Map Matching with Inverse Reinforcement Learning
Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 2547-2552.
Abstract
We study map-matching, the problem of estimating the route that is traveled by a vehicle, where the points observed with the Global Positioning System are available. A state-of-the-art approach for this problem is a Hidden Markov Model (HMM). We propose a particular transition probability between latent road segments by the use of the number of turns in addition to the travel distance between the latent road segments. We use inverse reinforcement learning to estimate the importance of the number of turns relative to the travel distance. This estimated importance is incorporated in the transition probability of the HMM. We show, through numerical experiments, that the error of map-matching can be reduced substantially with the proposed transition probability.
@inproceedings{ijcai2013p375,
title = {Map Matching with Inverse Reinforcement Learning},
author = {Osogami, Takayuki and Raymond, Rudy},
booktitle = {Proceedings of the Twenty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-13}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Francesca Rossi},
pages = {2547--2552},
year = {2013},
month = {8},
url = {https://ijcai.org/proceedings/2013/375},
}
TY - CPAPER TI - Map Matching with Inverse Reinforcement Learning AU - Osogami, Takayuki AU - Raymond, Rudy T2 - Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2013 SP - 2547 EP - 2552 UR - https://ijcai.org/proceedings/2013/375 ER -Copied
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