Hitoshi Sakurai

Yoshiyuki Yajima, Hemant Prasad, Daisuke Ikefuji et al.

This paper presents the optimal-control suggestion for congestion on freeways using data assimilation (DA) of distributed fiber-optic sensing (DFOS). To simultaneously maximize throughput and avoid/mitigate congestion, it is necessary to execute optimal control for the current traffic state as active transportation and demand management (ATDM) according to multi-objective optimization with real-time monitoring data. However, optimal control cannot be estimated due to intermittent observed data obtained from conventional sensors. To solve the issue, this paper proposes the ATDM optimal control estimation with DA of DFOS, which can monitor traffic flow in real time without dead zones. Our real-time DA method enables us to estimate the effectiveness of control scenarios by simulation. This paper also provides a method to uniquely determine the optimal-control solution among the Pareto solutions for multi-objective optimization. Throughput and mean speed across the entire road are considered as the objective functions. Variable speed limit (VSL) and inflow control are taken as ATDM examples. Validation results on a Japanese freeway show that (i) the optimal control scenario varies depending on the traffic state, especially congestion level; (ii) optimal control considering VSL alone improves throughput by 5-14% while the improvement rate for mean speed is 0-8%; (iii) throughput and mean speed are improved by 10-15% and 20-30%, respectively when VSL and inflow control are considered. This paper also implies the importance of balance management for the lane occupancy and proactive optimal control before congestion occurs.

Hemant Prasad, Daisuke Ikefuji, Shin Tominaga et al.

The distributed fiber-optic sensing (DFOS) system is a cost-effective wide-area traffic monitoring technology that utilizes existing fiber infrastructure to effectively detect traffic congestions. However, detecting single-lane abnormalities, that lead to congestions, is still a challenge. These single-lane abnormalities can be detected by monitoring lane change behaviour of vehicles, performed to avoid congestion along the monitoring section of a road. This paper presents a method to detect single-lane abnormalities by tracking individual vehicle paths and detecting vehicle lane changes along a section of a road. We propose a method to estimate the vehicle position at all time instances and fit a path using clustering techniques. We detect vehicle lane change by monitoring any change in spectral centroid of vehicle vibrations by tracking a reference vehicle along a highway. The evaluation of our proposed method with real traffic data showed 80% accuracy for lane change detection events that represent presence of abnormalities.