Dynamic Programming Approach for Online Freeway Flow Propagation Adjustment

Abstract

An optimization framework for online flow propagation adjustment in a freeway context was proposed. Instead of performing local adjustment for individual links separately, the proposed framework considers the interconnectivity of links in a traffic network. In particular, dynamic behavior in the mesoscopic simulation is approximated by the finite-difference method at a macroscopic level. The proposed model seeks to minimize the deviation between simulated density and anticipated density. By taking advantage of the serial structure of a freeway, an efficient dynamic programming algorithm has been developed and tested. The experiment results compared with analytic results as the base case showed the superior performance of dynamic programming methods over the classical proportion control method. The effect of varying update intervals was also examined. The simulation results suggest that a greedy method considering the impact of inconsistency propagation achieves the best trade-off in terms of computation effort and solution quality.

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