An Improved Actuated Signal Control of Intersection Based on VISVAP

To reduce travel time, the actuated signal controls have been implemented at urban intersections. However, the safety impacts of actuated signal controls thus far have rarely been examined. In this assessment of the safety impact of urban intersections with semi-actuated signal controls, the safety performance functions and EB approaches were applied. The semi-actuated signal controls have increased injuries and total crashes in all crash types by around 5.9% and 3.8%, respectively. Regarding the most common crash types, such as angle, sideswipe & rear-end, and head-on crashes, semi-actuated signal controls have been seen to decrease injuries by 7.7%. Total crashes have been reduced by over 9.2% through the use of semi-actuated signal controls. This may be result of optimal signal timings considering traffic conditions during peak time periods. In conclusion, safety impact factors which have been established in this study can be used to improve safety and minimize travel times using semi-actuated signal controls.

Macroscopic arterial traffic flow modeling is crucial for signal control because most of signal control systems are relying on macroscopic models to quickly generate performance to evaluate control strategies. However, because of the randomness of vehicle speed, most of the macroscopic models are lack of capability to adequately reproduce the stochastic nature of traffic dynamics, especially for arterials with actuated signal control. To solve this problem, this research proposed a new model that incorporates a simplified shockwave model with traffic diffusion theory, which is able to reproduce the stochastic nature of traffic dynamics. The simulation results and comparison with field observed data verify that the proposed model is not only to model the traffic dynamics on arterials, but it is also able to derive signal timings of actuated intersections. Such results are encouraging since this model can be applied to a variety of real-time applications such as arterial traffic flow estimation, performance prediction and signal optimization.

Traffic congestion impacts economics, health, and productivity. Traffic congestion is increasing in the urban areas and negatively impacts people in these areas due to air pollution, accidents, delays in travel time, and more. In Saudi Arabia, more than 500,000 people have died or been injured because of traffic. To improve this problem, we developed a rewarding system that encourages people to drive during off-peak hours. In addition, the system provides recommendations for a preferred departure time in order to avoid traffic congestion. Recommended departure time is based on historical data. This system will be used to create dataset of drivers and traffic information in order to build an intelligent recommendation system

Traffic flow on roads has increased manifolds from past few decades due to increase in number of vehicles and rise in population. With fixed road infrastructure and more vehicles on traffic routes lead to traffic congestion conditions especially in urban areas of developing nations. Traffic jams are normal in major cities which ultimately cause delay in travel time, more fuel consumption and more pollution. This manuscript propose a Multi-metric road guidance mechanism(MRGM) which considers multiple metrics to analyze the traffic congestion conditions and based on the conditions effective optimal routes are suggested to the vehicles. The Simulation of the proposed mechanism is performed with the SUMO by using the python script and the results show that proposed mechanism i.e MRGM outperforms other mechanism in terms of traffic efficiency, travel time, fuel consumption and pollution levels in the smart vehicular network.

Currently, in China, the single-objective optimization methods are commonly used for road intersection signal control, which has one single objective such as minimizing the number of stops, average delays, or queuing length. When the intersection traffic varies large with the time of day, using this method for intersection traffic control will always cause poor adaptability and low efficiency. To solve these problems, a multi-objective optimization method for signal control of single intersection is proposed, with the composite optimization objectives to minimize the average delay, the least number of stops, and the shortest vehicle queue length, which can set preferences of various optimization objectives to adapt to different traffic conditions. The method uses the Pareto multi-objective evolutionary algorithm to optimize signal timing parameters to get the best signal control plan. Simulation results show that this method can significantly improve the adaptability and efficiency of the intersection traffic control.

This paper presents an adaptive traffic signal controller algorithm that allows real time traffic flow data to control traffic signal timing for an isolated intersection. The proposed design allows greater flexibility and control of signal timings while keeping in view multiple criterions. It is based on simple rules that allow traffic flows on all intersection branches to control the timing of current signal phase without causing unrealistic delays on low volume side streets. The algorithm is also adjusted for capacity of different links. Detector data has been used to assess congestion situation and respond accordingly. A priority scheme for priority vehicles is also proposed. As a proof of concept, the proposed algorithm has been implemented on FPGA as a parameterized VERILOG model allowing adjustment to any intersection size and topology. An evaluator circuit was designed to assess the performance as compared to actuated signal controller. Improvement was observed in total stopped delay, number of stops and queue lengths.

With the continued rapid growth of urban areas, problems such as traffic congestion and environmental pollution have become increasingly common. Alleviating these problems involves addressing signal timing optimization and control, which are critical components of urban traffic management. In this paper, a VISSIM simulation-based traffic signal timing optimization model is proposed with the aim of addressing these urban traffic congestion issues. The proposed model uses the YOLO-X model to obtain road information from video surveillance data and predicts future traffic flow using the long short-term memory (LSTM) model. The model was optimized using the snake optimization (SO) algorithm. The effectiveness of the model was verified by applying this method through an empirical example, which shows that the model can provide an improved signal timing scheme compared to the fixed timing scheme, with a decrease of 23.34% in the current period. This study provides a feasible approach for the research of signal timing optimization processes.

Semi-exclusive right-of-way trams were considered in this research. Compared with subways, the travel time of trams fluctuates greatly due to manual driving and control by traffic signal at intersections. In this work, schedule optimisation was combined with signal timing optimisation of intersections along a tramline to improve the robustness of tram operation (i.e. the ability to resist relatively small-scale delays). On the basis of the normal needed travel time, an appropriate slack time is added to the timetable to absorb delays during tram operation. Based on historical tram trajectory data, the tram's travel time distribution at each running section/station was obtained. The PM-Band passive signal priority model was used to construct an integer programming problem to solve the slack time of timetable and signal timing at intersections. Finally, a tram robustness evaluation simulation model was established based on historical data and the robustness of the timetable was tested by adding random interference time in the tram travel process. The result shows that, compared with the traditional method, the modified timetable has great advantages in indicators such as average punctuality rate, standard deviation of the whole travel time and average delay time.

Adaptive signal control is a method that dynamically adjusts signal phases based on real-time traffic conditions, aiming to improve the efficiency of intersection light control. However, in practical applications, obtaining complete traffic spatiotemporal states at intersections is challenging given the limited data sources. To address this issue, this paper presents a novel deep reinforcement learning model, namely 3DQN-PSTER, which combines Dueling Deep Q Network, Double Deep Q Network, and Priority SumTree Experience Replay technology. This model effectively extracts traffic states from sampled floating car data (FCD) and achieves optimal timing schemes. To evaluate its performance, the proposed model is tested in a simulated intersection environment using actual intersection data. The model interacts with the environment by adjusting signal phases, updating traffic states, and obtaining optimal timing schemes. Simulation results demonstrate that the proposed model exhibits higher convergence efficiency and outperforms benchmark models. When applied, the reinforcement learning single control (RLSC) based on the 3DQN-PSTER model outperforms fixed-time signal control schemes and actuated signal control (ASC) schemes. Even in scenarios with low penetration ratios of FCD, RLSC still significantly outperforms ASC. Therefore, the signal timing optimization method proposed in this paper could greatly enhance intersection operation efficiency in both ideal overall data environments and partial states.

Some insight into the benefits of regularly and frequently maintaining traffic signal timing within and across jurisdictional boundaries is provided. The rapidly growing Cranberry Township, located at the crossroads of two interstate highways (Pennsylvania Turnpike [Interstate-76] and Interstate-79) 20 miles north of Pittsburgh, Pennsylvania, is used as a case study. Environmental and economic costs due to the absence of monitoring and updating traffic signal timing are estimated. While traffic signal timing optimization and coordination is a cost effective strategy to reduce travel time and its consequential air emissions along corridors, implementing such timing plans is only effective at and around the time of implementation, while travel demand remains constant. As time passes, with the fluctuation of vehicular demands, especially in rapidly developing areas, timing plans need to be monitored and modified. In Cranberry Township such developments and travel demand fluctuations result in a significant increase in costs of travel (time and fuel) and external environmental costs. Estimates show that timing plans that were at their optimal level at the time of implementation, if left unmodified, result in an 18% increase in the cost of fuel, a 13% increase in the cost of travel time, and an 11% increase in the external environmental costs in less than two years.

This study proposes a new multi-input multi-output optimal bilinear signal control method in which a bilinear dynamic model approximation is used to capture the nonlinear dynamics of the urban traffic networks. With signal green time splits as the control input and traffic delay changes as the output for each intersections in the network, a bilinear system model was developed, which, on the basis of linear system modeling, takes interactions among traffic delays and signal timing splits into consideration. Based on the bilinear system modeling framework, we conducted two steps in each time interval to derive traffic control strategies: (1) we used the normalized least-squared algorithm to estimate system parameters; and (2) we solved an online optimization problem to obtain the updated traffic control inputs for the signal timing that minimizes future traffic delays. We evaluated the proposed method in a microscopic traffic simulation environment (VISSIM) with a 35-intersection network of Bellevue city in Washington. Two different traffic demand patterns: (1) normal traffic demands; and (2) time-varying traffic demands were simulated to compare the performance of different control strategies. Experimental results show that (1) the proposed bilinear system model can better describe traffic system dynamics than linear-model based methods, such as our previously developed linear-quadratic regulator control; and (2) the proposed method outperforms the state-of-the-art signal control strategies, namely the max-pressure and the self-organizing traffic light control methods. We have also shown that the proposed method is applicable to all other possible network layouts and signal controller phasing structures. IEEE

Introduction Drag on city traffic has escalated to be a pivotal problem in almost all cities in the world, and commuter travel time, fuel consumption and emission have also soared. Fixed-time signal control systems have been traditionally used in traffic signal control, but their optimality highly depends on the time of day they are applied and they are not capable of real-time traffic control. In order to overcome these drawbacks, intelligent and adaptive traffic control approaches are being explored by both scientists and traffic developers. Objective The purpose of this research is to create and assess a dynamic traffic light control system by utilizing Snake Optimization (SO), a novel meta-heuristic algorithm. In order to minimize vehicle queues, cut down on delays, and enhance overall traffic flow, the main goal is to examine how well SO optimizes traffic signal timings based on real-time traffic data. by contrasting conventional static control techniques and meta-heuristics with the SO-based dynamic approach. Proposed Method To find the best traffic light phase durations at intersections, the suggested approach uses the Snake Optimization (SO) algorithm, a bio-inspired meta-heuristic technique. Real traffic data from the Algerian city of Algiers, which depicts realistic traffic volumes and patterns, is used to test the method. The SUMO (Simulation of Urban Mobility) simulator, an open-source program popular for simulating microscopic traffic, is used to construct the experimental framework. Traffic scenario modeling, control algorithm implementation, and performance evaluation in a simulated urban setting are all made possible by SUMO. The following steps are part of the optimization process: gathering of data, using the SUMO simulator to model traffic flow and intersections, incorporating the SO algorithm to modify traffic light timings in real time in response to inputs, evaluation of performance using metrics like vehicle throughput, average waiting time, reduction of CO2 emission and total travel time, and evaluation of the benefits obtained by the SO-based approach through comparison with a baseline static control system and ACO-DSOS meta-heuristic. Conclusion According to the simulation results, the suggested approach, which is based on the Snake Optimization algorithm, performs better than other meta-heuristics and conventional static control systems. The algorithm's effectiveness in controlling traffic flows is validated by quantitative comparisons. According to the study's findings, SO is a practical and successful strategy for maximizing traffic light control in practical settings.

Traffic congestion can be alleviated by infrastructure expansions, however, improving the existing infrastructure using traffic control is more plausible due to the obvious constraints on financial resources and physical space. Independent applications of Adaptive Traffic Signal Control (ATSC) and Ramp Metering (RM) have shown strong potential to effectively alleviate urban traffic congestion by adjusting the signal timings in real-time in response to traffic fluctuations to achieve desirable objectives (e.g., minimize delay at intersections or minimize travel time along freeways). This paper presents the problem formulation and the framework for addressing traffic control problem using an integrated solution combining ATSC and RM. According to that problem definition, an optimal closed-loop approach for ATSC and RM can be designed using a multi-agent reinforcement learning (MARL) approach. Authors' previous studies of MARL on a large-scale simulated network of 59 intersections in the lower downtown core of the City of Toronto for the morning rush hour have shown unprecedented reduction in the average intersection delay up to 39% at the network level, and travel time savings of up 2to 26% along the busiest routes in downtown Toronto. Additionally, authors' studies of MARL applied to a calibrated microsimulation model of the Gardiner Expressway in Toronto, Canada resulted in 50% reduction in total travel time compared with the base case scenario.

Traffic congestion and delays at signalized intersections remain a challenge in urban mobility. To minimize congestion, adaptive traffic signal control systems have been adopted across the United States. However, for these systems to function at their best, systems require an accurate estimation of traffic states, such as traffic density and queue length. This paper implements a relatively novel method, Physics-Informed Neural Networks (PINNs), for real-time traffic density estimation to enhance adaptive intersection signal control. The methodology leverages PINNs, which integrate both data-driven learning and physical constraints imposed by the Lighthill-Whitham-Richards (LWR) macroscopic traffic flow model. A simulation of a four-way intersection was used to generate realistic spatio-temporal traffic data to evaluate the performance of the model. Separate PINNs models, one trained for each incoming approach using simulation-based spatio-temporal density data, provide segment-level density predictions. These real-time density estimates are then employed within an adaptive traffic signal control algorithm designed to adjust green light phasing based on detected demand. The proposed PINNs control strategy was evaluated through simulation, comparing its performance against the Webster traffic timing method using key metrics such as average vehicle delay at the intersection and average vehicle travel time. The findings show an approximate 18% decrease in average vehicle delay and more than a 6% reduction in average travel time when utilizing adaptive traffic control based on the PINNs method for determining density values. These findings highlight the potential of leveraging PINNs for accurate, real-time traffic state estimation, which can effectively inform and improve the performance of adaptive traffic signal control systems, leading to reduced congestion and enhanced mobility at intersections.

We develop and assess centralized and decentralized signal control systems with short-term origin-destination (OD) demands as inputs. Considering each intersection turning movement as a virtual link, we assign traffic demand to paths based on minimal instantaneous travel time. Then, the optimal control is formulated using a G/G/n/FIFO open queueing network model (QNM). We also solve the issue of optimal control using a three-step naïve method for the centralized system with the new inputs. Because the optimization of large-scale network traffic signals can involve sizeable numbers of decision variables and nonlinear constraints, making it a nondeterministic polynomial time (NP) complete problem, we further decompose the centralized system into a decentralized system where the network is divided into subnetworks. Each subnetwork has a dedicated agent that optimizes signals within it. Furthermore, traffic demand for the entire network is decomposed into demands for subnetworks via path decomposition index (PDI). The proposed control systems are applied to test scenarios constructed using different demand profiles in grid networks. We also investigate the impact of network decomposition strategy on signal control system performance. Results show that network decomposition with smaller subnetworks results in less computational time (CT) but increased average travel time (ATT) and total travel delay (TTD).