This study examined the influence of the built environment surrounding rail stations on rail transit ridership and its spatiotemporal variations,aiming to enhance rail transit operational efficiency and inform station...This study examined the influence of the built environment surrounding rail stations on rail transit ridership and its spatiotemporal variations,aiming to enhance rail transit operational efficiency and inform station planning and development.Data from 159 metro stations in Nanjing,collected over a 14-d period,were analyzed to identify changes in weekday and weekend ridership patterns.The analysis included explanatory variables grouped into three categories:urban spatial variables,socioeconomic vari-ables,and transit service variables.A geographically and temporally weighted regression(GTWR)model was developed,and its performance was compared with that of ordinary least squares(OLS)and geographically weighted regression(GWR)models.The results demonstrated that the GTWR model outperformed others in analyzing the relationship between rail transit ridership and the built environment.In addition,the coefficients of explanatory variables showed significant variation across spatiotemporal dimensions,revealing distinct patterns.Notably,the influence of commuter flows led to more pronounced temporal heterogeneity in the coefficients observed on weekdays.These findings offer valuable insights for optimizing urban public transportation systems and advancing integrated urban rail development.展开更多
This study aims to explore the role of spatial heterogeneity in ridership analysis and examine the relationship between built environment, station attributes and urban rapid transit ridership at the station level.Alth...This study aims to explore the role of spatial heterogeneity in ridership analysis and examine the relationship between built environment, station attributes and urban rapid transit ridership at the station level.Although spatial heterogeneity has been widely acknowledged in spatial data analysis, it has been rarely considered in travel behavior studies.Four models(three global models-ordinary least squares(OLS), spatial lag model(SLM), spatial error model(SEM) and one local model-geographically weighted regression(GWR) model) are estimated separately to explore the relationship between various independent variables and station ridership, and identify the influence of spatial heterogeneity.Using the data of built environment and station characteristics, the results of diagnostic identify evidence the existence of spatial heterogeneity in station ridership for the metro network in Nanjing, China.Results of comparing the various goodness-of-fit indicators show that, the GWR model yields the best fit of the data, performance followed by the SEM, SLM and OLS model.The results also demonstrate that population, number of lines, number of feeder buses, number of exits, road density and proportion residential area have a significant impact on station ridership.Moreover, the study pays special attention to the spatial variation in the coefficients of the independent variables and their statistical significance.It underlines the importance of taking spatial heterogeneity into account in the station ridership analysis and the decision-making in urban planning.展开更多
This paper is mainly concerned with how to increase rail transit ridership and how to coordinate with multimodity to optimize the entire public transportation systems. Three case cities, Montreal, Toronto and San Fran...This paper is mainly concerned with how to increase rail transit ridership and how to coordinate with multimodity to optimize the entire public transportation systems. Three case cities, Montreal, Toronto and San Francisco, with metro systems are reviewed in different aspects, including urban planning, transport policy, flexible fare system, safety and security measure, special service, new technical application to improve the quality and value of its service for increasing revenues and profit, comtributing to the long term development of public transit. Some conclusions can be drawn: 1) urban planning should be closely connected with urban transport planning; 2) the role of government is predominant to implement railtransit; 3) the facilifies of railtransit should be advanced, reliable and safe; 4) quality service should be match with rail facilities; 5) special service for the disabled, yound and senior should be advocated.展开更多
The objective of this study was to quantify multimodal connectivity of HSR stations and its impact on ridership in four countries: France, Spain, Japan and China. In this study, multimodal connectivity is measured by ...The objective of this study was to quantify multimodal connectivity of HSR stations and its impact on ridership in four countries: France, Spain, Japan and China. In this study, multimodal connectivity is measured by the number of different modes of transportation connected to HSR stations, the number of installed arrival and departure facilities for each mode, the transfer time from connecting modes to boarding platforms at HSR stations, and the arrival time intervals of public transportation modes. Data were collected from HSR systems of these four countries. The relationship between ridership and the characteristics of multimodal connectivity was identified using regression models developed in this study. All the connectivity variables considered in this study influence ridership in these four countries in different ways. On the whole, bus, subway, and regional railroad services influence ridership significantly. For instance, the more bus services connected to the station, the higher the ridership. This trend is apparent in three of the four countries, France being the exception. Also, subway, light rail, and traditional rail are modes of high-capacity transportation. Their connection to HSR stations always implies high ridership for high-speed rail. The number of facilities also shows significant impacts on HSR ridership. For instance, the more bus and subway stops, and the more bicycle parking and taxi stands, the higher the ridership. Transfer time also has a significant influence.展开更多
At the beginning of the twentieth century, the United States was leading in the public transit sector, but following World War II, private automobiles became more affordable and gained popularity. Transportation infra...At the beginning of the twentieth century, the United States was leading in the public transit sector, but following World War II, private automobiles became more affordable and gained popularity. Transportation infrastructure investments that increased road capacity further facilitated the increase in automobile use at the expense of reduced public transit ridership. With the increase of dependency on automobiles and the continuing growth of private automobile ownership and use, various problems became major challenges in big cities of USA. These include traffic congestion, air pollution, road and parking infrastructure costs, energy consumption, traffic safety, fewer mobility options for the non-drivers, and a decline in the image and use of public transit. This study uses a medium sized city, Birmingham as a case study to investigate the potential of public transit to reduce automobile trips and in turn improve the overall performance of the road network by addressing the abovementioned challenges. An agent-based simulation model was developed for the Birmingham metropolitan region using the Multi-agent Transport Simulation (MATSim) platform. Three scenarios were considered with gradually increased transit ridership to identify the benefits of increased public transit. Traffic volume, network average speed, and travel times were used as performance measures for the evaluation of the designated scenarios. Results suggest that modal shifts toward public transit and reduction in travel demand for an automobile can result in improvements in speed and travel time for all users. Therefore, investments for improving transit quality and frequency of service, as well as campaigns to improve the image of public transit and make it a mode of choice for transportation users can increase transit ridership and, in turn, improve network operations, thus are deemed worthy for medium sized cities.展开更多
In this paper, a national-wide study is conducted to investigate the impacts of COVID-19 on the public transit ridership in the top twenty metropolitan areas in the U.S. At first, COVID-19 composite index was develope...In this paper, a national-wide study is conducted to investigate the impacts of COVID-19 on the public transit ridership in the top twenty metropolitan areas in the U.S. At first, COVID-19 composite index was developed to qualitatively measure the level of public fear toward COVID-19 in different metropolitan areas. After that, to analyze the impact of COVID-19 and some socioeconomic factors on transit ridership reduction during the COVID-19 pandemic, a random-effects panel data model was developed and the traditional correlation analysis was also conducted. According to the results of both analyses, it was found that the areas with higher median household income, a higher percentage of the population with a Bachelor’s degree or higher, higher employment rate, and a higher percentage of the Asian population are more likely to have more reductions in public transit ridership during the COVID-19 pandemic. On the other side, the areas with a higher percentage of the population in poverty, and a higher percentage of the Hispanic population are more likely to experience smaller reductions in public transit ridership.展开更多
为探究建成环境对城市轨道站点客流的非线性影响,以北京市轨道交通站点为研究对象,基于兴趣点(Point of Interest,POI)数据、手机信令数据、路网数据等多源数据,从社会经济与人口、土地利用、多模式衔接及站点特征4方面精细化刻画建成环...为探究建成环境对城市轨道站点客流的非线性影响,以北京市轨道交通站点为研究对象,基于兴趣点(Point of Interest,POI)数据、手机信令数据、路网数据等多源数据,从社会经济与人口、土地利用、多模式衔接及站点特征4方面精细化刻画建成环境,构建梯度提升决策树(Gradient Boosting Decision Tree,GBDT)模型揭示建成环境对不同时段及不同方向站点客流的相对贡献、非线性影响及阈值效应.研究结果表明:GBDT模型拟合效果优于普通最小二乘(Ordinary Least Squares,OLS)模型、自适应增强(Adaptive Boosting,AdaBoost)算法及随机森林(Random Forest,RF)模型;社会经济与人口属性对高峰时段客流的影响最大,对早晚高峰进出站客流的贡献均超过40%;土地利用属性对平峰进出站客流的影响最大,贡献为33.06%及49.10%;相对重要程度最高的变量对站点客流均表现出明显的非线性影响及阈值效应;距离市中心15~22 km的站点有更高的早高峰进站及晚高峰出站客流;高峰时段私家车接驳轨道交通的比例远大于平峰时段.研究结果可为城市轨道交通线网规划及城市土地利用布局提供理论支撑.展开更多
基金The National Key Research and Development Program of China(No.2022YFC3800201).
文摘This study examined the influence of the built environment surrounding rail stations on rail transit ridership and its spatiotemporal variations,aiming to enhance rail transit operational efficiency and inform station planning and development.Data from 159 metro stations in Nanjing,collected over a 14-d period,were analyzed to identify changes in weekday and weekend ridership patterns.The analysis included explanatory variables grouped into three categories:urban spatial variables,socioeconomic vari-ables,and transit service variables.A geographically and temporally weighted regression(GTWR)model was developed,and its performance was compared with that of ordinary least squares(OLS)and geographically weighted regression(GWR)models.The results demonstrated that the GTWR model outperformed others in analyzing the relationship between rail transit ridership and the built environment.In addition,the coefficients of explanatory variables showed significant variation across spatiotemporal dimensions,revealing distinct patterns.Notably,the influence of commuter flows led to more pronounced temporal heterogeneity in the coefficients observed on weekdays.These findings offer valuable insights for optimizing urban public transportation systems and advancing integrated urban rail development.
基金Under the auspices of National Natural Science Foundation of China(No.71771049)the Six Talent Peaks Project in Jiangsu Province(No.2016-JY-003)China Scholarship Council(No.201606090149)
文摘This study aims to explore the role of spatial heterogeneity in ridership analysis and examine the relationship between built environment, station attributes and urban rapid transit ridership at the station level.Although spatial heterogeneity has been widely acknowledged in spatial data analysis, it has been rarely considered in travel behavior studies.Four models(three global models-ordinary least squares(OLS), spatial lag model(SLM), spatial error model(SEM) and one local model-geographically weighted regression(GWR) model) are estimated separately to explore the relationship between various independent variables and station ridership, and identify the influence of spatial heterogeneity.Using the data of built environment and station characteristics, the results of diagnostic identify evidence the existence of spatial heterogeneity in station ridership for the metro network in Nanjing, China.Results of comparing the various goodness-of-fit indicators show that, the GWR model yields the best fit of the data, performance followed by the SEM, SLM and OLS model.The results also demonstrate that population, number of lines, number of feeder buses, number of exits, road density and proportion residential area have a significant impact on station ridership.Moreover, the study pays special attention to the spatial variation in the coefficients of the independent variables and their statistical significance.It underlines the importance of taking spatial heterogeneity into account in the station ridership analysis and the decision-making in urban planning.
文摘This paper is mainly concerned with how to increase rail transit ridership and how to coordinate with multimodity to optimize the entire public transportation systems. Three case cities, Montreal, Toronto and San Francisco, with metro systems are reviewed in different aspects, including urban planning, transport policy, flexible fare system, safety and security measure, special service, new technical application to improve the quality and value of its service for increasing revenues and profit, comtributing to the long term development of public transit. Some conclusions can be drawn: 1) urban planning should be closely connected with urban transport planning; 2) the role of government is predominant to implement railtransit; 3) the facilifies of railtransit should be advanced, reliable and safe; 4) quality service should be match with rail facilities; 5) special service for the disabled, yound and senior should be advocated.
文摘The objective of this study was to quantify multimodal connectivity of HSR stations and its impact on ridership in four countries: France, Spain, Japan and China. In this study, multimodal connectivity is measured by the number of different modes of transportation connected to HSR stations, the number of installed arrival and departure facilities for each mode, the transfer time from connecting modes to boarding platforms at HSR stations, and the arrival time intervals of public transportation modes. Data were collected from HSR systems of these four countries. The relationship between ridership and the characteristics of multimodal connectivity was identified using regression models developed in this study. All the connectivity variables considered in this study influence ridership in these four countries in different ways. On the whole, bus, subway, and regional railroad services influence ridership significantly. For instance, the more bus services connected to the station, the higher the ridership. This trend is apparent in three of the four countries, France being the exception. Also, subway, light rail, and traditional rail are modes of high-capacity transportation. Their connection to HSR stations always implies high ridership for high-speed rail. The number of facilities also shows significant impacts on HSR ridership. For instance, the more bus and subway stops, and the more bicycle parking and taxi stands, the higher the ridership. Transfer time also has a significant influence.
文摘At the beginning of the twentieth century, the United States was leading in the public transit sector, but following World War II, private automobiles became more affordable and gained popularity. Transportation infrastructure investments that increased road capacity further facilitated the increase in automobile use at the expense of reduced public transit ridership. With the increase of dependency on automobiles and the continuing growth of private automobile ownership and use, various problems became major challenges in big cities of USA. These include traffic congestion, air pollution, road and parking infrastructure costs, energy consumption, traffic safety, fewer mobility options for the non-drivers, and a decline in the image and use of public transit. This study uses a medium sized city, Birmingham as a case study to investigate the potential of public transit to reduce automobile trips and in turn improve the overall performance of the road network by addressing the abovementioned challenges. An agent-based simulation model was developed for the Birmingham metropolitan region using the Multi-agent Transport Simulation (MATSim) platform. Three scenarios were considered with gradually increased transit ridership to identify the benefits of increased public transit. Traffic volume, network average speed, and travel times were used as performance measures for the evaluation of the designated scenarios. Results suggest that modal shifts toward public transit and reduction in travel demand for an automobile can result in improvements in speed and travel time for all users. Therefore, investments for improving transit quality and frequency of service, as well as campaigns to improve the image of public transit and make it a mode of choice for transportation users can increase transit ridership and, in turn, improve network operations, thus are deemed worthy for medium sized cities.
基金supported in part by the United States Department of Transportation(USDOT)under grant#69A3551747133.
文摘In this paper, a national-wide study is conducted to investigate the impacts of COVID-19 on the public transit ridership in the top twenty metropolitan areas in the U.S. At first, COVID-19 composite index was developed to qualitatively measure the level of public fear toward COVID-19 in different metropolitan areas. After that, to analyze the impact of COVID-19 and some socioeconomic factors on transit ridership reduction during the COVID-19 pandemic, a random-effects panel data model was developed and the traditional correlation analysis was also conducted. According to the results of both analyses, it was found that the areas with higher median household income, a higher percentage of the population with a Bachelor’s degree or higher, higher employment rate, and a higher percentage of the Asian population are more likely to have more reductions in public transit ridership during the COVID-19 pandemic. On the other side, the areas with a higher percentage of the population in poverty, and a higher percentage of the Hispanic population are more likely to experience smaller reductions in public transit ridership.
