Survey of Traffic Congestion Detection using VANET

El-Sersy, Heba; El-Sayed, Ayman

doi:10.5120/cae-1520

Cited by 14 publications

(4 citation statements)

References 28 publications

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“…In [19][20][21], each floating car reports its time and location information via GPS or cellular phone, from which traffic parameters such as speed and congestion are calculated. Each vehicle in Vehicular ad hoc networks (VANETs) in [45,46] acts as a data collection device and transmits the information using vehicle-to-vehicle communication and vehicle-to-fixed roadside infrastructure communication. Each Inductive loop in [47] and traffic camera in [48] collect information when vehicles pass over or through sensor's area.…”

Section: Traffic Datamentioning

confidence: 99%

Large-Scale Road Network Congestion Pattern Analysis and Prediction Using Deep Convolutional Autoencoder

et al. 2021

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The transportation system, especially the road network, is the backbone of any modern economy. However, with rapid urbanization, the congestion level has surged drastically, causing a direct effect on the quality of urban life, the environment, and the economy. In this paper, we propose (i) an inexpensive and efficient Traffic Congestion Pattern Analysis algorithm based on Image Processing, which identifies the group of roads in a network that suffers from reoccurring congestion; (ii) deep neural network architecture, formed from Convolutional Autoencoder, which learns both spatial and temporal relationships from the sequence of image data to predict the city-wide grid congestion index. Our experiment shows that both algorithms are efficient because the pattern analysis is based on the basic operations of arithmetic, whereas the prediction algorithm outperforms two other deep neural networks (Convolutional Recurrent Autoencoder and ConvLSTM) in terms of large-scale traffic network prediction performance. A case study was conducted on the dataset from Seoul city.

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Section: Traffic Datamentioning

confidence: 99%

Large-Scale Road Network Congestion Pattern Analysis and Prediction Using Deep Convolutional Autoencoder

et al. 2021

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“…In most cities there is what they call peak times where roads and highways are filled with traffic which is almost immobile due to many reasons [4]. Planning in this kind of environment is not always easy as most of the times business players are found themselves right in the mid of the traffic congestion, stuck [5]. This becomes an impingement on the progress on a business and has possibility of costing high on the economy of such a business [6].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Transport Systems

Kazembe¹

2022

IJRASET

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Growing economic activities aand others are among the factors that make many cities busy places today especially in traffic systems. Road networks that seem to be spacious sometimes become completely congested so much so that traffic mobility looks standstill. This impacts negatively on traffic users resources in terms of time management, fuel and other resource. This paper discusses the background trend in traffic activities amid congestion environment and proposes an Intelligent Traffic System that uses Machine Learning technique of predictive classification and regression to help traffic users determine forehand on the congestion status of available roads or highways. This further suggests that if the road user tries to avoid congestion, then the congestion levels will minimize. For simplicity, the data that has been used for congestion has been from a csv file containing previous datasets of local roads or highways.

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“…The main reasons of using VANET are as follows: to organize traffic during unpredictable situations-for example, an accident-to inform drivers of traffic congestion detection [10][11][12][13] or about the weather, to control the intersection [14][15][16], for accident avoidance and emergency management, cf. [17][18][19], and to enable the fastest path planning [9,20].…”

Section: Introductionmentioning

confidence: 99%

Management of Delay Optimization at Intersections

Kuźmiński

Maśloch²,

Bazan

et al. 2020

Energies

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Congestion extends the time of the journey for both people and goods. Therefore, transport solutions should be optimized. Management scientists and technical scientists worked together in order to develop a proprietary solution to increase efficiency in terms of productivity improvements for intelligent transport systems. The most fundamental functions of management have been paired with a detailed analysis of city traffic. The authors developed a method for determining the order of vehicles at traffic lights and connected it with vehicle-to-vehicle communication and GPS signals. As a result, a novel method to increase the throughput of intersections is presented. This solution generates a sound signal in order to inform the driver that the preceding car has started moving forward. The proposed solution leads to the shortening of the reaction time of the drivers waiting in a queue. This situation is most common at red lights. Consequently, the traffic simulation shows that the discharge of queues at traffic lights may be quicker by up to 13.5%. Notably, that proposed solution does not require any modification of the infrastructure as well as any additional devices for vehicle-to-infrastructure communication at the road intersections. To conclude, proper implementation of the proposed solution will certainly contribute to efficiency improvements within intelligent transport systems, with the potential to reduce traffic jams.

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Survey of Traffic Congestion Detection using VANET

Cited by 14 publications

References 28 publications

Large-Scale Road Network Congestion Pattern Analysis and Prediction Using Deep Convolutional Autoencoder

Large-Scale Road Network Congestion Pattern Analysis and Prediction Using Deep Convolutional Autoencoder

Intelligent Transport Systems

Management of Delay Optimization at Intersections

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