Evolution of road traffic congestion control: A survey from perspective of sensing, communication, and computation

2021 ◽  
Vol 18 (12) ◽  
pp. 151-177
Author(s):  
Wenwei Yue ◽  
Changle Li ◽  
Guoqiang Mao ◽  
Nan Cheng ◽  
Di Zhou
Keyword(s):  
Congestion Control ◽  
Traffic Congestion ◽  
Road Traffic ◽  
Traffic Congestion Control ◽  
Road Traffic Congestion

Adaptive IoT Empowered Smart Road Traffic Congestion Control System Using Supervised Machine Learning Algorithm

2020 ◽  
Author(s):  
Ayesha Ata ◽  
Muhammad Adnan Khan ◽  
Sagheer Abbas ◽  
Muhammad Saleem Khan ◽  
Gulzar Ahmad
Keyword(s):  
Machine Learning ◽  
Control System ◽  
Congestion Control ◽  
Traffic Congestion ◽  
Road Traffic ◽  
Learning Algorithm ◽  
Supervised Machine Learning ◽  
Smart Systems ◽  
Traffic Congestion Control ◽  
Road Traffic Congestion

Abstract The concept of smart systems blessed with different technologies can enable many algorithms used in Machine Learning (ML) and the world of the Internet of Things (IoT). In a modern city many different sensors can be used for information collection. Algorithms that are cast-off in Machine Learning improves the capabilities and intelligence of a system when the amount of data collectedincreases. In this research, we propose a TCC-SVM system model to analyse traffic congestion in the environment of a smart city. The proposed model comprises an ML-enabled IoT-based road traffic congestion control system whereby the occurrence of congestion at a specific point is notified.

scholarly journals Road Traffic Congestion Management Based on a Search-Allocation Approach

2017 ◽  
Vol 18 (1) ◽  
pp. 25-33 ◽  
Author(s):  
Jamal Raiyn
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Traffic Congestion ◽  
Traffic Management ◽  
Road Traffic ◽  
Smart Cities ◽  
Self Regulation ◽  
Congestion Management ◽  
Shortest Route ◽  
Road Traffic Congestion

Abstract This paper introduces a new scheme for road traffic management in smart cities, aimed at reducing road traffic congestion. The scheme is based on a combination of searching, updating, and allocation techniques (SUA). An SUA approach is proposed to reduce the processing time for forecasting the conditions of all road sections in real-time, which is typically considerable and complex. It searches for the shortest route based on historical observations, then computes travel time forecasts based on vehicular location in real-time. Using updated information, which includes travel time forecasts and accident forecasts, the vehicle is allocated the appropriate section. The novelty of the SUA scheme lies in its updating of vehicles in every time to reduce traffic congestion. Furthermore, the SUA approach supports autonomy and management by self-regulation, which recommends its use in smart cities that support internet of things (IoT) technologies.

scholarly journals Research on an intelligent traffic congestion control model

2021 ◽  
Vol 336 ◽  
pp. 07001
Author(s):  
Bo Xu ◽  
Jianbing Chen ◽  
Wei Tang
Keyword(s):  
Congestion Control ◽  
Traffic Control ◽  
Traffic Congestion ◽  
Control Model ◽  
Status Quo ◽  
Key Technology ◽  
Related Research ◽  
The Status ◽  
Traffic Congestion Control

This paper summarizes the status quo of intelligent traffic congestion control and vehicle following on traffic road, puts forward the key technology model and its content of intelligent traffic control, elaborates the model and content in detail, and summarizes the research done, hoping to provide reference for the related research on intelligent traffic congestion control.

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