Multi-Dimensional And Multi-Granularity Acquisition Of Road Traffic States

The construction and development of Intelligent Transportation System (ITS) is an effective way to solve the traffic problems such as traffic jam. The real-time and accurate acquisition of the information of road traffic states is the basis of ITS construction, traffic information service and road traffic management. It plays a fundamental and critical role in the management and control in the operation of traffic system. The acquisition approaches of the road traffic states are the key problems to improve running and management efficiency of the urban traffic system.This paper takes the actual demand of the road traffic management and control as the background. The multi-dimensional and multi-granularity acquisition of road traffic states approaches are studied in this paper. Different methods for acquisition of road traffic states are put forward based on different levels of road traffic characteristics. The effectiveness of the proposed methods for obtaining traffic states is verified through analysis and application of examples. A relatively complete road traffic states acquisition system with different levels is formed. The main results of this paper are embodied in the following aspects:1) Based on the road traffic’s section properties, the road traffic states estimation method based on the road traffic information templates is proposed.The road traffic states estimation method based on the road traffic information templates is carried on the basis of the effective historical data. Firstly, the road traffic modes are divided into several different sub-modes according to different partition identifications; then data from road traffic detectors under different traffic modes are abstracted to analyze the regularity characteristics of the road traffic information and the road traffic information templates are finally obtained based on this regularity characteristics; finally the current traffic mode of the target link is judged and the corresponding traffic information templates are selected to obtain the current traffic sates data through the road traffic states information’s temporal correlation characteristics.2) Based on the road traffic’s link properties, the road traffic states estimation method based on virtual speed sensors is put forward.The road traffic states estimation method based on virtual speed sensors is carried on the basis of the soft measurement for the link’s traffic state. First, virtual speed sensors are defined by linear interpolation between adjacent traffic flow sensors. Secondly, virtual speed sensors are designed. The weight matrix related with the speed of virtual speed sensors and the speed data from traffic flow sensors are trained by least square. The speed of virtual speed sensors are estimated with the weight matrix and the speed data from traffic flow sensors by multiple linear regressions. Thirdly, the traffic state spatial distribution of the link (the speed spatial distribution on the link) can be gained. This approach can effectively obtain the traffic state of links without any traffic detector.3) Based on the road traffic’s region properties, the road traffic states estimation method based on matching of the regional traffic attracters is put forward.The road traffic states estimation method based on matching of the regional traffic attracters is carried on the basis of the analysis of road traffic’s region properties. Firstly, the road traffic running modes are divided into several different sub-modes according to different partition identifications, and the historical road traffic data are abstracted and preprocessed to obtain the characteristic information of road traffic running states under different modes, which are stored in the reference sequences of characteristics of traffic running states. Then, the concept of the regional traffic attracters of the target link is introduced and the historical traffic data of links in the region are abstracted and preprocessed to obtain the regional traffic attracters of the target link under different traffic running modes, which are also stored in the reference sequences of characteristics of traffic running states. Finally, the data of the current regional traffic attracters of the target link are abstracted, which are matched with the historical regional traffic attracters of the target link through certain rules. The road traffic running states data of the target link corresponding to the optimal matching are selected as the initial data for recovery and the initial recovery data are processed with Kalman Filter and the final recovery data are obtained. This method can real-time and effectively reflect the current road traffic states.4) Based on the road traffic’s network properties, the road traffic states estimation method based on compressive sensing is put forward.The road traffic states estimation method based on matching of the regional traffic attracters is carried on the basis of the analysis of road traffic’s network properties. To make sure that the compressive sensing theory can be applied on the estimation of traffic states of the road traffic network, the compressibility analysis of the road traffic states data is discussed. Then the road traffic states estimation approach based on compressive sensing is presented and the parameters setting referred in this road traffic states estimation approach based on compressive sensing is discussed. Finally one typical road network in Beijing is adopted for verification of the application of this road traffic states estimation algorithm. This method can realize the estimation of large area’s road traffic states of the road network.