Assessing travelers’ preferences for online bus-hailing service across various travel distances: Insights from Chinese metropolitan areas

Yan Zheng, Anxin Deng, Zijuan Yin and Wenquan Li

Transportation Research Part A: Policy and Practice, 2024, vol. 187, issue C

Abstract: This study conducts a stated preference survey to assess travelers’ preferences for online bus-hailing service across various travel distances in Chinese metropolitan areas. By collecting personal attributes, travel characteristics, and scenario variables (including fares, walking distance, waiting time, and travel time for various ground transportation services), we develop a travel mode choice model based on XGBoost and examine the positive and negative effects and elasticity of different influencing factors. Empirical results demonstrate that the XGBoost-based travel mode choice model performs best in predictive accuracy, proving its superior fitting capability. Meanwhile, travel distance is pivotal in determining travelers’ preferences. Short-distance travelers prioritize time efficiency, whereas as travel distance increases, travelers become less sensitive to time, and fares become the primary consideration. This shift provides service operators with opportunities to implement differentiated strategies. The remaining travel modes also significantly influence choice preferences, with travelers critically concerned about the time cost of regular bus service and highly sensitive to taxi/car-hailing service fares. Personal attributes and travel characteristics, such as gender, age, and the number of companions, also impact choice preferences, providing opportunities for service operators to offer personalized services. Elasticity analysis further confirms the role of each influencing factor in increasing the attractiveness of choosing online bus-hailing service. Ultimately, we derive significant insights at the planning level, including single-factor optimization and prioritizing optimization for different dual-factor combinations. These insights serve as a basis for urban managers to formulate and enhance online bus-hailing service, covering aspects such as fare incentive policies and route service frequencies.

Keywords: Online bus-hailing service; Travel behavior; Travel preferences; Machine learning; XGBoost (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.tra.2024.104159

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