Escape route guidance system for emergency situation in quay process of shipyard

2019 ◽  
Author(s):  
Dae-Eun Ko ◽  
Ho-Kyeong Kim
Keyword(s):  
Emergency Situation ◽  
Guidance System ◽  
Route Guidance ◽  
Escape Route

scholarly journals An Eco-Friendly Multimodal Route Guidance System for Urban Areas Using Multi-Agent Technology

2021 ◽  
Vol 11 (5) ◽  
pp. 2057
Author(s):  
Abdallah Namoun ◽  
Ali Tufail ◽  
Nikolay Mehandjiev ◽  
Ahmed Alrehaili ◽  
Javad Akhlaghinia ◽  
...  
Keyword(s):  
Traffic Flow ◽  
Urban Areas ◽  
Urban Environments ◽  
Ease Of Use ◽  
Transport Infrastructure ◽  
Guidance System ◽  
Planning System ◽  
Flow Density ◽  
Route Guidance ◽  
Agent Based

The use and coordination of multiple modes of travel efficiently, although beneficial, remains an overarching challenge for urban cities. This paper implements a distributed architecture of an eco-friendly transport guidance system by employing the agent-based paradigm. The paradigm uses software agents to model and represent the complex transport infrastructure of urban environments, including roads, buses, trolleybuses, metros, trams, bicycles, and walking. The system exploits live traffic data (e.g., traffic flow, density, and CO2 emissions) collected from multiple data sources (e.g., road sensors and SCOOT) to provide multimodal route recommendations for travelers through a dedicated application. Moreover, the proposed system empowers the transport management authorities to monitor the traffic flow and conditions of a city in real-time through a dedicated web visualization. We exhibit the advantages of using different types of agents to represent the versatile nature of transport networks and realize the concept of smart transportation. Commuters are supplied with multimodal routes that endeavor to reduce travel times and transport carbon footprint. A technical simulation was executed using various parameters to demonstrate the scalability of our multimodal traffic management architecture. Subsequently, two real user trials were carried out in Nottingham (United Kingdom) and Sofia (Bulgaria) to show the practicality and ease of use of our multimodal travel information system in providing eco-friendly route guidance. Our validation results demonstrate the effectiveness of personalized multimodal route guidance in inducing a positive travel behavior change and the ability of the agent-based route planning system to scale to satisfy the requirements of traffic infrastructure in diverse urban environments.

Forced-node route guidance system: incorporating both user equilibrium and system optimal benefits

2019 ◽  
Vol 13 (12) ◽  
pp. 1851-1859 ◽  
Author(s):  
Hossein Rahimi-Farahani ◽  
Amir Abbas Rassafi ◽  
Babak Mirbaha
Keyword(s):  
User Equilibrium ◽  
Guidance System ◽  
Route Guidance ◽  
System Optimal

scholarly journals Hierarchical Sarsa Learning Based Route Guidance Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Feng Wen ◽  
Xingqiao Wang ◽  
Xiaowei Xu
Keyword(s):  
State Space ◽  
Road Network ◽  
Large Scale ◽  
Road Networks ◽  
Modern Society ◽  
Guidance System ◽  
Route Guidance ◽  
Guidance Algorithm ◽  
Reinforcement Learning Models ◽  
Dynamic Route

In modern society, route guidance problems can be found everywhere. Reinforcement learning models can be normally used to solve such kind of problems; particularly, Sarsa Learning is suitable for tackling with dynamic route guidance problem. But how to solve the large state space of digital road network is a challenge for Sarsa Learning, which is very common due to the large scale of modern road network. In this study, the hierarchical Sarsa learning based route guidance algorithm (HSLRG) is proposed to guide vehicles in the large scale road network, in which, by decomposing the route guidance task, the state space of route guidance system can be reduced. In this method, Multilevel Network method is introduced, and Differential Evolution based clustering method is adopted to optimize the multilevel road network structure. The proposed algorithm was simulated with several different scale road networks; the experiment results show that, in the large scale road networks, the proposed method can greatly enhance the efficiency of the dynamic route guidance system.

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