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Dynamic Accessibility using Big Data: The Role of the Changing Conditions of Network Congestion and Destination Attractiveness

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Listed:
  • Borja Moya-Gómez

    (Universidad Complutense de Madrid (UCM))

  • María Henar Salas-Olmedo

    (Universidad Complutense de Madrid (UCM))

  • Juan Carlos García-Palomares

    (Universidad Complutense de Madrid (UCM))

  • Javier Gutiérrez

    (Universidad Complutense de Madrid (UCM))

Abstract

Accessibility is essentially a dynamic concept. However, most studies on urban accessibility take a static approach, overlooking the fact that accessibility conditions change dramatically throughout the day. Due to their high spatial and temporal resolution, the new data sources (Big Data) offer new possibilities for the study of accessibility. The aim of this paper is to analyse urban accessibility considering its two components –the performance of the transport network and the attractiveness of the destinations– using a dynamic approach using data from TomTom and Twitter respectively. This allows us to obtain profiles that highlight the daily variations in accessibility in the city of Madrid, and identify the influence of congestion and the changes in location of the population. These profiles reveal significant variations according to transport zones. Each transport zone has its own accessibility profile, and thus its own specific problems, which require solutions that are also specific.

Suggested Citation

  • Borja Moya-Gómez & María Henar Salas-Olmedo & Juan Carlos García-Palomares & Javier Gutiérrez, 2018. "Dynamic Accessibility using Big Data: The Role of the Changing Conditions of Network Congestion and Destination Attractiveness," Networks and Spatial Economics, Springer, vol. 18(2), pages 273-290, June.
    • Handle: RePEc:kap:netspa:v:18:y:2018:i:2:d:10.1007_s11067-017-9348-z
    DOI: 10.1007/s11067-017-9348-z
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    2. Mansour, Shawky & Alahmadi, Mohammed & Abulibdeh, Ammar, 2022. "Spatial assessment of audience accessibility to historical monuments and museums in Qatar during the 2022 FIFA World Cup," Transport Policy, Elsevier, vol. 127(C), pages 116-129.
    3. Yun Xiang & Chengcheng Xu & Weijie Yu & Shuyi Wang & Xuedong Hua & Wei Wang, 2019. "Investigating Dominant Trip Distance for Intercity Passenger Transport Mode Using Large-Scale Location-Based Service Data," Sustainability, MDPI, vol. 11(19), pages 1-17, September.
    4. Hu, Yujie & Downs, Joni, 2019. "Measuring and visualizing place-based space-time job accessibility," Journal of Transport Geography, Elsevier, vol. 74(C), pages 278-288.
    5. Shixiong Jiang & Wei Guan & Zhengbing He & Liu Yang, 2018. "Measuring Taxi Accessibility Using Grid-Based Method with Trajectory Data," Sustainability, MDPI, vol. 10(9), pages 1-16, September.
    6. Yujie Hu & Joni Downs, 2020. "Measuring and Visualizing Place-Based Space-Time Job Accessibility," Papers 2006.00268, arXiv.org.
    7. Tao, Sui & Cheng, Long & He, Sylvia & Witlox, Frank, 2023. "Examining the non-linear effects of transit accessibility on daily trip duration: A focus on the low-income population," Journal of Transport Geography, Elsevier, vol. 109(C).
    8. F. Crawford & D. P. Watling & R. D. Connors, 2023. "Analysing Spatial Intrapersonal Variability of Road Users Using Point-to-Point Sensor Data," Networks and Spatial Economics, Springer, vol. 23(2), pages 373-406, June.
    9. Shi, Yuji & Blainey, Simon & Sun, Chao & Jing, Peng, 2020. "A literature review on accessibility using bibliometric analysis techniques," Journal of Transport Geography, Elsevier, vol. 87(C).
    10. Tianlu Qian & Zheng Fu & Jie Chen & Shujie Qin & Changbai Xi & Jiechen Wang, 2023. "Evaluating multiscale and multimodal transport inequalities in Chinese cities with massive open-source path data," Journal of Geographical Systems, Springer, vol. 25(2), pages 237-264, April.
    11. Igor Lazov, 2019. "A Methodology for Revenue Analysis of Parking Lots," Networks and Spatial Economics, Springer, vol. 19(1), pages 177-198, March.
    12. Bimpou, Konstantina & Ferguson, Neil S., 2020. "Dynamic accessibility: Incorporating day-to-day travel time reliability into accessibility measurement," Journal of Transport Geography, Elsevier, vol. 89(C).
    13. Jianhua Ni & Jie Chen & Zheng Fu & Changbai Xi & Jiechen Wang, 2023. "Examining Commercial Center Accessibility Using a Modified 2SFCA method in Realistic Networks in Nanjing, China," Networks and Spatial Economics, Springer, vol. 23(4), pages 1025-1045, December.
    14. Panrawee Rungskunroch & Yuwen Yang & Sakdirat Kaewunruen, 2020. "Does High-Speed Rail Influence Urban Dynamics and Land Pricing?," Sustainability, MDPI, vol. 12(7), pages 1-18, April.
    15. Wang, Yafei & Chen, Bi Yu & Yuan, Hui & Wang, Donggen & Lam, William H.K. & Li, Qingquan, 2018. "Measuring temporal variation of location-based accessibility using space-time utility perspective," Journal of Transport Geography, Elsevier, vol. 73(C), pages 13-24.
    16. Jang, Seongman & Lee, Seungil, 2020. "Study of the regional accessibility calculation by income class based on utility-based accessibility," Journal of Transport Geography, Elsevier, vol. 84(C).
    17. Gutiérrez, Antonio, 2022. "Movilidad urbana y datos de alta frecuencia [Urban mobility and high frequency data]," MPRA Paper 114854, University Library of Munich, Germany.
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    20. Linlin Liu & Bohong Zheng & Chen Luo & Komi Bernard Bedra & Francis Masrabaye, 2022. "Access to City Center: Automobile vs. Public Transit," IJERPH, MDPI, vol. 19(9), pages 1-16, May.

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