A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Examining the spatial-temporal relationship between urban built environment and taxi ridership: Results of a semi-parametric GWPR model
With the advance of intelligent transportation systems (ITSs) and data acquisition systems (DASs), it becomes possible in recent to explore the determinants of urban taxi ridership using multi-source heterogeneous data. This paper aims to use floating car data, points-of-interests (POIs) data and housing-price data to assess the influence of the built environment on taxi ridership. Within a scale of 0.5 km grid, critical indicators related to the economic aspect, intermodal connection, and land use factors were obtained using the multi-source data in Shanghai. To capture the spatial and temporal heterogeneity, Semi-parametric Geographically Weighted Poisson Regression (SGWPR) models are built over different time dimensions. It is found that SGWPR models result in higher goodness-of-fit than the generalized linear models. More importantly, the results show the impacts of built environment factors on taxi demand are highly heterogeneous, positive or negative in different city areas, reflected in the significant temporal variations of the effects. Overall, these findings suggest that the built environment factors have significant impacts on urban taxi demand, and the spatial context should not be ignored. Findings in this paper are expected to help better understand the relationship between urban taxi demand and built environment factors, improving the service level of the urban taxi system, and offering valuable insights into future urban and transportation planning.
Examining the spatial-temporal relationship between urban built environment and taxi ridership: Results of a semi-parametric GWPR model
With the advance of intelligent transportation systems (ITSs) and data acquisition systems (DASs), it becomes possible in recent to explore the determinants of urban taxi ridership using multi-source heterogeneous data. This paper aims to use floating car data, points-of-interests (POIs) data and housing-price data to assess the influence of the built environment on taxi ridership. Within a scale of 0.5 km grid, critical indicators related to the economic aspect, intermodal connection, and land use factors were obtained using the multi-source data in Shanghai. To capture the spatial and temporal heterogeneity, Semi-parametric Geographically Weighted Poisson Regression (SGWPR) models are built over different time dimensions. It is found that SGWPR models result in higher goodness-of-fit than the generalized linear models. More importantly, the results show the impacts of built environment factors on taxi demand are highly heterogeneous, positive or negative in different city areas, reflected in the significant temporal variations of the effects. Overall, these findings suggest that the built environment factors have significant impacts on urban taxi demand, and the spatial context should not be ignored. Findings in this paper are expected to help better understand the relationship between urban taxi demand and built environment factors, improving the service level of the urban taxi system, and offering valuable insights into future urban and transportation planning.
Examining the spatial-temporal relationship between urban built environment and taxi ridership: Results of a semi-parametric GWPR model
Chen, Chao (author) / Feng, Tao (author) / Ding, Chuan (author) / Yu, Bin (author) / Yao, Baozhen (author)
2021-10-01
Chen , C , Feng , T , Ding , C , Yu , B & Yao , B 2021 , ' Examining the spatial-temporal relationship between urban built environment and taxi ridership: Results of a semi-parametric GWPR model ' , Journal of Transport Geography , vol. 96 , 103172 . https://doi.org/10.1016/j.jtrangeo.2021.103172
Article (Journal)
Electronic Resource
English
Built environment , Points of interests (POIs) , Semi-parametric geographically weighted Poisson regression (SGWPR) model , Spatial autocorrelation , Taxi ridership , /dk/atira/pure/sustainabledevelopmentgoals/sustainable_cities_and_communities , name=SDG 11 - Sustainable Cities and Communities , /dk/atira/pure/sustainabledevelopmentgoals/life_on_land , name=SDG 15 - Life on Land
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