Machine Learning Strategies for Optimizing Urban Parking: A Comparative Evaluation-Reference-Cited by-同舟云学术

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Machine Learning Strategies for Optimizing Urban Parking: A Comparative Evaluation

Published:2024-06-13 Issue: Volume:39 Page:678-689
ISSN:
Container-title:International Conference on Transportation and Development 2024
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Author:

Sneha Channamallu Sai¹²³⁴,Kermanshachi Sharareh¹²³⁴,Michael Rosenberger Jay¹²³⁴,Pamidimukkala Apurva¹²³⁴

Affiliation:

1. Ph.D. Student, Dept. of Civil Engineering, Univ. of Texas at Arlington, Arlington, TX.

2. Associate Vice Chancellor and Associate Dean of Research, Pennsylvania State Univ., State College, PA.

3. Professor, Dept. of Industrial Engineering, Univ. of Texas at Arlington, Arlington, TX.

4. Assistant Professor, Dept. of Civil Engineering, Univ. of Texas at Arlington, Arlington, TX.

Publisher

American Society of Civil Engineers

Link

https://ascelibrary.org/doi/pdf/10.1061/9780784485514.059

Reference42 articles.

1. Anwar, T., Asadullah, S., Azzam, M., and Abbas, K. 2019. Comparison of different machine learning models for parking occupancy prediction. In 2019 International Conference on Frontiers of Information Technology (FIT) (pp. 59–64). IEEE.

2. Prediction of parking space availability in real time;Caicedo F.;Expert Systems with Applications,2012

3. Parking Management and Modeling of Car Park Patron Behavior in Underground Facilities

4. Impact of Autonomous Vehicles on Traffic Crashes in Comparison with Conventional Vehicles

5. A review of smart parking systems;Channamallu S. S.;Transportation Research Procedia,2023

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