Understanding taxi ridership with spatial spillover effects and temporal dynamics

Zhu, Pengyu; Huang, Jie; Wang, Jiaoe; Liu, Yu; Li, Jiarong; Wang, Mingshu; Qiang, Wei

doi:10.1016/j.cities.2022.103637

Cited by 15 publications

(6 citation statements)

References 63 publications

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“…On the other hand, areas with higher housing price are located in the core business districts of Chengdu, such as Cao Tang Street, Chunxi Road, and Dongda Road, which are prone to serious traffic congestion caused by intensive traffic flow in the morning peak and evening peak, encouraging commuting by taxi instead of driving. This result supports similar findings in previous studies [ 11 , 49 ]. In contrast, during the late night, it is difficult for the housing price variable to explain late-night car-hailing trips, due to the lower total counts of DiDi trips and the need for diverse leisure needs of residents and a small number of overtime commuters.…”

Section: Discussionsupporting

confidence: 94%

Impact of built environment on residential online car-hailing trips: Based on MGWR model

Cao

Tian

et al. 2022

PLoS ONE

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With the development of smart mobile devices and global positioning technology, people’s daily travel has become increasingly dependent on online car-hailing. Meanwhile, it has also become possible to use multi-source data to explore the factors influencing urban residents’ car-hailing trips. Using online data on car-hailing trajectories, points of interest (POIs) data and other auxiliary data, the paper explores how the built environment impacts online car-hailing passengers. Within a 200 x 200m research grid, the unique spatiotemporal patterns of weekday car-hailing trips during a one-week period are analyzed, using statistics on pick-ups and drop-offs at different time of the day. By combining these data with built environment variables and various economic and traffic indicators, a multi-scale geographically weighted regression (MGWR) model is developed for different time scales. The MGWR model outperforms the classical geographically weighted regression (GWR) model and the ordinary least squares (OLS) regression model in terms of goodness of fit and all other aspects. More importantly, this study finds a high degree of temporal and spatial heterogeneity in the impact of built environment factors on local car-hailing trips across different regions, and the paper analyzes the business residence coefficient in detail. The study provides valuable insights to help improve the level of urban transportation services, as well as urban transportation planning and construction.

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Section: Discussionsupporting

confidence: 94%

Impact of built environment on residential online car-hailing trips: Based on MGWR model

Cao

Tian

et al. 2022

PLoS ONE

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“…With the emergence of big data, points of interest (POIs) extracted from digital maps are considered precise land-use data and provide new opportunities for the accurate and comprehensive measurement of land use. The different numbers of categories of POIs can describe detailed land-use characteristics and thus better reflect the situation in a city [21,22].…”

Section: Data Preprocessingmentioning

confidence: 99%

Predicting Station-Level Peak Hour Ridership of Metro Considering the Peak Deviation Coefficient

Zhao,

Wei,

et al. 2024

Sustainability

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Subway station-level peak hour ridership (SPR) is a crucial input parameter for multiple applications, including the planning, design, construction, and operation of stations. However, traditional SPR estimation techniques may produce biased results. A unified peak hour factor (PHF) extracted from the line level is generally set for all attributed stations, which ignores the possible peak deviation that arises between the station and line and the wide variation of PHFs in practice. This study presents a comprehensive and refined estimation framework for SPR that accommodates the peak deviation context by introducing the peak deviation coefficient (PDC). Moreover, the estimation of the PDC and PHF variability is improved by constructing spatial regression based relationship models. The empirical results show that the proposed approach exhibits wider applicability and a higher prediction precision across all types of peak periods considered as compared to conventional methods (i.e., MAPE decreases of 0.115–0.351). The findings demonstrate the importance of the consideration of the peak deviation scenario and the spatial dependency in SPR estimation to achieve better decision making. Moreover, the underlying influencing mechanism of the PHF and PDC at distinct peak periods is further revealed using the spatial model. This provides critical theoretical references and policy implications to prudently deploy land-use resources to balance the travel demand between peak and off-peak periods and thus enhance the line operation efficiency.

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“…However, the authors did not divide the dimensions of these variables. Zhu et al [ 29 ] quantified the built environment in terms of road network density, bus coverage, subway coverage, and other variables. They did not explicitly propose the dimension concept of the built environment.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Considering the scope of the study area and spatial resolution of the multisource data, 100m was chosen as the minimum side length of the grid. A review of the literature on the influence of the built environment on taxi travel demand revealed that most previous studies used 500m or 1000m as the side length of the grid [ 28 , 29 , 58 , 59 ]. Therefore, 1000m was selected as the maximum side length, with a side length interval of 100m.…”

Section: Study Area and Datamentioning

confidence: 99%

Influence of the built environment on taxi travel demand based on the optimal spatial analysis unit

Duan,

Yuan,

Mao

et al. 2023

PLoS ONE

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When discussing the influence of the built environment on taxi travel demand, few studies have considered the effect of the modifiable areal unit problem (MAUP) or the influence of the “5D” dimensions of the built environment (It refers to the consideration of the built environment from five dimensions of density, diversity, design, destination accessibility and distance to transit.) on taxi travel demand. Moreover, discussion of the nonlinear and linear relationships between taxi demand and environment variables is also lacking. To address these gaps, we constructed a “5D” dimension index system of built environment variables. The influence of the MAUP on the model results was discussed using the optimal parameter-based geographical detector (OPGD) model, and the optimal spatial analysis unit was selected. The OPGD and multiscale geographically weighted regression (MGWR) models were used to reveal the influence of different dimensions of the built environment on taxi travel demand from global and local perspectives, respectively. Finally, the central urban area of Xi’an was analyzed as an example. The results show the following: (1) Most built environment variables are sensitive to the influence of MAUP. (2) It is better to divide the space into regular hexagons than squares, and the optimal spatial analysis unit in this study is a regular hexagon grid with sides of 900m. (3) From a global perspective, the distance to the city center, commercial residence POI density, transportation facility POI density, and population density have the greatest influence on the demand for taxi travel. (4) From a local perspective, the MGWR model considering spatial heterogeneity and scale differences is superior to the GWR model, and the influence of built environment variables exhibited spatial heterogeneity. The proposed optimal spatial analysis unit can provide a basis for taxi demand forecasting and scheduling. This study provides a reference for urban planners and traffic managers to offer optimization strategies related to the built environment, promote healthy development of the taxi industry, and solve the problems of the urban transportation system.

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Understanding taxi ridership with spatial spillover effects and temporal dynamics

Cited by 15 publications

References 63 publications

Impact of built environment on residential online car-hailing trips: Based on MGWR model

Impact of built environment on residential online car-hailing trips: Based on MGWR model

Predicting Station-Level Peak Hour Ridership of Metro Considering the Peak Deviation Coefficient

Influence of the built environment on taxi travel demand based on the optimal spatial analysis unit

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