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Analysing the spatial-temporal characteristics of bus travel demand using the heat map

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  • Yu, Chang
  • He, Zhao-Cheng

Abstract

As the basic travel service for urban transit, bus services carry the majority of urban passengers. The characterisation of urban residents' transit trips can provide a first-hand reference for the evaluation, management and planning of public transport. Over the past two decades, data from smart cards have become a new source of travel survey data, providing more comprehensive spatial-temporal information about urban public transport trips. In this paper, a multi-step methodology for mining smart card data is developed to analyse the spatial-temporal characteristics of bus travel demand. Using the bus network in Guangzhou, China, as a case study, a smart card dataset is first processed to quantitatively estimate the travel demand at the bus stop level. The term ‘bus service coverage’ is introduced to map the bus travel demand from bus stops to regions. This dataset is used to create heat maps that visualise the regional distribution of bus travel demand. To identify the distribution patterns of bus travel demand, two-dimensional principal component analysis and principal component analysis are applied to extract the features of the heat maps, and the Gaussian mixture model is used for the feature clustering. The proposed methodology visually reveals the spatial-temporal patterns of bus travel demand and provides a practical set of visual analytics for transit trip characterisation.

Suggested Citation

  • Yu, Chang & He, Zhao-Cheng, 2017. "Analysing the spatial-temporal characteristics of bus travel demand using the heat map," Journal of Transport Geography, Elsevier, vol. 58(C), pages 247-255.
    • Handle: RePEc:eee:jotrge:v:58:y:2017:i:c:p:247-255
    DOI: 10.1016/j.jtrangeo.2016.11.009
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    3. Pengfei Lin & Jiancheng Weng & Dimitrios Alivanistos & Siyong Ma & Baocai Yin, 2020. "Identifying and Segmenting Commuting Behavior Patterns Based on Smart Card Data and Travel Survey Data," Sustainability, MDPI, vol. 12(12), pages 1-18, June.
    4. Shi, Linchang & Yang, Jiayu & Lee, Jaeyoung Jay & Bai, Jun & Ryu, Ingon & Choi, Keechoo, 2024. "Spatial-temporal identification of commuters using trip chain data from non-motorized mode incentive program and public transportation," Journal of Transport Geography, Elsevier, vol. 117(C).
    5. Wenping Liu & Chenlu Dong & Weijuan Chen, 2017. "Mapping and Quantifying Spatial and Temporal Dynamics and Bundles of Travel Flows of Residents Visiting Urban Parks," Sustainability, MDPI, vol. 9(8), pages 1-15, July.
    6. Yunjiao Zhou, 2020. "Spatial-temporal Dynamics of Population Aggregation during the Spring Festival based on Baidu Heat Map in Central Area of Chengdu City, China," Modern Applied Science, Canadian Center of Science and Education, vol. 14(4), pages 1-44, April.
    7. Merkebe Getachew Demissie & Lina Kattan, 2022. "Understanding the temporal and spatial interactions between transit ridership and urban land-use patterns: an exploratory study," Public Transport, Springer, vol. 14(2), pages 385-417, June.
    8. Yilei Tao & Ying Wang & Xinyu Wang & Guohang Tian & Shumei Zhang, 2022. "Measuring the Correlation between Human Activity Density and Streetscape Perceptions: An Analysis Based on Baidu Street View Images in Zhengzhou, China," Land, MDPI, vol. 11(3), pages 1-19, March.
    9. Cristina Pronello & Davide Longhi & Jean-Baptiste Gaborieau, 2018. "Smart Card Data Mining to Analyze Mobility Patterns in Suburban Areas," Sustainability, MDPI, vol. 10(10), pages 1-21, September.
    10. Rui Li & Youqin Huang, 2023. "COVID-19 pandemic and minority health disparities in New York City: A spatial and temporal perspective," Environment and Planning B, , vol. 50(5), pages 1194-1211, June.

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