Title: Bi-level optimisation model for greener transportation with intelligent transport system

Authors: Kun Liu

Addresses: Information Technology College, Beijing Normal University at Zhuhai, Guangdong Province, China

Abstract: In this paper, we propose a bi-level optimisation model (BLOM) with three algorithms. BLOM is intended for fuel saving and carbon dioxide emission reduction in both upper-level and lower-level model with intelligent transport system. Traffic signal schemes are optimised for minimising total fuel consumption passing through a road intersection in unit time in the upper-level model. At the same time, traffic signal information data are sent to the lowerlevel model in which vehicle motion states are optimised for greener transportation. Three algorithms include hybrid genetic algorithm and particle swarm optimisation in upper-level model with hybrid genetic algorithm and particle swarm optimisation in lower-level model (GA-PSO/GA-PSO), GA in upper-level model with PSO in lower-level model (GA/PSO) and GA in both level model (GA/GA) are realised to compare and improve the performance of the model. The simulation results derive GA-PSO/GA-PSO hybrid algorithm converges faster with the best resolution and least calculation time than other GA/PSO and GA/GA algorithms.

Keywords: bi-level optimisation; greener transportation; intelligent transport system.

DOI: 10.1504/IJRIS.2018.091124

International Journal of Reasoning-based Intelligent Systems, 2018 Vol.10 No.1, pp.26 - 31

Received: 03 May 2017
Accepted: 13 Jun 2017

Published online: 11 Apr 2018 *

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