Optimal Cooperative Driving at Signal-Free Intersections With Polynomial-Time Complexity
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Cooperative driving at signal-free intersections, which aims to improve driving safety and efficiency for connected and automated vehicles, has attracted increasing interest in recent years. However, existing cooperative driving strategies either suffer from computational complexity or cannot guarantee global optimality. To fill this research gap, this paper proposes an optimal and computationally efficient cooperative driving strategy with the polynomial-time complexity. By modeling the conflict relations among the vehicles, the solution space of the cooperative driving problem is completely represented by a newly designed small-size state space. Then, based on dynamic programming, the globally optimal solution can be searched inside the state space efficiently. It is proved that the proposed strategy can reduce the time complexity of computation from exponential to a small-degree polynomial. Simulation results further demonstrate that the proposed strategy can obtain the globally optimal solution within a limited computation time under various traffic demand settings.Keywords:
Signal timing
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Dynamic programming (DP) can be used to generate the optimal schedules of a smart home energy management system (SHEMS), however, it is computationally difficult because we have to loop over all the possible states, decisions and outcomes. This paper proposes a novel state-space approximate dynamic programming (SS-ADP) approach to quickly solve a SHEMS problem but with similar solutions as DP. The state-space approximations are made using a hierarchical approach, which involves clustering and machine learning. The proposed SS-ADP can generate the day-ahead value functions quickly without compromising the solution quality because it only loops over the necessary state-space. Our simulation results showed that the solutions from the SS-ADP approach are within 0.8% of the optimal DP solutions but saves the computational time by at least 20%. The paper also presents a fast real-time control strategy under uncertainty using the Bellman optimality condition and long short-term memory recurrent neural networks (LSTM-RNN). The Bellman equation uses the day-ahead value function from the SS-ADP and the instantaneous contribution function to make fast real-time decisions. The instantaneous contribution is calculated using the PV and load predicted using LSTM-RNN, which performs significantly better than the widely used persistence method.
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Due to the development of video perception technology, obtaining the volume of pedestrians and vehicles at a crosswalk has become much easier. Based on this development, this paper proposes a dynamic crosswalk signal timing optimization model and then analyzes the effects for three different signal timing strategies. First, we propose the dynamic signal timing optimization model by involving the delays of pedestrians and vehicles, as well as the fuel consumption cost, simultaneously. In the model, we design a dynamic signal timing strategy, using the volume of past cycles to predict the present volume, and then calculate the optimal signal timing by minimizing the total cost of the system. Second, the model is applied to a crosswalk in Beijing, China, as an example, and we compare and analyze the results of three timing strategies: Dynamic signal timing, optimal fixed timing, and current fixed timing. The results show that the dynamic signal timing is more efficient during the morning peak hour in terms of decreasing the total cost. Compared to the current fixed timing result, the vehicle delay and the fuel consumption decrease, while the pedestrian delay increases in both morning peak hour and flat hour for the other two signal timing strategies.
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Polynomial hierarchy
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Structural complexity theory
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Sixth International Conference on Traffic Engineering and Transportation System (ICTETS 2022) (2023)
A reasonable signal timing scheme can improve the traffic capacity of intersections. In order to solve the traffic congestion problem at the intersection of Gongyuan road and Daqiao road in Xuzhou City during the morning rush hour, this paper combines the road field survey data, uses Webster formula to optimize the signal timing scheme of intersections, uses VISSIM software to simulate, compares the average delay before and after optimization, and explains the optimized signal timing, The peak congestion at the intersection can be improved.
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This study evaluated existing traffic signal optimization programs including Synchro, TRANSYT-7F, and genetic algorithm optimization using real world data collected in Virginia. As a first step, a microscopic simulation model, VISSIM, was extensively calibrated and validated using field data. Multiple simulation runs were then made for signal timing plans such that drivers' behavior, day-to-day traffic variation, etc., were considered in the evaluation. Finally, long-term demand growth or changes were statistically modeled and evaluated, again using multiple simulation runs. Five timing plans were evaluated using the simulation test bed: 1) Virginia Department of Transportation's (VDOT's) former timing plan; 2) VDOT's current timing plan; 3) the genetic algorithm timing plan; 4) the Synchro timing plan; and 5) the TRANSYT-7F timing plan. The simulation study results indicated that the current practice of VDOT signal optimization procedure significantly improves upon its former one by reducing travel times by 17% and total system delay by 36%. The three optimized timing plans did not provide significant improvements. Evaluation of the Lee Jackson Memorial Highway network showed that the current VDOT signal optimization procedure significantly improved the performance of network operations. Thus, the study recommended that VDOT continue using its procedure for developing new timing plans, but that it evaluate its signal timing plan regularly so that it does not become outdated.
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Abstract The traffic conditions at the intersection have an increasingly high impact on the normal operation of the road. Improving the signal timing can effectively improve the congestion at the intersection. Taking Xi’an North Street intersection as an example, this paper improves signal timing by Webster timing method. The results before and after the improvement are simulated by VISSIM simulation software. The delay, queue length, parking time and parking times before and after the improvement are compared and analyzed, and the conclusion is drawn. The simulation results show that after optimizing the signal timing, the vehicle delay is reduced by 25.24%, the parking time is reduced by 35.16%, and the maximum queue length, average queue length and number of stops are also reduced. It can be seen that the optimized intersection operation status significant improvements have been made.
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The most popular tools for traffic simulation and signal timing optimization are macroscopic and deterministic in structure. Microscopic tools are also available and are gaining popularity because they simulate the stochastic nature of traffic flow. Performance of the optimized signal timing developed by any tool depends on the fidelity of the traffic models embedded in it. This study evaluates the performance of signal timing developed by the most popular signal-timing optimization tools. It examines signal timing from each tool in various macroscopic and microscopic simulation environments and includes statistical tests to find which tool produces signal timing of the highest quality. Each of the four tools selected (two macroscopic and two microscopic) can both optimize and evaluate signal timing. A real-world arterial with 12 signalized intersections serves as the test bed. To eliminate skewness and bias in the results, all experiments were performed under the same geometric and traffic characteristics in each tool. Saturation flow rates from each calibrated model were assessed to ensure that all the tools processed traffic demand consistently. The results indicate that, overall, VISSIM-based genetic algorithm optimization of signal timing and the Synchro programs produce signal timing of the highest quality and provide extremely similar performance.
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An unreasonable signal timing scheme is the leading cause of traffic jams, reducing capacity and increasing vehicle emissions. This paper proposed a multi-objective signal timing optimization method to improve traffic operation efficiency and environmental benefits at intersections. It built a nonlinear model with the effective green time as the decision variable, considering average delay, capacity, and stopping emissions. Experiments using an intersection in Guangzhou University Town demonstrate that the proposed method with the genetic algorithm is better than the Webster model, which can effectively solve the current signal timing problem. Specifically, the average delay and stopping emissions, in this case, were reduced by 15% and 11.4%, respectively, and the capacity was increased by 17.4%. In the optimization effect of intersections, the VISSIM simulation results are consistent with the model theoretical calculation results. The proposed signal timing optimization method could alleviate congestion and pollutant emissions at intersections.
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Deadlock prevention algorithms
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