Expressway traffic safety early warning system based on cloud architecture

Tian, Yujia; Xiao, Dianliang; Wang, Lu; Chen, Hong

doi:10.1016/j.comcom.2021.01.033

Cited by 4 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Shen combined Bayesian deep learning and Gaussian mixture clustering based on SSMs to predict the risk of road traffic collisions [33]. In addition, back propagation neural networks [34,35], generative adversarial networks [36,37], convolutional neural networks [38], XGBoost [39], long short-term memory [40], and random forests [41] have been widely used to measure driving risks.…”

Section: Modern Traffic Safety Analysis Methodsmentioning

confidence: 99%

Risk Measurement Model for Vehicle Group Based on Temporal and Spatial Similarities

Wen

Chen

Zhao

2022

Journal of Advanced Transportation

View full text Add to dashboard Cite

Vehicle rear-end collisions are primarily caused by tight car following in a continuous traffic flow, as well as a driver’s incorrect perception of the traffic environment ahead and delayed response. To facilitate an investigation pertaining to rear-end collision mechanisms and accurately measure the risk, the concept of a vehicle group is introduced herein. A risk measurement model for a vehicle group (RMVG) based on temporal and spatial similarities is proposed. First, vehicles are categorized based on their temporal and spatial similarities. Risk measurement metrics are defined based on the traffic composition, movement state, and conflict extent. Subsequently, vehicle group risk identification and risk measurement models based on an isolation forest are established. the rear-end collision risk of the vehicle groups is analyzed both qualitatively and quantitatively. Finally, the RMVG is tested using the vehicle trajectory data set of Longpan South Road, Nanjing City, Jiangsu Province, China, and the results are compared with those of a support vector machine and local outlier factor. The results show that the accuracy of the RMVG is higher than those of other models: its accuracy rate and specificity are 95.68% and 88.89%, respectively, whereas its false alarm rate is only 3.47%.

show abstract

Section: Modern Traffic Safety Analysis Methodsmentioning

confidence: 99%

Risk Measurement Model for Vehicle Group Based on Temporal and Spatial Similarities

Wen

Chen

Zhao

2022

Journal of Advanced Transportation

View full text Add to dashboard Cite

show abstract

“…Risk analysis is the first step in the design of a data flow risk monitoring system using the network dynamic risk monitoring system, and it is the most critical link in the data flow risk monitoring system of the expressway networking system. Conducting risk assessment and putting forward security requirements are the basis for formulating the data flow security strategy 17 . Risk analysis (also known as risk assessment and risk management) refers to the process of determining the security threat and vulnerability of expressway networking system assets and estimating the possible loss or impact.…”

Section: Methodsmentioning

confidence: 99%

“…Conducting risk assessment and putting forward security requirements are the basis for formulating the data flow security strategy. 17 Risk analysis (also known as risk assessment and risk management) refers to the process of determining the security threat and vulnerability of expressway networking system assets and estimating the possible loss or impact.…”

Section: Dynamic Risk Monitoring Systemmentioning

confidence: 99%

The data flow risk monitoring system of the expressway networking system based on deep learning

Zhong

Ding

Zhang

et al. 2022

Int J of Intelligent Sys

View full text Add to dashboard Cite

To ensure the reliability and safety of expressway networking systems, this paper designs a data flow risk monitoring system for expressway networking systems based on deep learning. The monitoring system is composed of data flow risk analysis, formulation of safety strategy, real-time monitoring, and disaster recovery. Data flow risk analysis is the basis for the operation of each part of the system. Meanwhile, indexes such as network management, data assets, and network resources are selected to build a data flow risk monitoring index system. The deep convolution neural network model is constructed, and the data flow risk monitoring index data are input into the deep convolution neural network to extract the index data features through the convolution and pooling process.Based on this, feature mapping is realized with a multilayer perceptron, and the index data risk classification results of data flow risk monitoring are output by the SoftMax classifier. The experimental resultsshow that the monitoring system can obtain accurate data flow risk analysis results which effectively reduces the data loss rate, alleviate the impact of different types of malicious attacks, and ensure the stability and security of the experimental object.

show abstract

“…For instance, in China, although accidents and accident victims are declining and expressways have an advantage over other highways in terms of safety, the road safety situation is still not optimistic. Mortality is rising, and the proportion of accident victims are getting high [1,2]. Zhang J noted that, it is necessary carry on the traffic accident at the intersection with the dividing fences and exits of the roads, to study the efficiency and safety issues on the expressways, as well as the problems of congestion in the city streets [3].…”

Section: Introductionmentioning

confidence: 99%

Analysis of existing problems of ensuring traffic safety in major cities of Uzbekistan

Abdunazarov

Shukurov

Nishonov

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

View full text Add to dashboard Cite

This article is devoted to the analysis of road traffic accidents and road safety in large cities of the Republic of Uzbekistan such as Bukhara, Fergana and Samarkand. And also, statistical data of road traffic accidents are processed throughout the republic. Such factors as transport risk, social risk and severity of the consequences of a road traffic accident have been identified. In conclusion, the author’s opinion on the factors affecting road safety in the republic is presented.

show abstract

Expressway traffic safety early warning system based on cloud architecture

Cited by 4 publications

References 16 publications

Risk Measurement Model for Vehicle Group Based on Temporal and Spatial Similarities

Risk Measurement Model for Vehicle Group Based on Temporal and Spatial Similarities

The data flow risk monitoring system of the expressway networking system based on deep learning

Analysis of existing problems of ensuring traffic safety in major cities of Uzbekistan

Contact Info

Product

Resources

About