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Multi-Modal Smart Traffic Signal Control Using Connected Vehicles

Rajvanshi, Kshitij
http://orcid.org/0000-0003-2347-3217
http://rave.ohiolink.edu/etdc/view?acc_num=ucin147981730919519

Abstract Details

2016, MS, University of Cincinnati, Engineering and Applied Science: Computer Science.
As the technology is advancing day by day, the intelligent transportation industry is also experiencing a advancement in vehicle communication technology. The future for the automotive industry are the self-driving vehicles. Next-generation cars and other automobiles are getting equipped with unique electronics sensors like LIDAR, ultrasonic sensors, radar sensors. These sensors monitor different aspects of vehicle movements such as vehicle's speed, position, longitudinal and lateral acceleration. The vehicle communication technology exists, but vehicles rarely communicate their information with the road side infrastructures. The connected vehicle initiative and the deployment of wireless communication techniques will help in improving vehicle safety and also reduce traffic congestion.The traffic signal control timing plans are designed in such way that they can minimize the vehicle travel delay based on conditions such as historical traffic volumes. In-pavement induction loop detectors and video detectors make small adjustments to signal timings, but they are unreliable and limited in terms of range. With the connected vehicle initiative, vehicles can communicate with the roadside infrastructure such as traffic signal control within 300 meters of an intersection through communication techniques like the Dedicated Short Range Communication (DSRC). A unique algorithm is proposed which uses a concept of vehicle platooning as the vehicle control model. Vehicle platooning helps in increasing the throughput of a particular road. The vehicle control is based on Cooperative Adaptive Cruise Control (CACC) mechanism. The proposed algorithm also uses a global nature inspired optimization algorithm known as Multi-objective Bat Algorithm. This algorithm takes into consideration different input such as the queue length of the intersection roads and actual flow rate and give out the optimized value of the green signal time for the next phase signal to be implemented. The effective performance of the intersection signal control model is measured on the basis of mobility and environmental factors through simulation studies. The suggested traffic signal control model can reduce average waiting time by a maximum of 30.7%, reduce fuel consumption by a maximum value of 11% and CO2 emissions by a maximum value of 9% based on the three simulation scenarios. To perform better than current systems,the minimum required for the connected vehicle penetration rate is 25%.
Dharma Agrawal, D.Sc. (Committee Chair)
Rui Dai, Ph.D. (Committee Member)
Chia Han, Ph.D. (Committee Member)
70 p.
Computer Science
Adaptive traffic control signal; Connected vehicle; Multi-objective Bat-Inspired algorithm; Vehicle platoons

Recommended Citations

Citations

  • Rajvanshi, K. (2016). Multi-Modal Smart Traffic Signal Control Using Connected Vehicles [Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin147981730919519

    APA Style (7th edition)

  • Rajvanshi, Kshitij. Multi-Modal Smart Traffic Signal Control Using Connected Vehicles. 2016. University of Cincinnati, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin147981730919519.

    MLA Style (8th edition)

  • Rajvanshi, Kshitij. "Multi-Modal Smart Traffic Signal Control Using Connected Vehicles." Master's thesis, University of Cincinnati, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin147981730919519

    Chicago Manual of Style (17th edition)

ucin147981730919519
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© 2016, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.