Scalable Stochastic Path Planning under Congestion

Kamilia Ahmadi, Vicki Allan

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 correspond 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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Paper Citation


in Harvard Style

Ahmadi K. and Allan V. (2021). Scalable Stochastic Path Planning under Congestion.In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-484-8, pages 454-463. DOI: 10.5220/0010394104540463


in Bibtex Style

@conference{icaart21,
author={Kamilia Ahmadi and Vicki Allan},
title={Scalable Stochastic Path Planning under Congestion},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2021},
pages={454-463},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010394104540463},
isbn={978-989-758-484-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Scalable Stochastic Path Planning under Congestion
SN - 978-989-758-484-8
AU - Ahmadi K.
AU - Allan V.
PY - 2021
SP - 454
EP - 463
DO - 10.5220/0010394104540463