Study On Weather Monitoring,Event Prediction And Application Of Dedicated Network System On Expressway

At present,the total mileage of expressways in China has exceeded 136,000 kilometers,of which "safe,green and smooth" are the three main elements.For Jiangsu Province,the main factors affecting traffic safety are visibility,"black ice" and other unfavorable weather conditions.Relying on best use of the existing monitoring videos,real-time monitoring analysis and indirect prediction of traffic events have been important measures to predict traffic accidents,assure safety as well as to improve management.In view of expressway's characteristic in broad region,wide range,and sophisticated management factors,cloud computing,big data,and hierarchical dynamic map technology have finally found a use for themselves.Therefore,combined with a number of provincial-level scientific research projects,based on the existing monitoring videos,dedicated research has been carried out for the environment conditions on expressway,prediction of traffic accidents,and construction of demo for integrated application of intelligent traffic.In this dissertation,the status of expressway monitoring at home and abroad is described and the causes of highway accidents are analyzed.The monitoring methods of visibility in videos,the real time monitoring ways of rain&snow ice,and indirect detection means of traffic events are studied.Then the experimental results are analyzed.Based on interpretation of the algorithm mechanism,the ELM-AID traffic incident detection algorithm and H-ELM traffic accident prediction algorithm as well as its technical implementation are shown,which enriches the content of LDM3(LocalDynamic Map/Multimedia/Management),an intelligent traffic integrated system architecture of ITS.Out of which:On the basis of research on the existing visibility measurement algorithm,a visibility detection algorithm based on trace norm is proposed to alleviate local optimal solution and noise sensitive problem.According to comparison experiments,the proposed algorithm has got better robustness and detection precision.Considering the problems of poor accuracy and lack of stability for the single technical method or means in detection of road icing,a method and device combined by various technology and means of optical,heat,temperature and capacitance is used for road surface icing detection.Based on the friction coefficient experiment of the indoor simulated scene and the actual scene,the friction coefficient of tire and asphalt pavement under the action of different phase water is studied,with illustration of the step-style descending relationship between them.In view of the extreme learning ability and good generalization performance of the ELM algorithm,ELM-AID traffic incident detection algorithm is studied,and advantages of ELM-AID in detection accuracy and operation speed are verified by comparison of the ELM-AID and the BP detection models.As to the over fitting problem single hidden layer ELM might run into,the H-ELM algorithm of multilayer architecture is studied and the corresponding prediction model is constructed.By means of comparing the two traffic event predictions,it is shown that the H-ELM has obtained comparatively greater superiority in the prediction efficiency.In view of the maturing technological development of big data and cloud computing,study work has been carried out for the multi space-time integrated road network with information convergences well as the data processing method.Based on the main data platforms of Hadoop,Spark and so on,corresponding big data storage model and distributed storage system as well as a distributed parallel computing system platform have been built-up,with fairly complete framework and solution of intelligent transportation information system coming into being.The innovative points or characteristics of the dissertation lie in:A road visibility detection algorithm based on trace-norm is proposed,which has alleviated the local optimal solution and noise sensitive problem,and simplified the calibration of reference objects.Experiments show that,the proposed method has better measurement performance.The measurement accuracy of this method increased by nearly 10%but calculation time decreased by more than 20%,compared to the method based on intensity feature points in the video and the method using the inherent intensity of the road surface.A comprehensive method for road icing detection is proposed to improve the accuracy,which has combined light,heat flow,temperature as well as capacitance detection methods to solve problems of poor detection accuracy,lack of stability when using single detection technology.Indirect event detection model for ELM-AID event and H-ELM model of multi-tier architecture is proposed.The former has obtained favorable classification performance and extreme learning ability,and more than 100 times speed acceleration than the traditional BP detection model,and the latter has non-supervision feature learning and supervised feature classification simultaneously,which is more suitable for mass data and cloud computing.The intelligent traffic integrated system framework of LDM3 has been optimized.With adoption of the mainstream big data platform such as Hadoop,Spark and so on,through means of distributed storage¶llel computing of traffic big data,the connotation of information convergence layer has got further enriched,and the integral structure and systematic solution of intelligent traffic information system has been developed.