Influencing Factor Analysis and Demand Forecasting of Intercity Online Car-Hailing Travel

Jincheng Wang, Qunqi Wu, Feng Mao, Yilong Ren, Zilin Chen and Yaqun Gao
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Jincheng Wang: School of Economics and Management, Chang’an University, Xi’an City 710064, China
Qunqi Wu: School of Economics and Management, Chang’an University, Xi’an City 710064, China
Feng Mao: Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
Yilong Ren: School of Transportation Science and Engineering, Beihang University, Beijing 100191, China
Zilin Chen: School of Transportation Science and Engineering, Beihang University, Beijing 100191, China
Yaqun Gao: School of Economics and Management, Tianjin Vocational Institute, Tianjin 300410, China

Sustainability, 2021, vol. 13, issue 13, 1-19

Abstract: Online car-hailing travel has become an important part of the urban transportation system and is gradually changing the mode of intercity travel. Analyzing and understanding the influencing factors of intercity online car-hailing travel hold great significance for planning and designing intercity transportation and transfer systems. However, few studies have analyzed the influencing factors of intercity car-hailing travel or forecast travel demand. This paper takes trips between Yinchuan and Shizuishan, China, as the research case and analyzes the influence of time, space, passengers, and the environment on intercity online car-hailing travel. The relationship between the urban built environment and intercity online car-hailing travel demand is also investigated through a geographically weighted regression (GWR) model. We find that the peak hours for intercity car-hailing trips are between 9:00 and 10:00 and between 16:00 and 18:00, which are significantly different from those for intracity trips. Weather conditions strongly affect the mobility of intercity trips. The urban built environment also has a significant impact on intercity car-hailing ridership, and residential districts and transportation facilities are the factors with the greatest influence on intercity online car-hailing travel. These results can provide practical help to city managers improve the management of intercity traffic and develop better transportation policies.

Keywords: intercity car-hailing travel; influencing factor analysis; geographical weighted regression; urban built environment (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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