Road traffic accident databases provide the basis for road traffic accident analysis, the data inside which usually has a radial, multidimensional, and multilayered structure. Traditional data mining algorithms such as association rules, when applied alone, often yield uncertain and unreliable results. An improved association rule algorithm based on Particle Swarm Optimization (PSO) put forward by this paper can be used to analyze the correlation between accident attributes and causes. The new algorithm focuses on characteristics of the hyperstereo structure of road traffic accident data, and the association rules of accident causes can be calculated more accurately and in higher rates. A new concept of Association Entropy is also defined to help compare the importance between different accident attributes. T-test model and Delphi method were deployed to test and verify the accuracy of the improved algorithm, the result of which was a ten times faster speed for random traffic accident data sampling analyses on average. In the paper, the algorithms were tested on a sample database of more than twenty thousand items, each with 56 accident attributes. And the final result proves that the improved algorithm was accurate and stable.
Video processing has become an efficient technique support for collecting parameters of urban traffic. Detection and tracking of multiple targets with an uncalibrated CCD camera is developed in this paper. In order to obtain moving targets from the video sequence efficiently, the paper presents Mixture Gaussian background model based on object-level, and moving objects are extracted after background subtraction. Moving multi-targets are tracked through integration of the motion and shape features by Kalman filter modeling. In order to ensure the continuity and the stabilization, occlusion processing is performed. The proposed approach is validated under real traffic scenes. Experimental results show that detection and tracking are robust and adaptive, can be well applied in real-world.
We have reported on the lateral photovoltaic effect of LaTiO3 films epitaxially grown on (100) SrTiO3 substrates. Under illumination of continuous 1064 nm laser beam on the LaTiO3 film through SrTiO3 substrate, the open-circuit photovoltage depended linearly on the illuminated position. The photosensitivity can be modified by bias current. These results indicated that the LaTiO3 films give rise to a potentially photoelectronic device for near infrared position-sensitive detection.
Traffic conflict between turning vehicles and pedestrians is the leading cause of pedestrian fatalities at signalized intersections. In order to provide a solution for evaluating intersection safety for vulnerable road users, this paper first determines the most important factors in analyzing pedestrian-vehicle conflict and puts forward a pedestrian safety conflict index (SCI) model to establish a quantitative standard for safety evaluation of two-or multiphase intersections. A safety level system is then designed based on SCI to help categorize and describe the safety condition of intersections applicable to the model. Finally, the SCI model is applied to the evaluation of two intersections in the city of Changchun, the result of which complies with expectation, indicating the model's potential in providing an improved approach for pedestrian-vehicle conflict evaluation study.
Based on the daily operation data of the urban road traffic management system; this paper analysis the demand of data mining of the traffic violations; pre-processes the data to data sets by the detection methods of proximity-based outlier. According to the characteristics of data traffic offense; combining the advantages of rough sets and association rules data mining; proposed two methods based on the joint data mining method. Finally; a city in the year 2008 road traffic management data; for example; using the text method; regularity of the traffic offense causes were analyzed; indicating that the method is effective.