Practical City Scale Stochastic Path Planning with Pre-computation
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Publisher
Springer International Publishing
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-10161-8_17
Reference34 articles.
1. Ahmadi, K.: Intelligent traffic management: from practical stochastic path planning to reinforcement learning based city-wide traffic optimization. Ph.D. Dissertation, All Graduate Theses and Dissertations, Utah State University (2021). https://doi.org/10.26076/a68e-ef6a
2. Ahmadi, K., Allan, V.H.: Stochastic path finding under congestion. In: International Conference on Computational Science and Computational Intelligence (CSCI), pp. 135–140. IEEE (2017). https://doi.org/10.1109/CSCI.2017.22
3. Ahmadi, K., Allan, V.H.: Congestion-aware stochastic path planning and its applications in real world navigation. In: Proceedings of the 13th International Conference on Agents and Artificial Intelligence, ICAART 2021, pp. 947–956. SCITEPRESS (2021). https://doi.org/10.5220/0010267009470956
4. Ahmadi, K., Allan, V.H.: Scalable stochastic path planning under congestion. In: Proceedings of the 13th International Conference on Agents and Artificial Intelligence, pp. 454–463. SCITEPRESS (2021). https://doi.org/10.5220/0010394104540463
5. Ahmadi, K., Allan, V.H.: Smart city: application of multi-agent reinforcement learning systems in adaptive traffic management. In: 2021 IEEE International Smart Cities Conference (ISC2), pp. 1–7 (2021). https://doi.org/10.1109/ISC253183.2021.9562951
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