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Dynamic accessibility mapping using floating car data: a network-constrained density estimation approach

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Listed:
  • Li, Qingquan
  • Zhang, Tong
  • Wang, Handong
  • Zeng, Zhe

Abstract

The concept of accessibility has been attracting increasing attention from transportation planners and researchers. The goal of “smart growth” is to promote accessibility rather than mobility. Accessibility mapping is a highly effective tool to support smart growth planning. Studies on accessibility mapping and analysis have proliferated over the last decade as Geographic Information Systems (GIS) has been widely adopted to produce accessibility maps. Nevertheless, a major negative aspect of these efforts is the lack of dynamic access measurement. Traffic conditions change significantly over space and time, leading to non-stationary travel time estimation which is central to accessibility measurement. In this paper, we propose a dynamic accessibility measure based on real-time travel speeds that are derived from floating car data (FCD). We introduce a novel integrated access measure to compute accessibility to Points of Interest (POIs) constrained by road networks. POI accessibility measures are obtained by means of both Network Kernel Density Estimation (NKDE) and Network Linear Density Estimation (NLDE). Therefore, we are able to capture temporal dynamics by taking into account speed variability using FCD during peak and off-peak hours on weekdays and weekends. We demonstrate that the proposed approach can capture the spatio-temporal dynamics of accessibility using FCD for Wuhan city in China. Empirical studies were conducted to examine the impacts of density estimation parameters (search bandwidths) and POI weights on the integrated accessibility. We find that search bandwidth and POI weights would impose profound influence on final accessibility values. Experiments also show that NKDE and NLDE would produce significantly different estimation results.

Suggested Citation

  • Li, Qingquan & Zhang, Tong & Wang, Handong & Zeng, Zhe, 2011. "Dynamic accessibility mapping using floating car data: a network-constrained density estimation approach," Journal of Transport Geography, Elsevier, vol. 19(3), pages 379-393.
  • Handle: RePEc:eee:jotrge:v:19:y:2011:i:3:p:379-393
    DOI: 10.1016/j.jtrangeo.2010.07.003
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    Cited by:

    1. Yu Liu & Chaogui Kang & Song Gao & Yu Xiao & Yuan Tian, 2012. "Understanding intra-urban trip patterns from taxi trajectory data," Journal of Geographical Systems, Springer, vol. 14(4), pages 463-483, October.
    2. Xintao Liu & Ziwei Lin & Jianwei Huang & He Gao & Wenzhong Shi, 2021. "Evaluating the Inequality of Medical Service Accessibility Using Smart Card Data," IJERPH, MDPI, vol. 18(5), pages 1-18, March.
    3. Tang, Jinjun & Liu, Fang & Wang, Yinhai & Wang, Hua, 2015. "Uncovering urban human mobility from large scale taxi GPS data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 438(C), pages 140-153.
    4. Shili Chen & Haiyan Tao & Xuliang Li & Li Zhuo, 2018. "Detecting urban commercial patterns using a latent semantic information model: A case study of spatial-temporal evolution in Guangzhou, China," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-20, August.
    5. Rongxiang Su & Zhixiang Fang & Ningxin Luo & Jingwei Zhu, 2018. "Understanding the Dynamics of the Pick-Up and Drop-Off Locations of Taxicabs in the Context of a Subsidy War among E-Hailing Apps," Sustainability, MDPI, vol. 10(4), pages 1-24, April.
    6. Liu, Xi & Gong, Li & Gong, Yongxi & Liu, Yu, 2015. "Revealing travel patterns and city structure with taxi trip data," Journal of Transport Geography, Elsevier, vol. 43(C), pages 78-90.
    7. Ke Nie & Zhensheng Wang & Qingyun Du & Fu Ren & Qin Tian, 2015. "A Network-Constrained Integrated Method for Detecting Spatial Cluster and Risk Location of Traffic Crash: A Case Study from Wuhan, China," Sustainability, MDPI, vol. 7(3), pages 1-16, March.
    8. Naqavi, Fatemeh & Sundberg, Marcus & Västberg, Oskar Blom & Karlström, Anders & Hugosson, Muriel Beser, 2023. "Mobility constraints and accessibility to work: Application to Stockholm," Transportation Research Part A: Policy and Practice, Elsevier, vol. 175(C).
    9. Horner, Mark & Downs, Joni, 2014. "Integrating people and place: A density-based measure for assessing accessibility to opportunities," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 7(2), pages 1-18.
    10. 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.
    11. Song Gao & Yaoli Wang & Yong Gao & Yu Liu, 2013. "Understanding Urban Traffic-Flow Characteristics: A Rethinking of Betweenness Centrality," Environment and Planning B, , vol. 40(1), pages 135-153, February.
    12. Salonen, Maria & Toivonen, Tuuli, 2013. "Modelling travel time in urban networks: comparable measures for private car and public transport," Journal of Transport Geography, Elsevier, vol. 31(C), pages 143-153.
    13. Panayotis Christidis & Nicolás Ibanez Rivas1, 2012. "European transport policy: methodology to assess accessibility impacts," Chapters, in: Karst T. Geurs & Kevin J. Krizek & Aura Reggiani (ed.), Accessibility Analysis and Transport Planning, chapter 16, pages 280-294, Edward Elgar Publishing.
    14. Xuewen Zhou & Xiaoxia Zhang & Zhimei Dai & Roosmayri Lovina Hermaputi & Chen Hua & Yonghua Li, 2021. "Spatial Layout and Coupling of Urban Cultural Relics: Analyzing Historical Sites and Commercial Facilities in District III of Shaoxing," Sustainability, MDPI, vol. 13(12), pages 1-20, June.
    15. Luyu Liu & Adam Porr & Harvey J. Miller, 2023. "Realizable accessibility: evaluating the reliability of public transit accessibility using high-resolution real-time data," Journal of Geographical Systems, Springer, vol. 25(3), pages 429-451, July.
    16. 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.
    17. Chelsey Palmateer & Andrew Owen & David Levinson, 2016. "The Impact of Analysis Boundaries in Accessibility Evaluations: A Case Study," Working Papers 149, University of Minnesota: Nexus Research Group.
    18. 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.
    19. Kang, Chaogui & Ma, Xiujun & Tong, Daoqin & Liu, Yu, 2012. "Intra-urban human mobility patterns: An urban morphology perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(4), pages 1702-1717.
    20. 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.
    21. Karst T. Geurs & Kevin J. Krizek & Aura Reggiani (ed.), 2012. "Accessibility Analysis and Transport Planning," Books, Edward Elgar Publishing, number 14718.
    22. 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.
    23. Fu, Xiao & Zuo, Yufan & Zhang, Shanqi & Liu, Zhiyuan, 2022. "Measuring joint space-time accessibility in transit network under travel time uncertainty," Transport Policy, Elsevier, vol. 116(C), pages 355-368.
    24. Escobar, D. & Cadena-Gaitan, C. & Garcia, F., 2014. "Accessibility analysis as an urban planning tool: Gas station location," MERIT Working Papers 2014-048, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    25. Guoqiang Shen & Zhangye Wang & Long Zhou & Yu Liu & Xiaoyi Yan, 2020. "Home-Based Locational Accessibility to Essential Urban Services: The Case of Wake County, North Carolina, USA," Sustainability, MDPI, vol. 12(21), pages 1-21, November.

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