Understanding urban mobility patterns from a spatiotemporal perspective: daily ridership profiles of metro stations

Gan, Zuoxian; Yang, Min; Feng, Tao; Timmermans, Harry

doi:10.1007/s11116-018-9885-4

Cited by 62 publications

(32 citation statements)

References 31 publications

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“…e POI dataset, which was collected by Google Place API, has typically been used to represent land use characteristics [29]. In addition to recording the name and location (longitude and latitude) of each point of interest, the dataset also categorizes POIs into 20 groups, such as administrative agencies, train and metro stations, shopping areas, and residential areas.…”

Section: Poi Datamentioning

confidence: 99%

Understanding Regional Mobility Patterns Using Car-Hailing Order Data and Points of Interest Data

Zheng

Chen

Xiong

et al. 2020

Journal of Advanced Transportation

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Car hailing is undergoing rapid global development, thereby providing new opportunities and challenges to operators and transport engineers due to uneven or irregular demand in certain areas. To date, only a limited number of studies have analyzed regional mobility patterns or anomaly detection. This study therefore proposes a methodology for recognizing regional mobility patterns using car-hailing order datasets and point of interest datasets. More specifically, we detect regional mobility patterns by incorporating regional intrinsic properties to a hierarchical mixture model termed latent Dirichlet allocation (LDA). This model can simulate the process of generating car-hailing order data and yield regional mobility patterns from spatial, temporal, and spatiotemporal perspectives. Moreover, by combining the trained results with future mobility records, we can measure similarities between areas and detect anomalous areas by calculating the perplexity. We also implement our workflow on a real-word car-hailing order dataset and reveal that it is possible to identify areas with similar or anomaly mobility patterns. This research will contribute to the design of regional transportation policies and customized bus services.

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Section: Poi Datamentioning

confidence: 99%

Understanding Regional Mobility Patterns Using Car-Hailing Order Data and Points of Interest Data

Zheng

Chen

Xiong

et al. 2020

Journal of Advanced Transportation

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“…It covered 6 lines and 112 stations, which makes it the fifth largest rail transit system in China (not including Hong Kong, Macao, Taiwan) (the top four cities are Shanghai, Beijing, Guangzhou and Shenzhen). The metro system carries 717 million passengers annually and its share in 2015 was about 34.8% of the passenger volume of public transport (Zhao et al, 2013;Gan et al, 2018).…”

Section: Datamentioning

confidence: 99%

Analysis of Metro Station Ridership Considering Spatial Heterogeneity

et al. 2019

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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.

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“…Thus, in order to maximize the universality of the survey results, it is necessary to select representative metro stations. Nanjing metro stations can be classified into seven categories according to the land use and metro ridership, including traffic hub and scenic spot stations, university influenced stations, employment-oriented stations, residential-oriented stations, spatial mismatched stations, mixed employment-oriented stations and mixed residential-oriented stations [38]. We selected the metro station with the largest shared bike trips from each type as the survey site, as shown in Figure 3.…”

Section: Survey and Data Collectionmentioning

confidence: 99%

Use Frequency of Metro–Bikeshare Integration: Evidence from Nanjing, China

Liu

Feng

et al. 2020

Sustainability

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Promoting a transition in individuals’ travel mode from car to an integrated metro and bikeshare systems is expected to effectively reduce the traffic congestion that results mainly from commute trips performed by individual automobiles. This paper focuses on the use frequency of an integrated metro–bikeshare by individuals, and presents empirical evidence from Nanjing, China. Using one-week GPS data collected from the Mobike company, the spatiotemporal characteristics of origin/destination for cyclists who would likely to use shared bike as a feeder mode to metro are examined. Three areas of travel-related spatiotemporal information were extracted including (1) the distribution of walking distances between metro stations and shared bike parking lots; (2) the distribution of cycling times between origins/destinations and metro stations; and (3) the times when metro–bikeshare users pick up/drop off shared bikes to transfer to/from a metro. Incorporating these three features into a questionnaire design, an intercept survey of possible factors on the use of the combined mode was conducted at seven functional metro stations. An ordered logistic regression model was used to examine the significant factors that influence groupings of metro passengers. Results showed that the high-, medium- and low-frequency groups of metro–bikeshare users accounted for 9.92%, 21.98% and 68.1%, respectively. Education, individual income, travel purpose, travel time on the metro, workplace location and bike lane infrastructure were found to have significant impacts on metro passengers’ use frequency of integrated metro–bikeshares. Relevant policies and interventions for metro passengers of Nanjing are proposed to encourage the integration of metro and bikeshare systems.

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Understanding urban mobility patterns from a spatiotemporal perspective: daily ridership profiles of metro stations

Cited by 62 publications

References 31 publications

Understanding Regional Mobility Patterns Using Car-Hailing Order Data and Points of Interest Data

Understanding Regional Mobility Patterns Using Car-Hailing Order Data and Points of Interest Data

Analysis of Metro Station Ridership Considering Spatial Heterogeneity

Use Frequency of Metro–Bikeshare Integration: Evidence from Nanjing, China

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