Timely and accurate detection of traffic incidents can effectively reduce personal casualties and property losses, and improve the ability of macro-control and scientific decisionmaking of traffic. The unbalance of traffic incident data has a great influence on the detection effect. Therefore, a traffic incident detection method based on factor analysis and weighted random forest (FA-WRF) is designed. Through the analysis of the change rule of traffic flow parameters to build the initial incident variable. The factor analysis (FA) method is used to reduce the dimension of the initial incident variables. Using Bootstrap improved algorithm to predetermine the data extraction standard of the training set. The MCC coefficient value is calculated for the classification effect of the decision tree after training, and is assigned to each tree as a weight value, so as to ensure that the trees with better classification ability have more voting power in the voting process, thus improve the overall classification performance of the random forest (RF) algorithm for unbalanced data. The detection performance is evaluated by the common criteria including the detection rate, the false alarm rate, the classification rate and the area under the curve of the receiver operating characteristic (AUC). Based on the location detector data from expressway, the incident data in which accounts for 6.5%, showing a typical unbalance. The experimental results indicate that the model based on FA-WRF has the better classification effect. Meanwhile it is competitive in processing unbalanced data classification compared with Support Vector Machine.
For the lack of quantitative basis of traffic sign combination information, this paper established a model of information quantity of urban road traffic signs by analyzing the driver’s information processing and the visual recognition of traffic signs combined with theories of informatics. It used various analytical methods to build a model of the relationship between the traffic sign information quantity (TSIQ) and the driver’s visual recognition. Based on factors, the relationship between the TSIQ and the driver’s visual recognition was studied and analyzed to provide a reference for the design of urban traffic sign layout information. The results show that the TSIQ can explain 61% of the driver’s recognition time (DRT). The more information the road traffic sign conveys, the longer DRT will be. The TSIQ’s threshold is 642 bits, and exceeding this value will cause information overload. Different influence factors have a certain impact on drivers’ visual recognition distance (VRD). The male VRD is shorter than the female. The VRD of the young driver is larger than the old driver. The VRD of a novice driver is longer than an experienced driver, while the visual recognition sign of an experienced driver is shorter.
Given the impact of traffic sign combinations (TSC) on the driver’s visual recognition, this paper analyzed the influence on the driver’s visual recognition process. It used the cognitive psychology theory to establish the information transmission model during the traffic sign combinations. It abstracted the information transmission model to construct the driver’s information processing model. Simultaneously, according to the analysis of the traffic sign combinations of the urban roads, this paper carried out the driver’s visual recognition simulation test when the traffic signs were combined, measured the reaction time of the driver’s visual recognition of multiple combinations of traffic signs, and analyzed the driver’s recognition time (DRT) range in the traffic sign combinations. It used correlation analysis, robust estimation, polynomial regression, and other methods to obtain a significant relationship between the driver’s recognition times in different traffic sign combinations (DTSC). Then it built polynomial regression analysis models, fitted the data, and visualized the fitting results. The results show that through the analysis of the experimental data, based on ensuring certain accuracy, the driver’s recognition time of the traffic sign combinations of the urban road increased appropriately. There is a significant relationship between different traffic sign combinations and the driver’s recognition time. As the number of traffic signs increases, the driver’s recognition time increases significantly. Besides, under certain conditions, gender, age, and the driving experience will impact the driver’s recognition time during the traffic sign combinations. The research results can provide the relevant theoretical basis for the setting of urban traffic signs, provide a powerful reference for the revision of various traffic sign setting standards and norms, and provide ideas for future research on the sign system.
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