Impacts of Non-Recurrent Events on Pheromone-Based Green Transportation System

Kian Lun Soon,Joanne Mun-Yee Lim,Rajendran Parthiban

doi:10.1109/access.2020.2964022

Kian Lun Soon, Joanne Mun-Yee Lim + Show 1 more

Open Access

https://doi.org/10.1109/access.2020.2964022

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Abstract

Green transportation has become a key research focus in recent years. As the evaluation of non-recurrent event is still in the infancy stage, the practicality of using existing pheromone-based system for green transportation is still an open question. To fill this gap, the impacts of non-recurrent events are assessed, when using the proposed Pheromone-based Green Transportation System (PGTS) under some practical scenarios such as accidental situation, heavy rain and their combination. First, accidental situation is evaluated by halting a vehicle on a target road with maximum allowable speed of 10 km/h. Second, heavy rain is modelled by reducing the maximum speed of the affected area by 20 km/h. Third, the performance of PGTS is also gauged based on the impacts from accidental situation and heavy rain. Based on Singapore traffic data, experimental results show that the proposed PGTS achieves competitive performance against all non-recurrent events.

Highlights

The application of swarm intelligence in transportation has been increasingly gaining attention in recent years
SCENARIOS UNDER NON-RECURRENT EVENTS To promote practicality, the proposed Pheromone-based Green Transportation System (PGTS) is evaluated under several non-recurrent events namely accidental situations, heavy rain and their combination, which are described in subsections below: 1) ACCIDENT An accident refers to the incident that causes slower traffic speed. the impact of an accident can be simulated in three different ways [19]:
To fill the gap of existing pheromone-based systems which focus on typical traffic conditions, this paper aims to assess the impacts of non-recurrent events accidents, rainy weather, and their combination through the proposed

Summary

INTRODUCTION

The application of swarm intelligence in transportation has been increasingly gaining attention in recent years. The improved pheromone-based scheme reduces the upstream congestion through a routing scheme and disseminates downstream traffic through a coordinated traffic light control strategy. After coordinating the traffic lights, line 8 removes the checked p from RdCon(t) and line 9 sets statestopcoordinate to 0, showing that there is a need to coordinate its r-hop upstream and m-hop downstream road segments. 3) COOPERATIVE GREEN VEHICLE ROUTING (CGVR) CGVR aims to direct vehicles away from entering the congested road to reduce upstream congestion, as given by Algorithm 1 Coordinated Traffic Light Control. Line 1 iteratively loops #RdCon while lines 2-15 recursively assign a greener path for each vehicle in each ODnei pair based on global distance, number of intersections, transport pheromone intensity and mean trip speed of m-hop downstream road segments. Line computes the probability of vehicles on route selection while line distributes

Get neighboring roads p Pnei connected to road p for p’ Pnei do

14 Remove the congested road p from Pcon

RESULTS AND DISCUSSIONS

CONCLUSION