Study On The Key Techonologies Of Expressway Network Traffic Suiveillance Systems

In recent years, freeways in our country have witnessed a rapid development and thenetwork of freeways in provinces has been formed gradually. At the same time, trafficcongestion in freeways has been increasingly serious with the development of society andeconomy. Therefore, it’s very important to obtain traffic condition data for monitoring trafficconditions in freeway network.In this paper, we focus on acquiring the traffic condition data and processingmulti-source data to achieve the goal of the combination of “visualization" and"measurability". Therefore, we study on the acquiring of traffic parameters based on freewaysurveillance video, models classification, detection of haze and multi-source trafficinformation fusion processing about freeways and other key issues. Concerning theshortcomings of the existing methods, it puts forward some innovative and practical solutionsand algorithms, which have been verified through abundant experimental data in actual roadnetwork operations. At the same time, through the engineering practice personally presidedover by the author himself about the construction of information platform about freewaysystem operation monitoring and service in Guangdong province, part of the implementationof technical research has been realized and good results has been achieved. A firm foundationhas been laid for the future development and pointing out the direction.The main work and achievements are summarized as follows:1. For the calibration of test system of single fixed cameras we comprehensively studiedthe existing test methods of testing the influence of the traffic most commonly occurring inthe scene of robustness of various factors, analyzed related reasons and improved constraintsabout reliability. To eliminate the influence of main factors, a kind of suitable application inthe situation of traffic detection background reference model based on the area code isestablished. And around the background reference model, a series of cutting, extensible, themethods of robustness test about traffic flow parameters adapting to the complex applicationenvironment as well as in full time were proposed. Through the comparison between actualtraffic scene detection results under different times and the traditional detection method, thevalidity and reliability of the test method have been verified. 2. On the basis of the analysis of the monitoring vehicle in the video image extractionmethod, three methods about the model classification have been proposed. The first one isbased on improved bag-of-word (BOW) models of the classification method, the second oneis based on sparse coding and low rank denoising algorithm, the third one is based on the edgeof the conviction in the network model. On this basis, the surveillance video is proposed inthis paper based on the freeway of multimodal fusion model classification method.Experimental results have verified the effectiveness and practicability of these models.3. This paper proposes a new fog haze level detecting method based on freewaysurveillance video. This method is different from the existing atmospheric physics modelbased on the idea of visibility detecting method. By using of the characteristics of imagecontrast, innovative statistical histogram is proposed using HSV color space as a featurevector, using the SVM classifier to detect fog haze level.The method does not need tocompute the visibility of camera calibration, therefore it is not limited to the scene referenceand be compatible with the original freeway network video monitoring system, making fulluse of the traffic camera video images. The cost is low. Through the experiment, the methodfunctions well in all kinds of foggy and fogless weather, as well as different fog haze levels.4. A novel freeway traffic congestion identification method based on the fuzzy clusteringmethod (FCM) has been put forward. Freeway traffic information data fusion processingtechnology is studied. With the real historical running data of Foshan-Kaiping Freeway inGuangdong Province,the traffic congestion identification method of this paper has beenvalidated. The experimental results show that this method can be applied to freewaymonitoring system very well. Based on queuing theory, the dispersion characteristics offreeway traffic are studied, including the analysis of the traditional cumulative reach-leavemodel to estimate the defects, and to estimate the maximum queue. And maximum queuelength estimation model and its solving process has been established and improved.Simulation experiment results show that the model can well match the actual traffic flow, andhas higher application value in the freeway management.5. A novel travel time estimation model based on limited mixture distribution wasproposed. It’s usually difficult to obtain the accurate travel time in freeway network as trafficdetector is limited. To integrate or fuse the travel time in freeway network which is estimated from single-point detector (such as microwave traffic detector), point-to-point detector(license plate recognition/toll data) and the floating car data, a novel travel time fusingestimation or prediction algorithm based on Kalman filtering is proposed to obtain moreaccurate and complete travel time in freeway network.On the basis of the above research results, in the final this paper takes the real project ofGuangdong provincial expressway network monitoring system platform as an example toanalyze the design, the overall orientation and functional structure of the system. The eightcore subsystems are introduced one by one: traffic flow detection subsystem, entrance ramplimiting subsystem, the main road control subsystem, exit ramp information subsystem, trafficillegal catch beat system, CCTV system and traffic information issuing system. This papercombines with the development trend of freeway monitoring technology and proposes thefuture technical development prospect of the system based on the realized system function.