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Exploring the potential of mobile phone records and online route planners for dynamic accessibility analysis

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  • García-Albertos, Pedro
  • Picornell, Miguel
  • Salas-Olmedo, María Henar
  • Gutiérrez, Javier

Abstract

Big Data sources offer new possibilities for urban mobility and accessibility studies. As people carry out their activities in a city, they leave behind a digital fingerprint that can be used to analyze the population’s daily mobility patterns and determine the exact times of travel between points of origin and destination at different times of the day. These data present high spatial and temporal resolution, and enable accurate and dynamic analysis of accessibility. The objective of this study was to conduct a dynamic analysis of urban accessibility considering its two main components: travel times and the attractiveness of destinations. To this end, we calculated travel times between transport zones using the Google Maps API and constructed origin and destination (OD) travel matrices from mobile phone records. Several scenarios were generated to analyze dynamic accessibility and the separate influence of its two components. We also conducted a cluster analysis to characterize transport zones according to their accessibility in each of the scenarios and times of day considered. Our results indicate that these new sources of geolocated data show considerable potential for use in time-sensitive accessibility studies, since they yield more accurate and realistic information than static or partially dynamic analyses. Such information could help politicians take better decisions concerning transport and land use.

Suggested Citation

  • García-Albertos, Pedro & Picornell, Miguel & Salas-Olmedo, María Henar & Gutiérrez, Javier, 2019. "Exploring the potential of mobile phone records and online route planners for dynamic accessibility analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 125(C), pages 294-307.
    • Handle: RePEc:eee:transa:v:125:y:2019:i:c:p:294-307
    DOI: 10.1016/j.tra.2018.02.008
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    References listed on IDEAS

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    Cited by:

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    2. Chen, Siyuan & Liu, Xin & Lyu, Cheng & Vlacic, Ljubo & Tang, Tianli & Liu, Zhiyuan, 2023. "A holistic data-driven framework for developing a complete profile of bus passengers," Transportation Research Part A: Policy and Practice, Elsevier, vol. 173(C).
    3. Sławomir Goliszek, 2021. "GIS tools and programming languages for creating models of public and private transport potential accessibility in Szczecin, Poland," Journal of Geographical Systems, Springer, vol. 23(1), pages 115-137, January.
    4. Antonello Ignazio Croce & Giuseppe Musolino & Corrado Rindone & Antonino Vitetta, 2021. "Estimation of Travel Demand Models with Limited Information: Floating Car Data for Parameters’ Calibration," Sustainability, MDPI, vol. 13(16), pages 1-23, August.
    5. Yadan Yan & Tianzhao Guo & Dongwei Wang, 2021. "Dynamic Accessibility Analysis of Urban Road-to-Freeway Interchanges Based on Navigation Map Paths," Sustainability, MDPI, vol. 13(1), pages 1-17, January.
    6. Alina Viorica Dumitrașcu & Camelia Teodorescu & Alexandra Cioclu, 2023. "Accessibility and Tourist Satisfaction—Influencing Factors for Tourism in Dobrogea, Romania," Sustainability, MDPI, vol. 15(9), pages 1-17, May.
    7. Fielbaum, Andrés & Jara-Diaz, Sergio, 2021. "Assessment of the socio-spatial effects of urban transport investment using Google Maps API," Journal of Transport Geography, Elsevier, vol. 91(C).
    8. Helai Huang & Jialing Wu & Fang Liu & Yiwei Wang, 2020. "Measuring Accessibility Based on Improved Impedance and Attractive Functions Using Taxi Trajectory Data," Sustainability, MDPI, vol. 13(1), pages 1-23, December.
    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. Amparo Moyano & Marcin Stępniak & Borja Moya-Gómez & Juan Carlos García-Palomares, 2021. "Traffic congestion and economic context: changes of spatiotemporal patterns of traffic travel times during crisis and post-crisis periods," Transportation, Springer, vol. 48(6), pages 3301-3324, December.
    11. Jairo Ortega & János Tóth & Tamás Péter, 2021. "A Comprehensive Model to Study the Dynamic Accessibility of the Park & Ride System," Sustainability, MDPI, vol. 13(7), pages 1-17, April.
    12. Siqi Lai & Brian Deal, 2022. "Parks, Green Space, and Happiness: A Spatially Specific Sentiment Analysis Using Microblogs in Shanghai, China," Sustainability, MDPI, vol. 15(1), pages 1-18, December.

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