Overview

In this research, we consider stochastic and dynamic transportation network problems. Particularly, we develop a variety of algorithms to solve the expected shortest path problem in addition to techniques for computing the total travel time distribution along a path in the network. First, we develop an algorithm for solving an independent expected shortest path problem. Next, we incorporate the inherent dependencies along successive links in two distinct ways to find the expected shortest path. Since the dependent expected shortest path problem cannot be solved with traditional deterministic approaches, we develop a heuristic based on the K-shortest path algorithm for this dependent stochastic network problem. Additionally, transient and asymptotic versions of the problem are considered. An algorithm to compute a parametric total travel time distribution for the shortest path is presented along with stochastically shortest path measures. The work extends the current literature on such problems by considering interactions on adjacent links. This work has been selected by scholars as being culturally important, and is part of the knowledge base of civilization as we know it. This work was reproduced from the original artifact, and remains as true to the original work as possible. Therefore, you will see the original copyright references, library stamps (as most of these works have been housed in our most important libraries around the world), and other notations in the work. This work is in the public domain in the United States of America, and possibly other nations. Within the United States, you may freely copy and distribute this work, as no entity (individual or corporate) has a copyright on the body of the work. As a reproduction of a historical artifact, this work may contain missing or blurred pages, poor pictures, errant marks, etc. Scholars believe, and we concur, that this work is important enough to be preserved, reproduced, and made generally available to the public. We appreciate your support of the preservation process, and thank you for being an important part of keeping this knowledge alive and relevant.

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