Adaptive Traffic Light Controller for Vehicular Ad-Hoc Networks

In metro cities, the monitoring, organizing and controlling road traffic has become a challenge to make human life easier. A vehicular Ad-Hoc Network (VANET) is a promising research area to reduce the day to day hurdles in traffic flows. It is used to collect and provide the vehicular information su...

Full description

Saved in:
Bibliographic Details
Published in:International Conference on Signal Processing and Communication (Online) pp. 338 - 344
Main Authors: Pughat, Anuradha, Pritam, Amrit, Sharma, Dikshant, Gupta, Saurabh
Format: Conference Proceeding
Language:English
Published: IEEE 01.03.2019
Subjects:
delay
Delays
Fuzzy Inference System
Fuzzy logic
Roads
traffic flow
traffic light controller
Urban areas
VANET
Vehicular ad hoc networks
ISBN:9781538694350, 1538694352
ISSN:2643-444X
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In metro cities, the monitoring, organizing and controlling road traffic has become a challenge to make human life easier. A vehicular Ad-Hoc Network (VANET) is a promising research area to reduce the day to day hurdles in traffic flows. It is used to collect and provide the vehicular information such as speed of a vehicle, operational conditions, traffic density, traffic flow and average waiting time etc. to the Traffic Light Controller (TLC) at a four-way intersection. Therefore, safe, simple and cost-effective solutions are required to increase transportation efficiency using VANET. Research on Artificial Intelligence technique e.g. Neuro-fuzzy controller, reinforce learning, evolutionary algorithm and static embedded controller is ongoing to adjust to the traffic-related problems but difficult to implement. We present a Traffic Light Controller (TLC) design to monitor Green Light-time Estimation (GLE) and delay for efficient traffic flow. This Adaptive TLC based on Fuzzy Inference System (FIS) observes current traffic condition around the intersection and controls the traffic signals to obtain efficient traffic flow. This paper compares and presents two approaches of TLC-first approach considers the traffic density and traffic flow rate for estimating green light time. The second approach observes the green light time on the basis of traffic flow rate, queue size, and vehicular communication data before controlling traffic light.
ISBN:9781538694350
1538694352
ISSN:2643-444X
DOI:10.1109/ICSC45622.2019.8938220