Bi-Level Programming Models Applied in Urban Transportation Network Design Problems

2013 ◽  
Vol 791-793 ◽  
pp. 1172-1175
Author(s):  
Chuan Song
Keyword(s):  
Network Design ◽  
Transportation Network ◽  
Urban Transportation ◽  
Programming Models ◽  
Future Research ◽  
Open Problems ◽  
Design Problems ◽  
Practical Applications ◽  
Network Design Problems ◽  
Comprehensive Survey

In this paper, we conduct a comprehensive survey on the past developments and recent advances of bilevel programming models, algorithms and practical applications in urban transportation network design problems. Moreover, based on this survey, some open problems and future research directions are proposed.

scholarly journals Review of Urban Transportation Network Design Problems Based on CiteSpace

2019 ◽  
Vol 2019 ◽  
pp. 1-22 ◽  
Author(s):  
Guo-Ling Jia ◽  
Rong-Guo Ma ◽  
Zhi-Hua Hu
Keyword(s):  
Mathematical Models ◽  
Network Design ◽  
Transportation Network ◽  
Urban Transportation ◽  
Design Problems ◽  
Future Directions ◽  
Comprehensive Review ◽  
Network Design Problems ◽  
The Future ◽  
Solution Methods

This paper provides a comprehensive review of urban transportation network design problems according to CiteSpace, including main problem classifications, mathematical models, and solution methods obtained from CiteSpace clusters. The review attempts to present the systematic picture of urban transportation network design and show the future directions of it.

scholarly journals A review of urban transportation network design problems

2013 ◽  
Vol 229 (2) ◽  
pp. 281-302 ◽  
Author(s):  
Reza Zanjirani Farahani ◽  
Elnaz Miandoabchi ◽  
W.Y. Szeto ◽  
Hannaneh Rashidi
Keyword(s):  
Network Design ◽  
Transportation Network ◽  
Urban Transportation ◽  
Design Problems ◽  
Network Design Problems

A hybrid meta-heuristic algorithm for solving real-life transportation network design problems

2013 ◽  
Vol 16 (1) ◽  
pp. 41 ◽  
Author(s):  
Saeed Asadi Bagloee ◽  
Madjid Tavana ◽  
Avishai Ceder ◽  
Claire Bozic ◽  
Mohsen Asadi
Keyword(s):  
Network Design ◽  
Heuristic Algorithm ◽  
Transportation Network ◽  
Real Life ◽  
Design Problems ◽  
Network Design Problems

Identifying Critical Links in Transportation Network Design Problems for Maximizing Network Accessibility

2020 ◽  
Vol 2674 (2) ◽  
pp. 237-251
Author(s):  
Yufeng Zhang ◽  
Alireza Khani
Keyword(s):  
Network Design ◽  
Large Scale ◽  
Programming Model ◽  
Transportation Network ◽  
Time Budget ◽  
Relaxation Method ◽  
Design Problems ◽  
Network Design Problems ◽  
Critical Links ◽  
Travel Time Budget

A significant amount of research has been performed on network accessibility evaluation, but studies on incorporating accessibility maximization into network design problems have been relatively scarce. This study aimed to bridge the gap by proposing an integer programming model that explicitly maximizes the number of accessible opportunities within a given travel time budget. We adopted the Lagrangian relaxation method for decomposing the main problem into three subproblems that can be solved more efficiently using dynamic programming. The proposed method was applied to several case studies, which identified critical links for maximizing network accessibility with limited construction budget, and also illustrated the accuracy and efficiency of the algorithm. This method is promisingly scalable as a solution algorithm for large-scale accessibility-oriented network design problems.

Hierarchical Reinforcement Learning

2021 ◽  
Vol 54 (5) ◽  
pp. 1-35
Author(s):  
Shubham Pateria ◽  
Budhitama Subagdja ◽  
Ah-hwee Tan ◽  
Chai Quek
Keyword(s):  
Reinforcement Learning ◽  
Future Research ◽  
Comprehensive Overview ◽  
Open Problems ◽  
Practical Applications ◽  
Hierarchical Reinforcement Learning ◽  
The Past ◽  
Agent Learning ◽  
Multi Agent ◽  
Supplementary Material

Hierarchical Reinforcement Learning (HRL) enables autonomous decomposition of challenging long-horizon decision-making tasks into simpler subtasks. During the past years, the landscape of HRL research has grown profoundly, resulting in copious approaches. A comprehensive overview of this vast landscape is necessary to study HRL in an organized manner. We provide a survey of the diverse HRL approaches concerning the challenges of learning hierarchical policies, subtask discovery, transfer learning, and multi-agent learning using HRL. The survey is presented according to a novel taxonomy of the approaches. Based on the survey, a set of important open problems is proposed to motivate the future research in HRL. Furthermore, we outline a few suitable task domains for evaluating the HRL approaches and a few interesting examples of the practical applications of HRL in the Supplementary Material.

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