Signal Timing Parameters Research Articles

Optimization Model of Regional Green Wave Coordination Control for the Coordinated Path Set

An optimization model of regional green wave coordination control for the coordinated path set is proposed based on the network’s directional and asymmetric actual coordination control demands. The model in this paper selects the coordinated path set as the optimization object and takes the best regional coordination control effect as the optimization objective. By selecting signal timing parameters, such as stage time, stage center point, offset, and cycle length, as optimization variables, the relationship between signal timing parameters and the effect of green wave coordination was established. The stage time of the coordinated stage at the upstream and downstream intersections is used to calculate the evaluation index of the coordination control benefit of the whole road network. The case study demonstrates that this model can optimize coordinated path chains with left-turn traffic flows under green wave coordination management. In terms of network performance, compared with Transyt and Synchro, the signal control scheme obtained by the proposed model can reduce the average delay by about 30% and the average number of stops by about 40%. To meet the requirements of regional green wave coordination control, the proposed model can optimize different signal timing parameters to obtain a wide range of applications of green wave coordinated control in road networks.

Effect of signal timing on vehicles’ near misses at intersections

Driving characteristics often vary between the different states of the signal. During red and yellow phase, drivers tend to speed up and reduce the following distance which in turn increases the possibility of rear end crashes. Intersection safety, therefore, relies on the correct modelling of signal phasing and timing parameters, and how drivers respond to its changes. This paper aims to identify the relationship between surrogate safety measures and signal phasing. Unmanned aerial vehicle (UAV) video data has been used to study a major intersection. Post encroachment time (PET) between vehicles was calculated from the video data as well as speed, heading and relevant signal timing parameters such as all red time, red clearance time, yellow time, etc. Random parameter ordered logit model was used to model the relationship between PET and signal timing parameters. Overall, the results showed that yellow time and red clearance time is positively related to PETs. The model was also able to identify certain signal phases that could be a potential safety hazard and would need to be retimed by considering the PETs. The odds ratios from the models also indicate that increasing the mean yellow and red clearance times by one second can improve the PET levels by 10% and 3%, respectively.

A Dynamic CAV-Dedicated Lane Allocation Method With the Joint Optimization of Signal Timing Parameters and Smooth Trajectory in a Mixed Traffic Environment

Owing to the difference in utilization efficiency of road between a connected-automated vehicle (CAV) and connected human-driven vehicle (CHV), caused by trajectory controllability, the optimization methods reported in literature for the mixed traffic of CAVs and CHVs at signalized intersections do not consider the dynamic adjustment of the approach lane utilization due to variations in the CAV penetration rate and traffic demand. Accordingly, a dynamic CAV-dedicated lane allocation method with the joint optimization of signal timing parameters and smooth trajectory is proposed to avoid using transitional or inefficient CAV-dedicated lanes and improve the performance of the intersections. In addition, a CAV trajectory control model for the CAV-dedicated lane is built to avoid the start-up loss time and maximize the utilization of green time. The delay and stops are weighted to form an integrated performance index (PI), and a PI model is established to evaluate the proposed method. A simplified solution procedure is designed to solve the joint optimization problem. The simulation results show that the proposed method in this paper can reduce the average PI per vehicle at intersections by 15.7% or more compared with that of a fully actuated signal control scheme, which indicates that it is necessary to drive the CAVs in one or more CAV-dedicated lanes when the CAV penetration rate exceeds a certain threshold. Compared with the existing signal and trajectory control approaches, the proposed method is more suitable for multi-lane signalized intersection with high saturation and high CAV penetration rate.

Investigation of Resonant Signal Timing Plans through Comprehensive Evaluation of Various Optimization Approaches

Transportation agencies periodically conduct signal retiming (i.e., optimization) to ensure efficient signal operations. Previous studies introduced the notion of the “resonant cycle length” (RCL), which is based on the premise that a good progression of traffic on the corridor mainline for various volume fluctuations can be achieved with an appropriate value of cycle lengths, where all other signal timing parameters (splits, offsets and phase sequences) remain unaltered. Several follow-up studies brought many inconsistencies in the previously introduced concept. For instance, authors would investigate the existence of the RCL by evaluating the performance of signal timing plans for not only coordinated movements (side streets and coordinated movements together), but would optimize all signal timing parameters (not only cycle lengths) while investigating. This study sheds light on the RCL concept and highlights the importance of all signal timing parameters for signal performance. In addition, we introduce a concept called the resonant signal timing plan (RSTP) as a refinement for the RCL, which represents a combination of signal timing parameters that (unaltered) retain an acceptable performance for a variety of traffic conditions. Results show that different sets of signal timing parameters cause plans to be resonant depending on the evaluation type.

Combined Dynamic Route Guidance and Signal Timing Optimization for Urban Traffic Congestion Caused by Accidents

When traffic congestion caused by accidents occurs on urban roads, the current signal control scheme may face failure. It is necessary to establish an effective dynamic route guidance scheme and formulate a reasonable signal timing scheme to alleviate the impact of the accident on urban traffic. In this paper, traffic congestion is divided into three states and the traffic evacuation strategies are formulated, respectively. Then, on the basis of analyzing the internal mechanism and spatiotemporal variation of queuing at intersections, a two-stage model is proposed to optimize the signal timing parameters. In the first stage, the basic timing parameters of each intersection, such as signal cycle and green ratio, are obtained. In the second stage, the offset optimization model is constructed to minimize the total delay time of the coordinated phase of all detour routes in the control scope, considering the early dissipation of queuing vehicles at the entrance of the intersection. This model is solved with a particle swarm optimization algorithm and simulated to the actual road network with VISSIM. As the experimental results show, this control method can greatly reduce the average vehicle delay, stop times, queue length, and the number of vehicles passing.

White Phase Intersection Control Through Distributed Coordination: A Mobile Controller Paradigm in a Mixed Traffic Stream

This study presents a vehicle-level distributed coordination strategy to control a mixed traffic stream of connected automated vehicles (CAVs) and connected human-driven vehicles (CHVs) through signalized intersections. We use CAVs as mobile traffic controllers during a newly introduced “white phase”, during which CAVs will negotiate the right-of-way to lead a group of CHVs while CHVs must follow their immediate front vehicle. The white phase will not be activated under low CAV penetration rates, where vehicles must wait for green signals. We have formulated this problem as a distributed mixed-integer non-linear program and developed a methodology to form an agreement among all vehicles on their trajectories and signal timing parameters. The agreement on trajectories is reached through an iterative process, where CAVs update their trajectory based on shared trajectory of other vehicles to avoid collisions and share their trajectory with other vehicles. Additionally, the agreement on signal timing parameters is formed through a voting process where the most voted feasible signal timing parameters are selected. The numerical experiments indicate that the proposed methodology can efficiently control vehicle movements at signalized intersections under various CAV market shares. The introduced white phase reduces the total delay by 3.2% to 94.06% compared to cooperative trajectory and signal optimization under different CAV market shares in our tests. In addition, our numerical results show that the proposed technique yields reductions in total delay, ranging from 40.2% – 98.9%, compared to those of a fully-actuated signal control obtained from a state-of-practice traffic signal optimization software.

Using CNN-LSTM to predict signal phasing and timing aided by High-Resolution detector data

This paper proposes a real-time signal timing prediction based on deep learning algorithms that takes various traffic flow parameters as input and predicts signal timing parameters. Signal retiming methods have been traditionally used to improve traffic flow at intersections by reducing delay and improving the level of service at signalized intersections. This can often be a lengthy process that includes several iterations of various optimization methods. In this paper, we have used detector data to calculate the traffic flow metrics at several intersections. This processed data is then used to predict the signal timing and phasing for the next six cycles with reasonable accuracy. Seventeen intersections from two distinct corridors have been used in this study. One of the corridors runs adaptive signal control and the other corridor runs actuated signal control. The proposed CNN-LSTM model shows that cycle length can be accurately predicted with an MAE of 7 to 16 s and phase duration can be predicted with an MAE of 3 to 8 s. The trained model was also successfully validated at five different unknown intersections.

Simulation Modelling of Permitted Left-Turn Saturation Flow Rate Based on Opposing Through-Flow Degree of Saturation

This paper presents the modelling of the saturation flow rate of the permitted left-turn in an exclusive lane. In the proposed model, the total permitted left-turn saturation flow rate is determined as a sum of saturation flow rates during the effective green time and the intergreen period. Primarily, the permitted left-turn saturation flow rate during the effective green time is modelled based on the opposing through-flow degree of saturation and the number of opposing through-flow lanes. The relation between the permitted left-turn saturation flow during the effective green time and these variables was examined using data from the simulation experiments in VISSIM. To our knowledge, this is the first study of the permitted left-turn saturation flow modelling based on the opposing through-flow degree of saturation instead of the opposing through-flow rate and signal-timing parameters. The proposed model was validated based on data collected at seven intersections with a permitted left-turn served in an exclusive lane. The permitted left-turn saturation flow rate could be accurately determined based on the opposing through-flow degree of saturation and the number of opposing lanes according to the RMSE of 58.4 pcu/h.

Signal timing parameters inference method at intersections using license plate recognition data

Signal timing parameters are essential components in traffic signal control (TSC). It affects not only traffic management, but also traffic safety. However, due to the confidential issues of the traffic management department, or lack of data integration from different signal manufacturers, it is intractable to obtain the city-scale signal timing data. In the previous studies, some existing estimation methods focused on a single parameter and fixed-timing scheme. To tackle this issue, this study attempts to develop an integrated parameters inference method based on license plate recognition (LPR) data, considering phase weight, average phase duration information and the overall phases of the intersections. In particular, the proposed method includes phase sequence inference model, cycle length inference model and phase duration inference model. To testify the performance of the proposed method, a real-world LPR dataset from Guangzhou, China, is applied. Numerical results show that the proposed method performs more efficiently on parameter inferences than the state-of-the-art (SOTA) approach. For instance, in the given research time period, the mean absolute error (MAE) of each phase duration is 2 s averagely (6.32 s in the SOTA approach), and the mean relative error (MRE) of cycle length is 0.91% (11.67% in the SOTA approach).

POINT: Partially Observable Imitation Network for Traffic Signal Control

• This is the first study in the area of traffic signal control that combines the optimization-based signal control algorithm with imitation learning network to generate optimal signal plans in real time. • The proposed model does not need 100% penetration rate of CVs or necessity of complete traffic information in online system. Instead, only partial observable traffic information collected in real time are required for the POINT model. • The proposed model accounts for the stochastic features of traffic flows and diversities of driving behaviors in offline expert signal plan collection. • The proposed model has largely reduced the online computational burden and ready to be implemented in real world. Smart traffic signals bring together transportation infrastructure and advance technologies to improve the mobility and efficiency of urban transportation network. Adaptive traffic signal control studies can be categorized into modeling-based approaches and learning-based approaches. In order to take advantages of these two systems, this study developed an offline-online combined Partial Observable Imitation Network for Traffic signal control (POINT). In the offline system, the traffic signal timing optimization problem was formulated as a Mixed Integer Nonlinear Programming (MINLP) given complete traffic information, i.e., second-by-second speeds and locations of all vehicles. The objective of MINLP is to minimize total travel delays considering individual vehicle trajectories under Connected Vehicle (CV) environment. The calculated optimal solutions under various traffic conditions were considered as the ”expert” decisions. In the online system, an imitation neural network model was developed to learn the ”expert” signal plans generated from offline system. Given partial observable traffic conditions in real time, e.g., the aggregate-level of traffic volume, the POINT model can compute the signal timing parameters in the online system. The numerical results demonstrated that the proposed method outperformed other state-of-the-art signal control method under high and unbalanced traffic demand levels in terms of reducing travel delays and queue length.

InterTwin: Deep Learning Approaches for Computing Measures of Effectiveness for Traffic Intersections

Microscopic simulation-based approaches are extensively used for determining good signal timing plans on traffic intersections. Measures of Effectiveness (MOEs) such as wait time, throughput, fuel consumption, emission, and delays can be derived for variable signal timing parameters, traffic flow patterns, etc. However, these techniques are computationally intensive, especially when the number of signal timing scenarios to be simulated are large. In this paper, we propose InterTwin, a Deep Neural Network architecture based on Spatial Graph Convolution and Encoder-Decoder Recurrent networks that can predict the MOEs efficiently and accurately for a wide variety of signal timing and traffic patterns. Our methods can generate probability distributions of MOEs and are not limited to mean and standard deviation. Additionally, GPU implementations using InterTwin can derive MOEs, at least four to five orders of magnitude faster than microscopic simulations on a conventional 32 core CPU machine.

Signal Control Method for Through and Left-Turn Shared Lane by Setting Left-Turn Waiting Area at Signalized Intersections

This paper proposes a signal control method for the through and left-turn shared lanes at signalized intersections to solve traffic conflicts between left-turn vehicles and opposing through vehicles by setting left-turn waiting area (LWA). Delays and stops are weighted to form an integrated performance index (PI) in a vehicle-to-infrastructure cooperation system. The PI models pertaining to all vehicles are established based on the LWA intersection. In addition, an optimized method of signal timing parameters is constructed by minimizing the average PI. VISSIM simulation shows that the average PI decreases by 6.51% compared with the original layout and signal timing plan of the intersection, since the increased delay of the side-road left-turn vehicles is insufficient to offset the reduced delay of the side-road through vehicles after the improvement. The sensitivity analysis shows that the greater the traffic volume of the phase including the through and left-turn shared lanes, the higher the operation efficiency of the LWA intersection compared with the typical permitted phase intersection. When the left-turn vehicles of the shared lanes in each cycle are less than the stop spaces, the LWA intersection can effectively reduce the average PI of the shared lanes. Furthermore, the more the stop spaces in the LWA, the lower the average PI in the same traffic conditions.

Kalman Filtering Method for Real-Time Queue Length Estimation in a Connected Vehicle Environment

Queue length estimation is of great importance for measuring traffic signal performance and optimizing traffic signal timing plans. With the development of connected vehicle (CV) technology, using mobile CV data instead of fixed detector data to estimate queue length has become an important research topic. This study focuses on real-time queue length estimation for an isolated intersection with CV data. A Kalman filtering method is proposed to estimate the queue length in real time using traffic signal timing and real-time traffic flow parameters (i.e., saturated flow rate, traffic volume, and penetration rate), which are estimated using CV trajectories data. A simulation intersection was built and calibrated using field data to evaluate the performance of the proposed method and the benchmark method. Results show that when the CV penetration rate is at 30%, the average values of mean absolute errors, mean absolute percentage errors, and root mean square errors are just 1.6 vehicles, 20.9%, and 2.5 vehicles, respectively. The performance of the proposed model is also better than the benchmark method when the penetration rate of CVs is higher than 20%, which proves the validity of the proposed method. Furthermore, sensitivity analysis indicates that the proposed method requires a high penetration rate of at least 30%.

Predicting Coordinated Actuated Traffic Signal Change Times using Long Short-Term Memory Neural Networks

Vehicle acceleration and deceleration maneuvers at traffic signals result in significant fuel and energy consumption levels. Green light optimal speed advisory systems require reliable estimates of signal switching times to improve vehicle energy/fuel efficiency. Obtaining these estimates is difficult for actuated signals where the length of each green indication changes to accommodate varying traffic conditions and pedestrian requests. This study details a four-step long short-term memory (LSTM) deep learning based methodology that can be used to provide reasonable switching time estimates from green to red and vice versa while being robust to missing data. The four steps are data gathering, data preparation, machine learning model tuning, and model testing and evaluation. The input to the models includes controller logic, signal timing parameters, time of day, traffic state from detectors, vehicle actuation data, and pedestrian actuation data. The methodology is applied and evaluated on data from an intersection in Northern Virginia. A comparative analysis is conducted between different loss functions including the mean squared error, mean absolute error, and mean relative error used in LSTM and a new loss function that is proposed in this paper. The results show that while the proposed loss function outperforms conventional loss functions in overall absolute error values, the choice of the loss function is dependent on the prediction horizon. Specifically, the proposed loss function is slightly outperformed by the mean relative error for very short prediction horizons (less than 20 s) and the mean squared error for very long prediction horizons (greater than 120 s).

Traffic Signal Timing and Trajectory Optimization in a Mixed Autonomy Traffic Stream

This study introduces a methodology for cooperative signal timing and trajectory optimization at intersections with a mix of connected automated vehicles (CAVs) and human-driven vehicles (HVs). We represent joint signal timing and trajectory control as a mixed-integer non-linear program, which is computationally complex. The developed methodology provides a balance between computational efficiency and solution quality by (a) linearizing the nonlinear constraints and reformulating the problem with a tight convex hull of the mixed-integer solutions and (b) decomposing the intersection-level program into several lane-level programs. Hence, a unique controller jointly optimizes the trajectories of CAVs on a lane and the signal timing parameters associated with that lane. This setting will allow finding near-optimal solutions with small duality gaps for complex intersections with different demand levels. Case study results show that the proposed methodology finds solutions efficiently with at most 0.1% duality gap. We compared the developed methodology with an existing signal timing and trajectory control approach and found 13% to 41% reduction in average travel time and 1% to 31% reduction in fuel consumption under different scenarios.

Optimization model for regional green wave coordinated control based on ring-and-barrier structure

It’s often hard for existing mathematical models to unify and optimize all control variables including phase time, signal phase sequence, offset and signal cycle at the same time. An optimization model for regional green wave coordinated control based on Ring-and-Barrier structure under unsaturated signalized network is proposed in this study. The constraints of signal timing parameters, such as signal cycle, signal phase sequence and phase time, are explored for the intersections based on Ring-and-Barrier structure. It is proved that the comprehensive optimization of phase time and signal cycle, signal phase sequence and offset can be achieved. And a three-dimensional time-space diagram is innovatively proposed to display the regional green wave coordinated control effect. The coordination effect of the model proposed in this paper is compared with the coordination effect of Synchro via a case study. It demonstrates that the regional signal timing scheme obtained by this model can lead to a better result in all aspects of each intersection, each artery and the whole control area. In the meanwhile, the proportion of bandwidth at each intersection is wider than 79% while satisfying the minimum demand of traffic flow at each intersection. To sum up, this model can improve the overall optimization effect of regional signal coordination control by optimizing the phase time, and achieve the comprehensive optimization of signal cycle, signal phase sequence, offset and phase time.

Assessing the impact of traffic signal performance on crash frequency for signalized intersections along urban arterials: A random parameter modeling approach

The recent development of Automated Traffic Signal Performance Measures (ATSPMs), has provided new opportunities and insights into traffic signal operations. As agencies begin to make decisions regarding investment in infrastructure and operation systems, it is imperative to understand the impacts these systems may have on safety. Past research has thoroughly investigated the impact of geometry and signal timing parameters on the safety of intersections, but little is understood on the relationship between improved signal performance and safety. This study uses vehicle trajectory data to create performance metrics for 121 signalized intersections on ten corridors near Columbus, Ohio. These metrics are used to understand the relationship between signal performance and safety. Two performance metrics, percent arrivals on green (POG) and level of travel time reliability (LOTTR), were used along with other volume and geometric data to model the total crash frequency on signalized mainline approaches. The crash data were modeled using a random parameters negative binomial approach. In consideration of potential unobserved heterogeneity between intersections, a correlated random parameters specification was tested alongside the traditional uncorrelated random parameters and fixed parameters model. Based on goodness of fit measures, the correlated random parameter model was chosen to interpret results because this model explains the complex cross-correlation among the estimates of random parameters. The elasticity values revealed a one percent increase in percent arrivals on green is associated with a reduction in total crashes by 1.12 %. The results of this study show the investment in signal operations and optimization result in an improvement in safety at signalized intersections. Further research should be explored to expand this study to additional intersections over a larger time period.

Collaborative Control Optimization on Reversible Lanes in Intersection Group under Intelligent Vehicle Infrastructure Cooperative System

Based on the Intelligent Vehicle Infrastructure Cooperative System (IVICS), the paper firstly analyzes the operating characteristics of the intersection group under real-time reversible lanes from the capacity; secondly, the signal phase sequence and signal timing parameters of the intersection are designed, and the green wave control is designed based on the phase difference model. The scheme provides traffic flowing in this section with a continuous green light signal. Finally, taking the intersection of Yongle East Road in Wuxi, Jiangsu Province as an example, it optimizes and controls the green wave of the intersection group and verifies the effectiveness of the phase difference model using the evaluation indicators of the number of stops, green wave speed, traffic capacity, and saturation flow. The results show that the optimization of the number of stops is 9.5% and the vehicle speed is optimized by 5.3%. The intersection capacity and saturation flow are greatly improved.

A real-time network-level traffic signal control methodology with partial connected vehicle information

This paper presents two algorithms to estimate traffic state in urban street networks with a mixed traffic stream of connected and unconnected vehicles and incorporates them in a real-time and distributed traffic signal control methodology. The first algorithm integrates connected vehicles (CV) and loop detector data to estimate the trajectory of unconnected vehicles based on car-following concepts. The second algorithm converts the temporal point vehicle detections to a spatial vehicle distribution on a link. The signal control methodology utilizes either algorithm to estimate traffic state on all network links at a time, optimizes the signal timing parameters over a prediction period constituting several time steps, implements the optimal decisions in the next time step, and continues this process until the end of the study period. We applied the methodology to a real-world case study network simulated in Vissim. The results show that both algorithms are effective under a wide range of CV market penetration rates in all tested demand patterns: at 0% market penetration rate, the proposed methodology reduced travel time by 2% to 10% and average delay by 7% to 20% compared to the existing signal timing parameters and traffic demand. At a 40% penetration rate, the proposed algorithms reduced travel time by 27% to 33%and average delay by 50% to 61% compared to the existing signal and demand pattern in the case study network. Similar trends were found for all other demand patterns tested in this study.

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.