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Detecting urban road network accessibility problems using taxi GPS data

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  • Cui, JianXun
  • Liu, Feng
  • Janssens, Davy
  • An, Shi
  • Wets, Geert
  • Cools, Mario

Abstract

Urban population growth and economic development have led to the creation of new communities, jobs and services at places where the existing road network might not cover or efficiently handle traffic. This generates isolated pockets of areas which are difficult to reach through the transport system. To address this accessibility problem, we have developed a novel approach to systematically examine the current urban land use and road network conditions as well as to identify poorly connected regions, using GPS data collected from taxis. This method is composed of four major steps. First, city-wide passenger travel demand patterns and travel times are modeled based on GPS trajectories. Upon this model, high density residential regions are then identified, and measures to assess accessibility of each of these places are developed. Next, the regions with the lowest level of accessibility among all the residential areas are detected, and finally the detected regions are further examined and specific transport situations are analyzed.

Suggested Citation

  • Cui, JianXun & Liu, Feng & Janssens, Davy & An, Shi & Wets, Geert & Cools, Mario, 2016. "Detecting urban road network accessibility problems using taxi GPS data," Journal of Transport Geography, Elsevier, vol. 51(C), pages 147-157.
    • Handle: RePEc:eee:jotrge:v:51:y:2016:i:c:p:147-157
    DOI: 10.1016/j.jtrangeo.2015.12.007
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    References listed on IDEAS

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    2. Sharma, Ishant & Mishra, Sabyasachee & Golias, Mihalis M. & Welch, Timothy F. & Cherry, Christopher R., 2020. "Equity of transit connectivity in Tennessee cities," Journal of Transport Geography, Elsevier, vol. 86(C).
    3. Chengming Li & Zhaoxin Dai & Weixiang Peng & Jianming Shen, 2019. "Green Travel Mode: Trajectory Data Cleansing Method for Shared Electric Bicycles," Sustainability, MDPI, vol. 11(5), pages 1-14, March.
    4. 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.
    5. Songkorn Siangsuebchart & Sarawut Ninsawat & Apichon Witayangkurn & Surachet Pravinvongvuth, 2021. "Public Transport GPS Probe and Rail Gate Data for Assessing the Pattern of Human Mobility in the Bangkok Metropolitan Region, Thailand," Sustainability, MDPI, vol. 13(4), pages 1-29, February.
    6. Xing, Jiping & Wu, Wei & Cheng, Qixiu & Liu, Ronghui, 2022. "Traffic state estimation of urban road networks by multi-source data fusion: Review and new insights," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 595(C).
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    8. Ming Sun & Xueyu Jiao, 2023. "Quantitative Identification Study of Epidemic Risk in the Spatial Environment of Harbin City," Sustainability, MDPI, vol. 15(9), pages 1-22, May.
    9. Hu, Beibei & Xia, Xuanxuan & Sun, Huijun & Dong, Xianlei, 2019. "Understanding the imbalance of the taxi market: From the high-quality customer’s perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    10. Yi Dang & Chengjin Wang & Peiran Chen, 2022. "Identification and Optimization Strategy of Urban Park Service Areas Based on Accessibility by Public Transport: Beijing as a Case Study," Sustainability, MDPI, vol. 14(12), pages 1-13, June.
    11. (Ato) Xu, Wangtu & Zhou, Jiangping & Yang, Linchuan & Li, Ling, 2018. "The implications of high-speed rail for Chinese cities: Connectivity and accessibility," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 308-326.
    12. 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.
    13. Suhono H. Supangkat & Rohullah Ragajaya & Agustinus Bambang Setyadji, 2023. "Implementation of Digital Geotwin-Based Mobile Crowdsensing to Support Monitoring System in Smart City," Sustainability, MDPI, vol. 15(5), pages 1-27, February.

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