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Machine Vision-Enabled Traffic Controller for Safer and Smoother Traffic Flow Around Construction Sites

conference contribution
posted on 2024-11-03, 13:34 authored by Wei Qin Chuah, Ruwan TennakoonRuwan Tennakoon, Reza HoseinnezhadReza Hoseinnezhad, Alireza Bab-HadiasharAlireza Bab-Hadiashar
With the continuous process of urbanization in many cities around the globe, construction works are increasingly interrupting traffic flow in urban roads. At construction sites, usually traffic control personnel are employed to instruct the traffic flow at the times obstruction. Employing manual labour flagmen for traffic control is costly and can potentially expose the workers to safety hazards. Alternative attempts such as using remotely controlled signs or timers are not ideal; the former still requires human input and the latter does not optimize traffic control based on traffic density. To tackle this problem, we propose a fully automated traffic controller solution that uses visual sensors and machine vision technology. This system can replace the human traffic controllers during lane closures on the event of a construction. The implementation of this system eliminates the requirement for conventional traffic controllers, reducing the overall health and safety risks to workers and drivers. Our solution can result in substantial cost savings by replacing the traffic controllers. In addition, with manual labour, traffic control performance can be affected by weather conditions or fatigue from long hour shifts. Thus, our solution can lead to improved road safety and robustness of traffic flow as well.

History

Related Materials

  1. 1.
    DOI - Is published in 10.1109/ITSC.2019.8917194
  2. 2.
    ISBN - Is published in 9781538670231 (urn:isbn:9781538670231)

Number

8917194

Start page

4255

End page

4260

Total pages

6

Outlet

Proceedings of the 2019 IEEE Intelligent Transportation Systems Conference (ITSC 2019)

Name of conference

ITSC 2019

Publisher

IEEE

Place published

United States

Start date

2019-10-27

End date

2019-10-30

Language

English

Copyright

© 2019 IEEE.

Former Identifier

2006106421

Esploro creation date

2021-08-11

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