Dynamic Programming Approach for Arterial Signal Optimization

Abstract

Link performance functions (LPFs) are essential components of macroscopic models for signal optimization, traffic assignment, or combined control-assignment models. Numerous studies have developed a variety of performance functions for signal optimization. Most of these studies attempted to use simplified LPFs that did not capture the important relationships and dependencies among the variables. Dependent LPFs are introduced; in these LPFs the performance on any link depends on the flow pattern not only on that link alone but also on the feeder links. Such functions represent with greater fidelity the performance characteristics of the link and can lead to better control and assignment. An optimization model explicitly considers flow interactions among successive links and applies it in a dynamic programming procedure to determine optimal signal settings. The procedure, which is an offshoot of the combination method for offset optimization, is illustrated for an arterial street and demonstrates its efficacy in comparison with existing optimization models.