Edge ML Technique for Smart Traffic Management in Intelligent Transportation Systems

被引:7
作者
Hazarika, Anakhi [1 ]
Choudhury, Nikumani [1 ]
Nasralla, Moustafa M. [2 ]
Khattak, Sohaib Bin Altaf [2 ]
Rehman, Ikram Ur [3 ]
机构
[1] Birla Inst Technol & Sci Pilani, Hyderabad Campus, Hyderabad 500078, India
[2] Prince Sultan Univ, Coll Engn, Smart Syst Engn Lab, Riyadh 11586, Saudi Arabia
[3] Univ West London, Sch Comp & Engn, London W5 5RF, England
来源
IEEE ACCESS | 2024年 / 12卷
关键词
Internet of Things; Delays; Roads; Edge computing; YOLO; Real-time systems; LoRa; Machine learning; Urban areas; Traffic congestion; Autonomous driving; Traffic control; IEEE 802.15.4 DSME MAC; Internet of Things (IoT); LoRaWAN; machine learning; EFFICIENT; SCHEME; BEACON;
D O I
10.1109/ACCESS.2024.3365930
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In urban traffic, a Dynamic Traffic Light System (DTLS) is an important aspect of automatic driving. DTLS estimates the time of the light signal from images of dynamically changing road traffic. In conventional traffic light systems, light signals are enabled at predefined or fixed time intervals without having information on the current traffic density on the road. This static behavior of the traffic light system increases unnecessary waiting time on the road, eventually creating traffic jams, environmental pollution, and other health emergencies. The smart traffic light system addresses these issues with self-learning algorithms and dynamically allows traffic to pass by learning current traffic density. In this paper, a vision-based DTLS is proposed using the YOLO (You Only Look Once) object detection algorithm that detects and counts the total number of vehicles on the roads of a traffic signal junction. The traffic signals are tuned based on the computed traffic to minimize the overall delay at that junction. Moreover, the traffic junctions are facilitated to communicate with the adjacent junctions to transmit the cumulative traffic delay observed. This delay is used to prioritize traffic passing through salient blocks like schools, offices, hospitals, etc. The paper aims to minimize the overhead incurred in both computations of traffic (using approximate computing) and in communication networks (using low-power technologies of IEEE 802.15.4 standard, specifically DSME MAC and/or LoRaWAN). The proposed system accomplishes its objective of smart city infrastructure by optimizing the traffic flow. Further, the paper provides a mechanism for green traffic corridors for emergency vehicles.
引用
收藏
页码:25443 / 25458
页数:16
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