QTNet: Theory-based Queue Length Prediction for Urban Traffic
Author:
Affiliation:
1. Sumitomo Electric System Solutions, Co., Ltd., Osaka, Japan
2. Kyoto University, Kyoto, Japan
Funder
Precursory Research for Embryonic Science and Technology
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3580305.3599890
Reference44 articles.
1. Modeling highway-traffic headway distributions using superstatistics
2. Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting;Bai L.;Advances in Neural Information Processing Systems,2020
3. Real-World Carbon Dioxide Impacts of Traffic Congestion
4. Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks;Bengio S.;Advances in Neural Information Processing Systems,2015
5. Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems
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1. Queue Length Prediction Using Traffic-theory-based Deep Learning;Transactions of the Japanese Society for Artificial Intelligence;2024-03-01
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