Real-Time Simulation of Dynamic Traffic Flow with Traffic Data Assimilation Approach

Yosuke Kawasaki; Yusuke Hara; Takuma Mitani; Masao Kuwahara

doi:10.20965/jdr.2016.p0246

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JDR Vol.11 No.2 pp. 246-254

(2016)

doi: 10.20965/jdr.2016.p0246

Paper:

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Real-Time Simulation of Dynamic Traffic Flow with Traffic Data Assimilation Approach

Yosuke Kawasaki, Yusuke Hara, Takuma Mitani, and Masao Kuwahara

Graduate School of Information Sciences, Tohoku University
6-6-06 Aramaki aza aoba, Aoba-ku, Sendai, Miyagi 980-8579, Japan

Received:

September 29, 2015

Accepted:

February 6, 2016

Online released:

March 18, 2016

Published:

March 1, 2016

Keywords:

traffic state estimation, data assimilation, kinematic wave theory, probe vehicle trajectory

Abstract

The real-time traffic state estimation we propose uses a state-space model considering the variability of the fundamental diagram (FD) and sensing data. Serious congestion was caused by vehicle evacuation in many Sanriku coast cities following the great East Japan earthquake on March 11, 2011. Many of the vehicles in these congested queues were caught in the enormous tsunami after the earthquake [1]. Safe, efficient evacuation and rescue and restoration require that dynamic traffic states be monitored in real time especially in natural disasters. Variational theory (VT) based on kinematic wave theory is used for the system model, with probe vehicle and traffic detector data used to for measurement data. Our proposal agrees better with simulated benchmark traffic states than deterministic VT results do.

Cite this article as:

Yosuke Kawasaki, Yusuke Hara, Takuma Mitani, , and Masao Kuwahara, “Real-Time Simulation of Dynamic Traffic Flow with Traffic Data Assimilation Approach,” J. Disaster Res., Vol.11, No.2, pp. 246-254, 2016.

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References

[1] Y. Hara and M. Kuwahara, “Traffic Monitoring immediately after a major natural disaster as revealed by probe data, – ,A case in Ishinomaki after the Great East Japan Earthquake,” Transportation Research Part A: Policy and Practice, Vol.75, pp. 1–15, 2015.
[2] M. L. John, S. Lakshmivarahan, and S. K. Dhall, “Dynamic Data Assimilation: A Least Squares Approach,” CAMBRIDGE, 2006.
[3] C.F. Daganzo, “On the Variational Theory of Traffic Flow: well-posedness, duality and applications,” Networks and Heterogeneous Media, Vol.1, No.4, pp. 601–619, 2006.
[4] B. Mehran, M. Kuwahara, and F. Naznin, “Implementing kinematic wave theory to reconstruct vehicle trajectories from fixed and probe sensor data,” Transportation Research Part C: Emerging Technologies, Vol.20, No.1, pp. 144–163, 2012.
[5] B. Mehran and M. Kuwahara, “Fusion of probe and fixed sensor data for short-term traffic prediction in urban signalized arterials,” Int. Journal of Urban Sciences, Vol.17, No.2, pp. 163–183, 2013.
[6] H. Chen and H.A. Rakha, “Real-time travel time prediction using particle filtering with a non-explicit state-transition model,” Transportation Research Part C: Emerging Technologies, Vol.43, No.1, pp. 112–126, 2014.
[7] C. Dong, C. Shao, S.H. Richards, and L.D. Han, “Flow rate and time mean speed predictions for the urban freeway network using state space model,” Transportation Research Part C: Emerging Technologies, Vol.43, No.1, pp. 20–32, 2014.
[8] C.G. Claudel and A.M. Bayen, “Convex Formulations of Data Assimilation Problems for a Class of Hamilton-Jacobi Equations,” SIAM Journal on Control and Optimization, Vol.49, No.2, pp. 383–402, 2011.
[9] W. Deng, H. Lei, and X. Zhou, “Traffic state estimation and uncertainty quantification based on heterogeneous data sources: A three detector approach,” Transportation Research Part B: Methodological, Vol.57, pp. 132–157, 2013.
[10] Y. Yuan, A. Duret, and H. van Lint, “Mesoscopic Traffic State Estimation based on a Variational Formulation of the LWR Model in Lagrangian-space Coordinates and Kalman Filter,” Transportation Research Procedia, Vol.10, pp. 82–92, 2015.
[11] A. Nantes, D. Ngoduy, A. Bhaskar, M. Miska, and E. Chung, “Real-time traffic state estimation in urban corridors from heterogeneous data,” Transportation Research Part C: Emerging Technologies, Vol.61, 2015.
[12] A.D. Patire, M. Wright, B. Prodhomme, and A. M. Bayen, “How much GPS data do we need?,” Transportation Research Part C: Emerging Technologies, Vol.58, pp. 325–342, 2015.
[13] E.I. Vlahogianni, M.G. Karlaftis, and J.C. Golias, “Short-term traffic forecasting: Where we are and where we’re going,” Transportation Research Part C: Emerging Technologies, Vol.43, No.1, pp. 3–19, 2014.
[14] G. Kitagawa, “Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models,” Journal of Computational and Graphical Statistics, Vol.5, No.1, pp. 1–25, 1996.
[15] J. Han, M. Kamber, and J. Pei, “DATA MINING Concepts and Techniques Third Edition,” The Morgan Kaufmann Series in Data Management Systems, 2011.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 International License.

[1] [1] Y. Hara and M. Kuwahara, “Traffic Monitoring immediately after a major natural disaster as revealed by probe data, – ,A case in Ishinomaki after the Great East Japan Earthquake,” Transportation Research Part A: Policy and Practice, Vol.75, pp. 1–15, 2015.

[2] [2] M. L. John, S. Lakshmivarahan, and S. K. Dhall, “Dynamic Data Assimilation: A Least Squares Approach,” CAMBRIDGE, 2006.

[3] [3] C.F. Daganzo, “On the Variational Theory of Traffic Flow: well-posedness, duality and applications,” Networks and Heterogeneous Media, Vol.1, No.4, pp. 601–619, 2006.

[4] [4] B. Mehran, M. Kuwahara, and F. Naznin, “Implementing kinematic wave theory to reconstruct vehicle trajectories from fixed and probe sensor data,” Transportation Research Part C: Emerging Technologies, Vol.20, No.1, pp. 144–163, 2012.

[5] [5] B. Mehran and M. Kuwahara, “Fusion of probe and fixed sensor data for short-term traffic prediction in urban signalized arterials,” Int. Journal of Urban Sciences, Vol.17, No.2, pp. 163–183, 2013.

[6] [6] H. Chen and H.A. Rakha, “Real-time travel time prediction using particle filtering with a non-explicit state-transition model,” Transportation Research Part C: Emerging Technologies, Vol.43, No.1, pp. 112–126, 2014.

[7] [7] C. Dong, C. Shao, S.H. Richards, and L.D. Han, “Flow rate and time mean speed predictions for the urban freeway network using state space model,” Transportation Research Part C: Emerging Technologies, Vol.43, No.1, pp. 20–32, 2014.

[8] [8] C.G. Claudel and A.M. Bayen, “Convex Formulations of Data Assimilation Problems for a Class of Hamilton-Jacobi Equations,” SIAM Journal on Control and Optimization, Vol.49, No.2, pp. 383–402, 2011.

[9] [9] W. Deng, H. Lei, and X. Zhou, “Traffic state estimation and uncertainty quantification based on heterogeneous data sources: A three detector approach,” Transportation Research Part B: Methodological, Vol.57, pp. 132–157, 2013.

[10] [10] Y. Yuan, A. Duret, and H. van Lint, “Mesoscopic Traffic State Estimation based on a Variational Formulation of the LWR Model in Lagrangian-space Coordinates and Kalman Filter,” Transportation Research Procedia, Vol.10, pp. 82–92, 2015.

[11] [11] A. Nantes, D. Ngoduy, A. Bhaskar, M. Miska, and E. Chung, “Real-time traffic state estimation in urban corridors from heterogeneous data,” Transportation Research Part C: Emerging Technologies, Vol.61, 2015.

[12] [12] A.D. Patire, M. Wright, B. Prodhomme, and A. M. Bayen, “How much GPS data do we need?,” Transportation Research Part C: Emerging Technologies, Vol.58, pp. 325–342, 2015.

[13] [13] E.I. Vlahogianni, M.G. Karlaftis, and J.C. Golias, “Short-term traffic forecasting: Where we are and where we’re going,” Transportation Research Part C: Emerging Technologies, Vol.43, No.1, pp. 3–19, 2014.

[14] [14] G. Kitagawa, “Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models,” Journal of Computational and Graphical Statistics, Vol.5, No.1, pp. 1–25, 1996.

[15] [15] J. Han, M. Kamber, and J. Pei, “DATA MINING Concepts and Techniques Third Edition,” The Morgan Kaufmann Series in Data Management Systems, 2011.