A geographically and temporally weighted regression model to explore the spatiotemporal influence of built environment on transit ridership

Ma, Xiaolei; Zhang, Jiyu; Ding, Chuan; Wang, Yunpeng

doi:10.1016/j.compenvurbsys.2018.03.001

Cited by 201 publications

(128 citation statements)

References 38 publications

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“…Recently, some scholars have tried to improve the GWR model according to the distribution characteristics of the research data. Ma et al [39] used a geographically and temporally weighted regression model to explore the relationship between the built environment and transit ridership considering the space-time relationship. The authors indicated that the results of geographically and temporally weighted regression model are superior to those obtained using the GWR method and the ordinary least square (OLS) model.…”

Section: Application Of Gwr In the Transportation Fieldmentioning

confidence: 99%

“…To measure the built environment, the 2016 Nanjing POI data, which were obtained from the Baidu map, were also used in this study. POI data are a kind of point data representing real geographical entities and include spatial information such as latitude and longitude and addresses, and attribute information such as name and category [39]. The data collected for this study include information about the boundaries of the county and district, urban roads, parks, metro stations, bus stations, public bicycle stations, parking lots, financial service areas, commercial buildings, retail industries, hotels, recreation, medical services, research and education, and corporate and residential communities.…”

Section: Nanjing Point Of Interest (Poi) Datamentioning

confidence: 99%

“…First, together with the characteristics of schooling behavior, we used population, employment, and the number of commercial and educational buildings to measure land use. Due to the limitations of our data and following Ma et al [39], the number of residents and jobs in the PCA have been replaced by the POI of the residential buildings and employment places.…”

Section: Selecting the Explanatory Variablesmentioning

confidence: 99%

See 2 more Smart Citations

The Influence of the Built Environment on School Children’s Metro Ridership: An Exploration Using Geographically Weighted Poisson Regression Models

Liu

Shi

et al. 2018

Sustainability

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Long-distance school commuting is a key aspect of students' choice of car travel. For cities lacking school buses, the metro and car are the main travel modes used by students who have a long travel distance between home and school. Therefore, encouraging students to commute using the metro can effectively reduce household car use caused by long-distance commuting to school. This paper explores metro ridership at the station level for trips to school and return trips to home in Nanjing, China by using smart card data. In particular, a global Poisson regression model and geographically weighted Poisson regression (GWPR) models were used to examine the effects of the built environment on students' metro ridership. The results indicate that the GWPR models provide superior performance for both trips to school and return trips to home. Spatial variations exist in the relationship between the built environment and students' metro ridership across metro stations. Built environments around metro stations, including commercial-oriented land use; the density of roads, parking lots, and bus stations; the number of docks at bikeshare stations; and the shortest distance between bike stations and metro stations have different impacts on students' metro ridership. The results have important implications for proposing relevant policies to guide students who are being driven to school to travel by metro instead.

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Section: Application Of Gwr In the Transportation Fieldmentioning

confidence: 99%

Section: Nanjing Point Of Interest (Poi) Datamentioning

confidence: 99%

Section: Selecting the Explanatory Variablesmentioning

confidence: 99%

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The Influence of the Built Environment on School Children’s Metro Ridership: An Exploration Using Geographically Weighted Poisson Regression Models

Liu

Shi

et al. 2018

Sustainability

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“…The focus of urban design work is people [2]. In the past few decades, most studies have focused on the impact of urban structures on population mobility [3][4][5]. However, little research has been conducted on the impacts of human activities on urban space in China and other developing countries, especially on waterfronts.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Relationship between Potential and Actual of Urban Waterfront Spaces in Wuhan Based on Social Networks

2019

Sustainability

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The geographical location of residents and the distribution of points of interest (POI) are key factors affecting the spatial value of urban waterfronts. This study designed an association scheme based on tourists’ geographical location information (obtained from social networks) and the distribution of facilities around lakes to evaluate the spatial value of urban waterfronts. Accordingly, it explored the causes of the current condition of the waterfronts. Using the distribution status of eight types of facilities, a multivariate regression model was established to predict the number of tourists that the lakes attract. Predicted results were compared with the actual condition. The clustering degree of various POI in the waterfronts was graded by using the kernel density estimation, and the difference between the predicted results and actual value was analyzed to reveal the current condition of the urban waterfronts and the reasons for their formation. On the basis of this survey, the situation of 21 major lakes within the third ring road in Wuhan, China was investigated. Results show that existing waterfronts in some areas have a considerable number of users, but the facilities fail to meet their needs. Thus, Wuhan city’s waterfront space needs to be used more effectively. This study can help with making targeted recommendations with reference to future city waterfront planning.

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“…Ma et al . [20] used a geographically and temporally weighted regression (GTWR) model to identify the spatiotemporal influence of the built environment on transit ridership.…”

Section: Introductionmentioning

confidence: 99%

Predicting subway passenger flows under different traffic conditions

et al. 2018

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Passenger flow prediction is important for the operation, management, efficiency, and reliability of urban rail transit (subway) system. Here, we employ the large-scale subway smartcard data of Shenzhen, a major city of China, to predict dynamical passenger flows in the subway network. Four classical predictive models: historical average model, multilayer perceptron neural network model, support vector regression model, and gradient boosted regression trees model, were analyzed. Ordinary and anomalous traffic conditions were identified for each subway station by using the density-based spatial clustering of applications with noise (DBSCAN) algorithm. The prediction accuracy of each predictive model was analyzed under ordinary and anomalous traffic conditions to explore the high-performance condition (ordinary traffic condition or anomalous traffic condition) of different predictive models. In addition, we studied how long in advance that passenger flows can be accurately predicted by each predictive model. Our finding highlights the importance of selecting proper models to improve the accuracy of passenger flow prediction, and that inherent patterns of passenger flows are more prominently influencing the accuracy of prediction.

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A geographically and temporally weighted regression model to explore the spatiotemporal influence of built environment on transit ridership

Cited by 201 publications

References 38 publications

The Influence of the Built Environment on School Children’s Metro Ridership: An Exploration Using Geographically Weighted Poisson Regression Models

The Influence of the Built Environment on School Children’s Metro Ridership: An Exploration Using Geographically Weighted Poisson Regression Models

Exploring the Relationship between Potential and Actual of Urban Waterfront Spaces in Wuhan Based on Social Networks

Predicting subway passenger flows under different traffic conditions

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