Exploring competitiveness of taxis to ride-hailing services from a multidimensional spatio-temporal perspective: A case study in Beijing, China

As the sharing economy expands in China, the emergence of ride-hailing services has diminished the market share of the taxi industry. As a regulated and publicly convenient service with a dedicated customer base, traditional taxi industry needs to improve its own competitiveness and maintain its market share. However, the specific circumstances under which taxis can gain a competitive edge over ride-hailing services are not well-understood. Aiming to uncover the competitive potential of taxis in the ride-source market, this study proposes a methodology to explore the Competition and Cooperation Relationship (CCR) between taxis and ride-hailing services on a multidimensional spatio-temporal scale. By taking Beijing, China as a case study, we first compare different impacts of Points of Interest (POI) on the traffic volume of taxis/ride-hailing services through Geographically and Temporally Weighted Regression (GTWR) models, and explore the corresponding times and locations where taxis/ride-hailing services are more likely to attract passengers. Based on the findings that there are strong correlations between traffic volume and spatio-temporal conditions, we establish the Competition-Cooperation Index (CCI) as a quantitative measure to characterize the CCR and then analyze the spatio-temporal distribution of CCI to identify the times and locations where taxis hold advantages in cooperative or competitive relationship relative to ride-hailing services. Furthermore, we investigate the underlying reasons for these patterns, discovering that CCI has a close connection with land use. The results of our analysis show that taxis exhibit competitive advantages over ride-hailing services under some specific circumstances and can further enhance their competitiveness by proposed targeted measures. The findings of this study provide valuable insights for both industries in formulating growth strategies and for governmental agencies in setting policies.