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Spatio-temporal autocorrelation of road network data

Author

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  • Tao Cheng
  • James Haworth
  • Jiaqiu Wang

Abstract

Modelling autocorrelation structure among space–time observations is crucial in space–time modelling and forecasting. The aim of this research is to examine the spatio-temporal autocorrelation structure of road networks in order to determine likely requirements for building a suitable space–time forecasting model. Exploratory space–time autocorrelation analysis is carried out using journey time data collected on London’s road network. Through the use of both global and local autocorrelation measures, the autocorrelation structure of the road network is found to be dynamic and heterogeneous in both space and time. It reveals that a global measure of autocorrelation is not sufficient to explain the network structure. Dynamic and local structures must be accounted for space–time modelling and forecasting. This has broad implications for space–time modelling and network complexity. Copyright Springer-Verlag 2012

Suggested Citation

  • Tao Cheng & James Haworth & Jiaqiu Wang, 2012. "Spatio-temporal autocorrelation of road network data," Journal of Geographical Systems, Springer, vol. 14(4), pages 389-413, October.
    • Handle: RePEc:kap:jgeosy:v:14:y:2012:i:4:p:389-413
    DOI: 10.1007/s10109-011-0149-5
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    3. Mojtaba Rajabi-Bahaabadi & Afshin Shariat-Mohaymany & Mohsen Babaei & Daniele Vigo, 2021. "Reliable vehicle routing problem in stochastic networks with correlated travel times," Operational Research, Springer, vol. 21(1), pages 299-330, March.
    4. Zhang, Liye & Meng, Qiang & Fang Fwa, Tien, 2019. "Big AIS data based spatial-temporal analyses of ship traffic in Singapore port waters," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 129(C), pages 287-304.
    5. Alireza Ermagun & David M Levinson, 2019. "Development and application of the network weight matrix to predict traffic flow for congested and uncongested conditions," Environment and Planning B, , vol. 46(9), pages 1684-1705, November.
    6. Lu, Feng & Liu, Kang & Duan, Yingying & Cheng, Shifen & Du, Fei, 2018. "Modeling the heterogeneous traffic correlations in urban road systems using traffic-enhanced community detection approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 227-237.
    7. Unsok Ryu & Jian Wang & Unjin Pak & Sonil Kwak & Kwangchol Ri & Junhyok Jang & Kyongjin Sok, 2022. "A clustering based traffic flow prediction method with dynamic spatiotemporal correlation analysis," Transportation, Springer, vol. 49(3), pages 951-988, June.
    8. Dongqing Zhang & Zhaoxia Guo, 2019. "On the Necessity and Effects of Considering Correlated Stochastic Speeds in Shortest Path Problems Under Sustainable Environments," Sustainability, MDPI, vol. 12(1), pages 1-14, December.
    9. Hongxia Ge & Siteng Li & Rongjun Cheng & Zhenlei Chen, 2022. "Self-Attention ConvLSTM for Spatiotemporal Forecasting of Short-Term Online Car-Hailing Demand," Sustainability, MDPI, vol. 14(12), pages 1-16, June.
    10. Christopher T. Boyko & Rachel Cooper, 2013. "Density and Decision-Making: Findings from an Online Survey," Sustainability, MDPI, vol. 5(10), pages 1-21, October.
    11. Qing Luo & Daniel A. Griffith & Huayi Wu, 2019. "Spatial autocorrelation for massive spatial data: verification of efficiency and statistical power asymptotics," Journal of Geographical Systems, Springer, vol. 21(2), pages 237-269, June.
    12. Zhang, Dongqing & Wallace, Stein W. & Guo, Zhaoxia & Dong, Yucheng & Kaut, Michal, 2021. "On scenario construction for stochastic shortest path problems in real road networks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 152(C).
    13. Ma, Tao & Zhou, Zhou & Antoniou, Constantinos, 2018. "Dynamic factor model for network traffic state forecast," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 281-317.
    14. Ma, Tao & Zhou, Zhou & Abdulhai, Baher, 2015. "Nonlinear multivariate time–space threshold vector error correction model for short term traffic state prediction," Transportation Research Part B: Methodological, Elsevier, vol. 76(C), pages 27-47.

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    More about this item

    Keywords

    Spatial autocorrelation; Network structure; Space–time autocorrelation; Space–time modelling; Travel time prediction; Network complexity; R41; C23; C52;
    All these keywords.

    JEL classification:

    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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