A multiscale spatial analysis of taxi ridership

Lyu, Tao; Wang, Yuanqing; Ji, Shujuan; Feng, Tao; Wu, Zhouhao

doi:10.1016/j.jtrangeo.2023.103718

Cited by 5 publications

(4 citation statements)

References 40 publications

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“…Future research could investigate how factors such as built environment features influence ride-hailing travel demand during peak and off-peak hours, both on weekdays and weekends. (2) Due to the similarity of temporal and spatial features in the demand for ride-hailing services within a certain period, this study only utilizes weekly trip volumes for analysis [38,46]. In future research, we plan to employ more data to compare and analyze differences between weeks, thereby providing a more accurate reflection of the nonlinear threshold effects of the built environment on ride-hailing travel services.…”

Section: Discussionmentioning

confidence: 99%

The Nonlinear and Threshold Effect of Built Environment on Ride-Hailing Travel Demand

Yin,

Zhao,

Tang

et al. 2024

Applied Sciences

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While numerous studies have explored the correlation between the built environment and ride-hailing demand, few have assessed their nonlinear interplay. Utilizing ride-hailing order data and multi-source built environment data from Nanjing, China, this paper uses the machine learning method, eXtreme Gradient Boosting (XGBoost), combined with Shapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDPs) to investigate the impact of built environment factors on ride-hailing travel demand, including their nonlinear and threshold effects. The findings reveal that dining facilities have the most significant impact, with a contribution rate of 30.75%, on predicting ride-hailing travel demand. Additionally, financial, corporate, and medical facilities also exert considerable influence. The built environment factors need to reach a certain threshold or within a certain range to maximize the impact of ride-hailing travel demand. Population density, land use mix, and distance to the subway station collectively influence ride-hailing demand. The results are helpful for TNCs to allocate network ride-hailing resources reasonably and effectively.

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Section: Discussionmentioning

confidence: 99%

The Nonlinear and Threshold Effect of Built Environment on Ride-Hailing Travel Demand

Yin,

Zhao,

Tang

et al. 2024

Applied Sciences

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“…For instance, Lain et al [72] proposed that travel emissions could be studied for different variables at various scales. Similarly, Lyu et al [73] also underscored the significance of multi-scale bandwidths for analyzing ride-hailing travel. This is because data points often exhibit a non-uniform distribution in space and time.…”

Section: Methods Applied In Existing Researchmentioning

confidence: 98%

Exploring the Spatio-Temporally Heterogeneous Impact of Traffic Network Structure on Ride-Hailing Emissions Using Shenzhen, China, as a Case Study

Gao,

Zhao,

Zeng

et al. 2024

Sustainability

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The rise of ride-hailing services presents innovative solutions for curbing urban carbon emissions, yet poses challenges such as fostering fair competition and integrating with public transit. Analyzing the factors influencing ride-hailing emissions is crucial for understanding their relationship with other travel modes and devising policies aimed at steering individuals towards more environmentally sustainable travel options. Therefore, this study delves into factors impacting ride-hailing emissions, including travel demand, land use, demographics, and transportation networks. It highlights the interplay among urban structure, multi-modal travel, and emissions, focusing on network features such as betweenness centrality and accessibility. Employing the COPERT (Computer Programme to Calculate Emissions from Road Transport) model, ride-hailing emissions are calculated from vehicle trajectory data. To mitigate statistical errors from multicollinearity, variable selection involves tests and correlation analysis. Geographically and temporally weighted regression (GTWR) with an adaptive kernel function is designed to understand key influencing mechanisms, overcoming traditional GTWR limitations. It can dynamically adjust bandwidth based on the spatio-temporal distribution of data points. Experiments in Shenzhen validate this approach, showing a 9.8% and 10.8% increase in explanatory power for weekday and weekend emissions, respectively, compared to conventional GTWR. The discussion of findings provides insights for urban planning and low-carbon transport strategies.

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“…Therefore, scholars often use the pick-up ratio of the taxi in a certain area as a cruising recommendation standard [8], but this study method also has certain drawbacks. On the one hand, due to the relationship between competition and game regarding taxis, high-demand areas are often also areas where a large number of taxis gather [9,10]. Therefore, there are certain limitations in the method of directly using passenger boarding and aligning data to determine passenger hotspots.…”

Section: Introductionmentioning

confidence: 99%

“…Firstly, the selection of model input variables is not comprehensive, and the index screening criteria are vague, resulting in some redundant variables with low correlation with output variables affecting the calculation accuracy of the model and reducing the upper limit of model prediction accuracy. Secondly, based on considering the high-demand area, the relevant researchers incorporate static indicators such as population, economy, and land use into the model to improve the accuracy of the model [9,10]. However, the real-time dynamics of taxi travel demand are not considered in modeling, so the research results have low reference values for real-time cruising range recommendation.…”

Section: Introductionmentioning

confidence: 99%

The Real-Time Dynamic Prediction of Optimal Taxi Cruising Area Based on Deep Learning

Wang,

et al. 2024

Sustainability

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A real-time, effective, and dynamic taxi cruising recommendation strategy is essential to solving the problem of taxi cruising passenger difficulty and urban road traffic congestion. This study focuses on two aspects of the real-time accessible range and pick-up ratio (PR) and proposes a real-time dynamic identification method for taxi optimal cruise-seeking area. Firstly, based on the cumulative opportunity method, a univariate temporal convolutional network (UTCN) accessible range dynamic prediction model is proposed to predict the real-time accessible range of taxis. Secondly, based on the gradient boosting decision tree (GBDT) model, the influencing factors with a high correlation with the PR are selected from the four dimensions of traffic characteristics, environmental meteorology, and time and space variables. Then, a multivariate univariate temporal convolutional network (MTCN) global grid PR prediction model is constructed, and the optimal taxi cruising area is identified based on the maximum PR. The results show that the taxi accessible range and PR of the same grid in different periods change with time, and based on the model comparison, the accessible range and PR prediction results of UTCN and MTCN algorithms in different periods are the best to identify the optimal cruising area of taxis in different periods. The main contribution of this study is that the proposed optimal cruising area prediction model has timeliness, accessibility, and dynamics. It can not only improve the probability of taxis receiving passengers and avoid taxis cruising aimlessly, but also solve the shortage of taxis in hotspots, thus shortening the waiting time of passengers. This provides a scientific basis for improving taxi cruising efficiency and the government’s formulation of taxi operation management policies, which can effectively promote the sustainable development of urban traffic.

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A multiscale spatial analysis of taxi ridership

Cited by 5 publications

References 40 publications

The Nonlinear and Threshold Effect of Built Environment on Ride-Hailing Travel Demand

The Nonlinear and Threshold Effect of Built Environment on Ride-Hailing Travel Demand

Exploring the Spatio-Temporally Heterogeneous Impact of Traffic Network Structure on Ride-Hailing Emissions Using Shenzhen, China, as a Case Study

The Real-Time Dynamic Prediction of Optimal Taxi Cruising Area Based on Deep Learning

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