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

2021 ◽  
Vol 18 (12) ◽  
pp. 151-177
Wenwei Yue ◽  
Changle Li ◽  
Guoqiang Mao ◽  
Nan Cheng ◽  
Di Zhou
2020 ◽  
Ayesha Ata ◽  
Muhammad Adnan Khan ◽  
Sagheer Abbas ◽  
Muhammad Saleem Khan ◽  
Gulzar Ahmad

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.

2017 ◽  
Vol 18 (1) ◽  
pp. 25-33 ◽  
Jamal Raiyn

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.

2021 ◽  
Vol 336 ◽  
pp. 07001
Bo Xu ◽  
Jianbing Chen ◽  
Wei Tang

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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