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 Modeling, Community Detection Methods, Distance Oracles, Approximation, Route Planning Under Uncertainty.
Abstract:
In this work, we propose a city scale path planning framework when edge weights are not fixed and are stochastically defined based on the mean and variance of travel time on each edge. Agents are car drivers who are moving from one point to another point in different time of the day/night. Agents can pursue two types of goals: first, the ones who are not willing to take risk and look for the path with highest probability of reaching destination before their desired arrival time, even if it may take them longer. The second group are the agents who are open to take a riskier decision if it helps them in having the shortest en-route time. In order to scale the path planning process and make it applicable to city scale, pre-computation and approximation has been used. The city graph is partitioned to smaller groups of nodes and each group is represented by one node which is called exemplar. For path planning queries, source and destination pair are connected to the respective exemplars c
orrespond to the direction of source to destination and path between those exemplars is found. Paths are stored in distance oracles for different time slots of day/week in order to expedite the query time. Distance oracles are updated weekly in order to capture the recent changes in traffic. The results show that, this approach helps in having a scalable path finding framework which handles queries in real time while the approximate paths are at least 90 percent as good as the exact paths.
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