The accurate estimation of road traffic states can provide decision making for travelers and traffic managers. In this work,an algorithm based on kernel-k nearest neighbor(KNN) matching of road traffic spatial charact...The accurate estimation of road traffic states can provide decision making for travelers and traffic managers. In this work,an algorithm based on kernel-k nearest neighbor(KNN) matching of road traffic spatial characteristics is presented to estimate road traffic states. Firstly, the representative road traffic state data were extracted to establish the reference sequences of road traffic running characteristics(RSRTRC). Secondly, the spatial road traffic state data sequence was selected and the kernel function was constructed, with which the spatial road traffic data sequence could be mapped into a high dimensional feature space. Thirdly, the referenced and current spatial road traffic data sequences were extracted and the Euclidean distances in the feature space between them were obtained. Finally, the road traffic states were estimated from weighted averages of the selected k road traffic states, which corresponded to the nearest Euclidean distances. Several typical links in Beijing were adopted for case studies. The final results of the experiments show that the accuracy of this algorithm for estimating speed and volume is 95.27% and 91.32% respectively, which prove that this road traffic states estimation approach based on kernel-KNN matching of road traffic spatial characteristics is feasible and can achieve a high accuracy.展开更多
With the wide applications of sensor network technology in traffic information acquisition systems,a new measure will be quite necessary to evaluate spatially related properties of traffic information credibility.The ...With the wide applications of sensor network technology in traffic information acquisition systems,a new measure will be quite necessary to evaluate spatially related properties of traffic information credibility.The heterogeneity of spatial distribution of information credibility from sensor networks is analyzed and a new measure,information credibility function(ICF),is proposed to describe this heterogeneity.Three possible functional forms of sensor ICF and their corresponding expressions are presented.Then,two feasible operations of spatial superposition of sensor ICFs are discussed.Finally,a numerical example is introduced to show the calibration method of sensor ICF and obtain the spatially related properties of expressway in Beijing.The results show that the sensor ICF of expressway in Beijing possesses a negative exponent property.The traffic information is more abundant at or near the locations of sensor,while with the distance away from the sensor increasing,the traffic information credibility will be declined by an exponential trend.The new measure provides theoretical bases for the optimal locations of traffic sensor networks and the mechanism research of spatial distribution of traffic information credibility.展开更多
基金Projects(LQ16E080012,LY14F030012)supported by the Zhejiang Provincial Natural Science Foundation,ChinaProject(61573317)supported by the National Natural Science Foundation of ChinaProject(2015001)supported by the Open Fund for a Key-Key Discipline of Zhejiang University of Technology,China
文摘The accurate estimation of road traffic states can provide decision making for travelers and traffic managers. In this work,an algorithm based on kernel-k nearest neighbor(KNN) matching of road traffic spatial characteristics is presented to estimate road traffic states. Firstly, the representative road traffic state data were extracted to establish the reference sequences of road traffic running characteristics(RSRTRC). Secondly, the spatial road traffic state data sequence was selected and the kernel function was constructed, with which the spatial road traffic data sequence could be mapped into a high dimensional feature space. Thirdly, the referenced and current spatial road traffic data sequences were extracted and the Euclidean distances in the feature space between them were obtained. Finally, the road traffic states were estimated from weighted averages of the selected k road traffic states, which corresponded to the nearest Euclidean distances. Several typical links in Beijing were adopted for case studies. The final results of the experiments show that the accuracy of this algorithm for estimating speed and volume is 95.27% and 91.32% respectively, which prove that this road traffic states estimation approach based on kernel-KNN matching of road traffic spatial characteristics is feasible and can achieve a high accuracy.
基金Project(61104164)supported by the National Natural Science Foundation of ChinaProject(2012AA112401)supported by the National High Technology Research and Development Program of ChinaProject(2012YJS059)supported by the Fundamental Research Funds for the Central Universities of China
文摘With the wide applications of sensor network technology in traffic information acquisition systems,a new measure will be quite necessary to evaluate spatially related properties of traffic information credibility.The heterogeneity of spatial distribution of information credibility from sensor networks is analyzed and a new measure,information credibility function(ICF),is proposed to describe this heterogeneity.Three possible functional forms of sensor ICF and their corresponding expressions are presented.Then,two feasible operations of spatial superposition of sensor ICFs are discussed.Finally,a numerical example is introduced to show the calibration method of sensor ICF and obtain the spatially related properties of expressway in Beijing.The results show that the sensor ICF of expressway in Beijing possesses a negative exponent property.The traffic information is more abundant at or near the locations of sensor,while with the distance away from the sensor increasing,the traffic information credibility will be declined by an exponential trend.The new measure provides theoretical bases for the optimal locations of traffic sensor networks and the mechanism research of spatial distribution of traffic information credibility.
