Signal Timing Optimization Research Articles

Travel Time Reliability-Based Signal Timing Optimization for Urban Road Traffic Network Control

Due to increasing traffic demand, many metropolitan areas are experiencing extensive traffic congestion, which demands for efficient traffic signal timing and optimization. However, conventional efficiency measure-based signal optimization cannot handle the ubiquitous uncertainty in the road networks, demanding for the incorporation of reliability measures into signal optimization, which is still in its early stage. Therefore, targeting this issue, based on the recent studies on recognizing travel time reliability (TRR) as an important reliability measure of road networks, a travel time reliability-based urban road traffic network signal timing optimization model is proposed in this paper, with the objective function to optimize a TTR measure, i.e., buffer time index. The proposed optimization model is solved using the heuristic particle swarm optimization approach. A case study is conducted using microscopic traffic simulation for a road network in the City of Nanjing, China. Results demonstrate that the proposed optimization model can improve travel time reliability of the road traffic network and the efficiency of the road traffic network as well. Future studies are recommended to expand the integration of travel time reliability into traffic signal timing optimization.

PGA: An Efficient Adaptive Traffic Signal Timing Optimization Scheme Using Actor-Critic Reinforcement Learning Algorithm

Advanced traffic signal timing method plays very important role in reducing road congestion and air pollution. Reinforcement learning is considered as superior approach to build traffic light timing scheme by many recent studies. It fulfills real adaptive control by the means of taking real-time traffic information as state, and adjusting traffic light scheme as action. However, existing works behave inefficient in complex intersections and they are lack of feasibility because most of them adopt traffic light scheme whose phase sequence is flexible. To address these issues, a novel adaptive traffic signal timing scheme is proposed. It''s based on actor-critic reinforcement learning algorithm, and advanced techniques proximal policy optimization and generalized advantage estimation are integrated. In particular, a new kind of reward function and a simplified form of state representation are carefully defined, and they facilitate to improve the learning efficiency and reduce the computational complexity, respectively. Meanwhile, a fixed phase sequence signal scheme is derived, and constraint on the variations of successive phase durations is introduced, which enhances its feasibility and robustness in field applications. The proposed scheme is verified through field-data-based experiments in both medium and high traffic density scenarios. Simulation results exhibit remarkable improvement in traffic performance as well as the learning efficiency comparing with the existing reinforcement learning-based methods such as 3DQN and DDQN.

Mixed Traffic Flow Signal Timing Optimization Method Considering E-Bike Expansion Influence

AbstractThe increase in e-bikes has aggravated traffic conflicts and casualties at intersections. However, existing signal control methods focus on motor vehicles, ignoring the influence of expansi...

Urban Traffic Signal Control Based on Multiobjective Joint Optimization

This paper proposes two algorithms for signal timing optimization of single intersections, namely, microbial genetic algorithm and simulated annealing algorithm. The basis of the optimization of these two algorithms is the original timing scheme of the SCATS, and the optimized parameters are the average delay of vehicles and the capacity. Experiments verify that these two algorithms are, respectively, improved by 67.47% and 46.88%, based on the original timing scheme.

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

Arriving at optimal signal timing parameters to improve the efficiency of traffic flow has been one of the major challenges faced by traffic engineers. The choice of a robust optimization framework and an accurate traffic model plays a significant role in determining optimal signal timing parameters. Though traffic flow is intuitively stochastic, few studies incorporate stochasticity in their optimization framework for traffic signal design. This study proposes two simulation-based stochastic optimization algorithms—an evolutionary algorithm-based framework and a simultaneous perturbation stochastic approximation (SPSA) algorithm-based framework for the optimal signal design of an isolated intersection using a calibrated microsimulation environment with reasonable accuracy. A software-in-the loop approach is used to control the traffic signals in the microsimulation environment. SPSA is a gradient descent algorithm with a powerful approach for approximating the gradient with just two function evaluations per gradient approximation. To evaluate the performance of the two frameworks, the study optimizes the signal timings for a case study on an isolated intersection in an urban arterial in Chennai. On comparing the two algorithms, it is found that SPSA performed better and took 100 function evaluations less than that taken by GA. A better (near optimal) initial solution is found to yield a faster rate of convergence for both algorithms. As the proposed optimization framework incorporates the stochastic nature of traffic in the optimization algorithm, it can accommodate the temporal variations in traffic and thereby provide traffic engineers a robust signal control strategy for improving the efficiency of traffic flow.

Optimal signal timing design for the intersection of Dazhi Street and Hexing Road

The phenomenon of intersection congestion has become a major problem in the city. Based on the intersection of Hexing road and Dazhi street, the delay phenomenon of Urban Road intersection is optimized. The intersection of Hexing road and Dazhi street is seriously delayed. In order to improve the road capacity of the intersection and reduce the delay of the intersection, the traffic characteristics parameters of the intersection during peak period are obtained, and the traffic capacity and intersection service level of the signalized intersection are analyzed and calculated. Based on the study of the basic content and research methods of signal timing at signalized intersections, the microscopic simulation software VISSIM was used to improve the traffic control scheme with serious delays at intersections and low urban road capacity, the simulation optimization is carried out by changing the signal timing and resetting the channelization scheme of intersection. Meanwhile, the signal timing of intersection is optimized. The results show that the traffic capacity of the intersection has been greatly improved by VISSIM simulation.

Signal Timing Optimization Model for Intersections in Traffic Incidents

The intersection control and management can alleviate the traffic congestion caused by traffic incidents. Therefore, it becomes essential to develop a signal optimization method for intersections influenced by traffic incidents, which will be beneficial to prevent congestion spreading. In this paper, the proposed model is capable of maximizing the intersection throughput by comprehensively considering the queue length as the penalty value. The headway of leaving vehicles is assumed to follow the Cowan’s M3 headway distribution, where formulas for queue length can be derived based on gap acceptance theory. To satisfy the conditions for efficiently identifying feasible solutions in a short time, a heuristic algorithm (simulated annealing algorithm) is employed to solve the model. The numerical results can validate that the proposed method can solve the problem more efficiently and alleviate the intersection congestion caused by the incidents more desirably. When the incident occurs away from the intersection stop line, the impacts on intersection throughput will be gradually weakened. The proposed method is capable of improving the signalized intersection throughput while preventing the congestion from spreading to the upstream intersection.

An analytical optimization approach to the joint trajectory and signal optimization problem for connected automated vehicles

Traffic conflict points (e.g., intersections, work-zones) cause travel delay, stop-and-go traffic, and excessive energy consumption. Efforts have been taken to improve traffic conflict point performance via trajectory control of connected automated vehicles (CAV) as the CAV technology emerges. One major challenge to these efforts is the complexity in optimization of CAV trajectories, particularly with joint signal timing optimization. This challenge poses barriers to real-time application requirements, scaling them up to address network level problems and drawing analytical insights into problem structures. To overcome this challenge, this paper aims to seek for an efficient and analytical solution to a joint vehicle trajectory and signal timing optimization problem. This problem simultaneously optimizes CAV trajectories and signal timing to minimize travel delay and energy consumption at a conflicting point with two traffic approaches. This study modifies the original complex formulation in two ways. First, the vehicle trajectory shape is simplified into a piece-wise quadratic function with no more than five segments. Second, instead of using the highly non-linear instantaneous fuel consumption function, a simplified macroscopic measure is proposed to approximate fuel consumption as an analytical quadratic function of signal red interval. These simplifications provide elegant theoretical properties that enable solving an analytical exact solution to this complex problem with parsimonious analytical insights. Numerical examples reveal that the proposed model can significantly reduce travel delay and fuel consumption. Moreover, it is demonstrated that the presented algorithm is highly efficient and appropriate for real-world traffic applications.

Incorporating Phase Rotation Into a Person-Based Signal Timing Optimization Algorithm

This article proposes a person-based traffic signal optimization algorithm that simultaneously optimizes signal phase sequence, cycle length, and green split to minimize total person delay at an intersection. It builds upon previous person-based work that uses a mathematical program to optimize signal timings based on real-time passenger car flows and knowledge of bus arrivals and passenger occupancies. Phase rotation is directly accommodated within the mathematical program by introducing new binary variables to describe the phase sequence. The program can either be solved directly or through an enumeration approach to reduce computational complexity. The proposed method is tested using numerical simulations of an intersection in Athens, Greece. The results reveal that phase rotation can reduce person delay significantly, especially when the intersection flow ratio is relatively low. These reductions in person delays are achieved by implicitly prioritizing the movement of high-occupancy buses to the detriment of lower-occupancy cars. Furthermore, to balance the person delay reduction and phase rotation frequency, a threshold can be applied in the phase sequence selection process to eliminate phase rotations actions with limited benefits.

Optimizing Signal Timing Control for Large Urban Traffic Networks Using an Adaptive Linear Quadratic Regulator Control Strategy

Traffic signal control is important for intersection safety and efficiency. However, most traffic signal control methods are designed for individual intersections or corridors. Although some adaptive control systems have been developed, the methods used are often proprietary and not published, making it difficult to evaluate their effectiveness. This study proposes an adaptive multi-input and multi-output traffic signal control method that not only can improve network-wide traffic operations in terms of reduced traffic delay and energy consumption, but also is more computationally feasible than existing centralized signal control methods. Considering intersection interactions, a linear dynamic traffic system model was built and adaptively updated to reflect how the signal control input of each intersection affects network-wide vehicle travel delay. Based on the system model, an adaptive linear-quadratic regulator (LQR) was designed to minimize both traffic delay and incremental changes in the control input. The proposed control method was evaluated in a microscopic traffic simulation environment with a 35-intersection network of Bellevue City, Washington. Simulation results show that the proposed method had shorter average traffic delays in the network when compared with the traffic delays controlled by the state-of-the-art max-pressure, self-organizing traffic lights, and independent deep Q network methods.

Signal Timing Optimization Algorithm for an Intersection Connected with an Urban Expressway

The off-ramps of urban expressways can assume various forms. Compared with other countries, many off-ramps in China have special Chinese characteristics; for example, the off-ramps are extremely short and connected to urban roads and intersections within a short distance. Thus, many foreign control methods may not be suitable for use in China. This paper provides a dual-phase signal timing optimization model for an intersection connected to the off-ramp of an urban expressway that has special Chinese characteristics. This model is solved by an improved genetic algorithm. To better evaluate traffic conditions, this paper also establishes methods by which stranded vehicles can be identified and the traffic flow at intersections can be predicted. The performance of the optimized signal timing is tested with PTV Vissim software, and the timing plan is calculated by MATLAB using an actual scenario in Xi’an, China. The optimized effect of the proposed model is tested under different flow conditions by changing the input traffic flow of the intersection. The results show that at the peak time, the stops, delays and queue lengths of the whole intersection are reduced by 29.0%, 27.8% and 23.7%, respectively, compared with the original plan and by 7.7%, 14.5% and 11.9%, respectively, compared with the plan from the single-phase model. This dual-phase model can prevent congestion from spillback onto urban expressways as well as reduce delays and stop times at intersections, especially in a supersaturated state. In general, this model can be used to address signal timing in ordinary urban intersections.

An alternative design for traffic intersections with work zones by using pre-signals

The lane closures caused by the work zone at the approaches create a negative impact on the operational effectiveness of the signalized intersections. This paper presents an innovative design for intersections with work zones to improve the intersection’s practical capacity. In this design, the lanes in the leg with work zone can be used dynamically as approach and exit lanes during different periods of a signal cycle by using the pre-signal. An optimization model for an optimal geometric layout and signal timing design is built to capture real-world operational constraints, including the lane assignment, the signal timing of the main signal and pre-signal, the distance of the mixed-usage area and the transition area, and the degree of saturation restriction. A case study and extensive numerical analysis demonstrate the effectiveness of the prospective design as compared with conventional designs under different geometric layout and traffic demand situations. Overall, the proposed design can lead to an increment in the practical capacity of the intersection with a work zone (by up to 30%) and reduce the average vehicular delay accordingly (by 50%) without necessitating an expansion of the intersection. The results show that the promising application of the proposed design when the length of the work zone is less than 160 m.

Research on Unregulated Emissions from Motor Vehicles at Intersection Based on the Optimized Traffic Signal Timing

This paper centers on optimizing the signal timing scheme to reduce motor vehicle unregulated emissions from motor vehicles at the intersection by the integration method of VISSIM and MOVES. Taking the intersection of Hong Kong Middle Road and Fuzhou South Road in Qingdao as the research object. In VISSIM, according to the traffic flow, the signal timing scheme is optimized. The localized MOVES emission model calculates emissions of formaldehyde, ammonia and benzene. Compared with emissions of regulated pollutants before and after the optimization for the signal timing scheme, the result shows the optimized signal timing improves the passing capacity of the road intersection, and emissions of formaldehyde, NH3 and benzene reduces by 22%, 7% and 14% respectively. The result indicates that the optimized signal timing can effectively reduce motor vehicle unregulated emissions at the road intersection, which provides an effective way to protect the environment.

Investigating the impacts of traffic signal timing on the urban traffic-related particulate matters (PMs): A case study in Xi'an, China

Characterizing the relationship between signal timing and particulate matter (PM) is important for a sustainable traffic signal control and accurate exposure assessment at emission hotspot locations. The impact of traffic signal timing and meteorological variables on size-resolved PM in the 0.25–10 μm range at the signalized intersection was investigated. The PMs, particle number concentrations (PNCs), and particle mass concentrations (PMCs) in the given size distribution were obtained at the signalized intersection in Xi'an. A comprehensive analysis method involving statistical analysis, regression analysis, simulation was used. The results demonstrated that the fine particles (PMC0.25-2.5 and PNC0.25-2.5) have a positive relationship with wind speed and vehicles per cycle, and a size ranges of PM, ranging from 2.5 to 10 μm, was greatly affected by those factors. Moreover, the regression findings via multivariate multiple linear regression (MMLR) showed that the coefficients of independent variables (temperature, and delay) were statistically significant, which indicated that those factors enormously affect fine particles. In addition, the simulation results from the CAL3QHC indicated the benefit of pollutant reduction (PM2.5 reduced by an average of 5.3%) becomes more significant as signal timing optimization. This study serves as a demonstration of the abilities of reasonable signal timing to improve air quality and make better at the service level of the intersection.

Traffic Signal Control Optimization in a Connected Vehicle Environment Considering Pedestrians

This paper proposes a connected vehicle-based traffic signal control scheme that seeks to improve both vehicle and pedestrian operations. Real-time information on vehicle speeds and locations is combined with knowledge of pedestrian arrivals to optimize signal timings that minimize a weighted average of vehicle and pedestrian delays. Such real-time pedestrian information might be available using existing sensors—such as pedestrian push buttons or infrared detectors—as well as in a connected environment. The algorithm implements a rolling-horizon optimization framework that optimizes signal phase sequences over some period but only implements the first phase in the optimized sequence. The results reveal that considering pedestrians in the optimization can improve delays to both pedestrians and vehicles compared with ignoring pedestrians. Within the proposed framework, average vehicle delay increases and average pedestrian delay decreases as more weight is assigned to pedestrian delay in the optimization. In general, the average person delay can be minimized when the relative weight between vehicle and pedestrian delay is consistent with the average occupancy rate of cars. However, a different weight may be chosen to prioritize pedestrian movement, if desired. These results are robust under varying demand levels and demand patterns. The effectiveness of the algorithm decreases as the information level of pedestrian arrivals decreases, and the algorithm becomes ineffective when information from fewer than 60% pedestrians is available. However, the detection of more than 60% of pedestrians can likely be achieved using existing technologies and thus would likely be available in a connected environment.

Multiple Objective Genetic Algorithms for Solving Traffic Signal Optimization Issue at a Complex Intersection: A Case Study in Taichung City, Taiwan

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.

Signal Timing Optimization Model Based on Bus Priority

This paper focuses on the optimization problem of a signal timing design based on the concept of bus priority. This optimization problem is formulated in the form of a bi-level programming model that minimizes average passenger delay at intersections and vehicle delay in lanes simultaneously. A solution framework that implements the differential evolution (DE) algorithm is developed to efficiently solve the model. A case study based on a real-world intersection in Beijing, China, is implemented to test the efficiency and applicability of the proposed modeling and computing methods. The experiment’s result shows that the optimization model can not only significantly improve the priority capacity of the buses at the intersection but also reduce the adverse impact of bus-priority approaches on the private vehicles for the intersections.

Network-Level Coordinated Speed Optimization and Traffic Light Control for Connected and Automated Vehicles

This study develops a methodology for coordinated speed optimization and traffic light control in urban street networks. We assume that all vehicles are connected and automated. The signal controllers collect vehicle data through vehicle to infrastructure communications and find optimal signal timing parameters and vehicle speeds to maximize network throughput while harmonizing speeds. Connected and automated vehicles receive these dynamically assigned speeds, accept them, and implement them. The problem is formulated as a mixed-integer non-linear program and accounts for the trade-offs between maximizing the network throughput and minimizing speed variations in the network to improve the network operational performance and at the same time smoothen the traffic flow by harmonizing the speed and reducing the number of stops at signalized intersections. A distributed optimization scheme is developed to reduce the computational complexity of the proposed program, and effective coordination ensures near-optimality of the solutions. The case study results show that the proposed algorithm works in real-time and provides near-optimal solutions with a maximum optimality gap of 5.4%. The proposed algorithm is implemented in Vissim. The results show that coordinated signal timing and speed optimization improved network performance in comparison with cases that either signal timing parameters or average speed of vehicles are optimized. The coordinated approach reduced the travel time, average delay, average number of stops, and average delay at stops by 1.9%, 5.3%, 28.5%, and 5.4%, respectively compared to the case that only signal timing parameters are optimized.

Deep Learning Approach for Predictive Analytics to Support Diversion during Freeway Incidents

Route diversion during incidents on freeways has been proven to be a useful tactic to mitigate non-recurrent congestion. However, the capacity constraints created by the signals on the alternative routes put limits on the diversion process since the typical time-of-day (TOD) signal control cannot handle the sudden increase in the traffic on the arterials because of diversion. Thus, there is a need for active transportation management strategies that support agencies in identifying the potential diversion routes for freeway incidents and the need for adjusting the traffic signal timing under different incident and traffic conditions. This paper investigates the use of a data analytic approach based on the long short-term memory (LSTM) deep neural network method to predict the alternative routes dynamically using incident attributes and traffic status on the freeway, and travel time on both the freeway and alternative routes during the incident. Additionally, a methodology is proposed for the development of special signal plans for the critical intersections on the alternative arterials based on the results from the LSTM neural network, combined with simulation modeling, and signal timing optimization. The methodology developed in the paper can be easily implemented by the transportation agencies, as it is based on data that are generally available to the agencies. The results from this paper indicate that the developed methodology can be used as part of a decision support system (DSS) to manage the traffic proactively during the incidents on the freeways.

A Platoon-Based Adaptive Signal Control Method with Connected Vehicle Technology.

One important objective of urban traffic signal control is to reduce individual delay and improve safety for travelers in both private car and public bus transit. To achieve signal control optimization from the perspective of all users, this paper proposes a platoon-based adaptive signal control (PASC) strategy to provide multimodal signal control based on the online connected vehicle (CV) information. By introducing unified phase precedence constraints, PASC strategy is not restricted by fixed cycle length and offsets. A mixed-integer linear programming (MILP) model is proposed to optimize signal timings in a real-time manner, with platoon arrival and discharge dynamics at stop line modeled as constraints. Based on the individual passenger occupancy, the objective function aims at minimizing total personal delay for both buses and automobiles. With the communication between signals, PASC achieves to provide implicit coordination for the signalized arterials. Simulation results by VISSIM microsimulation indicate that PASC model successfully reduces around 40% bus passenger delay and 10% automobile delay, respectively, compared with signal timings optimized by SYNCHRO. Results from sensitivity analysis demonstrate that the model performance is not sensitive to the number fluctuation of bus passengers, and the requested CV penetration rate range is around 20% for the implementation.