The use of traffic data from automatic monitoring systems to obtain day-to-day time series of vehicle traffic volumes and origin-destination flows in urban networks

Authors

  • Joana Maia Fernandes Barroso Universidade Federal do Ceará, Ceará – Brasil
  • João Lucas Albuquerque Oliveira Universidade Federal do Ceará, Ceará – Brasil
  • Francisco Moraes de Oliveira Neto Universidade Federal do Ceará, Ceará – Brasil https://orcid.org/0000-0002-7756-4619

DOI:

https://doi.org/10.14295/transportes.v29i2.2385

Keywords:

Day-to-day traffic volumes, Day-to-day origin-destination flows, Day-to-day traffic dynamics, Transport data analysis

Abstract

The understanding of travel pattern dynamics in the urban environment is essential for the transportation systems planning and operation. Recently, the increasing availability of massive traffic data from traffic monitoring systems, including automatic number plate recognition systems (TMS-ANPR), can allow an understanding of the day-to-day variability of traffic flows in large urban network systems. However, to enhance the data quality for analysis, it is essential to carry out a previous data treatment. This work presents a method for treatment of TMS-ANPR data. The main product of this data treatment are the day-to-day time series of traffic volumes and OD flows for different periods of a typical day, allowing the analysis of the multiday dynamic of travel behavior and of the model assumptions stated in the literature about such dynamic behavior. The proposed method, which can be applied to any type of TMS-ANPR, was applied to generate time series data from the TMS-ANPR of Fortaleza city, contributing to identify suspicious and atypical data, to define representative patterns of vehicular traffic and to estimate series of OD flows.

Downloads

Download data is not yet available.

Author Biography

Francisco Moraes de Oliveira Neto, Universidade Federal do Ceará, Ceará – Brasil

Possui Graduação em Engenharia Civil pela Universidade Federal do Ceará (2002), Mestrado em Engenharia de Transportes pela Universidade Federal do Ceará (2004), e Doutorado pela University of Tennessee-Knoxville (2010). Realizou estágio pós-doutoral no Centro de Pesquisa em Análise de Sistemas de Transportes (CTA - Center for Transportation Analysis) do Laboratório Nacional de Oak Ridge (ORNL - Oak Ridge National Laboratory), USA. Atualmente é Professor Adjunto do Departamento de Engenharia de Transportes da Universidade Federal do Ceará (UFC) e Professor Permanente do Programa de Pós-graduação em Engenharia de Transportes da UFC (PETRAN). Coordenador do Grupo de Pesquisa em Operação, Planejamento e Avaliação do Transporte Público - OPA-TP. Participa em pesquisa colaborativa com o Grupo de Pesquisa em Transportes, Trânsito e Meio Ambiente do Departamento de Engenharia de Transportes da UFC - GTTEMA/DET/UFC. Possui experiência profissional e acadêmica em Gerenciamento e Simulação de Sistemas Urbanos de Tráfego, e em Pesquisa Operacional. Tem interesse nas seguintes áreas: Análise de Sistemas de Transportes, com ênfase na Modelagem de Redes de Transportes de Passageiros, Modelagem Comportamental e Análise Espacial em Transportes.

References

Aggarwal, C. C. (2015) Data mining: the texbook. Springer International Publishing, Switzerland. DOI: 10.1007/978-3-319-14142-8.

Anda, C.; A. Erath and P. J. Fourie (2017) Transport modelling in the age of big data. International Journal of Urban Sciences, v. 21, p. 19–42. DOI: 10.1080/12265934.2017.1281150.

Bertini, R. L.; M. Lasky and C. M. Monsere (2005) Validating predicted rural corridor travel times from an automated license plate recognition system: Oregon’s frontier project. IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, v. 2005, p. 706–711. DOI: 10.1109/ITSC.2005.1520134.

Cascetta, E. (2009) Transportation System Analysis: Models and applications. Ed. Springer (2ª ed.), New York, USA.

Castillo, E.; J. M. Mennéndez and P. Jimenez (2008) Trip matrix and path flow reconstruction and estimation based on plate scanning and link observations. Transportation Research Part B: Methodological, v. 42, n. 5, p. 455–481. DOI: 10.1016/j.trb.2007.09.004.

Cheng, T.; J. Haworth and J. Wang (2012) Spatio-temporal autocorrelation of road network data. Journal of Geographical Systems, v. 14, n. 4, p. 389-413. DOI: 10.1007/s10109-011-0149-5.

Cremer, M. and H. Keller (1987) A New Class of Dynamic Methods for the Identification of Origin-Destination Flows. Trans-portation Research Part B: Methodological , v. 21, n. 2, p. 117–132. DOI: 10.1016/0191-2615(87)90011-7.

Ester, M.; H. P. Kriegel; J. Sander and X. Xu (1996) A density-based algorithm for discovering clusters in large spatial data-bases with noise. Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining. AAAI Press, Port-land, Oregon, USA, p. 226–231.

Giraud, T. (2018) OSRM: Interface Between R and the OpenStreetMap-Based Routing Service OSRM. R package version 3.1.1. Available in:

<https://cran.r-project.org/web/packages/osrm/index.html> (consulted on 08/18/2021).

Hazelton, M. L. (2000) Estimation of Origin-Destination Matrices from Link Flows on Uncongested Networks. Transportation Research Part B: Methodological, v. 34, n. 7, p. 549–566. DOI: 10.1016/S0191-2615(99)00037-5.

Hazelton, M. L. (2001) Inference for Origin-Destination Matrices: Estimation, Prediction and Reconstruction. Transportation Research Part B: Methodological, v. 35, n. 7, p. 667–676. DOI: 10.1016/S0191-2615(00)00009-6.

Hazelton, M. L. (2003) Some comments on origin–destination matrix estimation. Transportation Research Part A: Policy and Practice, v. 37, p. 811–822. DOI: 10.1016/S0965-8564(03)00044-2.

Jacques, J. and C. Preda (2014) Functional data clustering: A survey. Advances in Data Analysis and Classification, v. 8, p. 231–255. DOI: 10.1007/s11634-013-0158-y.

Järv, O.; R. Ahas and F. Witlox (2014) Understanding monthly variability in human activity spaces: A twelve-month study using mobile phone call detail records. Transportation Research Part C: Emerging Technologies, v. 38, p. 122–135. DOI: 10.1016/j.trc.2013.11.003.

Li, H.; R. Guensler; J. Ogle and J. Wang (2004) Using Global Positioning System Data to Understand Day-to-Day Dynamics of Morning Commute Behavior. Transportation Research Record: Journal of the Transportation Research Board, v. 1895, p. 78–84. DOI: 10.3141/1895-11.

Lima, L. S. (2017) Espraiamento Urbano por Autossegregação e seus Impactos na Acessibilidade Urbana de Fortaleza. Dissertação de Mestrado – Programa de Pós-Graduação em Engenharia de Transportes – PETRAN, Departamento de Engenharia de Transportes, Universidade Federal do Ceará, Fortaleza, Brasil, 2017. Disponível em: <http://www.repositorio.ufc.br/handle/riufc/30015> (acesso em 18/08/2021).

Liu, G.; Z. Ma; Z. Du and C. Wen (2011) The Calculation Method of Road Travel Time Based on License Plate Recognition Technology. Communications in Computer and Information Science. p. 385–389. DOI: 10.1007/978-3-642-22418-8_54.

Loureiro, C. F. G.; H. B. Meneses; F. M. Oliveira-Neto and M. M. Castro-Neto (2009) Managing Congestion in Large Brazilian Urban Area through Logical Interface between SCOOT and GIS Platform. Transportation Research Record: Journal of the Transportation Research Board, v. 2099, p. 76–84. DOI: 10.3141/2099-09.

Milne, D. and D. Watling (2019) Big data and understanding change in the context of planning transport systems. Journal of Transport Geography, v. 76, p. 235–244. DOI: 10.1016/j.jtrangeo.2017.11.004.

Oliveira, M. V. T. e C. F. G. Loureiro (2006) Análise dos Padrões de Variação Espaço-Temporal do Volume Veicular no Ambiente Urbano de Fortaleza. Anais do XX Congresso de Pesquisa e Ensino em Transportes, ANPET, Brasília, v. 1, p. 149–161.

Oliveira-Neto, F. M.; L. D. Han and M. K. Jeong (2012) Online license plate matching procedures using license-plate recogni-tion machines and new weighted edit distance. Transportation Research Part C: Emerging Technologies, v. 21, p. 306–320. DOI: 10.1016/j.trc.2011.11.003.

Oliveira-Neto, F. M., L. D. Han and M. K. Jeong (2013) An online self-learning algorithm for license plate matching. IEEE Transactions on Intelligent Transportation Systems, v. 14, p. 1806–1816. DOI: 10.1109/TITS.2013.2270107.

Pitombeira-Neto, A. R. and C. F. G. Loureiro (2016) A Dynamic Linear Model for the Estimation of Time-Varying Origin–Destination Matrices from Link Counts. Journal of Advanced Transportation, v. 50, n. 8, p. 2116-2129. DOI: 10.1002/atr.1449.

Pitombeira-Neto, A. R.; C. F. G. Loureiro and L. E. Carvalho (2018) Bayesian Inference on Dynamic Linear Models of Day-to-Day Origin-Destination Flows in Transportation Networks. Urban Science. v. 2, n. 4, p. 117. DOI: 10.3390/urbansci2040117.

Pitombeira-Neto, A. R.; F. M. Oliveira-Neto and C. F. G. Loureiro (2017) Statistical models for the estimation of the origin-destination matrix from traffic counts. Transportes (Rio de Janeiro), v. 25, p. 1-13. DOI: 10.14295/transportes.v25i4.1344.

Pitombeira-Neto, A. R.; C. F. G. Loureiro and L. E. Carvalho (2020) A Dynamic Hierarchical Bayesian Model for the Estimation of Day-to-Day Origin-Destination Flows in Transportation Networks. Networks and Spatial Economics. v. 20, n. 2, p. 499–527. DOI: 10.1007/s11067-019-09490-5.

Rao, W.; Y.-J. Wu; J. Xia; J. Ou and R. Kluger (2018) Origin-destination pattern estimation based on trajectory reconstruction using automatic license plate recognition data. Transportation Research Part C: Emerging Technologies, v. 95, p. 29–46. DOI: 10.1016/j.trc.2018.07.002.

Roess, R. P. and W. R. McShane (2004) Traffic Engegineering. Ed. Pearson/Prentice-Hall, New Jersey, USA.

Song, J.; C. Zhao; S. Zhong; T. A. S. Nielsen and A. V. Prishchepov (2019) Mapping Spatio-Temporal Patterns and Detecting the Factors of Traffic Congestion with Multi-Source Data Fusion and Mining Techniques. Computers, Environment and Urban Systems, v. 77, p. 101364. DOI: 10.1016/j.compenvurbsys.2019.101364.

Stathopoulos, A. and M. G. Karlaftis (2001) Temporal and Spatial Variations of Real-Time Traffic Data in Urban Areas. Trans-portation Research Record: Journal of the Transportation Research Board, Washington, D.C., USA, v. 1768, n. 1, p. 135-140. DOI: 10.3141/1768-16.

Tebaldi, C. and M. West (1998) Bayesian Inference on Network Traffic Using Link Count Data. Journal of the American Statisti-cal Association. v. 93, n. 442, p. 557–573. DOI: 10.1080/01621459.1998.10473707.

Vardi, Y. (1996) Network Tomography: Estimating Source-Destination Traffic Intensities from Link Data. Journal of the Ameri-can Statistical Association, v. 91, n. 433, p. 365–377. DOI: 10.1080/01621459.1996.10476697.

Weijermars, W. A. M. (2007) Analysis of urban traffic patterns using clustering. University of Twente, Enschede / Delft. Availa-ble in: <https://research.utwente.nl/en/publications/analysis-of-urban-traffic-patterns-using-clustering> (consulted on 08/18/2021).

Downloads

Published

2021-08-18

How to Cite

Fernandes Barroso, J. M. ., Albuquerque Oliveira, J. L., & de Oliveira Neto, F. M. (2021). The use of traffic data from automatic monitoring systems to obtain day-to-day time series of vehicle traffic volumes and origin-destination flows in urban networks . TRANSPORTES, 29(2), 2385. https://doi.org/10.14295/transportes.v29i2.2385

Issue

Section

Artigos