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JDR Vol.13 No.2 pp. 326-337
doi: 10.20965/jdr.2018.p0326
(2018)

Paper:

State-Space Model for Traffic State Estimation of a Two-Dimensional Network

Yosuke Kawasaki*,†, Yusuke Hara**, and Masao Kuwahara***

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

Corresponding author

**Graduate School of Engineering, University of Tokyo, Tokyo, Japan

***Graduate School of Information Sciences, Tohoku University, Miyagi, Japan

Received:
October 8, 2017
Accepted:
February 19, 2018
Online released:
March 19, 2018
Published:
March 20, 2018
Keywords:
kinematic wave theory, data assimilation, state-space model, route choice, probe data
Abstract

This study proposes a real-time monitoring method for two-dimensional (2D) networks via the fusion of probe data and a traffic flow model. In the Great East Japan Earthquake occurring on March 11, 2011, there was major traffic congestion as evacuees concentrated in cities on the Sanriku Coast. A tragedy occurred when a tsunami overtook the stuck vehicles. To evacuate safely and efficiently, the state of traffic must be monitored in real time on a 2D network, where all networks are linked. Generally, the traffic state is monitored only at observation points. However, observation data presents the risk of errors. Additionally, in the estimated traffic state of the 2D network, unlike non-intersecting road sections (i.e., one-dimensional), it is necessary to model user route choice behavior and origin/destination (OD) demand to input in the model. Therefore, in this study, we develop a state-space model that assimilates vehicle density and divergence ratio data obtained from probe vehicles in a traffic flow model that considers route choice. Our state-space model considers observational errors in the probe data and can simultaneously estimate traffic state and destination component ratio of OD demand. The result of simulated traffic model verification shows that the proposed model has good congestion estimation precision in a small-scale test network.

Cite this article as:
Y. Kawasaki, Y. Hara, and M. Kuwahara, “State-Space Model for Traffic State Estimation of a Two-Dimensional Network,” J. Disaster Res., Vol.13, No.2, pp. 326-337, 2018.
Data files:
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Last updated on Aug. 20, 2018