Highly precise and rapid global-optimal merchandise price prediction can offer reference value for enterprises' strategic decisions, such as procurement and risk assessment in the economic market. This paper proposes a merchandise price prediction method based on improving Deep Neural Networks(DNN) using the Grey Wolf-Particle Swarm Algorithm. To address the gradient vanishing or explosion caused by the input of multi-feature initial parameters into deep neural networks in merchandise price prediction, this paper optimizes the weight and bias parameters of DNN by improving the Chaotic Theory of the Grey Wolf-Particle Swarm Optimization Algorithm (IC-GWO-PSO). The concept of circular mapping in the GWO algorithm is introduced to avoid the problem of the particle swarm being prone to local optima and achieve a jump search to obtain the global optimal solution of the parameters of DNN. Moreover, the tent mapping in chaos theory is adopted to control the adjustment speed of particles and prevent them from exceeding the solution boundary, enhancing the exploration efficiency and convergence accuracy of the algorithm in the solution space. Experimental results indicate that IC-GWO-PSO outperforms baseline methods such as GWO-PSO and IGWO-IPSO in procurement price prediction. The three indicators, RMSE, R, and Fitness, reach 2.49, 0.9937, and 1583, respectively, demonstrating its performance advantages in global rapid solution seeking and precise price prediction.

As a real-time data sensor in vehicle networks, dash cams capture video with both static (object positions, scene layouts) and dynamic (motion paths, speed changes) spatio-temporal data. Analyzing this information helps predict scene changes and prevent accidents. To improve spatio-temporal feature representation and prediction accuracy, we propose a traffic accident prediction model based on spatio-temporal depth maps and Transformer, named the STDMP model. The model creates 3D depth maps using monocular estimation, aligns spatial-temporal features through two-stream architecture, and integrates GRU's sequential processing with Transformer's attention mechanism for precise short-term tracking and long-term dependency modeling. Experimental data shows the STDMP significantly outperforms baseline models on the DAD dataset. The key metrics (AP:64.3, mTTA:4.25) confirm the effective combination of two techniques: depth-based spatial modeling and Transformer's temporal feature capture.

Urban public transportation, epitomized by buses, has become integral in facilitating efficient and convenient mobility for citizens. Despite its growth, the prevalent bus operation paradigm (static departure schedules, linear route planning, underutilized spatio-temporal data, etc.) often fails to cater to the fluid travel needs of passengers. This study presents a novel dynamic scheduling method based on spatial graph convolution and proximal policy optimization, called DSPTR. Combining a bus dynamic scheduling model (BDS) and a passenger path planning model (PRP), DSPTR method judiciously determines bus departure intervals and formulates passenger travel routes. Leveraging the Weight Graph Sample and AggreGatE (WGraphSAGE), DSPTR adeptly extract road network characteristic, ensuring the feature representation quality of bus resource states. The Proximal Policy Optimization (PPO) further refines the scheduling by optimizing bus multi-route departures and passenger route recommendations. Through experiments and evaluations on real data in Shanghai, DSPTR outperforms baselines such as DDPG and A2C. Through the traffic resource scheduling plan generated by DSPTR, bus operating costs and passenger travel times can be significantly reduced, highlighting its potential to improve the operational efficiency of the urban public transportation system.