Adaptive Traffic Signal Control Research Articles

Genetic fuzzy logic traffic signal control with cell transmission modeling

This study develops an adaptive traffic signal control model based on an iterative genetic fuzzy logic controller (GFLC). The proposed model considers traffic flow and queue length as state variables and extension of green time as control variable, toward the minimization of total vehicle delays. For the learning efficiency of GFLC and the capability in capturing traffic behaviors, cell transmission model is used to replicate the traffic condition. To investigate, the performance of the proposed model in the case of an isolated intersection, comparisons to pretimed signal timing plans determining by Webster and total enumeration methods, and two queue-length based adaptive models are conducted. Results show that our proposed GFLC model performs best. As traffic flows vary more noticeably, the GFLC traffic signal control model performs even better than any timing plans. In the case of sequential intersections with four coordinated signal systems: simultaneous, progressive, alternate, and independent, the experimental example study also show that the proposed GFLC model can also perform better than current and pretimed timing plans, suggesting that the proposed GFLC signal control model is effective, robust, and adaptable.

Smart Urban Signal Networks: Initial Application of the SURTRAC Adaptive Traffic Signal Control System

In this paper, we describe a pilot implementation and field test of a recently developed approach to real-time adaptive traffic signal control. The pilot system, called SURTRAC (Scalable Urban Traffic Control), follows the perspective of recent work in multi-agent planning and implements a decentralized, schedule-driven approach to traffic signal control. Under this approach, each intersection independently (and asynchronously) computes a schedule that optimizes the flow of currently approaching traffic through that intersection, and uses this schedule to decide when to switch green phases. The traffic outflows projected by this schedule are then communicated to the intersection's downstream neighbors, to increase visibility of vehicles entering their respective planning horizons. This process is repeated as frequently as once per second in rolling horizon fashion, to provide real-time responsiveness to changing traffic conditions and coordinated signal network behavior. After summarizing this basic approach to adaptive traffic signal control and the domain challenges it is intended to address, we describe the pilot implementation of SURTRAC and its application to a nine-intersection road network in Pittsburgh, Pennsylvania. Both the SURTRAC architecture for interfacing with the detection equipment, hardware controller and communication network at a given intersection and the extensions required to account for unreliable sensor data are discussed. Finally, we present the results of a pilot test of the system, where SURTRAC is seen to achieve major reductions in travel times and vehicle emissions over pre-existing signal timings.

Adaptive Traffic Signal Control With Vehicular Ad hoc Networks

In this paper, we propose to use vehicular ad hoc networks (VANETs) to collect and aggregate real-time speed and position information on individual vehicles to optimize signal control at traffic intersections. We first formulate the vehicular traffic signal control problem as a job scheduling problem on processors, with jobs corresponding to platoons of vehicles. Under the assumption that all jobs are of equal size, we give an online algorithm, referred to as the oldest job first (OJF) algorithm, to minimize the delay across the intersection. We prove that the OJF algorithm is 2-competitive, implying that the delay is less than or equal to twice the delay of an optimal offline schedule with perfect knowledge of the arrivals. We then show how a VANET can be used to group vehicles into approximately equal-sized platoons, which can then be scheduled using OJF. We call this the two-phase approach, where we first group the vehicular traffic into platoons and then apply the OJF algorithm, i.e., the oldest arrival first (OAF) algorithm. Our simulation results show that, under light and medium traffic loads, the OAF algorithm reduces the delays experienced by vehicles as they pass through the intersection, as compared with vehicle-actuated methods, Webster's method, and pretimed signal control methods. Under heavy vehicular traffic load, the OAF algorithm performs the same as the vehicle-actuated traffic method but still produces lower delays, as when compared with Webster's method and the pretimed signal control method.

Urban Traffic Adaptive Signal Control through Intelligent Agent

As the population of the world grows rapidly, the problem of traffic has been increased in many parts of the world. Many governments of the world are devising ways to cater this with new and improved methods. Advances in computer technologies have changed transportation field. Based on those, Intelligent Transportation System has been proposed for the next generation transportation system. This system requires more utilisation of Artificial Intelligence. Intelligent agent may use Knowledge Based Expert System. Intelligent agent is suggested to be implemented in Traffic Adaptive Signal Control System as a part of Intelligent Transportation System. Previous applications of Knowledge Based Expert System in transportation field are briefly reviewed, and potential implementations are discussed by focusing on Traffic Adaptive Signal Control System. The project aim to simplify the problem at level of single traffic control due to limited capability of resources at a traffic signal in open space. So, an artificial agent at each signal must be rugged one system to face the extreme weather conditions and dusty and noisy area along-with shock prove because at road side it can be hit by some vehicle if situated in pole. So it should work in all extreme conditions.

SURTRAC: Scalable Urban Traffic Control

This paper defines and evaluates a pilot implementation of a recently developed approach to realtime, adaptive traffic signal control. The pilot system, which is called SURTRAC (Scalable Urban Traffic Control), integrates concepts from traffic control theory with recent work in the field of multi-agent planning and has several important distinguishing characteristics. First, to promote scalability and reliability, SURTRAC operates in a totally decentralized manner; each intersection independently and asynchronously allocates its time, based on current incoming vehicle flows. Second, SURTRAC aims at managing urban (grid-like) road networks with multiple (competing) traffic flows; network-level coordination is accomplished by communicating projected outflows to downstream neighbors, which gives these intersections a more informed basis for locally balancing competing inflows while simultaneously promoting establishment of larger green corridors. Third, SURTRAC truly operates in real-time; each intersection recomputes its allocation plan and re-communicates projected outflows as frequently as once per second in rolling horizon fashion, enabling both effective operation in tightly spaced signal networks and responsiveness to sudden changes in traffic conditions. After describing our basic approach to adaptive traffic signal control and the pilot implementation of SURTRAC, we present the results of a field test conducted on a nine-intersection road network in the East Liberty section of Pittsburgh, Pennsylvania. In this pilot test, SURTRAC is seen to achieve major reductions in travel times and vehicle emissions over pre-existing signal control.

Statistical Study of the Impact of Adaptive Traffic Signal Control on Traffic and Transit Performance

The Sydney Coordinated Adaptive Traffic System (SCATS) is designed to improve mobility on congested corridors with variable demand; SCATS is expected to improve transit performance. Because many urban corridors are heavily used by transit routes, it is important to determine how SCATS affects transit performance on congested corridors. However, there is limited research on evaluating the impact of SCATS on transit performance. To the best of the authors’ knowledge, this research presents the first field evaluation of SCATS with transit signal priority in the United States. The case study is based on before-and-after traffic and transit data along Powell Boulevard, one of the most congested urban arterials in the Portland, Oregon, metropolitan region. Using permanent traffic data collection stations, bus automated vehicle location and automatic passenger count data, and transit signal priority request data, this work presents the results of statistical tests and regressions to determine the impacts of SCATS. Statistically significant differences were observed in travel times and SCATS-related regression parameters. Overall, travel time changes or improvements related to SCATS seemed to depend greatly on the direction of travel and time of day.

Statistical Study of the Impact of Adaptive Traffic Signal Control on Traffic and Transit Performance

The Sydney Coordinated Adaptive Traffic System (SCATS) is designed to improve mobility on congested corridors with variable demand; SCATS is expected to improve transit performance. Because many urban corridors are heavily used by transit routes, it is important to determine how SCATS affects transit performance on congested corridors. However, there is limited research on evaluating the impact of SCATS on transit performance. To the best of the authors’ knowledge, this research presents the first field evaluation of SCATS with transit signal priority in the United States. The case study is based on before-and-after traffic and transit data along Powell Boulevard, one of the most congested urban arterials in the Portland, Oregon, metropolitan region. Using permanent traffic data collection stations, bus automated vehicle location and automatic passenger count data, and transit signal priority request data, this work presents the results of statistical tests and regressions to determine the impacts of SCATS. Statistically significant differences were observed in travel times and SCATS-related regression parameters. Overall, travel time changes or improvements related to SCATS seemed to depend greatly on the direction of travel and time of day.

CATS: An adaptive traffic signal system based on car-to-car communication

Traffic signal controls play an important role in regulating vehicular flow at road intersections. Traditional systems are not capable of adjusting the timing pattern in accordance with vehicular demand. This results in excessive delays for road users. Hence it is necessary to develop dynamic systems that can adjust the timing patterns according to traffic demand. In this paper, the design and implementation of an adaptive traffic signal control system based on car-to-car communication is presented. Also, a clustering algorithm is defined which will assist in estimating the density of vehicles approaching an intersection. The cycle time, which is calculated using the estimated density of vehicular traffic, helps in reducing both the waiting time for vehicles at intersections and queue length. It is also shown that the proposed solution is collision free at intersections. The proposed system is compared with a classic pre-timed system and an adaptive fuzzy logic system. The simulations also show that the data convergence time and the communication delay between vehicles and traffic signals do not compromise the efficiency of the system.

A Probe-Vehicle-Based Evaluation of Adaptive Traffic Signal Control

In 2005, the Cobb County Department of Transportation, Cobb County, GA, conducted an adaptive signal control pilot study implementing the Sydney Coordinated Adaptive Traffic System (SCATS) on 15 intersections. This paper presents the results of a before-and-after probe-vehicle-based operational comparison of optimized time-of-day (i.e., before control) and SCATS (i.e., after control) traffic control system performance. The focus of this operational analysis is the typical operating performance during the weekday peak, weekday off-peak, and weekend travel periods. Travel time data were collected using Global-Positioning-System (GPS)-equipped test vehicles. The results showed that both systems provided good performance, whereas neither the before time-of-day or after SCATS is clearly dominant, except on Cumberland Parkway, where SCATS control consistently provides equivalent or superior performance to that of the time-of-day control.

Distributed and adaptive traffic signal control within a realistic traffic simulation

As traffic congestion rises within urban centers around the world, the intelligent control of traffic signals within cities is becoming increasingly important. Previous research within the area of intelligent traffic signal control has several shortcomings, including a reliance on historical data, the use of centralized systems which cannot handle city-sized problem instances and solutions which are not capable of addressing real-world traffic scenarios (e.g., constantly varying volumes and complex network structures). The research reported here proposes algorithms capable of controlling traffic signals that rely on traffic observations made by available sensor devices and local communication between traffic lights. This solution allows signals to be updated frequently to match current traffic demand, while also allowing for significantly large problem sizes to be addressed. To evaluate the developed system, a realistic traffic model was developed using information supplied by the City of Ottawa, Canada. It was found, through simulation within the SUMO traffic simulation environment, that the proposed adaptive system resulted in higher overall network performance when compared to the current fixed signal plan controllers, which were recreated using information from the City of Ottawa. This work also includes examples of why fixed signal controllers are inferior to an adaptive control system.

Application of LVQ Neural Network in Real–Time Adaptive Traffic Signal Control

Real–time road traffic data analysis is the cornerstone for the modern transport system. The real–time adaptive traffic signal control system is an essential part for the system. This analysis is to describe a traffic scene in a way similar to that of a human reporting the traffic status and the extraction of traffic parameters such as vehicle queue length, traffic volume, lane occupancy and speed measurement. This paper proposed the application of two–stage neural network in real–time adaptive traffic signal control system capable of analysing the traffic scene detected by video camera, processing the data, determining the traffic parameters and using the parameters to decide the control strategies. The two–stage neural network is used to process the traffic scene and decide the traffic control methods: optimum priority or optimum locality. Based on simulation in the traffic laboratory and field testing, the proposed control system is able to recognise the traffic pattern and enhance the traffic parameters thus easing traffic congestion more effectively than existing control systems. Key words: Urban traffic control system, pattern recognition, two-stage neural network, adaptive control system

Traffic Signal Controller for Mixed Traffic Conditions

Fuzzy logic has been widely used to develop an adaptive traffic signal controller because it allows qualitative modelling of complex systems. However, existing research has developed Fuzzy Logic Traffic Signal Controller (FLTSC) based on non-mixed traffic conditions. These FLTSC are not appropriate to the mixed traffic conditions of developing countries where the traffic streams consist of different types of vehicles with a wide variation in their static, dynamic and operating characteristics. This paper describes the design and evaluation of an adaptive traffic signal controller based on fuzzy logic for an isolated four-way intersection with specific reference to mixed traffic in developing countries. The controller is designed to be responsive to real-time traffic demands. Video image processing has been proposed to capture traffic data such as maximum queue length (in metres) and average occupancy rate (in %) from each approach of the intersection. The proposed FLTSC uses maximum queue lengths and average occupancy rates collected during the previous cycle in order to estimate the number of seconds of green time required by each set of signal groups (stage) during the next cycle. The effectiveness of the proposed FLTSC was examined and analysed by the simulation program VISSIM. The performance of this controller is to be contrasted with the Fixed Time Controller (FTC) and Vehicle Actuated Controller - Extension Principle (VAC-EP) for different traffic conditions on a simulated four- way intersection. The simulation results indicate that the performance of the proposed FLTSC is generally better than the FTC and VAC-EP, especially in cases time-varying traffic.

Application of Reinforcement Learning as a Tool of Adaptive Traffic Signal Control on Isolated Intersections

For a long time it was believed that the systems responding to real time traffic would enable significant benefits. However, numerous limitations have appeared such as the existence of the models with high level of detail precision, the uncertainty in predicting future traffic flows, the difficulty in arrival time estimation, the lack of self-adjusting mechanism. The difficulties in optimising the signal control strategy have initiated new researches. The results highlight the artificial intelligence methods as a possible solution. These systems are characterized with the ability to accumulate and use knowledge, set a problem, learn, process, conclude, solve the problem and exchange knowledge. The research presented in this paper proposes an adaptive signal control performed by a control agent able to adapt to an optimal policy by learning from the environment. The goal to be achieved is minimization of the delays in the system. First, the problem of reinforcement learning has been set. The first computation results of the Q-learning application for adaptive traffic signal control are presented. It is concluded that the results obtained are in favor of the adaptive signal control strategy compared to the fixed and actuated signal control.

Vehicle actuation based short-term traffic flow prediction model for signalized intersections

Traffic flow prediction is an important component for real-time traffic-adaptive signal control in urban arterial networks. By exploring available detector and signal controller information from neighboring intersections, a dynamic data-driven flow prediction model was developed. The model consists of two prediction components based on the signal states (red or green) for each movement at an upstream intersection. The characteristics of each signal state were carefully examined and the corresponding travel time from the upstream intersection to the approach in question at the downstream intersection was predicted. With an online turning proportion estimation method, along with the predicted travel times, the anticipated vehicle arrivals can be forecasted at the downstream intersection. The model performance was tested at a set of two signalized intersections located in the city of Gainesville, Florida, USA, using the CORSIM microscopic simulation package. Analysis results show that the model agrees well with empirical arrival data measured at 10 s intervals within an acceptable range of 10%–20%, and show a normal distribution. It is reasonably believed that the model has potential applicability for use in truly proactive real-time traffic adaptive signal control systems.

Adaptive Traffic Control for Large-Scale Dynamic Traffic Assignment Applications

Dynamic traffic assignment (DTA) applications require traffic signal control data that are typically difficult to obtain and cumbersome to code in the required format. In addition, the evaluation of future scenarios requires future traffic signal settings consistent with the forecast demand. These signal settings are not known a priori and are costly to estimate. Intuitively, future signal timings need to be reasonably optimized so as to represent what the traffic management agency will do. In the literature, integration between traffic control and DTA models has been formulated as a bi-level or single-level optimization problem with system or user optimal constraints. Most existing solution procedures require certain nested structure with an inner-loop algorithm solving the problem of user-equilibrium or system-optimal assignment and the outer-loop algorithm searching for the optimal signal-timing settings. Most of these solution approaches remain only research tools without practical use because of computational intractability. This research proposes an efficient solution algorithm to the problem. An adaptive traffic signal control model is embedded in a simulation-based DTA model. For each inbound approach at an intersection of interest, the adaptive model uses upstream information and a dynamic rolling-horizon approach to project traffic flow conditions for a dynamic but short (projection) period. The adaptive model provides the signal settings during the entire process of traffic flow simulation and for every iteration of the solution algorithm. Thus, during the entire solution process, the experienced travel times and resulting traffic assignment flows are based on the adaptive (demand-responsive) signal settings, allowing the DTA flows and the adaptive signal settings to be generated simultaneously in a single-loop algorithmic structure. Simulation experiments illustrate the capabilities of the proposed approach.

Arterial Performance Evaluation on an Adaptive Traffic Signal Control System

This paper documents the evaluation of Sydney Coordinated Adaptive Traffic System (SCATS) at a major signalized arterial in Las Vegas. The evaluation was based on various performance measures, including primarily travel times and stops at selected routes along the arterial. Extensive travel time runs were conducted during the weekday and weekend peak periods. Arterial performance was compared between SCATS and conventional time-of-day (TOD) coordination plans. Based on the data and analysis results, a general conclusion was reached that no significant improvement on arterial progression was achieved with SCATS under normal traffic conditions. However, this conclusion was made based on a major limitation of using video detection in the field, which may have significantly limited SCATS from achieving its best performance.

Methodology to Evaluate Active Transportation and Demand Management Strategies

This paper describes a methodology for predicting the impacts of active transportation and demand management (ATDM) measures on highway performance that can be applied using either microsimulation or Highway Capacity Manual (HCM) analysis techniques.Active transportation and demand management (ATDM) is a comprehensive approach to facility management and operation that seeks to increase facility productivity by proactively balancing supply and demand to avoid or delay facility breakdown. Examples of ATDM measures include: adaptive ramp metering, congestion pricing, speed harmonization, traveler information systems, and adaptive traffic signal control systems. Incident management and work zone management programs may employ one or more of these ATDM measures.The proposed ATDM analysis methodology is employed as a “wrapper” to a “core” analysis tool, such as the HCM or microsimulation. The core analysis tool is applied several times to a set of demand/capacity scenarios. The demand/capacity scenarios are developed based on observations of annual fluctuations in weekday peak period demands and capacities for the facility. The scenario results are then combined according to their probability of occurrence in order to predict the full-year peak period performance of the street, highway, or freeway facility.The application of the methodology is illustrated on a real-world freeway facility. The Highway Capacity Manual FREEVAL procedure, the selected “core” analysis tool, was adapted to test ramp metering, HOV (High Occupancy Vehicle) lanes, and HOT (High Occupancy Toll) lanes within the ATDM evaluation methodology. FREEVAL was applied to each of several demand/capacity scenarios and the results combined into an estimate of average performance improvements for each ATDM measure. The predicted facility performance improvements were compared to a before/after field studies reported in the literature. © 2011 Published by Elsevier Ltd.

Iterative Learning Based Adaptive Traffic Signal Control

Iterative learning control (ILC), as a branch of data-driven control, has obtained plentiful achievements both in theoretical research and practical application over the past two decades. Taking the traffic signal control system as a plant system, the paper introduces the idea of the ILC and fuzzy logic to design an adaptive data-driven traffic signal controller to improve the capacity of the intersection. The key rule of the signal control logic is described by fuzzy iterative theory, and the control strategy can adapt itself to the changing of traffic flow by iterative learning and handle the uncertainty and randomness in traffic system by fuzzy logic, so as to avoid the modeling of complex transport system and take advantages of data-driven on non-model control. Finally, the proposed method is testified to be applicable and effective based on the simulation results by VISSIM. The simulation result indicates that the effect of the proposed method is more effective than the fixed and actuated control approaches.

Review of the Fuzzy Logic Based Approach in Traffic Signal Control: Prospects in Saudi Arabia

The first implementation of fuzzy logic controller in the literature appeared in 1977, which shows better performance compared to vehicle actuated controller for a very simplified intersection having two one-way streets based on a simple green time extension principle. After that, further development is taking place by adopting fuzzy logic based traffic signal control for two-way single intersection without turning vehicles, single intersection with all possible movements, multiple intersections, phase sequence and time determination, congested intersection and network, etc. Research in fuzzy logic based traffic signal control is getting inspired by the results which indicate better performance compared to traditional traffic signal controls, specifically during heavy and uneven traffic volume conditions. It can be expected that the fuzzy logic approach will not only contribute in the advancement of adaptive traffic signal control but will also contribute significantly in the future approach of transportation management system (TMS) by improving the performance of the adaptive controller and the overall decision making process of the TMS. However, there are only a few examples of this kind of traffic signal control or TMS in real life. Researchers in rapidly developing countries like Saudi Arabia should investigate the potential of the fuzzy logic based traffic signal control or TMS under the unique local conditions to curb the loss incurred due to congestion.

Adaptive traffic signal control using approximate dynamic programming

This paper presents a study on an adaptive traffic signal controller for real-time operation. The controller aims for three operational objectives: dynamic allocation of green time, automatic adjustment to control parameters, and fast revision of signal plans. The control algorithm is built on approximate dynamic programming (ADP). This approach substantially reduces computational burden by using an approximation to the value function of the dynamic programming and reinforcement learning to update the approximation. We investigate temporal-difference learning and perturbation learning as specific learning techniques for the ADP approach. We find in computer simulation that the ADP controllers achieve substantial reduction in vehicle delays in comparison with optimised fixed-time plans. Our results show that substantial benefits can be gained by increasing the frequency at which the signal plans are revised, which can be achieved conveniently using the ADP approach.