Signal Control Parameters Research Articles

Research on optimization method for traffic signal control at intersections in smart cities based on adaptive artificial fish swarm algorithm

The transportation environment of smart cities is complex and ever-changing, and traffic flow is influenced by various factors. With the increase of traffic flow in smart cities, optimizing traffic intersection signal control has become an important method to improve traffic efficiency and reduce congestion. To this end, a smart city traffic intersection(SCTI) signal control optimization method based on adaptive artificial fish swarm algorithm was studied. Establish the Equation of state of traffic flow at SCTIs to understand the actual traffic flow at SCTIs. On this basis, design SCTI signal control parameters, with the minimum average delay and average number of stops as objective functions, and construct an optimization model for SCTI signal control. By combining chaotic search theory and adaptively improving the artificial fish swarm algorithm, based on the adaptive artificial fish swarm algorithm, the intelligent city traffic intersection signal control optimization model is solved to achieve intelligent city traffic intersection signal control optimization. The experimental results show that the average delay of this method is 7.8 ms, the average number of stops is 2, and the travel time is 68.4 s s. Thus, it is proved that the method in this paper has a good optimization effect of traffic signal control at smart city intersections, which can improve the optimization efficiency of traffic signal control at smart city intersections and reduce traffic congestion at smart city intersections.

The Integrated optimization of intermittent lane intersection design and dynamic signal control: efficiency, safety, and fuel consumption

ABSTRACT Existing unconventional intersection designs can enhance the capacity of intersections to deal with repaid pressure on urban commuting, whereas they force vehicles to multi-stop and reorganize, resulting in safety risks and high fuel consumption. This paper proposes the Intermittent Lane Intersection (ILI) that combines the advantages of variable lane intersection and CTE (Combining the tandem control and exit lanes for left-turn control). An integrated optimization model is established to obtain the optimal geometric and signal control parameters, and a homothermic genetic algorithm is used to solve the model. Then, the application of ILI in different traffic demands of the Tianfu Third Street intersection is analyzed. Compared with the benchmark design (TI, CTE, and CTE with signal coordinated control), ILI can effectively reduce intersection delay, fuel consumption, and parking times. Additionally, ILI has great application superiority when the traffic arrival rate is [1500,1700] and the left-turn ratio is [0.40,0.60].

A Value Proposition of Cooperative Bus-Holding Transit Signal Priority Strategy in Connected and Automated Vehicles Environment

In this study, we assess a cooperative bus-holding transit signal priority strategy (C-BHTSP) in a connected and automated vehicles (CAV) environment. The research objectives of this study are to quantify the benefits of C-BHTSP to transit, its impact on opposing non-transit traffic, and the CAV market penetration rate required to neutralize that impact. A simulation model was developed in VISSIM using detailed, high-quality, up-to-date data for transit, non-transit traffic, and signal control parameters. Our assessment shows that the proposed strategy results in about a 60% reduction in transit delay compared to the base scenario. The results also show that a CAV MPR of 32% was found sufficient to overcome the negative impact of C-BHTSP on the opposing non-transit traffic while maintaining transit benefits. Higher MPRs produce substantial improvements in the overall travel time, delay, fuel consumption, and greenhouse gas emissions. The framework and findings of this study provide a blueprint for the decision-making process of implementing transit priority under varying CAV MPRs.

Traffic Signal Control Parameter Calculation Using Probe Data

In signal control, which is one of the main functions of traffic control systems, appropriate signal control parameters are calculated based on the measurement data from vehicle detectors installed on the road. However, the installation and maintenance of vehicle detectors is costly, so realization of a signal control system that can reduce the number of vehicle detectors used while maintaining control level is required. In this paper, we propose a method to calculate signal control parameters without using measurement data from vehicle detectors using traffic information obtained from probe data.

Dynamic Phase Signal Control Method for Unstable Asymmetric Traffic Flow at Intersections

This paper addresses the limitations that the phases proposed in variable phase sequencing studies for stochastic traffic flow are all predetermined and that the variable phase sequencing is only suitable for low traffic volume environment. It presents a dynamic phase signal control method for unstable asymmetric traffic flow with two primary operational objectives: the realization of a dynamic phase scheme in each cycle and optimization of signal control parameters. First, an asymmetric state of traffic flow at signalized intersections is defined, rules governing the generation of the dynamic phase of each cycle based on asymmetric state are proposed, and the delay variations of intersections adopting dynamic phase schemes are modeled. Next, a signal control parameter adjustment algorithm for the dynamic phase is constructed to maximize the positive benefits of delay variation. Last, the operational performance of the proposed method is validated using data collected from an intersection in Harbin, China, by VISSIM simulation. Furthermore, it is found that a higher asymmetric coefficient leads to lower efficiency of a symmetrical release phase scheme at intersections, and the increase of average delay becomes significant when the asymmetric coefficient threshold is greater than 0.2.

Koopman Operator Applications in Signalized Traffic Systems

This paper proposes Koopman operator theory and the related algorithm dynamical mode decomposition (DMD) for analysis and control of signalized traffic flow networks. DMD provides a model-free approach for representing complex oscillatory dynamics from measured data, and we study its application to several problems in signalized traffic. We first study a single signalized intersection, and we propose applying this method to infer traffic signal control parameters such as phase timing directly from traffic flow data. Next, we propose using the oscillatory modes of the Koopman operator, approximated with DMD, for early identification of unstable queue growth that has the potential to cause cascading congestion. Then we demonstrate how DMD can be coupled with knowledge of the traffic signal control status to determine traffic signal control parameters that are able to reduce queue lengths. Lastly, we demonstrate that DMD allows for determining the structure and the strength of interactions in a network of signalized intersections. All examples are demonstrated using a case study network instrumented with high resolution traffic flow sensors.

Effect of U-Turns and Heavy Vehicles on the Saturation Flow Rates of Left-Turn Lanes at Signalized Intersections

The Saturation Flow Rate (SFR) is a primary measure that can be used when estimating intersection capacity. Further, the efficiency of signal control parameters also depends on the accuracy of assumed SFR values. Driver behavior, type of movement, vehicle type, intersection layout, and other factors may have a significant impact on the saturation flow rate. Thus, it is expected that driving environments that have heterogeneous driver populations with different driving habits and cultures may have different SFRs. In practice, the proposed SFRs based on US standards (Highway Capacity Manual, 2016) have been adopted in the State of Qatar without validation or calibration to consider the local road environment and the characteristics of the driving population. This study aims to empirically analyze the saturation flow rates for exclusive left-turn lanes and shared left- and U-turn lanes at two signalized intersections in Doha city, while considering the effects of heavy vehicles and U-turn maneuvers. Empirical observations revealed that the average base SFR, i.e., when the influences from heavy vehicles and U-turns were excluded, could vary approximately from 1800 vehicles per hour per lane (vphpl) to 2100 vphpl for exclusive left-turning lanes and approximately from 1800 vphpl to 1900 vphpl for shared left- and U-turning lanes. Furthermore, this study proposed different adjustment factors for heavy vehicle and U-turn percentages which can be applied in practice in designing signalized intersections, particularly in the State of Qatar.

Nonlinear Decision Rule Approach for Real-Time Traffic Signal Control for Congestion and Emission Mitigation

We propose a real-time signal control framework based on a nonlinear decision rule (NDR), which defines a nonlinear mapping between network states and signal control parameters to actual signal controls based on prevailing traffic conditions, and such a mapping is optimized via off-line simulation. The NDR is instantiated with two neural networks: feedforward neural network (FFNN) and recurrent neural network (RNN), which have different ways of processing traffic information in the near past, and are compared in terms of their performances. The NDR is implemented within a microscopic traffic simulation (S-Paramics) for a real-world network in West Glasgow, where the off-line training of the NDR amounts to a simulation-based optimization aiming to reduce delay, CO2 and black carbon emissions. The emission calculations are based on the high-fidelity vehicle dynamics generated by the simulation, and the AIRE instantaneous emission model. Extensive tests are performed to assess the NDR framework, not only in terms of its effectiveness in reducing the aforementioned objectives, but also in relation to local vs. global benefits, trade-off between delay and emissions, impact of sensor locations, and different levels of network saturation. The results suggest that the NDR is an effective, flexible and robust way of alleviating congestion and reducing traffic emissions.

A Novel VANET-Assisted Traffic Control for Supporting Vehicular Cloud Computing

Vehicular Ad hoc Networks (VANETs) allow for vehicle-to-vehicle and vehicle-to-infrastructure communications using wireless local area network technologies. The distinctive features of their candidate applications (e.g., collision warning and local traffic information for drivers), resources (e.g., computational sources), and their ability to collect various data from their environment (e.g., vehicular traffic flow patterns) make VANETs a rich resource for information and resources. In this paper, we propose a new methodology to use VANETs to optimize signal control at traffic intersections as well as to create Vehicular Cloud (VC) computing environments. We theoretically analyze the traffic flow patterns in a given road intersection by using the diffusion approximation model. We calculate the probability of clearing the intersection and demonstrate the effect of the traffic patterns on the optimal choice of the traffic signal control parameters. Then, we employ our theoretical analysis to propose a potential solution to construct VANET-assisted VCs. Experimental results verify the correctness of our analysis.

Evaluation of analytical approximation methods for the macroscopic fundamental diagram

The Macroscopic Fundamental Diagram (MFD) describes the relation of average network flow, density and speed in urban networks. It can be estimated based on empirical or simulation data, or approximated analytically. Two main analytical approximation methods to derive the MFD for arterial roads and urban networks exist at the moment. These are the method of cuts (MoC) and related approaches, as well as the stochastic approximation (SA). This paper systematically evaluates these methods including their most recent advancements for the case of an urban arterial MFD. Both approaches are evaluated based on a traffic data set for a segment of an arterial in the city of Munich, Germany. This data set includes loop detector and signal data for a typical working day. It is found that the deterministic MoC finds a more accurate upper bound for the MFD for the studied case. The estimation error of the stochastic method is about three times higher than the one of the deterministic method. However, the SA outperforms the MoC in approximating the free-flow branch of the MFD. The analysis of the discrepancies between the empirical and the analytical MFDs includes an investigation of the measurement bias and an in-depth sensitivity study of signal control and public transport operation related input parameters. This study is conducted as a Monte-Carlo-Simulation based on a Latin Hypercube sampling. Interestingly, it is found that applying the MoC for a high number of feasible green-to-cycle ratios predicts the empirical MFD well. Overall, it is concluded that the availability of signal data can improve the analytical approximation of the MFD even for a highly inhomogeneous arterial.

Intersection signal timing optimization considering the travel safety of the elderly

With the rapid growth of the elderly population in China, the proportion of middle-aged and elderly pedestrians crossing streets at signalized intersections has been increasing gradually, mandating the consideration of the crossing characteristics and travel safety of the elderly in signal matching. This article proposes a new signal control parameter optimization method for intersections based on an improved genetic algorithm. According to the crossing characteristics and travel safety of the elderly, the average vehicle delay is used as the control objective, and the green signal ratio and cycle time are used as control variables. The improved genetic algorithm with an improved fitness calibration method and an adaptive cross-mutation function is used to solve the signal control model. Based on the optimization analysis of traffic signal control parameters at a traffic intersection, the study shows that the improved signal control method can effectively reduce the average vehicle delay compared to the Webster algorithm and the traditional genetic algorithm.

A neural network algorithm for queue length estimation based on the concept of k-leader connected vehicles

This paper presents a novel method to estimate queue length at signalised intersections using connected vehicle (CV) data. The proposed queue length estimation method does not depend on any conventional information such as arrival flow rate and parameters pertaining to traffic signal controllers. The model is applicable for real-time applications when there are sufficient training data available to train the estimation model. To this end, we propose the idea of “k-leader CVs” to be able to predict the queue which is propagated after the communication range of dedicated short-range communication (the communication platform used in CV system). The idea of k-leader CVs could reduce the risk of communication failure which is a serious concern in CV ecosystems. Furthermore, a linear regression model is applied to weigh the importance of input variables to be used in a neural network model. Vissim traffic simulator is employed to train and evaluate the effectiveness and robustness of the model under different travel demand conditions, a varying number of CVs (i.e. CVs’ market penetration rate) as well as various traffic signal control scenarios. As it is expected, when the market penetration rate increases, the accuracy of the model enhances consequently. In a congested traffic condition (saturated flow), the proposed model is more accurate compared to the undersaturated condition with the same market penetration rates. Although the proposed method does not depend on information of the arrival pattern and traffic signal control parameters, the results of the queue length estimation are still comparable with the results of the methods that highly depend on such information. The proposed algorithm is also tested using large size data from a CV test bed (i.e. Australian Integrated Multimodal Ecosystem) currently underway in Melbourne, Australia. The simulation results show that the model can perform well irrespective of the intersection layouts, traffic signal plans and arrival patterns of vehicles. Based on the numerical results, 20% penetration rate of CVs is a critical threshold. For penetration rates below 20%, prediction algorithms fail to produce reliable outcomes.

Effects of winter weather on traffic operations and optimization of signalized intersections

Abstract Adverse winter weather has always been a cause of traffic congestion and road collisions. To mitigate the negative impacts of winter weather, transportation agencies are under increasing pressure to introduce weather responsive traffic management strategies. Currently, most traffic signal control systems are designed for normal weather conditions and are therefore suboptimal regarding efficiency and safety for controlling traffic during winter snow events due to changes in traffic patterns and driver behaviors. The main objective of this research is to explore how to modify pre-timed traffic signal control parameters under adverse weather conditions to increase traffic efficiency and road safety. This research consists of two main components. First, we examine the impacts of winter weather on three key traffic parameters, i.e., saturation flow rate, start-up lost time, and free flow speed. Secondly, we investigate the potential benefits of implementing weather-specific signal control plans for uncoordinated intersections as well as coordinated corridors. Two case studies are conducted, each with varying levels of traffic demand and winter event severity, to compare the performance of different signal plans. Evaluation results from both Synchro and VISSIM show that implementing such signal plans is most beneficial for intersection with a medium level of traffic demand. It is also found that the benefit of implementing weather-responsive plans was more compelling at a coordinated-corridor level than at an uncoordinated-intersection level.

A Management Support System which Reviews the Pattern Table of Traffic Signal Control Parameters using Probe Information

A Management Support System which Reviews the Pattern Table of Traffic Signal Control Parameters using Probe Information

Multi-objective optimization of traffic signal timing using non-dominated sorting artificial bee colony algorithm for unsaturated intersections

Vehicle delay and stops at intersections are considered targets for optimizing signal timing for an isolated intersection to overcome the limitations of the linear combination and single objective optimization method. A multi-objective optimization model of a fixed-time signal control parameter of unsaturated intersections is proposed under the constraint of the saturation level of approach and signal time range. The signal cycle and green time length of each phase were considered decision variables, and a non-dominated sorting artificial bee colony (ABC) algorithm was used to solve the multi-objective optimization model. A typical intersection in Lanzhou City was used for the case study. Experimental results showed that a single-objective optimization method degrades other objectives when the optimized objective reaches an optimal value. Moreover, a reasonable balance of vehicle delay and stops must be achieved to flexibly adjust the signal cycle in a reasonable range. The convergence is better in the non-dominated sorting ABC algorithm than in non-dominated sorting genetic algorithm II, Webster timing, and weighted combination methods. The proposed algorithm can solve the Pareto front of a multi-objective problem, thereby improving the vehicle delay and stops simultaneously.

Robust Optimization of Signal Control Parameters for Unsaturated Intersection Based on Tabu Search-Artificial Bee Colony Algorithm

In order to overcome the drawback of the conventional signal timing optimization, a robust optimization algorithm for signal control parameters based on Tabu search-artificial bee colony algorithm is proposed under unsaturated flow condition. Based on the analysis of the characteristics of traffic signal control, a robust optimization model of signal control parameters is constructed by considering the minimum the average delay and the mean square error of average delay. As a consequence, the formation process of the initial solution to the bee colony is improved and the robust optimization model is solved by using the Tabu search-artificial bee colony algorithm. The proposed robust optimization model is validated by using an intersection in Zhangye City of China. The simulation results have shown that the robust optimization model and the algorithm are feasible and practicable. This robust model and algorithm can effectively deal with the volatility of traffic flow and reduce traffic delays.

A real-time traffic control method for the intersection with pre-signals under the phase swap sorting strategy.

To deal with the conflicts between left-turn and through traffic streams and increase the discharge capacity, this paper addresses the pre-signal which is implemented at a signalized intersection. Such an intersection with pre-signal is termed as a tandem intersection. For the tandem intersection, phase swap sorting strategy is deemed as the most effective phasing scheme in view of some exclusive merits, such as easier compliance of drivers, and shorter sorting area. However, a major limitation of the phase swap sorting strategy is not considered in previous studies: if one or more vehicle is left at the sorting area after the signal light turns to red, the capacity of the approach would be dramatically dropped. Besides, previous signal control studies deal with a fixed timing plan that is not adaptive with the fluctuation of traffic flows. Therefore, to cope with these two gaps, this paper firstly takes an in-depth analysis of the traffic flow operations at the tandem intersection. Secondly, three groups of loop detectors are placed to obtain the real-time vehicle information for adaptive signalization. The lane selection behavior in the sorting area is considered to set the green time for intersection signals. With the objective of minimizing the vehicle delay, the signal control parameters are then optimized based on a dynamic programming method. Finally, numerical experiments show that average vehicle delay and maximum queue length can be reduced under all scenarios.

Research on Straight Waiting Area Layout of an Intersection

The establishment of a waiting area in an intersection with a complex traffic flow is investigated. With the adoption of the basic conditions of a waiting area, capacity and delay models are established to examine the differences in the capacities of and delays in an intersection with and without a waiting area. Signal control parameters, such as green-light interval time and effective green-light time, are also studied. The increase in capacity and reduction in delay from the setting of a waiting area are deduced by using accurate mathematical equations. With the use of actual intersections as examples, traffic operation efficiency was analyzed through simulation. Simulation results can be summarized as follows: generally, under high flow rate and saturation conditions, setting a straight waiting area can adequately utilize the intersection spare space, improve usage efficiency of straight lanes, and maximally realize reciprocal transformation between time and space. Consequently, the capacity of straight lanes is improved, the delay per vehicle is reduced, the capacity of the intersection is enhanced to some extent, and the delay in the entire intersection is reduced, thereby verifying the validity and applicability of the model.

Study on Vehicle Delay Based on the Vehicle Arriving Distribution at Entrance Lanes of Intersection

Through analyzing vehicle arrival distribution at entrance lanes of signalized intersection, established the vehicle arrival distribution model, and then combined with the arrival distribution model, presented delay model of signalized intersection entrance lanes. In order to ensure the accuracy of the delay model, the arrival time of the vehicle was divided into different periods, and then we can get more accurate vehicle delay of different arriving periods. Finally, using partial data from urban signalized intersection, made a simulation for validating the validity of model. The result shows that there are small deviation between simulation and model calculation. Therefore, the Research results can provide the reference for coordinate control of adjacent intersections and signal control parameters optimization of target intersection.

A108 Transport Systems and Public Health: the Case of Traffic Congestion and Signal Control

Abstract Background In the context of transport systems, traffic congestion is always presented as an operational issue with negative socio-economic impacts. Rarely in engineering research and curricula is congestion portrayed as a public health issue-- a stress causing factor-- as in fact it is. It is accepted in the public health domain that repeated and prolonged driving in congested traffic conditions is a form of chronic stress and a serious health risk. Managing stress in difficult traffic conditions is a multifaceted activity in which proper traffic control can play a significant part. Methods This papers presents the human health side of traffic congestion and connect that to known and quantifiable traffic phenomena that, for modeling purposes, may be used as stress markers. Traffic control can thus be optimized with explicit consideration of stress. A case of a health-sensitive control algorithm for congested signalized systems is presented where control parameters are optimized to explicitly reduce the occurrence and intensity of unhealthy stress-inducing traffic conditions. The traffic signal control algorithm is formulated as a dynamic signal dynamic speed controller that directly and explicitly reduces the known stress-inducing operational traffic conditions. Results The resulting controller is combinatorial optimization problem. The control decision variables are traffic signal control parameters and network link speeds. Genetic Algorithms were used to solve the optimization. The results show that the signal and speed control generated markedly improved traffic flow where stress-inducing conditions were either eliminated or significantly reduced. Conclusions As extended and long-term exposure to congestion and difficult traffic conditions is a documented stressor with negative long term health impacts, current methods of signal control need to be modified to explicitly consider and minimize the impact of known stress-inducing traffic conditions.