IDEAS home Printed from https://ideas.repec.org/a/eee/jotrge/v58y2017icp247-255.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0966692316300357
    Download Restriction: no
    File URL: https://libkey.io/10.1016/j.jtrangeo.2016.11.009?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Yildirimoglu, Mehmet & Geroliminis, Nikolas, 2013. "Experienced travel time prediction for congested freeways," Transportation Research Part B: Methodological, Elsevier, vol. 53(C), pages 45-63.
    2. Tao, Sui & Rohde, David & Corcoran, Jonathan, 2014. "Examining the spatial–temporal dynamics of bus passenger travel behaviour using smart card data and the flow-comap," Journal of Transport Geography, Elsevier, vol. 41(C), pages 21-36.
    3. Bagchi, M. & White, P.R., 2005. "The potential of public transport smart card data," Transport Policy, Elsevier, vol. 12(5), pages 464-474, September.
    4. Daraio, Cinzia & Diana, Marco & Di Costa, Flavia & Leporelli, Claudio & Matteucci, Giorgio & Nastasi, Alberto, 2016. "Efficiency and effectiveness in the urban public transport sector: A critical review with directions for future research," European Journal of Operational Research, Elsevier, vol. 248(1), pages 1-20.
    5. Trasarti, Roberto & Olteanu-Raimond, Ana-Maria & Nanni, Mirco & Couronné, Thomas & Furletti, Barbara & Giannotti, Fosca & Smoreda, Zbigniew & Ziemlicki, Cezary, 2015. "Discovering urban and country dynamics from mobile phone data with spatial correlation patterns," Telecommunications Policy, Elsevier, vol. 39(3), pages 347-362.
    6. Demissie, Merkebe Getachew & Correia, Gonçalo Homem de Almeida & Bento, Carlos, 2013. "Exploring cellular network handover information for urban mobility analysis," Journal of Transport Geography, Elsevier, vol. 31(C), pages 164-170.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. Zhong, Jiaming & He, Zhaocheng & Tian, Chenyu, 2019. "Uncovering quasi-periodicity of transit behavior based on smart card data," Journal of Transport Geography, Elsevier, vol. 79(C), pages 1-1.
    5. 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.
    6. Yanbing Bai & Lu Sun & Haoyu Liu & Chao Xie, 2021. "Using Bus Ticketing Big Data to Investigate the Behaviors of the Population Flow of Chinese Suburban Residents in the Post-COVID-19 Phase," IJERPH, MDPI, vol. 18(11), pages 1-16, June.
    7. 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.
    8. 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.
    9. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Claudio Gariazzo & Armando Pelliccioni & Maria Paola Bogliolo, 2019. "Spatiotemporal Analysis of Urban Mobility Using Aggregate Mobile Phone Derived Presence and Demographic Data: A Case Study in the City of Rome, Italy," Data, MDPI, vol. 4(1), pages 1-25, January.
    2. Kaniz Fatima & Sara Moridpour & Tayebeh Saghapour, 2021. "Spatial and Temporal Distribution of Elderly Public Transport Mode Preference," Sustainability, MDPI, vol. 13(9), pages 1-15, April.
    3. Zijia Wang & Hao Tang & Wenjuan Wang & Yang Xi, 2020. "The Pattern of Non-Roundtrip Travel on Urban Rail and Its Application in Transit Improvement," Sustainability, MDPI, vol. 12(9), pages 1-16, April.
    4. Nadav Shalit & Michael Fire & Eran Ben-Elia, 2023. "A supervised machine learning model for imputing missing boarding stops in smart card data," Public Transport, Springer, vol. 15(2), pages 287-319, June.
    5. Liping Ge & Malek Sarhani & Stefan Voß & Lin Xie, 2021. "Review of Transit Data Sources: Potentials, Challenges and Complementarity," Sustainability, MDPI, vol. 13(20), pages 1-37, October.
    6. Avenali, Alessandro & Catalano, Giuseppe & D'Alfonso, Tiziana & Matteucci, Giorgio, 2020. "The allocation of national public resources in the Italian local public bus transport sector," Research in Transportation Economics, Elsevier, vol. 81(C).
    7. Liu, Yan & Wang, Siqin & Xie, Bin, 2019. "Evaluating the effects of public transport fare policy change together with built and non-built environment features on ridership: The case in South East Queensland, Australia," Transport Policy, Elsevier, vol. 76(C), pages 78-89.
    8. Kevin Credit & Zander Arnao, 2023. "A method to derive small area estimates of linked commuting trips by mode from open source LODES and ACS data," Environment and Planning B, , vol. 50(3), pages 709-722, March.
    9. D’Alfonso, Tiziana & Jiang, Changmin & Bracaglia, Valentina, 2016. "Air transport and high-speed rail competition: Environmental implications and mitigation strategies," Transportation Research Part A: Policy and Practice, Elsevier, vol. 92(C), pages 261-276.
    10. An, Ran & Zahnow, Renee & Pojani, Dorina & Corcoran, Jonathan, 2019. "Weather and cycling in New York: The case of Citibike," Journal of Transport Geography, Elsevier, vol. 77(C), pages 97-112.
    11. Campos-Alba, Cristina M. & Prior, Diego & Pérez-López, Gemma & Zafra-Gómez, Jose L., 2020. "Long-term cost efficiency of alternative management forms for urban public transport from the public sector perspective," Transport Policy, Elsevier, vol. 88(C), pages 16-23.
    12. Wang, Yihong & Correia, Gonçalo Homem de Almeida & de Romph, Erik & Timmermans, H.J.P., 2017. "Using metro smart card data to model location choice of after-work activities: An application to Shanghai," Journal of Transport Geography, Elsevier, vol. 63(C), pages 40-47.
    13. Avenali, Alessandro & Boitani, Andrea & Catalano, Giuseppe & D’Alfonso, Tiziana & Matteucci, Giorgio, 2016. "Assessing standard costs in local public bus transport: Evidence from Italy," Transport Policy, Elsevier, vol. 52(C), pages 164-174.
    14. Apanasevic, Tatjana & Rudmark, Daniel, 2021. "Crowdsourcing and Public Transportation: Barriers and Opportunities," 23rd ITS Biennial Conference, Online Conference / Gothenburg 2021. Digital societies and industrial transformations: Policies, markets, and technologies in a post-Covid world 238005, International Telecommunications Society (ITS).
    15. Mariano Gallo & Mario Marinelli, 2020. "Sustainable Mobility: A Review of Possible Actions and Policies," Sustainability, MDPI, vol. 12(18), pages 1-39, September.
    16. Tao, Sui & Rohde, David & Corcoran, Jonathan, 2014. "Examining the spatial–temporal dynamics of bus passenger travel behaviour using smart card data and the flow-comap," Journal of Transport Geography, Elsevier, vol. 41(C), pages 21-36.
    17. Christine Keller & Felix Glück & Carl Friedrich Gerlach & Thomas Schlegel, 2022. "Investigating the Potential of Data Science Methods for Sustainable Public Transport," Sustainability, MDPI, vol. 14(7), pages 1-26, April.
    18. Bantis, Thanos & Haworth, James, 2020. "Assessing transport related social exclusion using a capabilities approach to accessibility framework: A dynamic Bayesian network approach," Journal of Transport Geography, Elsevier, vol. 84(C).
    19. Mohammadi, Neda & Taylor, John E., 2017. "Urban energy flux: Spatiotemporal fluctuations of building energy consumption and human mobility-driven prediction," Applied Energy, Elsevier, vol. 195(C), pages 810-818.
    20. Fitzová, Hana & Matulová, Markéta, 2020. "Comparison of urban public transport systems in the Czech Republic and Slovakia: Factors underpinning efficiency," Research in Transportation Economics, Elsevier, vol. 81(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:jotrge:v:58:y:2017:i:c:p:247-255. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/journal-of-transport-geography .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.