Neural Networks In Transportation Research – Recent Applications
Author
Abstract
Suggested Citation
DOI: 10.20858/tp.2016.11.2.3
Download full text from publisher
References listed on IDEAS
- Zhao, Y. & Triantis, K. & Teodorovic, D. & Edara, P., 2010. "A travel demand management strategy: The downtown space reservation system," European Journal of Operational Research, Elsevier, vol. 205(3), pages 584-594, September.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Chen, Dongxu & Sun, Yu & Yang, Zhongzhen, 2020. "Optimization of the travel ban scheme of cars based on the spatial distribution of the last digit of license plates," Transport Policy, Elsevier, vol. 94(C), pages 43-53.
- Zhao, Y. & Triantis, K. & Murray-Tuite, P. & Edara, P., 2011. "Performance measurement of a transportation network with a downtown space reservation system: A network-DEA approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 47(6), pages 1140-1159.
- Nie, Yu (Marco) & Yin, Yafeng, 2013. "Managing rush hour travel choices with tradable credit scheme," Transportation Research Part B: Methodological, Elsevier, vol. 50(C), pages 1-19.
- Fan, Wenbo & Xiao, Feng & Nie, Yu (Macro), 2022. "Managing bottleneck congestion with tradable credits under asymmetric transaction cost," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
- Li, Xinwei & Yang, Hai & Ke, Jintao, 2023. "Booking cum rationing strategy for equitable travel demand management in road networks," Transportation Research Part B: Methodological, Elsevier, vol. 167(C), pages 261-274.
- Chen, Yinghao & Song, Xiaopeng & Cheng, Qixiu & An, Qinhe & Zhang, Yuan, 2021. "A cordon-based reservation system for urban traffic management," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 582(C).
- Celikoglu, Hilmi Berk, 2013. "Reconstructing freeway travel times with a simplified network flow model alternating the adopted fundamental diagram," European Journal of Operational Research, Elsevier, vol. 228(2), pages 457-466.
- Y. Zhao & K. Triantis & P. Edara, 2010. "Evaluation of travel demand strategies: a microscopic traffic simulation approach," Transportation, Springer, vol. 37(3), pages 549-571, May.
- Pilz, Danny & Schwerdfeger, Stefan & Boysen, Nils, 2022. "Make or break: Coordinated assignment of parking space for breaks and rest periods in long-haul trucking," Transportation Research Part B: Methodological, Elsevier, vol. 164(C), pages 45-64.
More about this item
Keywords
neural networks; transportation; prediction of road traffic parameters;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:exl:1trans:v:11:y:2016:i:2:p:27-36. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MPS Ltd. (email available below). General contact details of provider: https://www.exeley.com/journal/transport_problems .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.