Exploring real time traffic signalling using probabilistic approach in intelligent transport system

Mehta, Vatsal, Gandhi, Vaibhav ORCID: https://orcid.org/0000-0003-1121-7419 and Mapp, Glenford E. ORCID: https://orcid.org/0000-0002-0539-5852 (2018) Exploring real time traffic signalling using probabilistic approach in intelligent transport system. In: Mobi-UK 2018, 12-13 Sep 2018, University of Cambridge, Cambridge, UK. . [Conference or Workshop Item]

Abstract

Smart cities are developing rapidly over the past few years. The Intelligent Transport System (ITS) is a part of the smart cities which provides intelligence placed near the roadside or it can be developed in the vehicle. Static traffic light signal is a predefined and cannot change in Peak Hours as well as Non-Peak Hours. In dynamic traffic light signal all the signal are changed with reference to signal phase and according to predefined times during different times in day, which will change the signal timing to RED and GREEN in both peak and non-peak hours. In future there will be fully dynamic traffic light in which all the signal will change when any emergency vehicle as well as where there is que of traffic it will give way to the que of traffic. In real time this can be achieved through communication between vehicles and traffic infrastructure. I will be using Green Light Optimal Speed Advisory (GLOSA) system as well as Signal Phase and Timing (SPAT) and probabilistic approach as part of the methodology with the help of Vehicular ad-hoc Network (VANET). There will be some probabilistic approach in the research to get the appropriate timing for each of the vehicle. For this we will use VANET test-bed, Programmable Logic Controller (PLC) as well as HMI for displaying messages on the driver screen. The future work in this research will be that all the traffic signal will be controlled by the PLC and if there is any emergency vehicle or traffic queuing then they will get the first priority.

Item Type: Conference or Workshop Item (Presentation)
Research Areas: A. > School of Science and Technology
Item ID: 30715
Useful Links:
Depositing User: Vatsal Mehta
Date Deposited: 05 Aug 2020 08:27
Last Modified: 05 Aug 2020 08:27
URI: https://eprints.mdx.ac.uk/id/eprint/30715

Actions (login required)

View Item View Item