A Microsimulation-Based Stochastic Optimization Approach for Optimal Traffic Signal Design

Optimal Input Design for Dynamic Model Prediction Accuracy

Increased travel times are often observed on urban roads, with signalized intersections being the major bottlenecks. The inability of existing static signal timings in accommodating the actual demand fluctuations could be one of the contributing factors. A traffic-responsive signal control system that changes signal timings according to traffic volume fluctuations may alleviate this problem. However, such problems are conventionally formulated based on the data collected from location-based sensors, which are infrastructure intensive and costly and fail to capture mixed and disordered traffic conditions. Considering these limitations, this paper presents an optimal signal design using sample travel time information collected from mobile data sources such as GPS/Bluetooth/Wi-Fi sensors that work independently of the traffic conditions and are relatively cost-effective. The proposed adaptive signal design minimizes total intersection delay at isolated intersections for every cycle based on the traffic conditions observed in the previous cycle. The mathematical programming-based formulation uses shock waves formed during the red and green phases to estimate optimal-phase durations. Results revealed that the proposed design is capable of handling traffic flow fluctuations without requiring the entire traffic stream data. The system demonstrated that sample data from four probe vehicles per phase is adequate for real-time optimal signal design. Results showed that the proposed model outperformed the existing Webster’s signal design procedure with a delay reduction of 11.78% when compared theoretically and 10.41% when implemented in VISSIM.

Background: In optimal traffic signal timing, some researchers proposed a single objective genetic algorithm to optimize the timing plan at an isolated intersection. However, the genetic algorithm belongs to a natural selection procession. It means that a suggested model might have a local, optimal result instead of global optimization. A few researchers have tried to avoid local optimization values by making many assumptions for the suggested model, these estimations lacked comprehensive theoretical bases in the transportation field. Objective: The objective of this study is to contribute a comprehensive optimization solution, by applying multiple objective genetic algorithms, to minimize the effective green time and cycle length at a complex urban intersection. Methods: First, the fitness function was established by the minimum issues of average control delay and queue length at the complex isolated intersection. Secondly, constraint functions were identified based on a scientific basis to provide a comprehensive hypothetical model. After running the hypothetical model with single and multiple objective genetic algorithms and real traffic flow data, the results were compared between the use of multiple genetic algorithms and the use of a single-objective genetic algorithm, between an existing traffic signal timing plan and a suggested traffic signal timing plan. Then, the traffic simulation model for the complex intersection was generated to validate the effectiveness of the suggested method. Results: After comparison, the suggested model was found to be more efficient than the existing traffic signal timing at the complex intersection. Conclusion: This study demonstrated multiple objective genetic algorithms that overwhelmed the single objective genetic algorithm in optimal traffic signal timing. The multiple objective genetic algorithms could be effectively used to handle traffic optimization at a complex large-scale intersection. Furthermore, a comprehensive solution of applying multiple genetic algorithms to deal with traffic signal optimization has been generated in this research.

Multilateral wells (MLWs) equipped with multiple flow control devices (FCDs) are becoming increasingly favored within the oil sector due to their ability to enhance well-to-reservoir exposure and effectively handle unwanted fluid breakthrough. However, combining various types of FCDs in multilateral wells poses a complex optimization problem with a large number of highly correlated control variables and a computationally expensive objective function. Consequently, standard optimization algorithms, including metaheuristic and gradient-based approaches, may struggle to identify an optimal solution within a limited computational resource. This paper introduces a novel hybrid optimization (HO) framework combining particle swarm optimization (PSO) and Simultaneous Perturbation Stochastic Approximation (SPSA). It is developed to efficiently optimize the completion design of MLWs with various FCDs while overcoming the individual limitations of each optimization algorithm. The proposed framework is further enhanced by employing surrogate modelling and global sensitivity analysis to identify critical parameters (i.e., highly sensitive) that greatly affect the objective function. This allows for a focused optimization effort on these key parameters, ultimately enhancing global optimization performance. The performance of the novel optimization framework is evaluated using the Olympus benchmark reservoir model. The model is developed by three intelligent dual-lateral wells, with inflow control devices (ICDs) installed within the laterals and interval control valves (ICVs) positioned at the lateral junctions. The results show that the proposed hybrid optimization framework outperforms all industry-standard optimization techniques, achieving a Net Present Value of approximately USD 1.94 billion within a limited simulation budget of 2500 simulation runs. This represents a substantial 26% NPV improvement compared to the open-hole case (USD 1.54 billion NPV). This improvement is attributed to more efficient water breakthrough management, leading to a notable 24% reduction in cumulative water production and, consequently, a 26% increase in cumulative oil production.

Most of the literature on MIMO systems which assume Rayleigh flat fading, model the multipath channel propagation taking the channel correlation into account make simplifying assumptions on the statistics (e.g. uniform linear arrays with rich scattering all around the antennas). These assumptions lead to symmetric and separable correlation structure and leads to some insights on the impact of correlation on capacity and on the optimal signalling. In the recent past, several multiple-element antenna geometries, different the linear arrays, which exploit the ability to excite different spatial modes in the radiated E-field vector have been proposed in the cellular radio access link context. The aforementioned channel model is not rich to capture the EM properties of the antenna elements and its relation on the availability of scattering resources. In this paper, we propose a channel model based on EM theory which incorporates the joint impact of the antenna element properties (radiation pattern, mutual coupling between elements, etc.) and the scattering environment. This model provides a good compromise between complexity and tractability. We use this channel model to propose a rank and determinant criterion for array design in a multiple-antenna system for maximizing the ergodic capacity

In India, the growth of population plays an important role in increasing the congestion at intersections because every person uses a separate vehicle for movement due to this reason we know every day many people die in road accidents due to lack of traffic control system because all intersections are not well signalized. So, it is not possible to control the movement of vehicles by traffic police due to the increase in the number of vehicles at intersections. Therefore, all intersections should be well signalized and traffic signals are specially designed for the emergency vehicles like Ambulance, Fire vehicles because emergency vehicles face delay at intersections when traffic is at red light and it is very dangerous for our society. In this project, Design of traffic signals is done with the help of the Indian Road Congress (IRC) and Webster's method at Gajularamaram service road intersection. Traffic signals are a better option for effective transportation. Traffic signal systems are used to control the flow of vehicles with the help of traffic lights, where Many roads meet together and make a junction. Traffic volume studies are used to lay down mainly how many vehicles are moving on the road at a particular section during a particular time. Traffic signals are the best way to control vehicle movement at intersections without any accidents and conflicts.

This paper considers optimal input design when the intended use of the identified model is to construct a feed-forward controller based on measurable disturbances. The objective is to find a minimum power excitation signal to be used in system identification experiment, such that the corresponding model-based feed-forward controller guarantees, with a given probability, that the variance of the output signal is within given specifications. To start with, some low order model problems are analytically solved and fundamental properties of the optimal input signal solution are presented. The optimal input signal contains feed-forward control and depends of the noise model and transfer function of the system in a specific way. Next, we show how to apply the partial correlation approach to closed loop optimal experiment design to the general feed-forward problem. A framework for optimal input signal design for feed-forward control is presented and numerically evaluated on a temperature control problem.

The parameters of a transportation simulation model need to pass through a careful calibration process to ensure that the model’s output is as close as possible to the actual system. Owing to the computationally expensive and black-box nature of a simulation model, there is a need for robust and efficient calibration algorithms. This paper proposes a Bayesian optimization framework for the high-dimensional calibration problem of transportation simulation models. Bayesian optimization uses acquisition functions to determine more promising values for future evaluation, instead of relying on local gradient approximations. It guarantees convergence to the global optimum with a reduced number of evaluations, therefore is very computationally efficient. The proposed algorithm is applied to the calibration of a simulation network coded in simulation of urban mobility (SUMO), an open-source microscopic transportation simulation platform, and compared with a well-known method named simultaneous perturbation stochastic approximation (SPSA). To assess the calibration accuracy, speed distributions obtained from the two models calibrated using these two different methods are compared with the observation. For both the Bayesian optimization and SPSA results, the simulated and observed distributions are validated to be from the same distribution at a 95% confidence level for multiple sensor locations. Thus, the calibration accuracy of the two approaches are both acceptable for a stochastic transportation simulation model. However, Bayesian optimization shows a better convergence and a higher computational efficiency than SPSA. In addition, the comparative results of multiple implementations validate its robustness for a noisy objective function, unlike SPSA which may sometimes get stuck in a local optimum and fail to converge in a global solution.

CTM-based traffic signal optimization of mixed traffic flow with connected automated vehicles and human-driven vehicles

Hybrid fuzzy control for the goethite process in zinc production plant combining type-1 and type-2 fuzzy logics

The aim of this project is to promote the significant improvement of transportation efficiency and fuel economy by the cooperative method of traffic signal control and vehicle speed optimization. It formulates the optimal traffic signal timing and vehicles arrival time to minimize the total travel time of all vehicle and to optimize the engine power to minimize the fuel consumption of individual vehicles.

In traffic networks, appropriately determining the traffic signal plan of each intersection is a ünecessary condition for a reasonable level of service. This paper presents the development of a new system for automatically designing optimal actuated traffic signal plans with transit signal priority. It uses an optimization algorithm combined with a mesoscopic traffic simulation model to design and evaluate optimal traffic signal plans for each intersection in the traffic network, therefore reducing the need for human intervention in the design process. The proposed method can simultaneously determine the optimal logical structure, priority strategies, timing parameters, phase composition and sequence, and detector placements. The integrated system was tested by a real-world isolated intersection in Haifa city. The results demonstrated that this approach has the potential to efficiently design signal plans without human intervention, which can minimize time, cost, and design effort. It can also help uncover problems in the design that may otherwise not be detected.

This work aims to minimize average delay for signalized traffic network along Jalan UMS under oversaturated condition using decentralized genetic algorithm (DGA). Relieving traffic network is a key challenge for a nation for improving its socio-economic systems and welfare to society. Control the traffic signal timing becomes a cost effective solution to reduce congestion since the space constraint limits the authority to build more infrastructures in city. Regrettably, most of the current traffic signal control systems are not yet optimized. They are preset based on historical traffic data; thus they are inadequate to cope with the dynamic changes in real traffic scenario. Therefore, this work proposes DGA to optimize the traffic network signal for reducing average delay during the morning peak hour. A comprehensive traffic model based on Public Works Department, Malaysia has been developed as the platform. The average delay experienced by vehicles to traverse the crossed intersection during morning peak hour is used as the performance metric to evaluate performances of the proposed algorithm. Simulation results show developed DGA is able to reduce the average network delay by optimizing the traffic signal.

The design of optimal input signals for the identification of linear systems with feedback is considered for the case when the loop gain is large. The latter condition permits simplification of the description of the complete system and leads to a practical procedure for the design of the optimal input signal. Results of application to a diffusion furnace are included.

Traffic congestion poses serious challenges to urban infrastructures through the unpredictable dynamical loading of their vehicular arteries. Despite the advances in traffic light control systems, the problem of optimal traffic signal timing is still resistant to straightforward solutions. Fundamentally nonlinear, traffic flows exhibit both locally periodic dynamics and globally coupled correlations under deep uncertainty. This paper introduces Oscillator-Based modelling and control using Efficient neural Learning for Intelligent road traffic Signal Calculation (OBELISC), an end-to-end system capable of modelling the cyclic dynamics of traffic flow and robustly compensate for uncertainty while still keeping the system feasible for real-world deployments. To achieve this goal, the system employs an efficient representation of the traffic flows and their dynamics in populations of spiking neural networks. Such a computation and learning framework enables OBELISC to model and control the complex dynamics of traffic flows in order to dynamically adapt the green light phase. In order to emphasize the advantages of the proposed system, an extensive experimental evaluation on real-world data completes the study.