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Correlation analysis of day-to-day origin-destination flows and traffic volumes in urban networks

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  • Barroso, Joana Maia Fernandes
  • Albuquerque-Oliveira, João Lucas
  • Oliveira-Neto, Francisco Moraes

Abstract

The trip patterns on an urban network can be represented by two main variables: origin-destination flows (OD flows), defined as the number of trips between two locations over a given time period, and traffic volumes, defined as the number of vehicles that cross a street over a given time interval. Past research on the dynamic of traffic assignment and OD estimation suggested that the traveler's decisions vary on a day-to-day basis and that their most recent decisions may affect their current travel decisions. Based on these assumptions, this study analyzed the autocorrelation of a set of day-to-day series of traffic volumes and OD flows generated from a large collection of traffic sensors, identifying the data's correlation structure over different locations and OD pairs in an urban network. To this end, a method for data treatment of the 2017 dataset from the traffic monitoring system of Fortaleza, Brazil, was employed, which consisted in the following major steps: data cleaning due to equipment failure, definition of traffic profiles for typical and atypical months, definition of daily traffic periods, selection of suitable devices to obtain OD flows, and detection of outliers in the time series. The traffic profiles and the daily traffic periods were defined by applying clustering techniques. The analysis of autocorrelation was performed after controlling for seasonal effects in the data by applying regression analysis. This study contributes to understand how the dynamic of trip patterns varies over space due to the spatial distribution of the city's activities and the network's spatial centrality. The analysis of 144 sets of traffic volumes throughout 2017 suggests that the autocorrelation of traffic volumes should be higher in congested central areas where multiple options of route are available. It seems that, for large congested networks, which present many uncertain factors (e.g., accidents, variable weather, multiple paths, etc.), part of the users do not have complete knowledge of the network's performance, and must rely on experience and habit to decide their routes, especially at more centralized locations of the network. The analysis of serial correlation in the series of sample OD flows between regions showed that the city's central area, where more commercial and service-related activities take place, seems to influence the dynamic of OD flows, probably due to the occurrence of more non-commuting trips to the central area of the city.

Suggested Citation

  • Barroso, Joana Maia Fernandes & Albuquerque-Oliveira, João Lucas & Oliveira-Neto, Francisco Moraes, 2020. "Correlation analysis of day-to-day origin-destination flows and traffic volumes in urban networks," Journal of Transport Geography, Elsevier, vol. 89(C).
    • Handle: RePEc:eee:jotrge:v:89:y:2020:i:c:s0966692320309765
    DOI: 10.1016/j.jtrangeo.2020.102899
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    References listed on IDEAS

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    1. Hazelton, Martin L. & Parry, Katharina, 2016. "Statistical methods for comparison of day-to-day traffic models," Transportation Research Part B: Methodological, Elsevier, vol. 92(PA), pages 22-34.
    2. Anselmo Ramalho Pitombeira-Neto & Carlos Felipe Grangeiro Loureiro & Luis Eduardo Carvalho, 2020. "A Dynamic Hierarchical Bayesian Model for the Estimation of day-to-day Origin-destination Flows in Transportation Networks," Networks and Spatial Economics, Springer, vol. 20(2), pages 499-527, June.
    3. Hazelton, Martin L., 2000. "Estimation of origin-destination matrices from link flows on uncongested networks," Transportation Research Part B: Methodological, Elsevier, vol. 34(7), pages 549-566, September.
    4. Parthasarathi, Pavithra & Levinson, David, 2018. "Network structure and the journey to work: An intra-metropolitan analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 118(C), pages 292-304.
    5. Cantarella, Giulio E. & Watling, David P., 2016. "A general stochastic process for day-to-day dynamic traffic assignment: Formulation, asymptotic behaviour, and stability analysis," Transportation Research Part B: Methodological, Elsevier, vol. 92(PA), pages 3-21.
    6. Thomas, Tom & Tutert, Bas, 2015. "Route choice behavior in a radial structured urban network: Do people choose the orbital or the route through the city center?," Journal of Transport Geography, Elsevier, vol. 48(C), pages 85-95.
    7. Stefano de Luca & Roberta Di Pace & Silvio Memoli & Luigi Pariota, 2020. "Sustainable Traffic Management in an Urban Area: An Integrated Framework for Real-Time Traffic Control and Route Guidance Design," Sustainability, MDPI, vol. 12(2), pages 1-20, January.
    8. G. E. Cantarella & E. Cascetta, 1995. "Dynamic Processes and Equilibrium in Transportation Networks: Towards a Unifying Theory," Transportation Science, INFORMS, vol. 29(4), pages 305-329, November.
    9. Parry, Katharina & Hazelton, Martin L., 2013. "Bayesian inference for day-to-day dynamic traffic models," Transportation Research Part B: Methodological, Elsevier, vol. 50(C), pages 104-115.
    10. Shixu Liu & Hao Yan & Said M. Easa & Lidan Guo & Yingnuo Tang, 2018. "Analysis of Stability-To-Chaos in the Dynamic Evolution of Network Traffic Flows under a Dual Updating Mechanism," Sustainability, MDPI, vol. 10(11), pages 1-17, November.
    11. G. E. Cantarella & D. P. Watling, 2016. "Modelling road traffic assignment as a day-to-day dynamic, deterministic process: a unified approach to discrete- and continuous-time models," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 5(1), pages 69-98, March.
    12. Julien Jacques & Cristian Preda, 2014. "Functional data clustering: a survey," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(3), pages 231-255, September.
    13. Katharina Parry & David P. Watling & Martin L. Hazelton, 2016. "A new class of doubly stochastic day-to-day dynamic traffic assignment models," EURO Journal on Transportation and Logistics, Springer;EURO - The Association of European Operational Research Societies, vol. 5(1), pages 5-23, March.
    14. Hazelton, Martin L., 2003. "Some comments on origin-destination matrix estimation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 37(10), pages 811-822, December.
    15. Camila Soares Henrique Fontenele Garcia & Rosário Macário & Ezequiel Dantas de Araújo Girão Menezes & Carlos Felipe Grangeiro Loureiro, 2018. "Strategic Assessment of Lisbon’s Accessibility and Mobility Problems from an Equity Perspective," Networks and Spatial Economics, Springer, vol. 18(2), pages 415-439, June.
    16. Hazelton, Martin L., 2001. "Inference for origin-destination matrices: estimation, prediction and reconstruction," Transportation Research Part B: Methodological, Elsevier, vol. 35(7), pages 667-676, August.
    17. Dalumpines, Ron & Scott, Darren M., 2017. "Determinants of route choice behavior: A comparison of shop versus work trips using the Potential Path Area - Gateway (PPAG) algorithm and Path-Size Logit," Journal of Transport Geography, Elsevier, vol. 59(C), pages 59-68.
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