Real-time Road Traffic Information Research Articles

Traffic Image Analysis Based on Stacked Denoising Autoencoder Neural Network

This study aims to explore major neural network models - Stacked Denoising Autoencoder (SDAE), Deep Belief Network (DBN), Backpropagation - that have recently garnered attention and propose the most suitable and reliable artificial neural network model for real-time road traffic information collection. In this study, to enhance the reliability of experimental results, numerous experiments were conducted under identical conditions (such as parameter values and network configuration) by setting different initial values for the weight vector. The results of the experiments were statistically validated to draw conclusions. The research results showed that the SDAE model exhibited the most superior performance, while the accuracy of the DBN was somewhat lower compared to the SDAE model. On the other hand, the Backpropagation model demonstrated a relatively low predictive accuracy compared to both models, particularly showing a significant influence of the initial values

A Survey on IoT-Based Smart Cars, their Functionalities and Challenges

The Internet of Things (IoT) is a modern technology buzzword that makes everyday objects smarter. The concept of the Internet of Things extends beyond smartphones and computers to make vehicles, homes and communities intelligent. The most pressing concern in relation to growing vehicle use is the rising frequency of traffic accidents. The issue with modern automobiles is that they do not offer us safety, which might lead to tragic accidents. The concept of IoT in vehicles was developed to ensure the safety and efficiency of road conditions. It is a dynamic mobile communication system between cars and humans that is supposed to deliver real-time road traffic information, safeguard journey comfort, and increase transport efficiency. The paper reviews the evolution of smart cars throughout the 6 stages of automation and compares the various methodologies that use IoT.

Crowdsensing-Based Consensus Incident Report for Road Traffic Acquisition

Real-time road traffic information brings great convenience for drivers. Various road information acquisitions are enabled by recent mobile crowdsensing paradigm. However, the accuracy of information can not be guaranteed, and appropriate incentive mechanism is still unavailable. In this paper, we study the problem of extracting the actual road traffic information according to the reports from an amount of unknown contributors. To obtain the accurate road traffic result with high probability, we establish a reputation system to evaluate the reliability of each contributor, which takes both location and time deviation factors into account. We also design an incentive mechanism to elicit the truthful report of each qualified contributor. Furthermore, we improve the existing answer inference methods and derive the correct result in an efficient way. Extensive simulations are carried out to evaluate the proposed algorithms.

Comparison of Data Dissemination Protocols for Road Traffic Collecting Application in a Vehicular Ad hoc Network

Providing real-time road traffic information to drivers is a critical step to improve road traffic efficiency by allowing appropriate routes to be chosen. In a Vehicular Ad hoc Network (VANET), a query message can be disseminated along several road paths for collecting road traffic information. While several VANET protocols have been proposed to accomplish such task, they were evaluated in different settings, environments, and a limited scale. To gain better insights for actual deployment, it is necessary to explore their relative performance advantages and limitations. In this paper, we compare Slotted 1 persistence, Efficient Directional Broadcast (EDB), Data dissemination pRotocol In VEhicular networks (DRIVE), and Road Traffic Collecting (RTC) protocols under a large scale city networks, high vehicle density, multiple query sessions, and the presence of interfering background traffic. The evaluation focuses on the average percentage of targeted road segment coverage, the total number of transmitted messages, and the completion delay time. The results show that EDB outperforms other protocols in terms of the road segment coverage with highest number of transmitted messages while RTC yields a lower number of transmitted messages with less road segment coverage. However, EDB requires road-side units at every intersection and its performance dramatically drops under the failure of road-side units.