A reinforcement learning-based routing algorithm for large street networks

Author:

Li Diya1,Zhang Zhe12ORCID,Alizadeh Bahareh3,Zhang Ziyi2,Duffield Nick24,Meyer Michelle A.5,Thompson Courtney M.1,Gao Huilin6,Behzadan Amir H.3

Affiliation:

1. Department of Geography, Texas A&M University, College Station, TX, USA

2. Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX, USA

3. Department of Construction Science, Texas A&M University, College Station, TX, USA

4. Texas A&M Institute of Data Science, College Station, TX, USA

5. Department of Landscape Architecture & Urban Planning, Texas A&M University, College Station, TX, USA

6. Department of Civil & Environmental Engineering, Texas A&M University, College Station, TX, USA

Funder

National Oceanic and Atmospheric Administration (NOAA), U.S. Department of Commerce

Texas A&M TAMIDS Data Resource Development Program

Publisher

Informa UK Limited

Subject

Library and Information Sciences,Geography, Planning and Development,Information Systems

Reference111 articles.

1. Abe K. et al. 2019. Solving np-hard problems on graphs by reinforcement learning without domain knowledge. ArXiv abs/1905.11623 .

2. Agarap A.F. 2018. Deep learning using rectified linear units (relu). arXiv preprint arXiv:1803.08375 .

3. Human-centered flood mapping and intelligent routing through augmenting flood gauge data with crowdsourced street photos

4. Alizadeh B. et al. 2021. Feasibility study of urban flood mapping using traffic signs for route optimization. arXiv preprint arXiv:2109.11712 .

5. Debunking the Myths of Influence Maximization

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