Abstract
The rapid development of online car-hailing services (OCSs) has a huge impact on traditional taxi service (TTS) and is triggering a revolution in the taxi industry. Due to the differences in age, monthly income level, etc., travelers’ using frequencies of taxi service are different. It is necessary for online car-hailing platforms and traditional taxi companies to know the choice behavior of different types of passengers to enhance competitiveness. Based on the survey data of taxi passengers in Nanjing, China, the passengers are characterized by using frequency per week as infrequent passengers, moderately frequent passengers, and frequent passengers. The group characteristics and the differences among groups are analyzed. Further, three binary logit models are applied to analyze the taxi service choice behavior of different groups. The model results show the significant factors vary among three types of passengers. The result indicates that the impacts of safety level improvement, comfort level improvement and travel cost reduction for OCS on passengers’ choice behavior are higher than that of safety level decreasing, comfort level decreasing, and travel cost increasing. Moderately frequent passengers are more sensitive to comfort level than travel cost. The conclusions contribute to both the OCS and TTS business strategies. The results also provide insights into taxi industry management for governments.
Highlights
Compared with transit options with large vehicles, the taxi plays a small and personalized role, providing door-to-door transport services, and can function as a complement to existing public transit services [1,2]
The travel cost of online car-hailing services (OCSs) is higher than traditional taxi service (TTS) = 0, The travel cost of OCS is lower than TTS = 1
Comfort level (OCS is more comfortable than TTS) Travel cost (The travel cost of OCS is lower than TTS)
Summary
Compared with transit options with large vehicles, the taxi plays a small and personalized role, providing door-to-door transport services, and can function as a complement to existing public transit services [1,2]. To help in making policies, one group of scholars developed aggregate supply and demand models to examine the effectiveness and consequences of policies mainly associated with entry and fare controls [4,5,6,7,8,9,10,11,12,13]. Another group of scholars focused on modeling the spatiotemporal equilibrium of taxi supply and demand [14,15,16,17,18,19,20]. These regulations may raise the cost for consumers and drivers as they lead to a misallocation of resources and potentially insufficient supply to meet consumer demand [21]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
