Modeling taxi cruising time based on multi-source data: a case study in Shanghai

Yuebing Liang, Zhan Zhao () and Xiaohu Zhang
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Yuebing Liang: The University of Hong Kong
Zhan Zhao: The University of Hong Kong
Xiaohu Zhang: The University of Hong Kong

Transportation, 2024, vol. 51, issue 3, No 1, 790 pages

Abstract: Abstract Vacant cruising is an inevitable part of taxi services caused by spontaneous demand, and the efficiency of cruising strategies has purported impact on the profit of individual drivers. Extensive studies have been conducted to analyze taxi cruising patterns and propose effective cruising strategies. However, existing studies mainly focused on the collective behavior of certain driver groups and failed to capture cruising behavior patterns at the individual driver or trip level. Also, prior studies considered different types of factors affecting taxi cruising, but we still lack an integrated model to compare their relative importance. In this study, we analyze trip-level cruising time and the associated external and internal factors using a taxi trajectory dataset in Shanghai, China. A trajectory annotation technique is introduced to segment taxi trajectories into different phases. Various external (supply and demand, traffic condition and built environment) and internal (cruising strategies and historical driver performance) factors are derived from taxi trajectories and other data sources. A spatiotemporal embedding method is devised to capture unobserved effects over time and space. The impacts of external and internal factors on taxi cruising time are examined using regression and XGBoost—a machine learning model. The results show external and internal factors are both important in determining taxi cruising time. Cruising strategies contribute 49.0% in taxi cruising time, which implies effective cruising strategies can greatly reduce vacant cruising time. Additionally, nonlinear associations of some variables (e.g., supply–demand patterns, traffic speed) with taxi cruising time are discussed.

Keywords: GPS data; Taxi cruising; Trajectory mining; Urban mobility; Machine learning (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s11116-022-10348-y

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