Exploring the spatial variation of ridesourcing demand and its relationship to built environment and socioeconomic factors with the geographically weighted Poisson regression

Yu, Haitao; Peng, Zhong-Ren

doi:10.1016/j.jtrangeo.2019.01.004

Cited by 179 publications

(100 citation statements)

References 35 publications

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“…First, although recent research started to explore the contributing factors to ridesourcing demand (e.g., Alemi et al, 2018;Yu and Peng, 2019), only a limited number of factors were tested. This research explores a wide range of built environment factors and their effects on ridescouing demand.…”

Section: Discussionmentioning

confidence: 99%

“…However, it did not consider the effects of built environment on ridesourcing. Yu and Peng (2019) explored the contributing factors to ridesourcing demand and focused the effects of built environment and socioeconomic factors. Several land use and transportation factors were examined and found to have significant effects on ridesourcing demand.…”

Section: Literature Reviewmentioning

confidence: 99%

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The impacts of built environment on ridesourcing demand: A neighbourhood level analysis in Austin, Texas

Yu¹,

Peng

2019

Urban Studies

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Recently, the explosive growth of ridesourcing, or on-demand ridesharing, has attracted a great deal of attention from researchers and planners. Despite its transformative impacts on mobility, limited studies have examined how built environment affects its use. In this study, we investigate the impacts of built environment on ridesourcing demand. We employ structural equation modelling to account for the complex relationships among study variables, and investigate the impacts at census block group level by using RideAustin data in Austin, Texas. Findings reveal strong impacts of built environment on ridesourcing demand and significant temporal heterogeneity. The models show that greater population/employment/service job densities, road density, pavement completeness, land use mix and job accessibility by transit produce more ridesourcing demand. Access to the commuter rail (MetroRail) also leads to greater demand. Furthermore, time-of-day (TOD) models demonstrate that these effects vary significantly according to the time of day. Our research has implications for policy making and for travel demand modelling of ridesourcing.

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Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

The impacts of built environment on ridesourcing demand: A neighbourhood level analysis in Austin, Texas

Yu¹,

Peng

2019

Urban Studies

Self Cite

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“…The above mixed GWR model turns to the standard GWR model when global effects can be ignored and, when local effects can be ignored, the mixed GWR model turns to the traditional multiple linear regression (MLR) model. The MLR model (e.g., Rath et al, 2020 ) assumes that the explanatory variables are spatially stationary over the whole study area, which, therefore, only provides global estimates ( Yu & Peng, 2019 ).…”

Section: Methodsmentioning

confidence: 99%

Association of built environment attributes with the spread of COVID-19 at its initial stage in China

Zhang

2021

Sustainable Cities and Society

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Highlights Focus on the spread of COVID-19 at its initial stages across the whole China. Reveal spatial clusters and outliers of the spread of COVID-19. Estimate a mixed geographically weighted regression (GWR) model. Capture both the global and local effects of inter-/intra-city built environment. Derive important policy implications from the built environment planning.

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“…Spatio-temporal analysis is one of the most attractive directions amongst these various studies. For example, a spatial econometric model is widely used to explore the relationships between travel demand and a set of factors [16][17][18][19][20][21]. From the temporal perspective, applying time series models or dividing time into several periods is the most commonly used approach to understand the variations of travel demand at different times, such as weekdays and weekends, the morning peak, the evening peak and late at night [22,23].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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.

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Exploring the spatial variation of ridesourcing demand and its relationship to built environment and socioeconomic factors with the geographically weighted Poisson regression

Cited by 179 publications

References 35 publications

The impacts of built environment on ridesourcing demand: A neighbourhood level analysis in Austin, Texas

The impacts of built environment on ridesourcing demand: A neighbourhood level analysis in Austin, Texas

Association of built environment attributes with the spread of COVID-19 at its initial stage in China

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

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