Optimization of Time of Day Plan Scheduling Using a Multi-Objective Evolutionary Algorithm

Coordinating traffic signals can provide great savings to motorists in terms of reduced delays and number of vehicular stops. In order to maximize benefits, engineers need to use a mechanism by which the most optimal timing plans are activated when the traffic patterns change. Common ways of accomplishing this need is by using Time of Day (TOD) plan scheduling, or Traffic Responsive Plan Selection (TRPS). Out of the two modes, the TOD mode is by far the most common. Engineers, however, typically use their judgment to determine the TOD plan scheduling. Unless traffic patterns change at certain times of the day and remain constant until the next change—which is highly unlikely—it is very difficult to determine what the optimal break point would be. In addition, engineers would also face the challenge of selecting the timing plan that would be active during every scheduling period. This paper proposes the use of a multiobjective evolutionary algorithm to address these challenges. The authors introduce the Degree of Detachment (DOD) as a performance measure of scheduling continuity. A high DOD translates into frequent changes in timing plans. Whereas a zero DOD translates into a one timing plan applied throughout the day. The authors then use a non-dominated sorting genetic algorithm (NSGAII) to optimize the TOD scheduling. This approach results in different Pareto fronts, corresponding to different DODs, where engineers can evaluate the incremental benefits associated with increasing the frequency of timing plan changes. Optimization of Time of Day Plan Scheduling Using a Multi-Objective Evolutionary Algorithm