Key Technology of Real-Time Road Navigation Method Based on Intelligent Data Research

Tang, Haijing; Liang, Yu; Huang, Zhongnan; Wang, Taoyi; He, Lin; Du, Yicong; Yang, Xu; Ding, Guofu

doi:10.1155/2016/1874945

Cited by 4 publications

(5 citation statements)

References 13 publications

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“…Also, a traffic forecasting method was presented in our previous work [24]. We aim to integrate it into our SODPP algorithm as a future work to solve dynamic instability problems considering that the same information is shared by users and they travel in the same network area independently from the present or future congestion.…”

Section: Discussionmentioning

confidence: 99%

Application of Angle Related Cost Function Optimization for Dynamic Path Planning Algorithm

et al. 2018

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Abstract:In recent years, Intelligent Transportation Systems (ITS) have developed a lot. More and more sensors and communication technologies (e.g., cloud computing) are being integrated into cars, which opens up a new design space for vehicular-based applications. In this paper, we present the Spatial Optimized Dynamic Path Planning algorithm. Our contributions are, firstly, to enhance the effective of loading mechanism for road maps by dividing the connected sub-net, and building a spatial index; and secondly, to enhance the effect of the dynamic path planning by optimizing the search direction. We use the real road network and real-time traffic flow data of Karamay city to simulate the effect of our algorithm. Experiments show that our Spatial Optimized Dynamic Path Planning algorithm can significantly reduce the time complexity, and is better suited for use as a real-time navigation system. The algorithm can achieve superior real-time performance and obtain the optimal solution in dynamic path planning.

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Section: Discussionmentioning

confidence: 99%

Application of Angle Related Cost Function Optimization for Dynamic Path Planning Algorithm

et al. 2018

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“…Tang et al proposed a realtime traffic noise prediction model based on Kalman filter theory. rough the traffic noise collected by the coil detector, the traffic noise in the future period of the section was predicted [8]. Rui et al argued that nonparametric methods can produce better results than those with parameters and nonparametric methods had a better processing power for obtaining spatiotemporal relationships and nonlinear effects [9].…”

Section: Related Workmentioning

confidence: 99%

Construction and Analysis of Urban Traffic Noise Prediction Model Based on PSO Algorithm and BRF Network Structure

Qiu¹

2022

Scientific Programming

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Due to the current nonlinear traffic noise and changing natural environment, accurate prediction of traffic noise has become a major challenge. In order to improve the accuracy of traffic noise prediction, this paper constructs a short-term traffic noise prediction model based on PSO algorithm and BRF network structure. The model can predict the noise state of future traffic according to historical traffic information. Particle swarm optimization (PSO) network is used for short-term traffic prediction to deal with the problems of low accuracy and long-time consumption. A BRF (one-dimensional convolution)-PSO short-term traffic noise prediction model is proposed. BRF method is used to extract the spatial features of traffic noise and compared with PCA (principal component analysis)-PSO model and PSO model. The results show that the prediction accuracy of BRF-PSO model is 2% higher than that of PCA-PSO model and 6% higher than that of PSO model, which proves the effectiveness of BRF-PSO model. This study provides a technical reference for the short-term traffic noise prediction of intelligent city and the effective improvement of urban traffic environment.

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“…Probability. In [20] we have presented a traffic flow forecasting algorithm based on transition probability. This method first analyzes the transfer probability of upstream of the target road and then makes the prediction of the traffic flow at the next time by using the traffic flow equation.…”

Section: Traffic Flow Forecasting Algorithm Based On Transitionmentioning

confidence: 99%

“…Finally, it uses the proposed model to predict the traffic flow at the next time. We have not included details of that algorithm in this paper; readers who are interested can find out more in [20].…”

Section: Traffic Flow Forecasting Algorithm Based On Transitionmentioning

confidence: 99%

“…The implementation of this method is based on two kinds of traffic big data: one is road real-time traffic information; the other is the prediction of the traffic flow of the corresponding road network based on the traffic flow prediction algorithm [20]. The accumulated real-time data will be used alone or in conjunction with the corresponding prediction data to form the big data group.…”

Section: Introductionmentioning

confidence: 99%

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Application of Data Science Technologies in Intelligent Prediction of Traffic Congestion

Yang

Luo

Gao

et al. 2019

Journal of Advanced Transportation

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In recent years, with the rapid development of economy, more and more urban residents, while owning their own motor vehicles, are also troubled by the traffic congestion caused by the backward traffic facilities or traffic management methods. The loss of productivity, car accidents, high emissions, and environmental pollution caused by traffic congestion has become a huge and increasingly heavy burden on all countries in the world. Therefore, the prediction of urban road network traffic flow and the rapid and accurate evaluation of traffic congestion are of great significance to the study of urban traffic solutions. This paper focuses on how to apply data science technologies on vehicular networks data to present a prediction method for traffic congestion based on both real-time and predicted traffic data. Two evaluation frameworks are established, and existing methods are used to compare and evaluate the accuracy and efficiency of the presented method.

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Key Technology of Real-Time Road Navigation Method Based on Intelligent Data Research

Cited by 4 publications

References 13 publications

Application of Angle Related Cost Function Optimization for Dynamic Path Planning Algorithm

Application of Angle Related Cost Function Optimization for Dynamic Path Planning Algorithm

Construction and Analysis of Urban Traffic Noise Prediction Model Based on PSO Algorithm and BRF Network Structure

Application of Data Science Technologies in Intelligent Prediction of Traffic Congestion

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