Open Access

ARTICLE

A Multi-Mode Public Transportation System Using Vehicular to Network Architecture

by Settawit Poochaya^1,*, Peerapong Uthansakul¹, Monthippa Uthansakul¹, Patikorn Anchuen², Kontorn Thammakul³, Arfat Ahmad Khan⁴, Niwat Punanwarakorn⁵, Pech Sirivoratum⁵, Aranya Kaewkrad⁵, Panrawee Kanpan⁵, Apichart Wantamee⁵

1 School of Telecommunication Engineering, Suranaree University of Technology, Nakhon Ratchasima, 30000, Thailand
2 Office of Graduate Studies, Navaminda Kasatriyadhiraj Royal Air Force Academy, Bangkok, 10220, Thailand
3 School of Mechanical Engineering, Suranaree University of Technology, Nakhon Ratchasima, 30000, Thailand
4 School of Manufacturing Engineering, Suranaree University of Technology, Nakhon Ratchasima, 30000, Thailand
5 Thaicom Public Company Limited, Patumthani, 12140, Thailand

* Corresponding Author: Settawit Poochaya. Email:

Computers, Materials & Continua 2022, 73(3), 5845-5862. https://doi.org/10.32604/cmc.2022.031162

Received 11 April 2022; Accepted 23 May 2022; Issue published 28 July 2022

Abstract

The number of accidents in the campus of Suranaree University of Technology (SUT) has increased due to increasing number of personal vehicles. In this paper, we focus on the development of public transportation system using Intelligent Transportation System (ITS) along with the limitation of personal vehicles using sharing economy model. The SUT Smart Transit is utilized as a major public transportation system, while MoreSai@SUT (electric motorcycle services) is a minor public transportation system in this work. They are called Multi-Mode Transportation system as a combination. Moreover, a Vehicle to Network (V2N) is used for developing the Multi-Mode Transportation system in the campus. Due to equipping vehicles with On Board Unit (OBU) and 4G LTE modules, the real time speed and locations are transmitted to the cloud. The data is then applied in the proposed mathematical model for the estimation of Estimated Time of Arrival (ETA). In terms of vehicle classifications and counts, we deployed CCTV cameras, and the recorded videos are analyzed by using You Only Look Once (YOLO) algorithm. The simulation and measurement results of SUT Smart Transit and MoreSai@SUT before the covid-19 pandemic are discussed. Contrary to the existing researches, the proposed system is implemented in the real environment. The final results unveil the attractiveness and satisfaction of users. Also, due to the proposed system, the CO₂ gas gets reduced when Multi-Mode Transportation is implemented practically in the campus.

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Keywords

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