Short-term traffic flow prediction is important to realize real-time traffic instruction. However, due to the existing strong nonlinearity and non-stationarity in short-term traffic volume data, it is hard to obtain a satisfactory result through the traditional method. To this end, this paper develops an innovative hybrid method based on the time varying filtering based empirical mode decomposition (TVF-EMD) and least square support vector machine (LSSVM). Specifically, TVF-EMD is firstly used to deal with the implied non-stationarity in the original data by decomposing them into several different subseries. Then, the LSSVM models are established for each subseries to capture the linear and nonlinear characteristics embedded in the original data, and the corresponding prediction results are superimposed to obtain the final one. Finally, case studies based on two groups of data measured from an arterial road intersection are employed to evaluate the performance of the proposed method. The experimental results indicate it outperforms the other involved models. For example, compared with the LSSVM model, the average improvements by the proposed method in terms of the indexes of mean absolute error, mean relative percentage error, root mean square error and root mean square relative error are 7.397, 15.832%, 10.707 and 24.471%, respectively.

Landslides are widespread and highly destructive geological hazards that pose serious threats to infrastructure and densely populated areas. Conducting scientific and accurate predictions of landslide spatial distribution is therefore of great practical importance for supporting landslide prevention, risk management, and the reduction in casualties and economic losses. Landslides are driven by multiple variables, including elevation, road distance, river distance, slope and land use, with complex nonlinear interactions that traditional linear models cannot accurately capture. This study adopts a Categorical Boosting model (CatBoost) as the base prediction model, which demonstrates strong performance in capturing interactions among multiple variables and achieves relatively robust landslide spatial distribution predictions without complex feature engineering. However, CatBoost is highly sensitive to hyperparameters and difficult to manually optimize. Based on the Nutcracker Optimization Algorithm (NOA), which features an efficient search strategy, a multi-level improved Nutcracker Optimization Algorithm (COLNOA) is proposed to optimize its hyperparameters. The proposed algorithm integrates Circle Chaotic Mapping into the initial population construction of the NOA to generate two distinct populations and enables information exchange between them during the evolutionary process, thereby enhancing global search capability. In addition, Opposition-Based Learning and lateral mutation strategies are introduced to update inferior individuals in each iteration, improving their search capability. Based on these improvements, a COLNOA-CatBoost prediction model is developed. The proposed model is applied to a case study in Wanzhou District, Chongqing, China. The results show that the proposed model achieves a recall of 0.863, an F1-score of 0.860, and an accuracy of 0.865, outperforming baseline models such as decision trees. Compared with the original CatBoost model, recall, F1-score, and accuracy are improved by 34.8%, 35.0%, and 35.1%, respectively. The spatial prediction results indicate that high-risk landslide areas in Wanzhou District are mainly concentrated in regions such as Zouma Town, medium-risk areas in Xintian Town, low-risk areas in Fenshui Town, and very low-risk areas in Longju Town. Further analysis of terrain and landforms indicates that the high-risk areas for landslides in Wanzhou District are mainly related to steep slopes, deep river valleys, exposed or cut slopes at the foot of the slope, runoff convergence, and road excavation slopes. The extremely low and low-risk areas are mostly distributed in the middle and low mountain and hilly areas with relatively flat terrain, weak river cutting and engineering disturbance. This is consistent with the previous correlation analysis that the number of landslides increases with increasing slope and decreases with increasing elevation, distance from rivers, and distance from roads. Overall, the proposed model provides an effective approach for landslide spatial distribution prediction.

Traffic safety reliability is an important index in the safety assessment of road traffic systems, which is helpful to effectively prevent traffic accidents and enhance safety. After the traffic safety reliability of road traffic systems is defined, the safety reliability models of traffic participants are set up by analyzing correlation of traffic participant behavior, and the safety reliability model of vehicles is established on the logical machinery relationship of vehicles. The road safety reliability model is built on the characteristics of road composition. Finally, the safety reliability model of road traffic system is constructed on the internal structure relationship within the road traffic system. All these models will lay a certain foundation for traffic safety theory research.