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Analyzing travel destinations distribution using large-scaled GPS trajectories: A spatio-temporal Log-Gaussian Cox process

Author

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  • Tang, Jinjun
  • Zhao, Chuyun
  • Liu, Fang
  • Hao, Wei
  • Gao, Fan

Abstract

Under the increasingly serious city diseases, it is very important to deeply understand the spatio-temporal correlation between travel destination and urban built environment. The spatio-temporal point process has been widely used in many specific fields to explore the evolution of events. We employ a spatio-temporal Log-Gaussian Cox process model (LGCP) to analyze travel destination, which consists of three different components: a spatial component λ(s), a temporal component μ(t), and a separable log-Gaussian stochastic intensity field exp{Υ(s,t)}, to model and predict the destination. The spatial component and temporal component are modeled by the Poisson log linear regression model combined with covariates of interest separately. Spatial covariates including land use, demographics, travel origins and road network, and temporal covariates including the periodic function, weekdays and weekend variation, are selected as influencing factors to analyze the impact of building environment on travel destinations under different combinations of spatial and temporal covariates. Then, an extensible Markov chain Monte Carlo (MCMC) algorithm is applied to simulate Gaussian random field, and the fitted LGCP model can be used to obtain the prediction distribution of Gaussian random field beyond the last time point of data observation. In the experiment, taxi destination data collected in Shenzhen city May 6 to May 23, 2019 are used to validate modeling performance. The results in experiment shows the space–time positions of destination simulated by LGCP model are very similar to those observed. This study can provide strategy for urban planners and transportation administration to conduct reasonable policy to balance travel demand and public resources.

Suggested Citation

  • Tang, Jinjun & Zhao, Chuyun & Liu, Fang & Hao, Wei & Gao, Fan, 2022. "Analyzing travel destinations distribution using large-scaled GPS trajectories: A spatio-temporal Log-Gaussian Cox process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 599(C).
    • Handle: RePEc:eee:phsmap:v:599:y:2022:i:c:s0378437122002515
    DOI: 10.1016/j.physa.2022.127305
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    1. Yue Hou & Zhiyuan Deng & Hanke Cui & M. Irfan Uddin, 2021. "Short-Term Traffic Flow Prediction with Weather Conditions: Based on Deep Learning Algorithms and Data Fusion," Complexity, Hindawi, vol. 2021, pages 1-14, January.
    2. Soria, Jason & Stathopoulos, Amanda, 2021. "Investigating socio-spatial differences between solo ridehailing and pooled rides in diverse communities," Journal of Transport Geography, Elsevier, vol. 95(C).
    3. Huang, Ying & Xu, Wangtu (Ato), 2021. "Spatial and temporal heterogeneity of the impact of high-speed railway on urban economy: Empirical study of Chinese cities," Journal of Transport Geography, Elsevier, vol. 91(C).
    4. Anders Brix & Peter J. Diggle, 2001. "Spatiotemporal prediction for log‐Gaussian Cox processes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(4), pages 823-841.
    5. Tang, Jinjun & Bi, Wei & Liu, Fang & Zhang, Wenhui, 2021. "Exploring urban travel patterns using density-based clustering with multi-attributes from large-scaled vehicle trajectories," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 561(C).
    6. Jun, Myung-Jin & Choi, Keechoo & Jeong, Ji-Eun & Kwon, Ki-Hyun & Kim, Hee-Jae, 2015. "Land use characteristics of subway catchment areas and their influence on subway ridership in Seoul," Journal of Transport Geography, Elsevier, vol. 48(C), pages 30-40.
    7. Lei Zhang & Guoxing Zhang & Zhizheng Liang & Ekene Frank Ozioko, 2018. "Multi-features taxi destination prediction with frequency domain processing," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-22, March.
    8. Tilman M. Davies & Martin L. Hazelton, 2013. "Assessing minimum contrast parameter estimation for spatial and spatiotemporal log‐Gaussian Cox processes," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 67(4), pages 355-389, November.
    9. Tang, Jinjun & Gao, Fan & Han, Chunyang & Cen, Xuekai & Li, Zhitao, 2021. "Uncovering the spatially heterogeneous effects of shared mobility on public transit and taxi," Journal of Transport Geography, Elsevier, vol. 95(C).
    10. Yanjie Ji & Xinwei Ma & Mingyuan Yang & Yuchuan Jin & Liangpeng Gao, 2018. "Exploring Spatially Varying Influences on Metro-Bikeshare Transfer: A Geographically Weighted Poisson Regression Approach," Sustainability, MDPI, vol. 10(5), pages 1-23, May.
    11. Taylor, Benjamin M. & Davies, Tilman M. & Rowlingson, Barry S. & Diggle, Peter J., 2013. "lgcp: An R Package for Inference with Spatial and Spatio-Temporal Log-Gaussian Cox Processes," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 52(i04).
    12. Zhengyi Zhou & David S. Matteson & Dawn B. Woodard & Shane G. Henderson & Athanasios C. Micheas, 2015. "A Spatio-Temporal Point Process Model for Ambulance Demand," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 6-15, March.
    13. Singhal, Abhishek & Kamga, Camille & Yazici, Anil, 2014. "Impact of weather on urban transit ridership," Transportation Research Part A: Policy and Practice, Elsevier, vol. 69(C), pages 379-391.
    14. Yang Liu & Yanjie Ji & Zhuangbin Shi & Liangpeng Gao, 2018. "The Influence of the Built Environment on School Children’s Metro Ridership: An Exploration Using Geographically Weighted Poisson Regression Models," Sustainability, MDPI, vol. 10(12), pages 1-16, December.
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