The customized bus operating mode based on passenger demand is an effective way to solve the problem of bus services in low travel density areas such as urban fringe areas, ensure the profitability of bus enterprises, and promote the development of customized bus and other emerging bus. First, this study introduces the concept and operating principle of customized bus, determines the advantages and disadvantages of customized bus, evaluates the relevant theories of customized bus lines and station planning, and determines the principles of customized bus lines and station planning. Second, according to the characteristics of customized bus, this study proposes a novel customized bus line and station planning method completely based on passenger travel demand, including travel demand data processing, traffic community division, joint station planning, the establishment of a customized bus line planning model, and the solution of the planning model. Finally, the proposed planning method and improved ant colony optimization and clustering are verified by simulation experiments. The experimental results show that the station line planning method proposed in this paper can better realize the line planning of demand-responsive customized bus as well as meet diverse passenger travel needs.

In the development of smart cities, the transportation system plays a crucial role, with road congestion being particularly prominent under conditions of long-distance travel and high traffic volumes. This paper proposes a Vehicle Path Planning and Prediction Algorithm (VPPPA) based on an attention mechanism for complex traffic intersections collaboration in intelligent transportation systems. Our proposed algorithm is designed to plan and analyze traffic flow at urban road intersections. Firstly, attention mechanisms are used to balance the number of vehicles at different traffic intersections, with a particular focus on alleviating congestion at critical intersections during peak hours. Secondly, a Convolutional Neural Network (CNN) is employed to capture the spatial relationships between different road segments and intersections. Moreover, the Long Short-Term Memory and CNN (LSTM-CNN) architecture effectively captures the important temporal correlations in the traffic flow data. Thirdly, the spatiotemporal attention mechanism of vehicles captures the local spatial correlation characteristics between the target intersection and adjacent intersections along the traffic network. Finally, our proposed VPPPA model leverages the advantages of the LSTM-CNN architecture, enhancing learning efficiency during the training process and extracting valuable information. Experimental results show that the proposed VPPPA has significant advantages and greater efficiency in reducing average travel time and improving throughput across various intersections.

In this paper, the finite-time synchronization problem of delayed memristive neural networks (MNNs) is addressed by adopting a flux-controlled memristor model. First, based on the presented memristor model, the MNNs behave as a class of continuous systems with 22n2+n variables. Then, the finite-time controllers are proposed to synchronize the voltage and flux states between the drive and response MNNs, respectively. Based on the inequality techniques and the Lyapunov method, new synchronization criteria for delayed MNNs are derived. Finally, the results are verified by numerical simulations.

The rapid increase of the number of motor vehicles over the last several decades has driven a corresponding increase in the severity of traffic congestion, which has exhibited a considerable human impact. Intelligent anti-collision control via inter-vehicle communication technology can facilitate vehicles adhering to spacing speed standards and improve road utilization and traffic efficiency. In this paper, we apply a deep learning image estimation model based on joint attention mechanism. The network framework uses a deep estimation network Yolov5 and a location based VANET information fast transmission strategy to work together. This paper mainly considers vehicle distance measurement technology as a point of entry to study multi-sensor information fusion for vehicle collision prevention technology based on the self-organizing vehicular ad-hoc network (VANET). This paper also proposes a solution based on the strategy of one-way transmission of shared information and dynamic valuation of a cluster distance threshold with vehicle density. The proposed vehicle anti-collision control algorithm is designed to realize dynamic vehicle control via inter-vehicle communication. In this paper, the two-way coupling of traffic flow and network simulator is used to randomly generate vehicle nodes on the road, and the behavior of the anti-collision system is simulated. The experimental results show that the predetermined control goal is achieved, which demonstrates the effectiveness of the proposed algorithm.

This article explores complex-valued inertial neural networks (CVINNs) with distributed delays (DDs). By constructing two new feedback controllers, some novel results on fixed-time stabilization (FTS) and preassigned-time stabilization (PTS) of CVINNs are established. Unlike most of the previous works, FTS and PTS obtained here are explored without dividing the original complex-valued system into two separate real valued subsystems. Eventually, to verify the effectiveness and reliability of the results of this article, we provide several numerical examples. The FTS and PTS of CVINNs are successfully implemented at T = 6, 5.5, and 5, and the settling time is not affected by system parameters and initial values.