The rapid detection and confirmation of Suspicious Regions of Forest Fire (SRoFF) are critical for timely alerts and firefighting operations. In the early stages of forest fires, small flames and heavy occlusion lead to low accuracy, false detections, omissions, and slow inference in existing target-detection algorithms. We constructed the Suspicious Regions of Forest Fire Dataset (SRFFD), comprising publicly available datasets, relevant images collected from online searches, and images generated through various image enhancement techniques. The SRFFD contains a total of 64,584 images. In terms of effectiveness, the individual augmentation techniques rank as follows (in descending order): HSV (Hue Saturation and Value) random enhancement, copy-paste augmentation, and affine transformation. A detection model named SRoFF-Yolover is proposed for identifying suspicious regions of forest fire, based on the YOLOv8. An embedding layer that effectively integrates seasonal and temporal information into the image enhances the prediction accuracy of the SRoFF-Yolover. The SRoFF-Yolover enhances YOLOv8 by (1) adopting dilated convolutions in the Backbone to enlarge feature map receptive fields; (2) incorporating the Convolutional Block Attention Module (CBAM) prior to the Neck’s C2fLayer for small-target attention; and (3) reconfiguring the Backbone-Neck linkage via P2, P4, and SPPF. Compared with the baseline model (YOLOv8s), the SRoFF-Yolover achieves an 18.1% improvement in mAP@0.5, a 4.6% increase in Frames Per Second (FPS), a 2.6% reduction in Giga Floating-Point Operations (GFLOPs), and a 3.2% decrease in the total number of model parameters (#Params). The SRoFF-Yolover can effectively detect suspicious regions of forest fire, particularly during winter nights. Experiments demonstrated that the detection accuracy of the SRoFF-Yolover for suspicious regions of forest fire is higher at night than during daytime in the same season.

Pavement cracks are the main manifestations of the asphalt pavement distress for high-grade highway. Pavement cracks will not only affect the road appearance and the comfort of driving, but also easily lead to structural damage to pavement, hence shorten the life of road. The type of cracks varies according to the cause of distress. To some extent, the type of cracks shows the extent of pavement damage. The identification of the type of cracks plays an important role in exploring the causes of distress, detecting the extent of pavement damage, and finding a way to prevent the distress. In this paper, based on support vector machine, an identification algorithm of detecting the horizontal, longitudinal and net cracks of asphalt pavement is proposed. According to the geometry of different cracks, we use the number of pixels, the aspect ratio, the level projection, and the vertical projection of the connected area of the cracks, to design a classifier based on the support vector machine; and to identify the type of cracks. The experimental results show that the rates of correctly identifying different types of cracks by using the identification algorithm based on the support vector machine are as follows: 96.3% for the horizontal cracks, 97.2% for the longitudinal cracks, and 94.3% for the net cracks.

Accurate forecasting of short-term traffic flow played an important role in Intelligent Transportation Systems (ITS) to prevent or mitigate congestions in metropolitan areas. The prediction model based on the Support Vector Regression (SVR) was built to improve the prediction accuracy of short-term traffic flow of urban expressway. The data pre-processing and model-parameter-selection were discussed. The prediction model was validated by short-term traffic flow data, which was collected at a certain expressways in Beijing. The experiment result showed that the prediction model could achieve the highest accuracy while ϵ was equal to 0.25. The predicted data showed very good agreements with the ground-truth data, and the result was satisfactory. On the other hand, the maximum relative error is 0.405% for the prediction model based on SVR. The large errors happened before 7:00 and after 22:00, when traffic were not so heavy. The prediction model is of high precision and quite feasible for applications.

In the rapidly changing environment, the flexibility is important for logistics projects to make investment decision. Based on analysis of uncertainty in logistics investment projects, the common value is comparatively analyzed between logistics project and real-option. The model based on real option pricing is established to make investment decision for the logistics project. By analyzing logistics project investment, empirical studies are used to develop the investment decision-making method. Studies in this paper show that logistics project decision-making methods using option pricing theory take full account of potential investment value of investment projects, so it can effectively deal with the investment decision-making in the environment of uncertainty, and make investment decision-making more scientific.