Abstract
Online car-hailing travel is an increasingly popular mode of urban transport. A fundamental understanding of the relationship between the urban built environment and online car-hailing travel is essential for developing the corresponding traffic strategy and addressing sustainable urban planning and design. However, the varying impact of the urban built environment on online car-hailing travel in the spatial dimension has not been sufficiently investigated. This paper aims to fill this gap by using geographically weighted regression (GWR) to check the spatial heterogeneity of the likely influence. The result shows that the GWR model is superior to the global model (OLS) from the perspective of goodness of fit. The study finds that the recreation and entertainment Point of Interest (POI) and the residential district POI are the most influential factors on night online car-hailing travel. Land-use mix is found to have a positive effect on online car-hailing travel, and online car-hailing services can be a complementary mode for public transport, especially in suburban areas.
Highlights
Online car-hailing travel is becoming an emerging and fast-growing mode of transportation in cities, because of its convenient booking service and flexible door-to-door service (e.g., Uber, Lyft, and Didi)
Online car-hailing travel is undeniably becoming a key component of urban mobility
Previous studies have attempted to explore travel patterns [1], accessibility [2], or carpooling algorithm [3] to provide a better on-demand ride service, to the best of the authors’ knowledge, few efforts have been made to investigate the links between the built environment and online car-hailing travel
Summary
Online car-hailing travel is becoming an emerging and fast-growing mode of transportation in cities, because of its convenient booking service and flexible door-to-door service (e.g., Uber, Lyft, and Didi). Previous studies have attempted to explore travel patterns [1], accessibility [2], or carpooling algorithm [3] to provide a better on-demand ride service, to the best of the authors’ knowledge, few efforts have been made to investigate the links between the built environment and online car-hailing travel. Understanding such relationships will be critical when developing traffic strategies or addressing urban planning and design [4,5,6]. This paper aims to fill this gap by examining the spatio-temporal relationships between online car-hailing travel and the built environment using a geographical weighted regression (GWR) model
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