Impact of transportation network companies on urban congestion: Evidence from large-scale trajectory data

Qian, Xinwu; Tian, Lei; Xue, Jiawei; Lei, Zengxiang; Ukkusuri, Satish V.

doi:10.1016/j.scs.2020.102053

Cited by 50 publications

(35 citation statements)

References 25 publications

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“…ey further investigated how the rise of TNCs infected traffic states and emissions [28]. Yang et al [29] studied walking travel demand at intersections using walking counts over ten years in Chittenden County, Vermont, USA.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Medicine is closely related to the house, entertainment, and shopping variable with correlation coefficients of 0.901, 0.952, and 0.918. e house variable is related to entertainment and shopping with correlation coefficients of 0.877 and 0.869. e shopping variable is related to entertainment, with a correlation coefficient of 0.885. e remaining coefficients are below 0.7. According to Qian et al [28], variables with coefficients higher than 0.7 are deleted. us, beauty, restaurant, medicine, house, and shopping variables are removed from the explanatory variable in the experiment.…”

Section: Multicollinearity Problemmentioning

confidence: 99%

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Understanding City-Wide Ride-Sourcing Travel Flow: A Geographically Weighted Regression Approach

Chen

Ochieng

et al. 2021

Journal of Advanced Transportation

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The emerging ride-sourcing service has become an important element of urban mobility. A challenging question underlying the provision of such service is how and to what extent the built environment affects origin-destination (OD) travel flows. This paper employs the geographically weighted regression (GWR) model to analyze the OD-based ride-sourcing travel flow. It makes a comparison with the existing ordinary least square (OLS) model and spatial autocorrelation model (SAM). We have collected ride-sourcing order data in Hangzhou, China, to provide an accurate source for acquiring ride-sourcing travel flow. We investigate the effects of the residential area, points of interest (POIs), and transit stations on ride-sourcing travel flow among traffic analysis zones (TAZs). The results show the following: (a) GWR has better goodness-of-fit than SAM and OLS. (b) Residential area, enterprise, and bus stations have positive correlations with ride-sourcing OD flows, but education and subway stations have negative correlations. We have further investigated the issue and found that it is not a causal relationship between the bus station and OD flow, due to collinearity between the two variables. The bus station builds on locations with high demand, but its capacity is not large enough to reduce the ride-sourcing flow to a low level, which results in a positive coefficient. (c) Based on the estimated coefficients, the prediction of ride-sourcing flows is feasible, supporting the impact analysis for urban land use and transportation planning. This paper contributes to understanding OD-based ride-sourcing travel flow distributions and provides a framework of long-term OD flow prediction for urban land use and transportation planning.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Multicollinearity Problemmentioning

confidence: 99%

Understanding City-Wide Ride-Sourcing Travel Flow: A Geographically Weighted Regression Approach

Chen

Ochieng

et al. 2021

Journal of Advanced Transportation

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“…In order to implement appropriate urban traffic policies, i.e. traffic information for deploying ET charging stations, a model of traffic conditions around the city is fundamental [17], [18]. Vehicle traffic modeling has been approached through different methodologies and techniques.…”

Section: A Urban Traffic Modelingmentioning

confidence: 99%

Urban Traffic Flow Mapping of an Andean Capital: Quito, Ecuador

et al. 2020

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Several efforts have been devoted to developing sustainable cities to address global environmental challenges and the growth of urban areas. In particular, transportation has various issues such as air pollution, noise, and traffic, which have to be addressed by collecting significant information of the traffic and road conditions of the cities. Automating the data extraction process and street network construction will allow building more useful models to study traffic behavior. This work presents a network modeling approach to identify interest points (extreme and internal) of the city, through a winner-takes-all edge trimming, and mapping the traffic flow between them. Such points can be considered as entries of an Origin-Destination matrix, where such information can be used to model traffic behavior between interest points. The case study of Quito, Ecuador is considered. Besides, to address environmental issues, this paper encourages the replacement of internal combustion taxis with electric vehicles. From the understanding of the vehicle traffic behavior, a pre-feasibility siting of electric taxi (ET) charging stations was carried out. The results will allow performing the sizing of each charging station considering electric power network constraints. This work aims to ensure a sustainable transportation system based on this crucial information. INDEX TERMS charging station, driving time and distance, electric vehicle, graph trimming, urban traffic network

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“…Despite the growing amount of research exploring TNCs' emissions, effects on transportation [15][16][17][18][19][20][21][22], accessibility [13][14][15][16][17][18][19][20][21][22][23], and the TNC experience , there is currently no study that explores the perceptions of TNC drivers towards climate change. Therefore, we aim (1) to explore TNC drivers' beliefs of climate change, (2) to understand how they perceive climate change impacts their TNC business, and (3) to examine how TNC drivers see their role in contributing to or mitigating climate change.…”

Section: Introductionmentioning

confidence: 99%

Transportation Network Companies: Drivers’ Perceptions of Ride-Sharing Regarding Climate Change and Extreme Weather

Brügger

Watts

2021

Climate

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The transportation sector is a major factor contributing to climate change. Transportation Network Companies (TNC) may become part of solutions to reduce emissions and their drivers play an important role in doing so. This study aims to understand TNC driver’s perceptions of climate change, to understand how climate change and extreme weather affects their business and how they see their role in contributing to or mitigating climate change. We conducted an in-person survey of TNC drivers in Nevada, USA, and analyzed the derived information with descriptive statistics and content analysis. Among the 75 TNC drivers, almost half believe climate change is happening and is caused by human activities. We found TNC drivers and their business are affected by extreme weather events. Currently the drivers do not see their role in mitigating climate change and lack the awareness of green initiatives already in place by TNCs’. We conclude that TNCs could increase their climate change responsibility by providing driver incentives for cars with reduced emissions or by geographically expanding customer incentives for using sustainable TNC options such as car-pooling. By doing so, TNC may play a role in reducing global greenhouse gas emissions and traffic congestion; thus, contributing to improved sustainable transportation practices.

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Impact of transportation network companies on urban congestion: Evidence from large-scale trajectory data

Cited by 50 publications

References 25 publications

Understanding City-Wide Ride-Sourcing Travel Flow: A Geographically Weighted Regression Approach

Understanding City-Wide Ride-Sourcing Travel Flow: A Geographically Weighted Regression Approach

Urban Traffic Flow Mapping of an Andean Capital: Quito, Ecuador

Transportation Network Companies: Drivers’ Perceptions of Ride-Sharing Regarding Climate Change and Extreme Weather

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