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Identifying the Temporal Characteristics of Intra-City Movement Using Taxi Geo-Location Data

Identifying the Temporal Characteristics of Intra-City Movement Using Taxi Geo-Location Data

Wenbo Zhang, Xinwu Qian, Satish V. Ukkusuri
Copyright: © 2018 |Pages: 21
ISBN13: 9781522552109|ISBN10: 1522552103|EISBN13: 9781522552116
DOI: 10.4018/978-1-5225-5210-9.ch014
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MLA

Zhang, Wenbo, et al. "Identifying the Temporal Characteristics of Intra-City Movement Using Taxi Geo-Location Data." Intelligent Transportation and Planning: Breakthroughs in Research and Practice, edited by Information Resources Management Association, IGI Global, 2018, pp. 311-331. https://doi.org/10.4018/978-1-5225-5210-9.ch014

APA

Zhang, W., Qian, X., & Ukkusuri, S. V. (2018). Identifying the Temporal Characteristics of Intra-City Movement Using Taxi Geo-Location Data. In I. Management Association (Ed.), Intelligent Transportation and Planning: Breakthroughs in Research and Practice (pp. 311-331). IGI Global. https://doi.org/10.4018/978-1-5225-5210-9.ch014

Chicago

Zhang, Wenbo, Xinwu Qian, and Satish V. Ukkusuri. "Identifying the Temporal Characteristics of Intra-City Movement Using Taxi Geo-Location Data." In Intelligent Transportation and Planning: Breakthroughs in Research and Practice, edited by Information Resources Management Association, 311-331. Hershey, PA: IGI Global, 2018. https://doi.org/10.4018/978-1-5225-5210-9.ch014

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Abstract

In this chapter, the authors focus on temporal patterns of urban taxi trips and explore determinant factors in conjunction with geodatabase at aggregate level. Zero-Inflated Negative Binomial model is proposed in light of count data nature and excessive number of O-D pairs with zero trip. Three typical time slots on weekdays, as well as weekends, are introduced as case study to check temporal variations of intra-city movement. The results indicate that trip distance, land use, socioeconomics, and built environment are significant variables that affect the number of taxi trips between two locations. In particular, longer travel and worse economy conditions, such as low employment and average annual income and more population under poverty, may prevent more movements, which have more impacts during peak hours. A better transit system may reduce the taxi trips, except for areas with more subway stations. Develpoed area for instance more commercial or residential area is more likely to attract more visits by taxis, as well as dense public facilities but with more temporal variations.

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