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

Guo, Ruili; Huang, Zhengdong

doi:10.1155/2020/7538508

Cited by 11 publications

(8 citation statements)

References 48 publications

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“…Existing studies on transit ridership forecasting show that the passenger flows of transfer stations is larger than that of the ordinary stations, controlling the land use variables [ 28 , 29 ]. They normally identify the transfer stations by setting a dummy variable [ 30 , 31 ], which however ignores the spatial features of transfer stations on the metro network. To avoid this issue, this paper employs the concept of betweenness centrality in the graph theory as a reference, defined as, where:…”

Section: Methodsmentioning

confidence: 99%

Deviation of peak hours for metro stations based on least square support vector machine

Yu,

Cui,

Dai

2023

PLoS ONE

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The station-level ridership during the peak hour is one of the key indicators for the design of station size and relevant facilities. However, with the operation of metro system, it cannot be ignored that, in many cities, the station peak and the city peak may not be simultaneously occurred. As the current ridership forecasting methods use the city peak as the point of reference, stations with wide differences of ridership in between would experience disorders due to serious underestimates of passenger demand during the actual peak. Accordingly, this study fully considers the phenomenon that the metro station peak is not identical to the city peak and focuses on the concept of the peak deviation coefficient (PDC), the ratio of the station peak ridership to the city peak ridership. It investigates how metro ridership determinants affects the PDC using the least square support vector machine (LSSVM) model. A land-use function complementarity index is employed as one of the independent variables, which is newly proposed in this study that describes the relationship of the commute land use around an individual station with that along the whole network. This method can help to resolve the ridership amplification indicator for a fine-grained station-level forecasting. The results for Xi’an metro indicate that the LSSVM is an effective method to scrutinize the nonlinear effects of e.g., land use attributes, on the temporal distribution features of the metro ridership. Compared to the ratio of commute land use measured for individual stations, the land-use function complementarity index can better explain and predict the severity of peak deviation phenomenon, controlling other independent variables in the model.

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Section: Methodsmentioning

confidence: 99%

Deviation of peak hours for metro stations based on least square support vector machine

Yu,

Cui,

Dai

2023

PLoS ONE

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“…The quality of the supply is made up of and affected by internal factors, such as the infrastructure, fleet size, frequency, reliability, and quality of service. A single factor such as frequency can be affected by a number of subsequent internal factors, such as the platform design, signalling, vehicles available, passenger boarding/offloading, and so forth; and will vary to align appropriately with changing demand (Guo & Huang, 2020). There are also external factors which affect the balance between supply and demand.…”

Section: Stationsmentioning

confidence: 99%

Toronto's Future Rapid Transit Network: Evaluating Existing and Proposed Rapid Transit Services to a 2041 Forecast of Transit Needs and Transit Demands

Mandal

2024

Preprint

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<p>The Toronto Transit Commission (TTC), Toronto’s transit service provider, currently has 3 lines of rapid transit service intended for continued operation, and 4 new rapid transit projects that are underway. This study is to determine if the existing and new lines of rapid transit ideally meet present and future demands, and to locate spatial mismatch between demand and transit placement if they do not. Select socio-economic groups are also examined for their transit needs. These groups are selected by definitions of youth, elderly, language, recent immigrants, migrants, renters, and unemployed. Population projections to 2041 were examined to assess future outcomes. Catchment areas were employed around rapid transit stations when gauging transit line services. This was followed with statistical and mapping outcomes. These methods helped determine where demand for rapid transit is being adequately met now, and in the future, and where it might not be. Results show that downtown remains the densest area of rapid transit users. The Ontario Line has the greatest outcome in this study to provide service to a densely populated part of the city. In spite of this, existing lines seem to be better situated to meet future demand than the future lines. Transit lines in the network prove themselves to serve their purposes and target areas with high need and demand for transit services. Existing and new lines will experience higher growth rates than the rest of the city. Most of the proposed lines are shown to be developed in areas identified as coldspots. Other high demand clusters in the city remain unaddressed by the network. Albeit, the proposed lines do go a long way to providing much needed rapid transit to parts of the city. Overall, the densification of riders will increase more rapidly along present rapid transit lines, followed by the new lines. This study also identifies some spatial mismatch with respect to present and future lines.</p>

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“…Modeling and estimating public transit ridership is essential for analyzing the project viability of stations and the development of urban areas (Zhao et al, 2013). There are three main categories of DRMs: traditional, spatial (Guo and Huang, 2020), and machine learning models. In the traditional category, ordinary least squares (OLS) regression is considered as the basic approach (An et al, 2019; Kim et al, 2016; Sohn and Shim, 2010; Zhao et al, 2013).…”

Section: Literature Reviewmentioning

confidence: 99%

Transferability analysis of built environment variables for public transit ridership estimation in Wuhan, China

Fang¹,

Zhang

Zheng³

2022

Transactions in Urban Data, Science, and Technology

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New stations (such as metro stations) will bring remarkable changes to the local transportation and economic development. Understanding patterns of factors which importantly impact on public transit ridership in the surrounding areas of new stations is essential to their construction planning, like estimating the possible ridership. Built environment variables with high importance magnitude, which were thought applicable to estimate public transit ridership in other areas of the same category, were described as transferable variables (TVs) in this study. A transferability analysis method of the built environment for the ridership estimation was constructed by adopting partial least square regression (PLSR) based on available data. Taking Wuhan, China as an example, this study analyzed the changes and differences of the built environment variables in different categories of pedestrian catchment areas (PCAs) of metro stations on the importance and transferability magnitude for the metro and taxi ridership, based on the metro and taxi data of one week in January, April, and June. Performances of the ridership estimation based on TVs and all the built environment variables were compared. This study inferred that (1) most of the land use variables (about 85%) showed important influence on the metro and taxi ridership, while only about 18% of the other variables showed key impact. The importance magnitude of the built environment variables was mainly related to PCA categories and public transportation modes, but less related to time. (2) Highly important built environment variables also tended to be highly transferable. Transferability magnitude of the built environment variables for the ridership was related to PCA categories and types of public transport. (3) Compared to all the built environment variables, using TVs, the relative accuracy of the metro and taxi ridership estimation was around 20% and 18% higher respectively.

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

Cited by 11 publications

References 48 publications

Deviation of peak hours for metro stations based on least square support vector machine

Deviation of peak hours for metro stations based on least square support vector machine

Toronto's Future Rapid Transit Network: Evaluating Existing and Proposed Rapid Transit Services to a 2041 Forecast of Transit Needs and Transit Demands

Transferability analysis of built environment variables for public transit ridership estimation in Wuhan, China

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