Authors:
Kamilia Ahmadi
and
Vicki H. Allan
Affiliation:
Computer Science Department, Utah State University, Logan, Utah, U.S.A.
Keyword(s):
Stochastic Path Planning, Multi-Agent Systems, Congestion-Aware Modelling, Non-Linear Objective, Route Planning under Uncertainty.
Abstract:
In the realm of path planning, algorithms use edge weights in order to select the best path from an origin point to a specific target. This research focuses on the case where the edge weights are not fixed. Depending on the time of day/week, edge weights may change due to the congestion through the network. The best path is the path with minimum expected cost. The interpretation of best path depends on the point of view of car drivers. We model two different goals: 1) drivers who look for the path with the highest probability of reaching the destination before the deadline and 2) the drivers who look for the best time slot to leave in order to have a smallest travel time while they meet the deadline. Both of the goals are modelled based on the cost of the path which is highly dependent on the level of congestion in the network. Minimizing the paths’ cost helps in reducing traffic in the city, alleviates air pollution, and reduces fuel consumption. Findings show that using our propose
d intelligent path planning algorithm which satisfies users’ goals and picks the least congested path is more cost efficient than picking the shortest-length path. Also, we show how agents’ goals and selection of cost function impacts paths’ choice.
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