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Understanding taxi driver’s cruising behavior with ZIP model

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Abstract

The taxi drivers’ cruising pattern was learned using GPS trajectory data collected in Shenzhen, China. By employing zero-inflated Poisson model, the impacts of land use and previous pick-up experience on cruising decision were measured. The cruising strategies of different types of drivers as well as the top one driver were examined. The results indicate that both land use and previous pick-up experience affect travel behavior with the former’s influence (7.07×10−4 measured by one of the coefficients in zero-inflated Poisson model) being greater than the latter’s (4.58×10−t) in general, but the comparison also varies across the types of drivers. Besides, taxi drivers’ day-to-day learning feature is also proved by the results. According to comparison of the cruising behavior of the most efficient and inefficient driver, an efficient cruising strategy was proposed, that is, obeying the distribution of land use in choice of cruising area, while learning from pick-up experience in selection of detailed cruising location. By learning taxi drivers’ cruising pattern, the development of measures of regulating travel behaviors is facilitated, important factor for traffic organization and planning is identified, and an efficient cruising strategy for taxi drivers is provided.

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References

  1. WONG K I, WONG S C, YANG Hai. Modeling urban taxi services in congested road networks with elastic demand [J]. Transport Research B, 2001, 35(9): 819–842.

    Article  Google Scholar 

  2. LIU Yu, WANG Fa-hui, XIAO Yu, GAO Song. Urban land uses and traffic ‘source-sink areas’: Evidence from GPS-enabled taxi data in Shanghai [J]. Landscape and Urban Planning, 2012, 106(1): 73–87.

    Article  Google Scholar 

  3. YUAN Jing, ZHENG Yu, ZHANG Liu-hang, XIE Xing, SUN Guang-zhong. Where to find my next passenger [C]// Proceedings of the 13th International Conference on Ubiquitous Computing. New York, 2011: 109–118.

    Chapter  Google Scholar 

  4. LIU Liang, ANDRIS C, RATTI C. Uncovering cabdrivers’ behavior patterns from their digital traces [J]. Computers, Environment and Urban Systems, 2010, 34(6): 541–548.

    Article  Google Scholar 

  5. KIM H, OH Jun-seok, JAYAKRISHNAN R. Effect of taxi information system on efficiency and quality of taxi services [J]. Transportation Research Record: Journal of the Transportation Research Board, 2005, 1903: 96–104.

    Article  Google Scholar 

  6. SUN Jian, ZHANG Lun. Vehicle actuation based short-term traffic flow prediction model for signalized intersections [J]. Journal of Central South University, 2012, 19(1): 287–298.

    Article  Google Scholar 

  7. XU Hong-li, ZHOU Jing, XU Wei. A decision-making rule for modeling travelers’ route choice behavior based on cumulative prospect theory [J]. Transportation Research Part C, 2011, 19(2): 218–228.

    Article  Google Scholar 

  8. CAO Xin-yu, MOKHTARIAN P L. How do individuals adapt their personal travel? Objective and subjective influences on the consideration of travel-related strategies for San Francisco Bay Area commuters [J]. Transport Policy, 2005, 12(4): 291–302.

    Article  Google Scholar 

  9. ASPEL A D, WILLIS W G, FAUST D. School psychologists’ diagnostic decision-making processes: Objective-subjective discrepancies [J]. Journal of School Psychology, 1998, 36(2): 137–149.

    Article  Google Scholar 

  10. RAJU P S, LONIAL S C, MANGOLD W G. Differential effects of subjective knowledge, objective knowledge, and usage experience on decision making: An exploratory investigation [J]. Journal of Consumer Psychology, 1995, 4(2): 153–180.

    Article  Google Scholar 

  11. PARK C W, GARDNER M P, THUKRAL V C. Self-perceived knowledge: Some effects on information processing for a choice task [J]. American Journal of Psychology, 1988, 101(3): 401–424.

    Article  Google Scholar 

  12. BRUCKS M. The effects of product class knowledge on information search behavior [J]. Journal of Consumer Research, 1985, 12(1): 1–16.

    Article  MathSciNet  Google Scholar 

  13. RAJU P S, REILLY M D. Product familiarity and information processing strategies: An exploratory investigation [J]. Journal of Business Research, 1980, 8(2): 187–212.

    Article  Google Scholar 

  14. RAO A R, MONROE K B. The moderating effect of prior knowledge on cue utilization in product evaluations [J]. Journal of Consumer Research, 1988, 15(2): 253–264.

    Article  Google Scholar 

  15. MOORMAN C, DIEHL K, BRINBERG D, KIDWELL B. Subjective knowledge, search locations, and consumer choice [J]. Journal of Consumer Research, 2004, 31(3): 673–680.

    Article  Google Scholar 

  16. BETTMAN J R, PARK C W. Effects of prior knowledge and experience and phase of the choice process on consumer decision processes [J]. Journal of Consumer Research, 1980, 7(3): 234–248.

    Article  Google Scholar 

  17. ALBA J W, HUTCHINSON J W. Knowledge calibration: What consumers know and what they think they know [J]. Journal of Consumer Research, 2000, 27(2): 123–156.

    Article  Google Scholar 

  18. BEARDEN W O, DAVID M H, RANDALL L R. Consumer self-confidence: Refinements in conceptualization and measurement [J]. Journal of Consumer Research, 2001, 28(1): 121–134.

    Article  Google Scholar 

  19. PARK C W, LESSIG V P. Familiarity and its impacts on consumer decision biases and heuristics [J]. Journal of Consumer Research, 1981, 8(2): 223–230.

    Article  Google Scholar 

  20. PARK C W, DAVID L M, LAWRENCE F. Consumer knowledge assessment [J]. Journal of Consumer Research, 1994, 21(1): 71–82.

    Article  Google Scholar 

  21. CHIOU J. The effects of attitude, subjective norm, and perceived behavioral control on consumers’ purchase intentions: The moderating effects of product knowledge and attention to social comparison information [C]// Proceedings of the National Science Council. ROC (C), 1998: 298–308.

    Google Scholar 

  22. BLACKWELL M, ENGEL T. Point pattern analysis using spatial inferential statistics [EB/OL]. Briggs Henan University, 2006, http://www.utdallas.edu/-briggs/henan/7PointPat.ppt

    Google Scholar 

  23. LAMBERT D. Zero-inflated Poisson regression, with an application to defects in manufacturing [J]. Technometrics, 1992, 34(1): 1–14.

    Article  MATH  Google Scholar 

  24. GREENE W H. Accounting for excess zeros and sample selection in Poisson and negative binomial regression models [ER/OL]. Working paper NO. 94-10. New York: New York University, 1994.

    Google Scholar 

  25. HALL D B. Zero-inflated Poisson and binomial regression with random effects: A case study [J]. Biometrics, 2000, 56(4): 1030–1039.

    Article  MATH  MathSciNet  Google Scholar 

  26. AGARWAL D K, GELFAND A E, CITRON-POUSTY S. Zero-inflated models with application to spatial count data [J]. Environmental and Ecological Statistics, 2002, 9(4): 341–355.

    Article  MathSciNet  Google Scholar 

  27. SIMONOFF J S. Analyzing Categorical Data [M]. Berlin: Springer-Verlag, 2003: 95–102.

    Book  Google Scholar 

  28. MINAMI M, LENNERT-CODY C E, GAO Wei-qiang, ROM’AN-VERDESOTO M. Modeling shark bycatch: The zero-inflated negative binomial regression model with smoothing [J]. Fisheries Research, 2007, 84(2): 210–221.

    Article  Google Scholar 

  29. ANSELIN L. A test for spatial autocorrelation in seemingly unrelated regressions [J]. Economics Letters, 1988, 28(4): 335–341.

    Article  MathSciNet  Google Scholar 

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Correspondence to Fang Zong  (宗芳).

Additional information

Foundation item: Project(50908099) supported by the National Natural Science Foundation of China; Project(201104493) supported by the Doctoral Program of Higher Education of China

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Zong, F. Understanding taxi driver’s cruising behavior with ZIP model. J. Cent. South Univ. 21, 3404–3410 (2014). https://doi.org/10.1007/s11771-014-2315-7

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  • DOI: https://doi.org/10.1007/s11771-014-2315-7

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