A Method for Estimation of Traffic States Considering Floating Car Data in a Large Network

This study proposes a computationally efficient method for the traffic states estimation by using floating car data. The traffic states in a road network are obtained by solving a full information maximum likelihood estimation (FIMLE) problem under user equilibrium (UE) constraints. Solving the FIML...

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Published inJournal of the Eastern Asia Society for Transportation Studies Vol. 15; pp. 1029 - 1048
Main Authors DUAN, Kaifeng, TANI, Ryuichi, UCHIDA, Kenetsu, ZUSHII, Keita, NAGAOKA, Osamu
Format Journal Article
LanguageEnglish
Published Eastern Asia Society for Transportation Studies 2024
Subjects
Floating car data
Full Information Maximum-likelihood estimation
Sensitivity analysis
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Abstract This study proposes a computationally efficient method for the traffic states estimation by using floating car data. The traffic states in a road network are obtained by solving a full information maximum likelihood estimation (FIMLE) problem under user equilibrium (UE) constraints. Solving the FIMLE problem under UE constraints requires much computations time. Therefore, a solution algorithm based on the sensitivity analysis for UE is proposed in this study. We estimate the traffic states in the Asahikawa network in Hokkaido, Japan by applying the method proposed in this study.
AbstractList This study proposes a computationally efficient method for the traffic states estimation by using floating car data. The traffic states in a road network are obtained by solving a full information maximum likelihood estimation (FIMLE) problem under user equilibrium (UE) constraints. Solving the FIMLE problem under UE constraints requires much computations time. Therefore, a solution algorithm based on the sensitivity analysis for UE is proposed in this study. We estimate the traffic states in the Asahikawa network in Hokkaido, Japan by applying the method proposed in this study.
Author TANI, Ryuichi
ZUSHII, Keita
NAGAOKA, Osamu
DUAN, Kaifeng
UCHIDA, Kenetsu
Author_xml – sequence: 1
  fullname: DUAN, Kaifeng
  organization: Graduate School of Engineering, Hokkaido University
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  fullname: TANI, Ryuichi
  organization: Faculty of Engineering, Hokkaido University
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  fullname: UCHIDA, Kenetsu
  organization: Faculty of Engineering, Hokkaido University
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  fullname: ZUSHII, Keita
  organization: DOCON CO., LTD
– sequence: 5
  fullname: NAGAOKA, Osamu
  organization: DOCON CO., LTD
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References Uchida, K., Kato, T. (2017): A simplified network for travel time reliability analysis in a road network. Journal of advanced transportation.
Ramezani, M., Geroliminis, N. (2012): On the estimation of arterial route travel time distribution with Markov chains. Transportation Research Part B. 46(10),1576-1590
Yang, H., M.GH. Bell. (2009): Sensitivity analysis of network traffic equilibrium revisited: the corrected approach. 4th IMA International Conference on Mathematics in Transport Institute of Mathematics and its Applications, September, London, United Kingdom.
Smith, M.J., (1979): The existence, uniqueness and stability of traffic equilibrium. Transportation Research, 13B, 295-304
Jenelius, E., Koutsopoulos, H.N. (2013): Travel time estimation for urban road networks using low frequency probe vehicle data. Transportation Research Part B. 53, 64-81
Liu, H.X., Ma, W. (2009): A virtual vehicle probe model for time dependent travel time estimation on signalized arterials. Transportation Research Part C. 17(1), 11-26
Tani, R., Owada, T., Uchida, K. (2018): Path travel time estimating method by incomplete traffic data. International Journal of Intelligent Transportation Systems Research
Wei, C., Asakura, Y. (2013): A Bayesian approach to traffic estimation in stochastic user equilibrium networks. Transportation Research Part C. 36, 446-459
Tani, R., Uchida, K. (2021): An Estimation Method for Spatiotemporal Traffic States Based on Incomplete Traffic Observation. The 8th International Symposium on Dynamic Traffic Assignment.
Hara, Y., Suzuki, J., Kuwahara, M. (2018): Network-wide traffic state estimation using a mixture Gaussian graphical model and graphical lasso. Transportation Research Part C. 86, 622-638.
Hazelton, M.L. (2010): Bayesian inference for network-based models with a linear inverse structure. Transportation Research Part B. 44(5), 674-685
Tani, R., Uchida, K. (2018): A stochastic user equilibrium assignment model under stochastic demand and supply following lognormal distribution. Asian Transport Studies, 5(2):326-348.
Tani, R., Sumalee, A., Uchida, K. (2021): Travel time reliability-based optimization problem for CAVs dedicated lanes. Transportmetrica A: Transport Science. 18(3):1-56
Zheng, F., Van Zuylen, H. (2013): Urban link travel time estimation based on sparse probe vehicle data. Transportation Research Part C. 31, 145-157
Tobin, R.L., Frizes, T.L. (1988): Sensitivity analysis for equilibrium network flows. Transportation Science, 22, 242-250.
References_xml – reference: Tani, R., Owada, T., Uchida, K. (2018): Path travel time estimating method by incomplete traffic data. International Journal of Intelligent Transportation Systems Research
– reference: Tani, R., Uchida, K. (2018): A stochastic user equilibrium assignment model under stochastic demand and supply following lognormal distribution. Asian Transport Studies, 5(2):326-348.
– reference: Tani, R., Uchida, K. (2021): An Estimation Method for Spatiotemporal Traffic States Based on Incomplete Traffic Observation. The 8th International Symposium on Dynamic Traffic Assignment.
– reference: Ramezani, M., Geroliminis, N. (2012): On the estimation of arterial route travel time distribution with Markov chains. Transportation Research Part B. 46(10),1576-1590
– reference: Wei, C., Asakura, Y. (2013): A Bayesian approach to traffic estimation in stochastic user equilibrium networks. Transportation Research Part C. 36, 446-459
– reference: Tobin, R.L., Frizes, T.L. (1988): Sensitivity analysis for equilibrium network flows. Transportation Science, 22, 242-250.
– reference: Hazelton, M.L. (2010): Bayesian inference for network-based models with a linear inverse structure. Transportation Research Part B. 44(5), 674-685
– reference: Hara, Y., Suzuki, J., Kuwahara, M. (2018): Network-wide traffic state estimation using a mixture Gaussian graphical model and graphical lasso. Transportation Research Part C. 86, 622-638.
– reference: Jenelius, E., Koutsopoulos, H.N. (2013): Travel time estimation for urban road networks using low frequency probe vehicle data. Transportation Research Part B. 53, 64-81
– reference: Liu, H.X., Ma, W. (2009): A virtual vehicle probe model for time dependent travel time estimation on signalized arterials. Transportation Research Part C. 17(1), 11-26
– reference: Smith, M.J., (1979): The existence, uniqueness and stability of traffic equilibrium. Transportation Research, 13B, 295-304
– reference: Zheng, F., Van Zuylen, H. (2013): Urban link travel time estimation based on sparse probe vehicle data. Transportation Research Part C. 31, 145-157
– reference: Tani, R., Sumalee, A., Uchida, K. (2021): Travel time reliability-based optimization problem for CAVs dedicated lanes. Transportmetrica A: Transport Science. 18(3):1-56
– reference: Yang, H., M.GH. Bell. (2009): Sensitivity analysis of network traffic equilibrium revisited: the corrected approach. 4th IMA International Conference on Mathematics in Transport Institute of Mathematics and its Applications, September, London, United Kingdom.
– reference: Uchida, K., Kato, T. (2017): A simplified network for travel time reliability analysis in a road network. Journal of advanced transportation.
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Snippet This study proposes a computationally efficient method for the traffic states estimation by using floating car data. The traffic states in a road network are...
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SubjectTerms Floating car data
Full Information Maximum-likelihood estimation
Sensitivity analysis
Title A Method for Estimation of Traffic States Considering Floating Car Data in a Large Network
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