Real-time Adaptive Signal Control Research Articles

Adaptive Signal Control for Overflow Prevention at Isolated Intersections Based on Fuzzy Control

Nonrecurrent congestion on urban roads may cause overflow, possibly leading to gridlock at intersections. Signal control is one of the most important measures to prevent such phenomena. For nonrecurrent congestion with uncertain bottleneck capacity, however, existing signal control methods have mainly adopted the strategy of cutting off the green signal of the overflow movement while the release priority of green time has not been considered. Consequently, the green time allocated to overflow movements is too long, aggravating overflow and congestion. Therefore, a signal control method is proposed to prevent overflow caused by uncertain bottleneck capacity. The proposed method extends existing real-time adaptive signal control to explicitly consider the risk of queue spillover. When an overflow risk is detected, the green times of overflow movements will be reassigned using fuzzy control to determine the release priority depending on the queue length and red time. Numerical studies were conducted for two scenarios with different downstream bottleneck capacities. The results demonstrate that the proposed method not only outperforms existing methods in respect of intersection efficiency but also reduces the risk of overflow. The proposed method achieves a 27.7% to 33.8% reduction in the maximum queue length of overflow movements compared with a traditional adaptive signal control method and a 10.9% to 11.0% decrease compared with a modified signal control method with a cutting-off strategy. The sensitivity analysis indicates that the average delay resulting from the proposed method is 51.1% and 18.0% less than the delays resulting from the traditional and modified methods, respectively.

Effects analysis of reward functions on reinforcement learning for traffic signal control.

The increasing traffic demand in urban areas frequently causes traffic congestion, which can be managed only through intelligent traffic signal controls. Although many recent studies have focused on reinforcement learning for traffic signal control (RL-TSC), most have focused on improving performance from an intersection perspective, targeting virtual simulation. The performance indexes from intersection perspectives are averaged by the weighted traffic flow; therefore, if the balance of each movement is not considered, the green time may be overly concentrated on the movements of heavy flow rates. Furthermore, as the ultimate purpose of traffic signal control research is to apply these controls to the real-world intersections, it is necessary to consider the real-world constraints. Hence, this study aims to design RL-TSC considering real-world applicability and confirm the appropriate design of the reward function. The limitations of the detector in the real world and the dual-ring traffic signal system are taken into account in the model design to facilitate real-world application. To design the reward for balancing traffic movements, we define the average delay weighted by traffic volume per lane and entropy of delay in the reward function. Model training is performed at the prototype intersection for ensuring scalability to multiple intersections. The model after prototype pre-training is evaluated by applying it to a network with two intersections without additional training. As a result, the reward function considering the equality of traffic movements shows the best performance. The proposed model reduces the average delay by more than 7.4% and 15.0% compared to the existing real-time adaptive signal control at two intersections, respectively.

Smart Infrastructure for Future Urban Mobility

Real‐time traffic signal control presents a challenging multiagent planning problem, particularly in urban road networks where, unlike simpler arterial settings, there are competing dominant traffic flows that shift through the day. Further complicating matters, urban environments require attention to multimodal traffic flows (vehicles, pedestrians, bicyclists, buses) that move at different speeds and may be given different priorities. For the past several years, my research group has been developing and refining a real‐time, adaptive traffic signal control system to address these challenges, referred to as scalable urban traffic control (Surtrac). Combining principles from automated planning and scheduling, multiagent systems, and traffic theory, Surtrac treats traffic signal control as a decentralized online planning process. In operation, each intersection repeatedly generates and executes (in rolling horizon fashion) signal‐timing plans that optimize the movement of currently sensed approaching traffic through the intersection. Each time a new plan is produced (nominally every couple of seconds), the intersection communicates to its downstream neighbors what traffic it expects to send their way, allowing intersections to construct longer horizon plans and achieve coordinated behavior. Initial evaluation of Surtrac in the field has demonstrated significant performance improvements, and the technology is now deployed and operating in several U.S. cities. More recent work has focused on integrating real‐time adaptive signal control with emerging connected vehicle technology, and exploration of the opportunities for enhanced mobility that direct vehicle (or pedestrian) to infrastructure communication can provide. Current technology development efforts center on vehicle route sharing, smart transit priority, safe intersection crossing for pedestrians with disabilities, real‐time incident detection, and integrated optimization of signal control and route choice decisions. This article provides an overview of this overall research effort.

A dynamic optimization method for adaptive signal control in a connected vehicle environment

In a connected vehicle environment, vehicle location, speed, and other traffic information are readily available; hence, such environments provide new data sources for traffic signal control optimization. Existing adaptive signal control systems based on fixed detectors cannot directly obtain vehicle location and speed information, and thus, cannot provide accurate information about real-time traffic flow changes. This study presents a dynamic optimization method for adaptive signal control in a connected vehicle environment. First, the proposed method developed a dynamic platoon dispersion model to predict vehicle arrivals by using connected vehicle data. Then, a signal timing optimization model is constructed by regarding the minimization of average vehicle delay as the optimization objective, and setting the green time duration of each phase as a constraint. To achieve real-time adaptive signal control, a genetic algorithm is adopted to solve the optimization model through rolling optimization. Finally, a real-world road network was modeled in Vissim to validate the proposed method. Simulation results show that compared with the classical adaptive signal control algorithm, the proposed method is able to reduce vehicle delays and queue lengths at least 50% penetration rates. At 100% penetration rate, the proposed method improved the average vehicle delay and the average queue length by 22.7% and 24.8%, respectively. Moreover, it catered to all directions in a balanced manner.

A Two-Level Rolling Optimization Model for Real-time Adaptive Signal Control

Recently, dynamic traffic flow prediction models have increasingly been developed in a connected vehicle environment, which will be conducive to the development of more advanced traffic signal control systems. This paper proposes a rolling optimization model for real-time adaptive signal control based on a dynamic traffic flow model. The proposed method consists of two levels, i.e., barrier group and phase. The upper layer optimizes the length of the barrier group based on dynamic programming. The lower level optimizes the signal phase lengths with the objective of minimizing vehicle delay. Then, to capture the dynamic traffic flow, a rolling strategy was developed based on a real-time traffic flow prediction model. Finally, the proposed method was compared to the Controlled Optimization of Phases (COP) algorithm in a simulation experiment. The results showed that the average vehicle delay was significantly reduced, by as much as 17.95%, using the proposed method.

A real-time adaptive signal control in a connected vehicle environment

The state of the practice traffic signal control strategies mainly rely on infrastructure based vehicle detector data as the input for the control logic. The infrastructure based detectors are generally point detectors which cannot directly provide measurement of vehicle location and speed. With the advances in wireless communication technology, vehicles are able to communicate with each other and with the infrastructure in the emerging connected vehicle system. Data collected from connected vehicles provides a much more complete picture of the traffic states near an intersection and can be utilized for signal control. This paper presents a real-time adaptive signal phase allocation algorithm using connected vehicle data. The proposed algorithm optimizes the phase sequence and duration by solving a two-level optimization problem. Two objective functions are considered: minimization of total vehicle delay and minimization of queue length. Due to the low penetration rate of the connected vehicles, an algorithm that estimates the states of unequipped vehicle based on connected vehicle data is developed to construct a complete arrival table for the phase allocation algorithm. A real-world intersection is modeled in VISSIM to validate the algorithms. Results with a variety of connected vehicle market penetration rates and demand levels are compared to well-tuned fully actuated control. In general, the proposed control algorithm outperforms actuated control by reducing total delay by as much as 16.33% in a high penetration rate case and similar delay in a low penetration rate case. Different objective functions result in different behaviors of signal timing. The minimization of total vehicle delay usually generates lower total vehicle delay, while minimization of queue length serves all phases in a more balanced way.

Development and Testing of an Automatic Turning Movement Identification System at Signalized Intersections

Vehicle turning movement data from signalized intersections is utilized for numerous applications in the field of transportation. Such applications include real-time adaptive signal control, dynamic traffic assignment, and traffic demand estimation. However, it is very time consuming and costly to obtain vehicle turning movement information manually. Previous efforts to simplify this process were focused on solving the problem using an O-D matrix, but this method proved to be inaccurate and unreliable with the existing data acquisition system. Another study involved the identification of vehicle turning movements from the detector information, but the presence of shared lanes led to uncertainties in vehicle matching, thus limiting application of the method only to intersections without shared lanes. In light of those unsuccessful attempts, this paper develops and tests a system called the Automatic Turning Movement Identification System (ATMIS), which estimates vehicle turning movements at a signalized intersection in real time, regardless of its geometry. The results from lab experiments as well as a field test show that the algorithm is very promising and may potentially be expanded for field applications.

Optimization of Control Parameters for Adaptive Traffic-Actuated Signal Control

The authors propose a real-time adaptive signal control model that aims to maintain the adaptive functionality of actuated controllers while improving the performance of traffic-actuated control systems. In this model, a flow-prediction algorithm is formulated to estimate the future vehicle arrival flow for each signal phase at the target intersection on the basis of the available signal-timing data obtained from previous control cycles. Optimal timing parameters are determined on the basis of these estimations and are used as signal-timing data for further optimizations. To be consistent with the operation logic of existing signal-control devices, this model is developed to optimize the basic control parameters that can be found in modern actuated controllers. Microscopic simulation is used to test and evaluate the proposed control model in a calibrated network consisting of 38 actuated signals. Simulation results indicate that this model has the potential to improve the performance of the signalized network under different traffic conditions.

NEW LOOP DETECTOR INSTALLATION GUIDELINES FOR REAL-TIME ADAPTIVE SIGNAL CONTROL SYSTEMS

A real-time adaptive signal control system, named COSMOS, generated signal control parameters automatically using collected information from loop detectors. Therefore, the reliability of loop detector information can affect the operational effectiveness of the signal control system. In this paper, a set of practical loop detector installation guidelines was developed through extensive field experiments. Results indicated that a laying depth of 12 cm provided good performance regardless of detector types tested. In addition, a laying depth less than 9 cm should not be selected in practice. The limit lengths of feeder cable were determined as 1,000 m for octagon loop detector and 900 m for circle loop detector, respectively. It was also recommended that the number of soldering should be less than 6 times when the feeder cable length was below 600 m, and 4 times when the length was in a range between 600 m and 1,000 m.

Traffic Probe Data Processing for Full-Scale Deployment of Vehicle-Infrastructure Integration

Vehicle-infrastructure integration (VII) is expected to provide greatly improved transportation planning and operations data by enabling many–and eventually all–road vehicles to function as traffic data probes. This means that VII probe data can be processed to produce useful information about the operation of an arterial roadway based on studies using a traffic microsimulation. Simulation provides a “truth model” representation of all vehicle trajectories, which serves as the basis for comparison with probe data sampling strategies under development in the VII program. Modifications to the probe data sampling strategies are suggested to enhance the quality of the data, especially its timeliness. Applications to weather condition detection, incident detection, and realtime adaptive traffic signal control, each with significantly different data requirements, were considered, and the ability of the probe sampling system to support these applications was evaluated. The least-demanding applications, such as weather condition detection, can be served well even at low market penetrations of VII-capable vehicles, but the most demanding applications (real-time adaptive signal control) appear to require the majority of vehicles to be equipped. Recommendations were defined for additional research needed to support the development and deployment of a viable VII traffic probe data system.

Analysis of intersection delay under real-time adaptive signal control

The United States Department of Transportation has recently begun implementation of the national demonstration project for suburban Advanced Traffic Management Systems (ATMS) utilizing the Sydney Coordinated Adaptive Traffic System (SCATS). SCATS is an automated, real time, traffic responsive signal control strategy. The expected benefit from the system comes from its ability to constantly modify signal timing patterns to most effectively accommodate changing traffic conditions. The objectives of this research study were to analyze the differences in certain delay parameters which would occur as a result of implementing SCATS signal control. The study employed a macroscopic simulation procedure to compute intersection delay under both a strategy that changed signal timings once per hour and SCATS signal control. A comparison of delay under both forms of control is presented. The study findings demonstrated mixed results regarding the benefit of SCATS control. A general conclusion of the study was that SCATS distributed the delay across competing approaches more evenly. However, in some cases this resulted in an increase in the total intersection delay. The observed delay change was attributed primarily to the saturation equalization objective of the SCATS control program. SCATS attempts to allocate green time to the intersection approaches based on the degree of saturation. Under this philosophy the system is able to balance the percentage of green time between all approaches, resulting in more uniform delay.