Understanding the impact of built environment on metro ridership using open source in Shanghai

An, Dong; Tong, Xin; Liu, Kun; Chan, Edwin H.W.

doi:10.1016/j.cities.2019.05.013

Cited by 101 publications

(43 citation statements)

References 33 publications

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“…Its size varies in extant studies. Some scholars employed a comparatively small circle with a radius of 400m [42], while some applied a longer distance of 500 m or 600m [43,44]. Plus, some researchers adopted the 10 minutes' walking circle, of which the radius was approximately 800 m [45,46].…”

Section: Study Areamentioning

confidence: 99%

How Do Land Use Types and Transport Conditions Affect Population Aggregation Around Suburban Metro Stations: A Case Study in Shanghai

Yunzheng¹

2021

URP

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With the acceleration of urban sprawl, many cities have extended their metro lines into peripheral areas. It is hoped that the expansion of transport network can promote the construction of less developed regions and help relieve overpopulation in city centers. When measuring the spatial quality of station catchment areas, land use and transport conditions are two main dimensions that should be paid attention to. But how they affect population distribution hasn't been carefully studied yet. Up to now, there have been a substantial amount of studies relating to the comparatively well-developed transit stations in city centers whereas suburban stations still require further exploration. To fill this gap, our study aims to learn about current land use types, transport conditions and population aggregation around 97 suburban metro stations in Shanghai based on available information and data from Baidu Map and employs a stepwise regression approach to examine how the environmental variables affect population aggregation. The study shows that six land use variables and three transport-related variables are significantly associated with population aggregation on weekday while four land use variables and three transport-related variables show their significance on weekend. By finding out the coefficients of different land use and transport variables, this study hopes to guide government and urban planners in future planning in order to attract more people to live and work around suburban metro stations.

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Section: Study Areamentioning

confidence: 99%

How Do Land Use Types and Transport Conditions Affect Population Aggregation Around Suburban Metro Stations: A Case Study in Shanghai

Yunzheng¹

2021

URP

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“…A univariate state-space model with AFC data clustering has also been developed to estimate shortterm ridership at the station level [34]. POI data may help identifying characteristics of built environment so as to implement ridership forecast [35]. According to the description by Cervero [13], station-level transit ridership was a function of the station environment and transit service; thus, defining the catchment area of the station was critical.…”

Section: Direct Ridership Forecastingmentioning

confidence: 99%

Mass Rapid Transit Ridership Forecast Based on Direct Ridership Models: A Case Study in Wuhan, China

Guo

Huang

2020

Journal of Advanced Transportation

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Many large cities rely on Mass Rapid Transit (MRT) to increase passenger mobility. For efficiency, MRT stations should be arranged to attract maximal number of travelers. It is therefore important to develop methods for estimating MRT ridership forecasting models, which are important for policies on land use development or new MRT lines. Direct ridership models (DRMs) at the station level are superior in estimating the benefits of transit-oriented development policies. In this paper, a principal component regression (PCR) is proposed to overcome the issue of multicollinearity that widely occurs in multivariate regression analyses for DRM modeling, especially the ordinary least squares regression. Based on the analysis of 72 MRT stations in Wuhan, China, four principal components are obtained to explain the potential linkage to MRT ridership, which include built-environment related factors, jobs-housing spatial structure related factors, station attributes, and the large compound. Nineteen significant determinants have been identified, among which the four factors of office building area, land use mix, the number of restaurants, and financial institutions are the most influential factors. Built-environment-related factors exert more significant impact on MRT ridership than others. The distance to city center and the number of bus lines around stations have negative association with MRT demand. The proposed PCR-based DRM provides insights for forecasting transit demand brought about by new metro lines and forecasting the consequences of land use development.

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“…Unfortunately, studies referring to exploring the spatial characteristic of the mode shift and its relationship with the built environment under the LPR policy are still lacking. Enormous literature has confirmed the critical role of the built environment on transit ridership [32,33]. However, the issue of how restricted travel demands transfer among the different modes under different built environments has been largely neglected in the past years.…”

Section: Literature Reviewmentioning

confidence: 99%

Effects of Vehicle Restriction Policies on Urban Travel Demand Change from a Built Environment Perspective

Cheng

Huang

et al. 2020

Journal of Advanced Transportation

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License plate restriction (LPR) policy presents the most straightforward way to reduce road traffic and emissions worldwide. However, in practice, it has aroused great controversy. This policy broke the original structure of the urban transportation mode, which needed some matching strategies to adapt to this change. Investigating this travel demand change is a challenging task because it is greatly influenced by features of the local built environment. Fourteen variables from four dimensions, location, land-use diversity, distance to transit, and street design, are used to depict the built environment; moreover, the severe collinearity underlies these feature variables. To solve the multicollinearity among the variables and high-dimensional problem, this study utilizes two different penalization-based regression models, the LASSO (least absolute shrinkage and selection operator) and Elastic Net regression algorithms, to achieve the variable selection and explore the impacts of the built environment on the change of travel demand triggered by the LPR policy. Travel demand changes are assessed by the relative variation in taxi ridership in each traffic analysis zone based on the taxi GPS data. Built environment variables are measured using the transportation network data and the Baidu Map Service points of interest (POI) data. The results show that regions with a higher level of public transportation service and a higher degree of the land mix have a stronger resilience to the vehicle restriction policy. Besides, the contribution rate of public transportation is stable as a whole, while the contribution rate of richness depends on specific types of land use. The conclusions in this study can provide in-depth insights into the influence of the LPR policy and underpin traffic complementary policies to ensure the effectiveness of LPR.

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Understanding the impact of built environment on metro ridership using open source in Shanghai

Cited by 101 publications

References 33 publications

How Do Land Use Types and Transport Conditions Affect Population Aggregation Around Suburban Metro Stations: A Case Study in Shanghai

How Do Land Use Types and Transport Conditions Affect Population Aggregation Around Suburban Metro Stations: A Case Study in Shanghai

Mass Rapid Transit Ridership Forecast Based on Direct Ridership Models: A Case Study in Wuhan, China

Effects of Vehicle Restriction Policies on Urban Travel Demand Change from a Built Environment Perspective

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