predicts future behavior. Psychological Bulletin. 1998;124(1):54-74.

Benshoof VA. Characteristics of drivers' route selection behaviour. Traffic Engineering and Control. 1970;11(12):604-606.

Duda RO. Pattern Classification, 2nd ed. American: John Wiley; 2003.

Ban X. Research on mutual relationship of land use structure and industrial structure [Master thesis]. China University of Geosciences; 2012.

Liu Y, Wang F, Xiao Y, Gao S. Urban land uses and traffic 'source-sink areas': Evidence from GPS-enabled taxi data in Shanghai. Landscape and Urban Planning. 2012;106(1):73-87.

Pas EI. Intra-personal variability and model goodness-of-fit. Transportation Research A. 1987;21(6):431-438.

Pas EI, Sundar S. Intra-personal variability in daily urban travel behavior: Some additional evidence. Transportation. 1995;22(2):135-150.

Castro PS, Zhang D, Chen C, Li S, Pan G. From taxi GPS traces to social and community dynamics: A survey. ACM Computing Surveys. 2013;46(2):17-17.

Zong F. Understanding taxi driver's cruising pattern with GPS data. Journal of Central South University of Technology. 2014;21(8):3404-3410.

Salanova JM, Estrada MA, Mitsakis E, Stamos I. Agent based modeling for simulating taxi services. Journal of Traffic and Logistics Engineering. 2013;1(2):159-163.

Song ZQ, Tong CO. A simulation based dynamic model of taxi service. Proceedings of the First International Symposium on Dynamic Traffic Assignment (DTA2006); 2006 June 21-23; Leeds, UK: Institute for Transport Studies; 2006. p. 355-360.

How to Cite
Zong F, Sun X, Zhang H, Zhu X, Qi W. Understanding Taxi Drivers’ Multi-day Cruising Patterns. PROMET [Internet]. 2015Dec.17 [cited 2020Feb.26];27(6):467-76. Available from: http://traffic.fpz.hr/index.php/PROMTT/article/view/1641

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