simobility
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sash-ko
Implement paper: "MOVI: A Model-Free Approach to Dynamic Fleet Management"
MOVI, a Deep Q-network (DQN)-based framework that directly learns the optimal vehicle dispatch policy
MOVI: A Model-Free Approach to Dynamic Fleet Management
I already started working on it and going to use this repo for all machine learning related papers https://github.com/sash-ko/ai-transportation
@yabirgb there is an interesting part is this paper - demand prediction with CNNs, it could be a topic on its own. A bit more details you can find in the technical report https://www.dropbox.com/s/ujqova12lnklgn5/dynamic-fleet-management-TR.pdf?dl=0
@yabirgb there is an interesting part is this paper - demand prediction with CNNs, it could be a topic on its own. A bit more details you can find in the technical report https://www.dropbox.com/s/ujqova12lnklgn5/dynamic-fleet-management-TR.pdf?dl=0
Ok I'm going to take a look, surely seems something interesting. Yesterday I finished the paper about T-share and I believe it has some ideas that can be mixed or used in the MOVI design although I need to reread it.
@sash-ko I'm playing around the topic of demand prediction using images and found this papers that might be interesting https://arxiv.org/pdf/1911.03441.pdf and https://arxiv.org/abs/1701.04245 (Yesterday I was trying to learn pytorch that is new for me)
In MOVI paper there is only a small chapter about CNNs for demand prediction
In MOVI paper there is only a small chapter about CNNs for demand prediction
I implemented the demand prediction as the paper mentions plus other variations that I made. Currently I'm training and comparing the models using the data from https://www1.nyc.gov/site/tlc/about/tlc-trip-record-data.page converting PULocation and DOLocation to a coordinate inside the region. I'll prepare a document with the comparison.
Moreover I'm implementing the model from https://arxiv.org/pdf/1911.03441.pdf to compare it too