As a flourishing basic transportation service in recent years, online car-hailing has made great achievements in metropolitan cities. Accurate spatiotemporal forecasting plays a significant role in the deployment of a network for online car-hailing demand services. A self-attention mechanism in convolutional long short-term memory (ConvLSTM) is proposed to accurately predict the online car-hailing demand. It can more effectively address the disadvantage that ConvLSTM is not good at capturing spatial correlation over a large spatial extent. Furthermore, it can generate features by aggregating pair-wise similarity scores of features at all positions of input and memory, and thus obtain the function of long-range spatiotemporal dependencies. First, the online car-hailing trajectories dataset was converted into images after geographic grid matching, and image enhancement was performed by cropping. Then, the effectiveness of the ConvLSTM embedded with a self-attention mechanism (SA-ConvLSTM) was demonstrated by comparing it to existing models. The experimental results showed that the proposed model performed better than the existing models, and including spatiotemporal information in images would perform better predictions than including spatial information in time-series pixels.
With the continuous advancement of electronic technology, auto parts manufacturing institutions are gradually applying electronic throttles to automobiles for precise control. Based on the visual angle model (VAM), a car-following model considering the electronic throttle angle of the preceding vehicle is proposed. The stability conditions are obtained through linear stability analysis. By means of nonlinear analysis, the time-dependent Ginzburg–Landau (TDGL) equation is derived first, and then the modified Korteweg-de-Vries (mKdV) equation is derived. The relationship between the two is thus obtained. Finally, in the process of numerical simulations and exploration, it is shown how the visual angle and electronic throttle affect the stability of traffic flow. The simulation results in MATLAB software verify the validity of the model, indicating that the visual angle and electronic throttle can improve traffic stability.
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