Modeling and Control Using Stochastic Distribution Control Theory for Intersection Traffic Flow

This work investigated stochastic distribution control theory-based traffic signal optimization to achieve a smooth and uniform flow of vehicles through signalized intersections. In this context, the static and linear dynamic stochastic distribution models were developed to express the relationship between the signal timing and the traffic queue length together with its probability density function. Two stochastic distribution control algorithms were designed to control the signal timing at intersections such that the probability density function of the traffic queue of each intersection road segment is made as narrow and as small as possible. Also, a recursive input-output traffic queue estimation model was proposed, which is data-driven and dynamic in nature, to calculate real-time traffic queue length using traffic signal timings and loop-detector data. The control algorithms were evaluated for a one-signal corridor, two-signal corridor, and 2 x 2 network of signalized intersections. MATLAB simulation examples are provided to demonstrate the use of the proposed algorithms and comparison to the existing widely-used semi-actuated control has been made. Desired results were obtained.

Automated summary

This work focuses on using stochastic distribution control theory to optimize traffic signal timing and achieve smooth and uniform flow of vehicles at signalized intersections. Two stochastic distribution control algorithms are designed to minimize the probability density function of traffic queues at each intersection. A data-driven and dynamic recursive input-output traffic queue estimation model is proposed to calculate real-time queue lengths. Simulations demonstrate the effectiveness of the algorithms compared to commonly used semi-actuated control.