Effects of metro transit on the ownership of mobility instruments in Xi’an, China

Huang, Xiaoyan; Cao, Xinyu; Yin, Jiangbin; Cao, Xiaoshu

doi:10.1016/j.trd.2016.09.014

Cited by 55 publications

(24 citation statements)

References 42 publications

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“…Vehicle ownership levels grew with the ever-increasing dependence on private transportation and the increased vehicle miles traveled. It is widely acknowledged that the growth of vehicle ownership is a major cause of traffic congestion and air pollution [22]. Ridesharing is emerging as a potentially effective way to reduce individuals' reliance on vehicle ownership by shifting personal transportation mode choice from owned assets to on-demand mobility services, which would reduce traffic jams and lower emissions.…”

Section: Introductionmentioning

confidence: 99%

Examining the Relationship between Household Vehicle Ownership and Ridesharing Behaviors in the United States

Zhang

2018

Sustainability

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To improve the sustainability and efficiency of transport systems, communities and government agencies throughout the United States (US) are looking for ways to reduce vehicle ownership and single-occupant trips by encouraging people to shift from driving to using more sustainable transport modes (such as ridesharing). Ridesharing is a cost-effective, sustainable and effective alternative transportation mode that is beneficial to the environment, the economy and society. Despite the potential effect of vehicle ownership on the adoption of ridesharing services, individuals’ ridesharing behaviors and the interdependencies between vehicle ownership and ridesharing usage are not well understood. This study aims to fill the gap by examining the associations between household vehicle ownership and the frequency and probability of ridesharing usage, and to estimate the effects of household vehicle ownership on individuals’ ridesharing usage in the US. We conducted zero-inflated negative binomial regression models using data from the 2017 National Household Travel Survey. The results show that, in general, one-vehicle reduction in households was significantly associated with a 7.9% increase in the frequency of ridesharing usage and a 23.0% increase in the probability of ridesharing usage. The effects of household vehicle ownership on the frequency of ridesharing usage are greater for those who live in areas with a higher population density than those living in areas with a lower population density. Young people, men, those who are unable to drive, individuals with high household income levels, and those who live in areas with rail service or a higher population density, tend to use ridesharing more frequently and are more likely to use it. These findings can be used as guides for planners or practitioners to better understand individuals’ ridesharing behaviors, and to identify policies and interventions to increase the potential of ridesharing usage, and to decrease household vehicle ownership, depending on different contextual features and demographic variables. Comprehensive strategies that limit vehicle ownership and address the increasing demand for ridesharing have the potential to improve the sustainability of transportation systems.

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Section: Introductionmentioning

confidence: 99%

Examining the Relationship between Household Vehicle Ownership and Ridesharing Behaviors in the United States

Zhang

2018

Sustainability

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“…An explosive growth of car ownership and use has also brought a series of problems, such as traffic congestion, energy consumption and air pollution [4,5], which attracts more attention from the government and policy makers in China [6]. Therefore, studies on the determiners and how they influence the car ownership and use have gained wide interests among transportation researchers [7][8][9][10]. To address the challenges, it is widely acknowledged that a solution to reduce car dependency by promoting urban development patterns [11][12][13][14][15][16][17][18][19][20], in which built environment plays an important role [21].…”

Section: Introductionmentioning

confidence: 99%

Application of Bayesian Multilevel Models Using Small and Medium Size City in China: The Case of Changchun

et al. 2018

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Concerns about transportation energy consumption and emissions force urban planners and policy makers to pay more attention to the effects of car ownership and use on the environment in China. However, few studies have investigated the relationship between the built environment and car ownership and use in China, especially in mid-sized and small cities. This study uses Changchun, China as a case study and examines the potential impacts of the built environment and socio-demographics on car ownership and use for commuting simultaneously using Bayesian multilevel binary logistic models. Furthermore, the spatial autocorrelation of car ownership and use is recognized across traffic analysis zones (TAZs), which are specifically represented by the conditional autoregressive (CAR) model. The estimated results indicate that socio-demographic characteristics have significant effects on car ownership and use. Moreover, the built environment measured at the TAZ level still shows a significant association with other factors controlled. Specifically, it suggests that denser residential density, compact land use, better transit services and street connectivity can reduce car dependency more effectively. This study provides new insights into how the built environment influences the car ownership and use, which can be useful for urban planners and policy makers to develop strategies for reducing car dependency.

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“…In the context of rail-based TOD, the metro now accounts for the largest share of public transit ridership in many metropolises of China, such as Nanjing, Beijing, and Shanghai [1][2][3]. Due to its efficient and reliable characteristics, researchers have found metro system can inhibit people's enthusiasm to drive and reduce automobile dependency for mobility [4,5]. Thus, considering the boost in metro ridership, it is crucial to investigate metro travel behavior and identify the spatio-temporal influences of the built environment on station-based travel distances at the station level.…”

Section: Introductionmentioning

confidence: 99%

Incorporating Smart Card Data in Spatio-Temporal Analysis of Metro Travel Distances

Chen

2019

Sustainability

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The primary objective of this study is to explore spatio-temporal effects of the built environment on station-based travel distances through large-scale data processing. Previous studies mainly used global models in the causal analysis, but spatial and temporal autocorrelation and heterogeneity issues among research zones have not been sufficiently addressed. A framework integrating geographically and temporally weighted regression (GTWR) and the Shannon entropy index (SEI) was thus proposed to investigate the spatio-temporal relationship between travel behaviors and built environment. An empirical study was conducted in Nanjing, China, by incorporating smart card data with metro route data and built environment data. Comparative results show GTWR had a better performance of goodness-of-fit and achieved more accurate predictions, compared to traditional ordinary least squares (OLS) regression and geographically weighted regression (GWR). The spatio-temporal relationship between travel distances and built environment was further analyzed by visualizing the average variation of local coefficients distributions. Effects of built environment variables on metro travel distances were heterogeneous over space and time. Non-commuting activity and exurban area generally had more influences on the heterogeneity of travel distances. The proposed framework can address the issue of spatio-temporal autocorrelation and enhance our understanding of impacts of built environment on travel behaviors, which provides useful guidance for transit agencies and planning departments to implement targeted investment policies and enhance public transit services.

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Effects of metro transit on the ownership of mobility instruments in Xi’an, China

Cited by 55 publications

References 42 publications

Examining the Relationship between Household Vehicle Ownership and Ridesharing Behaviors in the United States

Examining the Relationship between Household Vehicle Ownership and Ridesharing Behaviors in the United States

Application of Bayesian Multilevel Models Using Small and Medium Size City in China: The Case of Changchun

Incorporating Smart Card Data in Spatio-Temporal Analysis of Metro Travel Distances

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