scholarly journals Does Urban Rail Transit Discourage People from Owning and Using Cars? Evidence from Beijing, China

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Shasha Liu ◽  
Enjian Yao ◽  
Toshiyuki Yamamoto
Keyword(s):  
Travel Distance ◽  
Urban Rail Transit ◽  
Mediating Effect ◽  
Car Ownership ◽  
Lower Probability ◽  
Rail Transit ◽  
Car Use ◽  
Urban Rail ◽  
Catchment Areas ◽  
Beijing China

With the rapid urbanization and motorization, many cities are developing urban rail transit (URT) to reduce car dependence. This paper explores the URT effect on car ownership and use based on the home-based work tour data in Beijing, China. Considering the mediating effects of car ownership and travel distance simultaneously, we develop a structural equation model to examine the complex relationship among URT, car ownership, travel distance, and car use. The results indicate that URT plays an important role in reducing car dependence. Living within URT catchment areas by itself is not significantly associated with car ownership and use, but if the workplace is near a URT station, people are less likely to own and use cars. People who both live and work near URT station areas have lower probability of owning and using cars. Moreover, car ownership and travel distance mediate the relationship between URT and car use, and the mediating effect of car ownership is greater than travel distance. Our study verifies that URT does discourage people from owning and using cars, which may have important implications for developing cities to make response to the ongoing motorization.

Study of the Influencing Factors of Urban Rail Transit Travel Behavior: A Case Study of Xi'an

2015 ◽  
Vol 744-746 ◽  
pp. 2049-2052
Author(s):  
Yao Wu ◽  
Jian Lu ◽  
Yue Chen
Keyword(s):  
Travel Time ◽  
Influencing Factors ◽  
Travel Behavior ◽  
Family Income ◽  
Binary Logistic Regression ◽  
Travel Distance ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Binary Logistic Regression Model ◽  
Urban Rail

In order to study the factors influencing urban rail transit travel behavior, a questionnaire was conducted for residents’ selection of rail transit in Xi'an. Based on the collected data from 1105 valid questionnaires, a binary logistic regression model was established to analyze the influencing factors quantitatively. The results showed that seven factors have statistically significant for rail transit travel behavior including age, occupation, family income, average monthly household transportation costs (T-cost), travel purpose, travel distance, and travel time. Odds ratio analysis revealed that young people and staff were more likely to choose rail transit; the probability of selecting rail transit increased with the increase of family income and the T-cost. In addition, more and more people tend to rail travel with the increase of travel distance and travel time.

scholarly journals Improved Huff Model for Estimating Urban Rail Transit Station Catchment Areas considering Station Choices

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhenjun Zhu ◽  
Yudong He ◽  
Xiucheng Guo ◽  
Yibang Zhang ◽  
Junlan Chen
Keyword(s):  
Urban Rail Transit ◽  
Rail Transit ◽  
Transit Oriented Development ◽  
Urban Rail ◽  
Catchment Areas ◽  
Feeder Bus ◽  
Transit Station ◽  
Bus Services ◽  
Additive Form

Estimating urban rail transit station catchment areas is of great significance to deepening our understanding of Transit-Oriented Development in Chinese megacities. This study investigated station choices of residents and considered that residents may not only pay attention to the proximity to stations when the URT system develops into a relatively mature network. An improved Huff model was proposed to calculate the probability of residents’ station choice, which considered the station attractiveness. The station attractiveness is measured by three variables: walk score, public transport accessibility level, and service and facility index. The additive form based on multicriteria decision is adopted to incorporate experts’ opinions on the importance of three variables. In this study, extended catchment areas that can be accessed by cycling and feeder bus services are adopted to replace the conventional pedestrian-oriented catchment areas. A case study of Xi’an, China, was used to validate the applicability of the proposed methodology. The results revealed that the methodology effectively solved the problem. The findings could be used as a reference and provide technical support to policymakers and city planners with regard to the transport facilities configuration for URT station catchment areas, which contributes to facilitating transit-oriented development.

scholarly journals How Do Different Treatments of Catchment Area Affect the Station Level Demand Modeling of Urban Rail Transit?

2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Hongtai Yang ◽  
Xuan Li ◽  
Chaojing Li ◽  
Jinghai Huo ◽  
Yugang Liu
Keyword(s):  
Linear Regression ◽  
Catchment Area ◽  
Goodness Of Fit ◽  
Urban Rail Transit ◽  
Distance Decay ◽  
Rail Transit ◽  
Demand Modeling ◽  
Urban Rail ◽  
Catchment Areas ◽  
Log Linear

Direct demand modeling is a useful tool to estimate the demand of urban rail transit stations and to determine factors that significantly influence such demand. The construction of a direct demand model involves determination of the catchment area. Although there have been many methods to determine the catchment area, the choice of those methods is very arbitrary. Different methods will lead to different results and their effects on the results are still not clear. This paper intends to investigate this issue by focusing on three aspects related to the catchment area: size of the catchment area, processing methods of the overlapping areas, and whether to apply the distance decay function on the catchment area. Five catchment areas are defined by drawing buffers around each station with radius distance ranging from 300 to 1500 meters with the interval of 300 meters. Three methods to process the overlapping areas are tested, which are the naïve method, Thiessen polygon, and equal division. The effect of distance decay is considered by applying lower weight to the outer catchment area. Data from five cities in the United States are analyzed. Built environment characteristics within the catchment area are extracted as explanatory variables. Annual average weekday ridership of each station is used as the response variable. To further analyze the effect of regression models on the results, three commonly used models, including the linear regression, log-linear regression, and negative binomial regression models, are applied to examine which type of catchment area yields the highest goodness-of-fit. We find that the ideal buffer sizes vary among cities, and different buffer sizes do not have a great impact on the model’s goodness-of-fit and prediction accuracy. When the catchment areas are heavily overlapping, dividing the overlapping area by the number of times of overlapping can improve model results. The application of distance decay function could barely improve the model results. The goodness-of-fit of the three models is comparable, though the log-linear regression model has the highest prediction accuracy. This study could provide useful references for researchers and planners on how to select catchment areas when constructing direct demand models for urban rail transit stations.

scholarly journals Automobile usage and urban rail transit expansion: evidence from a natural experiment in Beijing, China

2016 ◽  
Vol 21 (5) ◽  
pp. 557-580 ◽  
Author(s):  
Lunyu Xie
Keyword(s):  
Natural Experiment ◽  
Urban Rail Transit ◽  
Rail Transit ◽  
Diary Data ◽  
Urban Rail ◽  
Travel Diary ◽  
Before And After ◽  
The Cross ◽  
The Impact ◽  
Beijing China

AbstractUsing individual travel diary data collected before and after a rail transit expansion in urban Beijing, the impact of urban rail accessibility improvement on the usage of rail transit, automobiles, buses, walking and bicycling, as well as the cross-area externality induced by congestion alleviation, is estimated. The results show that rail transit usage significantly increased for commuters residing in the affected areas and that the additional rail passengers were previously auto users, rather than bus passengers. The cross-area externality is estimated as small, which implies that the congestion alleviation was not large enough (yet) to change the travel mode choices of commuters residing in areas that did not experience the improvement. Furthermore, the results show that neither the number of commute work trips nor their length increased, indicating that the quantity of travel was not increased by the rail transit expansion.

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