Design and analysis of traffic incident detection based on random forest

In order to avoid the noise and over fitting and further improve the limited classification performance of the real decision tree, a traffic incident detection method based on the random forest algorithm is presented. From the perspective of classification strength and correlation, three experiments are performed to investigate the potential application of random forest to traffic incident detection： comparison with a different number of decision trees; comparison with different decision trees; comparison with the neural network. The real traffic data of the 1-880 database is used in the experiments. The detection performance is evaluated by the common criteria including the detection rate, the false alarm rate, the mean time to detection, the classification rate and the area under the curve of the receiver operating characteristic （ROC）. The experimental results indicate that the model based on random forest can improve the decision rate, reduce the testing time, and obtain a higher classification rate. Meanwhile, it is competitive compared with multi-layer feed forward neural networks （MLF）.

Monitoring Freeway Incident Detection Using a Hotelling T2 Control Chart

In real-life freeway transportation system, a few number of incident observation （very rare event） is available while there are large numbers of normal condition dataset. Most of researches on freeway incident detection have considered the incident detection problem as classification one. However, because of insufficiency of incident events, most of previous researches have utilized simulated incident events to develop freeway incident detection models. In order to overcome this drawback, this paper proposes a wavelet-based Hotelling 7a control chart for freeway incident detection, which integrates a wavelet transform into an abnormal detection method. Firstly, wavelet transform extracts useful features from noisy original traffic observations, leading to reduce the dimensionality of input vectors. Then, a Hotelling T2 control chart describes a decision boundary with only normal traffic observations with the selected features in the wavelet domain. Unlike the existing incident detection algorithms, which require lots of incident observations to construct incident detection models, the proposed approach can decide a decision boundary given only normal training observations. The proposed method is evaluated in comparison with California algorithm, Minnesota algorithm and conventional neural networks. The experimental results present that the proposed algorithm in this paper is a promising alternative for freeway automatic incident detections.

Fusing moving average model and stationary wavelet decomposition for automatic incident detection:case study of Tokyo Expressway

Traffic congestion is a growing problem in urban areas all over the world. The transport sector has been in full swing event study on intelligent transportation system for automatic detection. The functionality of automatic incident detection on expressways is a primary objective of advanced traffic management system. In order to save lives and prevent secondary incidents, accurate and prompt incident detection is necessary. This paper presents a methodology that integrates moving average （MA） model with stationary wavelet decomposition for automatic incident detection, in which parameters of layer coefficient are extracted from the difference between the upstream and downstream occupancy. Unlike other wavelet-based method presented before, firstly it smooths the raw data with MA model. Then it uses stationary wavelet to decompose, which can achieve accurate reconstruction of the signal, and does not shift the signal transfer coefficients. Thus, it can detect the incidents more accurately. The threshold to trigger incident alarm is also adjusted according to normal traffic condition with con- gestion. The methodology is validated with real data from Tokyo Expressway ultrasonic sensors. Ex- perimental results show that it is accurate and effective, and that it can differentiate traffic accident from other condition such as recurring traffic congestion.

Incident Detection Method of Expressway Based on Traffic Flow Simulation Model

The expressway traffc incidents have the characteristics of high harmful, strong destructive and refractory.Incident detection can guarantee smooth operation of the expressway, reduce traffc congestion and avoid secondary accident by informing the accident, detection and treatment timely. In this paper, an incident detection method is proposed using the toll station data that takes into account the traffc ratio at the entrances and crossway in the network. The expressway traffc simulation model is improved and a simulation algorithm is established to describe the movement of the vehicles. A numerical example is experimented on the expressway network of Shandong province. The proposed method can effectively detect the expressway incidents, and dynamically estimate the traffc network states so as to provide advice for the highway management department.

Millimeter-wave emission characteristics of bilayer radar-infrared compound stealth material

To achieve radar and infrared stealth, an infrared stealth layer is usually added to the radar absorbing material（RAM） of stealth aircraft. By analyzing the millimeter-wave（MMW） emissivities of three stealth materials, this Letter investigates the impact of the added infrared stealth layer on the originally ＂hot＂ MMW emission of RAM. The theoretical and measured results indicate that, compared with the monolayer RAM, the MMW emission of the bilayer material is still strong and its emissivity is reduced by 0.1–0.2 at almost every incident angle.The results partially demonstrate the feasibility of detecting stealth aircraft coated with this bilayer stealth material.