A panel analysis of the effect of the urban environment on the spatiotemporal pattern of taxi demand. (January 2020)

• Record Type: Journal Article
• Authors: Liu, Qian; Ding, Chuan; Chen, Peng
• Is Part Of: Travel behaviour and society. Volume 18(2020)
• Page Start: 29
• Page End: 36
• Publication Date: 2020-01
• Subjects: Taxi demand -- Temporal factors -- Built environment -- Generalized additive mixed model; Transportation -- Periodicals; Population geography -- Periodicals; 303.48305
• DOI: 10.1016/j.tbs.2019.09.003
• Languages: English
• ISSNs: 2214-367X

Abstract

Highlights: Taxi demand is higher in densely developed areas. Taxi demand is higher in areas with denser secondary roads. Taxi demand is lower in areas with more bus stops. Taxi demand is higher during weekdays, peak hours, and warmer days. Despite taxi demand being positively associated with the rainy season, this relationship presents strong nonlinearity. Abstract: Taxis are an indispensable part of the public transportation system; however, the industry is confronting many challenges, such as the development of mobile apps for reservations and the increase in large-scale ridesharing. Taxi demand is impacted by many spatial and temporal factors, such as the location, the built environment, the time of day. This study analyzes various factors associated with temporal and spatial densities of taxi pick-up locations using a generalized additive mixed model. The key findings are as follows: (1) taxi demand is higher in densely developed areas, which are characterized by high degrees of mixed land use, high population densities, dense road junctions, and high percentages of residential, commercial and public space; (2) taxi demand is higher in areas with denser secondary roads; (3) taxi demand is lower in areas with more bus stops; (4) taxi demand is higher during weekdays, peak hours, and warmer days (autumn season); and (5) despite taxi demand being positively associated with the rainy season, this relationship has strong nonlinearity. To optimize the efficiency of taxi system, there should be more loading spaces for taxis in activity-concentrated areas, which are characterized by a higher population density, high levels of mixed land use and dense road junctions. In addition, taxi drivers should search for passengers in underserved areas to reach potential passengers during nonpeak hours. Future research may use data recorded via mobile apps to examine taxi overtime waiting, rejection of service and prepaid empty trips to provide a complete image of taxi demand.