A panel analysis of the effect of the urban environment on the spatiotemporal pattern of taxi demand

  • Qian Liu
  • , Chuan Ding*
  • , Peng Chen
  • *Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Pages (from-to)29-36
Number of pages8
JournalTravel Behaviour and Society
Volume18
DOIs
StatePublished - Jan 2020

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 15 - Life on Land
    SDG 15 Life on Land

Keywords

  • Built environment
  • Generalized additive mixed model
  • Taxi demand
  • Temporal factors

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