Optimal Control Design for Traffic Flow Maximization Based on Data-Driven Modeling Method

This paper proposes enhanced prediction and control design methods for improving traffic flow with human-driven and automated vehicles. To achieve accurate prediction for the entire time horizon, data-driven and model-based prediction methods were integrated. The goal of the integration was to accurately predict the outflow of the traffic network, which was selected as the highway section in this paper. The proposed novel prediction method was used in the optimal design for calculating controlled inflows on highway ramps. The goal of the design was to reach the maximum outflow of the traffic network, even against disturbances on uncontrolled inflows of the network. The control design leads to an optimization problem based on the min–max principle, i.e., the traffic outflow is considered to be maximized by controlled inflows and to be minimized by uncontrolled inflows. The effectiveness of the prediction and the control methods through simulation examples are illustrated, i.e., traffic outflow can be maximized by the control system under various uncontrolled inflow values.

Optimizing Locations of Primary Schools in Rural Areas of China

Scientific location selection of schools is an important way to optimize the allocation of educational resources, improve the efficiency of operating schools, and realize the balanced development of education, especially in rural areas. Many studies have considered the location of schools, but most have omitted the impact of transportation network conditions and the time cost differences caused by different travel speeds under different road conditions. The object of this study is to minimize the total transportation costs for students, construction costs for new schools, and the construction and upgrading costs for roads on a traffic network with travel time uncertainty indicated by different travel time scenarios. A mixed-integer programming model for this problem was proposed. Furthermore, a hybrid simulated annealing algorithm was used to solve the problem. Finally, a practical case study was used to illustrate the application of the proposed mathematical model. The results showed that the traffic network has an important influence on the optimization location of rural schools, and the improvement of traffic network conditions can greatly reduce the time required for students to travel to school.

A Novel Routing Protocol for Realistic Traffic Network Scenarios in VANET

The vehicular ad hoc network (VANET) has traditional routing protocols that evolved from mobile ad hoc networks (MANET). The standard routing protocols of VANET are geocast, topology, broadcast, geographic, and cluster-based routing protocols. They have their limitations and are not suitable for all types of VANET traffic scenarios. Hence, metaheuristics algorithms like evolutionary, trajectory, nature-inspired, and ancient-inspired algorithms can be integrated with standard routing algorithms of VANET to achieve optimized routing performance results in desired VANET traffic scenarios. This paper proposes integrating genetic algorithm (GA) in ant colony optimization (ACO) technique (GAACO) for an optimized routing algorithm in three different realistic VANET network traffic scenarios. The paper compares the traditional VANET routing algorithm along with the metaheuristics approaches and also discusses the VANET simulation scenario for experimental purposes. The implementation of the proposed approach is tested on the open-source network and traffic simulation tools to verify the results. The three different traffic scenarios were deployed on Simulation of Urban Mobility (SUMO) and tested using NS3.2. After comparing them, the results were satisfactory and it is found that the GAACO algorithm has performed better in all three different traffic scenarios. The realistic traffic network scenarios are taken from Dehradun City with four performance metric parameters including the average throughput, packet delivery ratio, end-to-end delay, and packet loss in a network. The experimental results conclude that the proposed GAACO algorithm outperforms particle swarm intelligence (PSO), ACO, and Ad-hoc on Demand Distance Vector Routing (AODV) routing protocols with an average significant value of 1.55%, 1.45%, and 1.23% in three different VANET network scenarios.

Research on Simulation Calculation of the Safety of Tight-Lock Coupler Curve Coupling

Once a train breaks down on a busy railway line, it will affect the whole traffic network. However, when a rescue locomotive is hooked up to the broken train for towing it to the next station, it is common that coupling dislocation occurs, which results in damages to couplers and the driver’s cab. To ensure the safety of the trains during the coupling, it becomes crucial to determine whether they can be linked safely and automatically under different line conditions. In this paper, position and pose of the rescue locomotive and the broken train on the line are calculated by geometric analytical calculation method, which determines the position relation of their couplers. Then a so-called “coupling characteristic triangle” was proposed to determine whether trains can be safely and automatically linked on the railway line. The triangles are constructed by the peak points of the couplers head of the front vehicle and the rear one and border lines of secure coupling area on the couplers. By judging the shape of the triangle, it can directly judge whether their couplers can be connected successfully. The method has been applied to check the safety of the trains during coupling on the Nanchang urban railway Line 4. The results show that the maximum swing angle of the coupler reaches 17.3957° in the straight–curve section with a radius of 325 m. At this time, coupling is most difficult, and trains need to be connected manually through the tractor; all the calculation results are verified in the actual line. By comparing different calculation methods for judging coupling safety, it is shown that the method proposed in this paper is accurate, efficient, and users can judge coupling safety more intuitively.