Understanding Spatio-Temporal Characteristics of Urban Travel Demand Based on the Combination of GWR and GLM

Jinjun Tang, Fan Gao, Fang Liu, Wenhui Zhang and Yong Qi
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Jinjun Tang: School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China
Fan Gao: School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China
Fang Liu: School of Transportation Engineering, Changsha University of Science and Technology, Changsha 410205, China
Wenhui Zhang: Traffic School, Northeast Forestry University, Harbin 150040, China
Yong Qi: School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China

Sustainability, 2019, vol. 11, issue 19, 1-19

Abstract: Taxis are an important part of the urban public transit system. Understanding the spatio-temporal variations of taxi travel demand is essential for exploring urban mobility and patterns. The purpose of this study is to use the taxi Global Positioning System (GPS) trajectories collected in New York City to investigate the spatio-temporal characteristic of travel demand and the underlying affecting variables. We analyze the spatial distribution of travel demand in different areas by extracting the locations of pick-ups. The geographically weighted regression (GWR) method is used to capture the spatial heterogeneity in travel demand in different zones, and the generalized linear model (GLM) is applied to further identify key factors affecting travel demand. The results suggest that most taxi trips are concentrated in a fraction of the geographical area. Variables including road density, subway accessibility, Uber vehicle, point of interests (POIs), commercial area, taxi-related accident and commuting time have significant effects on travel demand, but the effects vary from positive to negative across the different zones of the city on weekdays and the weekend. The findings will be helpful to analyze the patterns of urban travel demand, improve efficiency of taxi companies and provide valuable strategies for related polices and managements.

Keywords: travel demand; geographically weighted regression; generalized linear model; spatial heterogeneity (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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