Conference Papers 2020

Seminar
Day 2 (10 Sep 2020), Session 5, Optimisation in Modelling, 13:00 - 15:00

Status
Accepted, documents submitted

Submitted by / Abstract owner
Shohel Amin

Authors
Dr. Shohel Amin
Louis Froger
Dr. Olivier Haas

Short abstract
This paper develops a computational resolution of a problem which can be reduced to finding the best path for emergency vehicle using the Ant Colony Optimization (ACO) algorithm

Abstract
Rail transport of hazardous materials is a major safety concern for urban areas and an efficient emergency response plan is required in case of rail accident involving hazardous materials. Failure to response immediate aftermath of train accidents involving the release of hazardous materials can lead to catastrophic consequence. The emergency services face challenges to coordinate the activities because the response time plays a crucial role in minimising the adverse impacts. Integration of the real-time traffic information with emergency vehicles dispatch in assigning the appropriate response vehicles and guide these vehicles through the fastest routes requires to achieve the efficient performance of emergency response. This paper develops a computational resolution of a problem which can be reduced to finding the fastest path for emergency vehicle using the Ant Colony Optimization (ACO) algorithm. ACO is an adaptative algorithm that transfers information from past environment to a new one to iteratively build a relevant solution with a heuristic-based method. This study applies A* algorithm considering the distance as crow flies to bring a faster solution. The road network within 5 km buffer zone of rail tracks at Angers city in France is considered as a case study. The case study enables to testing the algorithm in a complex urban environment where the exposure of people and properties towards hazardous materials released in freight train accident is high. The designed system computes the right number of emergency vehicles required depending on disaster characteristics and dynamically choice the best route starting from different places using real-time data. The heuristic ACO method developed in this paper solves efficiently the routing problem with providing quite suitable and diversified solutions in a reasonable computing time.

Programme committee
Transport Models