Artificial Neural Network–Based Heuristic Optimal Traffic Signal Timing

Abstract

A software called Optimal Traffic Signal Control System (OTSCS) was developed by us for testing the feasibility of dynamically controlling a traffic signal by finding optimal signal timing to minimize delay at signalized intersections. It also was designed as a research tool to study the learning behavior of artificial neural networks and the properties of heuristic search methods. It consists of a level-of-service evaluation model that is based on an artificial neural network and a heuristic optimization model that interacts with the level-of-service evaluation model. This article discusses the latter model, named the Optimal Traffic Signal Timing Model (OTSTM). The OTSTM was applied to determine optimal signal timing of twophase traffic signals to evaluate the model's performance. Two search methods were employed: a depth-first search method (an enumeration method) and a direction-search method that we developed. It was found that the OTSTM with the direction search resulted in "optimal" signal timings similar to the depth-first search, which would always produce a global optimal timing. Yet the cost of the direction search, as measured by the CPU time of the computer used for analysis, was found to be much less than the cost of obtaining an optimal solution by the depth-first search cases-more than 10 times less. The study showed that once

Comparing Ant Colony Optimization and Genetic Algorithm Approaches for Solving Traffic Signal Coordination under Oversaturation Conditions

“…While these approaches focus on switching between under‐ and oversaturated timing strategies, a second set of studies focuses on the identification of optimal cycle lengths and green times for oversaturated conditions alone. Lieberman and Chang (2005) used a mixed‐integer linear programming approach for this problem, and heuristic optimization methods have also been successfully applied (Saito and Fan, 2000; Varia and Dhingra, 2004; Sun et al, 2006; Teklu et al, 2007; Maher, 2008). A GA approach was used to minimize total delay through identifying phase sequences and proportions of green times (Foy et al, 1992).…”

Neural Networks in Civil Engineering: 1989–2000

“…The BP neural network is used by Lingras and Adamo (1996) to estimate average and peak hourly traffic volumes, by Ivan and Sethi (1998) for traffic incident detection, by Sayed and Abdelwahab (1998) for classification of road accidents for road improvements, and by Park and Rilett (1999) to predict the freeway link travel times for one through five time periods into the future. Saito and Fan (2000) present an optimal traffic signal timing model that uses the BP algorithm to conduct an analysis of the level of service at a signalized intersection by learning the complicated relationship between the traffic delay and traffic environment at signalized intersections. Gagarin et al (1994) discuss the use of a radial-Gaussianbased neural network for determining truck attributes such as axle loads, axle spacing, and velocity from strainresponse readings taken from the bridges over which the truck is traveling.…”

Robust Optimal Lane Allocation for Isolated Intersections

“…Other systems including Optimization Policies for Adaptive Control (OPAC) (Gartner, ) and PRODYN (Henry et al., ) are acyclic; that is, the sequence of phases is not predetermined and the signal plans can change at any time step. Besides, extensive studies on TD prediction (Jiang and Adeli, , , ), freeway flows (Adeli and Ghosh‐Dastidar, ; Dharia and Adeli, ; Hooshdar and Adeli, ), freeway work zones (Adeli and Jiang, ; Jiang and Adeli, , , b), travel time reliability (Shahabi et al., ; Uchida, ), more complex adaptive control models (Haijema and Hendrix, ; Feng et al., ; Tong et al., ) and effective heuristic algorithms (Saito and Fan, ; Varia and Dhingra, ; Adeli and Jiang, ; Putha et al., ) have also been conducted.…”