Modeling, Estimation, and Control in Highway Traffic Based on Discrete Event Dynamic Systems

Petri net (PN) is a useful tool for the modeling and analysis of complex systems and has been widely used in a variety of practical systems. This dissertation aims at studying highway transportation systems using Petri nets and investigating several fundamental problems related to the modeling, state/structure estimation, and control of highway traffic.

This dissertation starts with two kinds of modeling schemes. The first one uses the Probabilistic Petri net to model a highway segment. The traffic movement probabilities have also been shown. The second scheme uses the traditional Petri net structure to model the traffic network around a city’s metropolitan area, where places represent the destinations of interests and tokens represent time units.

After that, two estimation algorithms and one control algorithm have been proposed, respectively, based on external observations. The first algorithm deals with labeled Petri nets and the objective is to estimate the minimum initial marking that has (have) the smallest token sum. The second algorithm estimates the Petri net structures from the observations of finite token change sequences in terms of the minimum number of transitions and connections. At last, the traffic volume control algorithm is to keep the traffic volume within capacity. The controller will be applied in each evolution step depending on observation.

Since we have been focusing on the optimization problems of the structure and markings of the Petri net, it is directly related to the optimal route planning problems in highway traffic scenarios. Thus, we can obtain optimized traveling routes by applying proposed algorithms to the traffic systems.