Adaptive Traffic Control Research Articles

Operational performance evaluation of adaptive traffic control systems: A Bayesian modeling approach using real-world GPS and private sector PROBE data

This evaluation ascertained the operational impacts of the SUTRAC (Scalable Urban Traffic Control) Adaptive Signal Control Technology (ASCT) in an urban corridor consisting of 23 intersections in Pittsburgh, Pennsylvania. A combination of real-world GPS floating car runs and private sector probe data from INRIX was used to assess the impact of the ASCT. Data were collected with the ASCT active and inactive to determine the operational impacts on the mainline and cross streets. The ASCT was found to produce significant improvements in the number of stops made along the corridor. On Baum and Center, travel times improved during the AM and PM peak in the WB direction. Speeds were also observed to improve significantly during the Midday period on Baum EB and during the AM and PM peak periods on Baum WB. Similarly, statistically significant improvements in speed were observed on Center WB during the AM and PM periods, while a statistically significant decrease in speed was observed during the Midday period. Six months of private sector probe data was used to examine travel time reliability along the corridor, and reliability was also found to have improved. Further, Bayesian models were calibrated to account for variations in speeds and acceleration/deceleration. The Bayesian models revealed that driving was less volatile with the ASCT system in operation over instantaneous periods, which also points towards improved operations. The findings of this study are generally consistent with past evaluations of other ASCTs, indicating that the SURTRAC system is another potential tool for managing congestion on signalized urban arterial networks.

An Algorithm based on VANET Technology to Count Vehicles Stopped at a Traffic Light

Vehicular Ad hoc Networks (VANETs) have gained considerable attention in the past few years due to their promising applicability in relation to the Intelligent Transportation Systems (ITSs). This emerging new technology will provide timely information to develop adaptive traffic light control systems that will allow a significant optimization of the vehicular traffic flow. In this paper, we introduce a novel algorithm for counting vehicles stopped at a traffic light using VANET technology. The algorithm is based on the idea of the propagation of a count request message from the RSU (originating unit) toward the vehicles that are at the end of the waiting line, and the propagation of a response message (with the number of vehicles counted) in the opposite direction, that is, from the vehicles at the end of the line toward the RSU. For this, our algorithm uses BEACON messages periodically to exchange the necessary information between any two 1-hop neighbors. Using the data received from BEACON messages, each vehicle can maintain its own neighbors list. To validate and evaluate the performance of our proposal, we use Veins (Vehicle in Network Simulation) and TraCI (Traffic Control Interface). The former is a framework that ties together a network simulator (OMNeT++) with a road traffic simulator (SUMO), and the latter is an API for the communications between both simulators by providing TCP connections between each other. The results of the simulations performed in different scenarios are encouraging since they indicate that the proposed algorithm efficiently computes a number of vehicles very close to the real one, using a few control messages.

Time‐of‐day breakpoints optimisation through recursive time series partitioning

Traffic signal control systems often operate with a fixed time strategy when practical conditions prohibit adaptive traffic control built upon real-time traffic data. One of the most important challenges to have good performance for a fixed time strategy is to optimally identify the breakpoints that divide one day into different partitions, which is a time-of-day (TOD) breakpoints optimisation problem. Various solutions to this problem have been proposed based on classic clustering methods. However, these methods require empirical adjustment since they are not capable of incorporating the temporal information among traffic data. In this study, the TOD breakpoints optimisation problem is formulated as a time series data partitioning problem. A recursive algorithm is proposed to partition one day into several time periods based on the dynamic programming reformulation of the original problem. The appropriate number of partitions is determined through the elbow method. Then the authors present a case study based on the real data from Qingdao City in China that evaluates the proposed method against the existing ones. From simulation experiments, they illustrate that the proposed method is more effective in terms of operational performance measures such as maximum queue length and delay time than the existing ones.

Effectiveness of adaptive control of traffic light intersection on isolated multi-lane intersections

The use of information technology in traffic management can increase the capacity of traffic intersections and reduce vehicle delays. In cities, adaptive traffic control at traffic lights intersections has been well established. Adaptive regulation is applied mainly on isolated traffic lights, where the traffic flow has a predominantly exponential distribution of intervals. It was established experimentally that the effectiveness of the method decreases with a supersaturated traffic flow, which makes it difficult to find a “cutoff point”, a convenient moment for switching the traffic light signal. The article shows that at isolated multi-band intersections, the search for a convenient signal switching time is difficult at low loading levels. At uninsulated intersections, the incoming flow will have clear periods of time without vehicles, which makes the operation of adaptive traffic control in these conditions at a multi-lane crossing more efficient. This suggests the need for further study of the patterns of distribution of intervals between cars in urban environments at the approaches to non-isolated intersections and at the approach to intersections operating without coordination with other traffic lights.

Formal modeling of a complex adaptive air traffic control system

Air traffic control system in airports is one of the most complex systems in the context of air traffic management due to the huge number of requirements. In order to help engineers to develop such complex system we propose a predefined model that includes the essence of air traffic control and the standard requirements. We develop this model using the Event-B formal method which is based on set theory and allows theorems proving. Event-B is also hinged on refinement which means starting with an abstract model and then enriching it in successive steps. Event-B has been successfully applied in several transportation systems and shows no bugs. This encourages us to use it in this critical system to guarantee a strong assurance of bugs’ absence and to ensure model correctness. Our approach provides a standard model to start with in order to model any airport control system, which allows engineers to focus on more typical requirements that are not developed here.

Energy‐efficient and traffic‐adaptive Z‐medium access control protocol in wireless sensor networks

Both the backoff time and idle listening are the key issues affecting the performance of wireless sensor networks. Therefore, the energy-efficient and traffic-adaptive Z-medium access control protocol is proposed in this study. Firstly, it avoids idle listening by setting a fixed contention window in single round to effectively improve the fairness in the backoff strategy. At the same time, it ensures the nodes with less residual energy have priority to access channel and realise the load balance. Secondly, the protocol uses the improved explicit contention notification (ECN) mechanism to reduce the energy consumption caused by sending the ECN messages repeatedly in a period of time and prolong the network lifetime. Lastly, the channel flags are introduced at the top of data frames to make an appointment on the channel so as to avoid the idle listening. Simulation results show that, when, compared with other similar algorithms, the collision rate is reduced by at least 14.2%, the time when the first node dies is prolonged by at least 135 rounds, and the average network energy consumption is reduced by at least 23%.

Development and Simulation of Adaptive Traffic Light Controller Using Artificial Bee Colony Algorithm

Development and Simulation of Adaptive Traffic Light Controller Using Artificial Bee Colony Algorithm

Towards computer vision-based approach for an adaptive traffic control system

ABSTRACTIn this paper, an adaptive traffic control system (ATCS) is proposed using the state of the art of video processing techniques. We illustrate how the system controls standard four-way intersections using three parameters; namely, average vehicles flow speed, level of crowdedness of vehicles, and a critical state timer. These parameters are detected from traffic videos using our computer vision algorithm. The ATCS decision-making process has been designed to adapt to predefined priorities over the traffic parameters. The validation of the proposed ATCS has been tested using four synchronized test videos in order to feed the proposed ATCS with different traffic information. Experimental results show a complete adaptation for the traffic flow.

Upgrade Evaluation of Traffic Signal Assets: High-resolution Performance Measurement Framework

Agencies that have large-scale traffic signal systems under their purview often have to face asset upgrade decisions. As one of the most advanced traffic control technologies, Adaptive Traffic Control Systems (ATCS) are among the options that must be taken into consideration. Having in mind the complexity of benefits and costs stemming from ATCS investments, there is a need for information-rich performance measures (PM) used in the evaluation and decision-making. However, individual PMs are often not suitable for evaluating the multidimensionality of ATCS operations, due the inherent variability of ATCS control parameters. To expand the range of PMs used in ATCS evaluation, this research develops a new PM, i.e., average arrivals on green ratio, and proposes a refinement of average delay PM to account for queue formation. The paper also presents an application framework for a multi-criteria analysis, assuming a combination of the proposed and existing PMs. In addition to presenting the analytical PM formulation, the evaluation methodology uses microsimulation for a case study comparison between actuated-coordinated and ATCS operations. The results include a comparison between previous and proposed PMs, based on the processed simulation data as well as field data. In conclusion, the proposed PMs have a high transferability potential, low data collection cost, and high data quality, thus being suitable for use in decision processes for signal asset investment. Finally, this research opens up further opportunities for advancing decision-support methods for traffic operations asset management.

3-D Data Processing to Extract Vehicle Trajectories from Roadside LiDAR Data

High-resolution vehicle data including location, speed, and direction is significant for new transportation systems, such as connected-vehicle applications, micro-level traffic performance evaluation, and adaptive traffic control. This research developed a data processing procedure for detection and tracking of multi-lane multi-vehicle trajectories with a roadside light detection and ranging (LiDAR) sensor. Different from existing methods for vehicle onboard sensing systems, this procedure was developed specifically to extract high-resolution vehicle trajectories from roadside LiDAR sensors. This procedure includes preprocessing of the raw data, statistical outlier removal, a Least Median of Squares based ground estimation method to accurately remove the ground points, vehicle data clouds clustering, a principle component-based oriented bounding box method to estimate the location of the vehicle, and a geometrically-based tracking algorithm. The developed procedure has been applied to a two-way-stop-sign intersection and an arterial road in Reno, Nevada. The data extraction procedure has been validated by comparing tracking results and speeds logged from a testing vehicle through the on-board diagnostics interface. This data processing procedure could be applied to extract high-resolution trajectories of connected and unconnected vehicles for connected-vehicle applications, and the data will be valuable to practices in traffic safety, traffic mobility, and fuel efficiency estimation.

Intelligent Software-Defined Mesh Networks With Link-Failure Adaptive Traffic Balancing

In the software defined networking (SDN)-based wireless mesh network (WMN) architecture (SD-WMN), the network is monitored and managed in a centralized manner. Although the use of SDN simplifies the WMN management, it is still not clear whether the SDN architecture, which was originally designed for wired links, can effectively handle the dynamic topology of WMNs. This paper addresses the traffic balancing issue introduced by node mobility. To reduce the response time of SDN controller for dynamic network topology, a two-layer supervised learning model is proposed that helps the controller to predict the node mobility and link failure probability. Then, an alternative route selection scheme is proposed, which achieves optimal traffic balancing while minimizing the control plane overhead. Efficiency of the proposed SD-WMN architecture is validated in the widely used network simulator-ns-3. Our results demonstrate the network throughput enhancement achieved by the proposed SD-WMN architecture with link-failure adaptive traffic control.

High performance adaptive traffic control for efficient response in vehicular ad hoc networks

Nowadays, with the invention of CUDA, a parallel computing platform and programming model, there is a dramatic increase in computing performance by harnessing the power of the GPU. GPU computing with CUDA can be utilised to find efficient solutions for many real world complex problems. One such problem is traffic signal control problem, which takes care of conflicting movements at the intersections to avoid accidents and ensure smooth flow of traffic for the commuters in safe and efficient manner. Adaptive traffic control (ATC) algorithm is used in literature to reduce the average queue length at the intersections. This algorithm has serial implementation on a single CPU and hence takes large computation time. In this paper, we propose a high performance adaptive traffic control for proving efficient responses and hence reducing average queue length that results in decrease in the overall waiting time at the intersections. We tested our proposed approach with varying number of vehicles for two real world networks. The performance of the proposed algorithm is compared with its serial counterpart.

Calculation of vehicle delay at signal-controlled intersections with adaptive traffic control algorithm

By widely introducing information technology tools in the field of traffic control, it is possible to increase the capacity of hubs and reduce vehicle delays. Adaptive traffic light control is one of such tools. Its effectiveness can be assessed through traffic flow simulation. The aim of this study is to create a simulation model of a signal-controlled intersection that can be used to assess the effectiveness of adaptive control in various traffic situations, including the presence or absence of pedestrian traffic through an intersection. The model is based on a numerical experiment conducted using the Monte Carlo method. As a result of the study, vehicle delays, queue length and duration of traffic light cycles are calculated subject to different intensities of incoming traffic flows, and the presence or absence of pedestrian traffic.

Simulation of queue length and vehicle delays on signal-controlled intersection

The extensive use of information technology in the field of traffic control will increase the traffic capacity of intersection points and transports. To assess the efficacy of changing options for the road traffic organization or the reorganization of intersection points, you need to know the average delay in vehicles and the length of the queue. The adaptive traffic light control is one of such tools. Simulation modeling of traffic flows use for the definition of its work. The aim of this work is to create a simulation model of controlled intersection, which can evaluate the efficiency of the application the adaptive regulation in various traffic situations, including the availability or deficiency of pedestrian traffic through the intersection. The numerical experiment in in the model pass with using of the Monte Carlo method, which can to draw a conclusion about the calculated parameter on the basis of the result of the reproduction of the calculation model.

High performance adaptive traffic control for efficient response in vehicular ad hoc networks

High performance adaptive traffic control for efficient response in vehicular ad hoc networks

Calculation of vehicle delay at signal-controlled intersections with adaptive traffic control algorithm

Calculation of vehicle delay at signal-controlled intersections with adaptive traffic control algorithm

SYSTEM ANALYSIS OF MAJOR TRENDS IN DEVELOPMENT OF ADAPTIVE TRAFFIC FLOW MANAGEMENT METHODS

Adaptive algorithms, which current traffic systems are based on, exist for many decades. Information technologies have developed significantly over this period and it makes more relevant their application in the field of transport. This paper analyses modern trends in the development of adaptive traffic flow control methods. Reviewed the most perspective directions in the field of intelligent transport systems, such as high-speed wireless communication between vehicles and road infrastructure based on such technologies as DSRC and WAVE, traffic jams prediction having such features as traffic flow information, congestion, velocity of vehicles using machine learning, fuzzy logic rules and genetic algorithms, application of driver assistance systems to increase vehicle’s autonomy. Advantages of such technologies in safety, efficiency and usability of transport are shown. Described multi-agent approach, which uses V2I-communication between vehicles and intersection controller to improve efficiency of control due to more complete traffic flow information and possibility to give orders to separate vehicles. Presented number of algorithms which use such approach to create new generation of adaptive transport systems.

Traffic light control using deep policy‐gradient and value‐function‐based reinforcement learning

Recent advances in combining deep neural network architectures with reinforcement learning (RL) techniques have shown promising potential results in solving complex control problems with high-dimensional state and action spaces. Inspired by these successes, in this study, the authors built two kinds of RL algorithms: deep policy-gradient (PG) and value-function-based agents which can predict the best possible traffic signal for a traffic intersection. At each time step, these adaptive traffic light control agents receive a snapshot of the current state of a graphical traffic simulator and produce control signals. The PG-based agent maps its observation directly to the control signal; however, the value-function-based agent first estimates values for all legal control signals. The agent then selects the optimal control action with the highest value. Their methods show promising results in a traffic network simulated in the simulation of urban mobility traffic simulator, without suffering from instability issues during the training process.

Group-based hierarchical adaptive traffic-signal control part I: Formulation

A group-based adaptive traffic-control method for isolated signalized junctions is developed that includes a hierarchical structure comprising tactical and local levels of signal timing optimization. The control method optimizes the signal timings in adaptive traffic-control systems, and takes full advantage of flexible new technologies to incorporate the most up-to-date traffic information, as collected in real time. The definitions, combinations, and sequencing of the cycle structure stages are generated automatically using a procedure for optimizing the signal-timing plans in response to online data from traffic detectors. This new method provides a wider search space and improves the efficiency of the signal-control systems, thus improving the junction performance, minimizing delays, and maximizing capacity in real time. A multi-resolution strategy is proposed for updating the elements of the signal plans cycle-by-cycle and adjusting the current green signal timing second-by-second. The group-based variables and parameters for the proactive global-optimization method utilize lane-based predictive traffic-flow information, such as arrival and discharge rates, expressed as the slopes of polygonal delay formulas. Therefore, there is a high degree of flexibility in the tactical identification of the optimal signal plan in response to the real-time predicted traffic information, the objective function of the polygonal delay formula, and the direct differential equations for the adaptive group-based variables. The reactive local signal-control policy, which is formed based on the max-pressure strategy, is developed to locally adjust the current green signal time and to accommodate delicate demand fluctuations second-by-second at the fine-resolution level. The most appropriate cycle-structure for the tactical level of control is identified using a group-based global-optimization procedure that takes advantage of the latest available information. In part II of this study (Lee et al. 2017), the effectiveness of the proposed methods is validated based on the actualized mathematical frameworks, computer simulations, and a case study, using the appropriate computer programs.

Group-based hierarchical adaptive traffic-signal control Part II: Implementation

In part I of this study (Lee et al., 2017), the formulation of a theoretical framework for a group-based adaptive traffic-control method for isolated signalized junctions is presented, which includes tactical and local levels of signal timing optimization. The global level control aims to determine the time-varying cycle structure, with a resolution of cycles, and the real-time adjustment of the green phase, with a resolution of seconds, based on longer-term traffic information observed by traffic detectors. Overall, the purpose of the study is to actualize a multi-resolution strategy for a group-based adaptive signal-control method and establish a microscopic simulation platform to implement the proposed methodology and test its effectiveness. To actualize the global proactive-optimization scheme, in this paper, a rolling-horizon approach to the temporal and spatial variables, signal structures for four-arm intersections, and discrete directional search methods is applied using the developed mathematical framework. The formulation of the group-based max-pressure policy is realized using the logical form of the local reactive-control policy at a typical directional three-lane, four-arm approach to an isolated intersection. The integrated group-based adaptive traffic-signal control is actualized using VISSIM, Fortran, and VBA based on the developed tactical and local levels of signal timing optimization. The results of the computer simulations and the case study presented in this paper show that the integrated group-based adaptive traffic-signal-control logic outperforms the other methods over a wide range of traffic conditions, from free-flowing traffic to extreme congestion. Moreover, the proposed models perform much better than the existing fixed-signal plan and the actuated signal-control in asymmetric traffic conditions.