Agent Recommendation for Agent-Based Urban-Transportation Systems

2011 ◽  
Vol 26 (6) ◽  
pp. 77-81 ◽  
Author(s):  
Cheng Chen ◽  
Shuang Shuang Li ◽  
Bo Chen ◽  
Ding Wen
Keyword(s):  
Urban Transportation ◽  
Transportation Systems ◽  
Agent Based

Cloud Computing for Agent-Based Urban Transportation Systems

2011 ◽  
Vol 26 (1) ◽  
pp. 73-79 ◽  
Author(s):  
ZhenJiang Li ◽  
Cheng Chen ◽  
Kai Wang
Keyword(s):  
Cloud Computing ◽  
Urban Transportation ◽  
Transportation Systems ◽  
Agent Based

scholarly journals Sustainable urban transportation development in China: A behavioral perspective

2021 ◽  
Author(s):  
Shuai Ling ◽  
Shoufeng Ma ◽  
Ning Jia
Keyword(s):  
Traffic Management ◽  
Rapid Development ◽  
Demand Management ◽  
Urban Transportation ◽  
Transportation Systems ◽  
Transportation Demand ◽  
Special Cases ◽  
Traffic Systems ◽  
Four Levels ◽  
Transportation Development

AbstractThe rapid development of economics requires highly efficient and environment-friendly urban transportation systems. Such requirement presents challenges in sustainable urban transportation. The analysis and understanding of transportation-related behaviors provide one approach to dealing with complicated transportation activities. In this study, the management of traffic systems is divided into four levels with a structural and systematic perspective. Then, several special cases from the perspective of behavior, including purchasing behaviors toward new energy vehicles, choice behaviors toward green travel, and behavioral reactions toward transportation demand management policies, are investigated. Several management suggestions are proposed for transportation authorities to improve sustainable traffic management.

Autonomous Ship Navigation Under Deep Learning and the Challenges in COLREGs

2018 ◽  
Author(s):  
Lokukaluge P. Perera
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Collision Avoidance ◽  
Situation Awareness ◽  
Type System ◽  
Performance Standards ◽  
Transportation Systems ◽  
Agent Based ◽  
Distributed Intelligence ◽  
System Intelligence

A general framework to support the navigation side of autonomous ships is discussed in this study. That consists of various maritime technologies to achieve the required level of ocean autonomy. Decision-making processes in autonomous vessels will play an important role under such ocean autonomy, therefore the same technologies should consist of adequate system intelligence. Each onboard application in autonomous vessels may require localized decision-making modules, therefore that will introduce a distributed intelligence type strategy. Hence, future ships will be agent-based systems with distributed intelligence throughout vessels. The main core of this agent should consist of deep learning type technology that has presented promising results in other transportation systems, i.e. self-driving cars. Deep learning can capture helmsman behavior, therefore that type system intelligence can be used to navigate autonomous vessels. Furthermore, an additional decision support layer should also be developed to facilitate deep learning type technology including situation awareness and collision avoidance. Ship collision avoidance is regulated by the Convention on the International Regulations for Preventing Collisions at Sea, 1972 (COLREGs) under open sea areas. Hence, a general overview of the COLREGs and its implementation challenges, i.e. regulatory failures and violations, under autonomous ships are also discussed with the possible solutions as the main contribution of this study. Furthermore, additional considerations, i.e. performance standards with the applicable limits of liability, terms, expectations and conditions, towards evaluating ship behavior as an agent-based system on collision avoidance situations are also illustrated in this study.

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