Adaptive Traffic Signal Control System Research Articles

Fusing crowdsourced data to an adaptive wireless traffic signal control system architecture

In this study, a novel wireless adaptive traffic signal control System architecture is proposed, in which crowdsourced data is integrated to detect the traffic state on each approach of an intersection. Under the wireless architecture, the green signal timing is calculated using a travel time-based Max Pressure control algorithm, which is performed on a single-board computer (SBC, RPi 4). The resulting green timings are sent from SBC to the master controller (ESP32) over serial UART and then to the slave (ESP32) wirelessly using ESP-NOW; slave ESP32 is placed on each approach of the intersection. It integrates a fail-safe mechanism to avoid any vehicular conflicts. The proposed system architecture also embeds a crowdsourced data-based pre-emption of intersections for efficient movement of emergency vehicles. The efficacy of the proposed system is compared using a simulation tool with real-world data. With respect to actuated and Max Pressure based on traffic flow, the proposed algorithm reduces the total delay by 54.8% and 22.96%, respectively. Further, the total queue dissipation time decreases by 8.32% and 5.45%, respectively. It outperforms the actuated signal control as well as Max Pressure based on traffic flows. The capability of transforming existing traffic signal controllers into wireless adaptive traffic signal control systems makes it a very affordable and scalable solution.

Elevating adaptive traffic signal control in semi‐autonomous traffic dynamics by using connected and automated vehicles as probes

AbstractIn this work, the connected vehicle's messages are used to create an enhanced adaptive traffic signal control (ATSC) system for improved traffic flow. Few existing studies use connected and automated vehicles (CAVs) to develop traffic signal control algorithms under hybrid connected and autonomous conditions. The proposed approach focuses on a four‐phase traffic intersection with both CAVs and human‐driven vehicles (HVs). CAVs share real‐time state information, and a model called Roads Dynamic Segmentation estimates queuing procedures and vehicle fleet numbers on dynamic road sections. This information is used in the Store and Forward Model (SFM) to predict intersection queuing length. The ATSC system, based on model predictive control (MPC), aims to minimize intersection queue length while considering traffic constraints (undersaturated, saturated, and oversaturated) and avoiding free‐flow problems due to queue overflow. To reduce computational complexity, a linear‐quadratic‐regulator (LQR) is used. Real‐world vehicle trajectories and the SUMO tool are used for experimental purposes. Results show that the proposed method reduces average delay by 38.50% and 33.42% compared to fixed timing and traditional MPC in cases of oversaturated traffic flow with 100% CAV penetration. Even with a penetration rate of only 20%, average delay decreases by 13.65% and 6.50%, respectively. This study showcases not only the potential benefits of CAV in enhancing traffic, but also enables the optimal utilization of green duration in signalized intersection control systems. This helps prevent traffic congestion and ensures the efficient and smooth movement of traffic flow.

Adaptive green split optimization for traffic control with low penetration rate trajectory data

Adaptive traffic signal control systems often rely on expensive physical detection infrastructure. However, with the advent of widespread trajectory data, it is now possible to implement adaptive control entirely avoiding such costs. We present two simple adaptive control policies which only require sample delay and number of stops, with the goal to mitigate the presence of oversaturation. The simplicity stems from the necessity of controlling under any trajectory penetration rate. The two policies differ on the possibilities of the control infrastructure to be implemented. The first one minimizes oversaturation by deviating from a reference pre-timed signal plan. This signal plan can be an existing one or an estimated one from aggregating trajectory data. The second policy creates first a set of green split plans to be then selected by a control logic. This second policy is intended to be used in SCATS-like systems where signal plans are limited to a pre-defined discrete set. We propose a plan selection logics or alternatively, the original plan selection policy can be used as well. Both policies are tested in the field, achieving a significant reduction in delay, oversaturation and spillover ratios. Lastly, we test an application of this policy as an enhancement of SCATS systems in the presence of malfunctioning physical detectors.

Priority-Based Adaptive Traffic Signal Control System for Smart Cities

Priority-Based Adaptive Traffic Signal Control System for Smart Cities

A review of reinforcement learning applications in adaptive traffic signal control

In urban areas, the problem of recurring daily congestion is constantly increasing. A possible solution is seen in the application of adaptive traffic signal control (ATSC) systems for the control of signalized intersections. While available ATSC systems can achieve an increase in the Level of Service, the focus of ATSC research has shifted towards the application of reinforcement learning (RL) techniques, which allow the controller to learn the optimal control policy by direct interaction with the environment. This paper describes the fundamentals of traffic signal control, RL algorithms and approaches and their application to ATSC, with a discussion on the impact of connected and autonomous vehicles on future traffic signal control. In conclusion, a summary of open research questions and possible directions for future research in the domain of RL-based traffic signal control is given.

Dynamic Weight-based Multi-Objective Reward Architecture for Adaptive Traffic Signal Control System

Dynamic Weight-based Multi-Objective Reward Architecture for Adaptive Traffic Signal Control System

A Comparative Analysis of Machine Learning Algorithms in Design Process of Adaptive Traffic Signal Control System

In recent decades machine learning technology has proved its efficiency in most sectors by making human life easier. With this popularity and efficiency, it is applied to design traffic signal control systems to mitigate traffic congestion and distribute waiting delays. Hence, many researchers around the world are working to address this issue. As a part of the solution, this article presents a comparative analysis of various machine learning models to come up with a suitable model for an isolated intersection. In this context, eight machine learning models including Linear Regression, Ridge, Lasso, Support Vector Regression, k-Nearest Neighbour, Decision Tree, Random Forest, and Gradient Boosting Regression Tree are selected. Shivakumara Swamiji Circle (SSC), one of the intersections in Tumakuru, Karnataka, India is selected as a case study area. Essential data is collected from SSC through videography. The selected models are developed to predict green time based on traffic classification and volume in Passenger Car Units (PCU) for each phase on the PyCharm platform. The models are evaluated based on various performance metrics. Results revealed that all the selected models predict green splits with 91% accuracy using traffic classification as input, whereas, models showed 85% accuracy with PCU as input. And also, Gradient Boosting Regression Tree is the best suitable model for the selected intersection, whereas, Decision Tree is not referred model for this application.

Real-time signal-vehicle coupled control: An application of connected vehicle data to improve intersection safety

The proliferation of Connected Vehicles and their ability to collect a large amount of data presents an opportunity for the real-time safety optimization of traffic networks. At intersections, Adaptive Traffic Signal Control (ATSC) systems and dynamic speed advisories are among the proactive real-time safety interventions that can assist in preventing rear-end collisions. This study proposes a Signal-Vehicle Coupled Control (SVCC) system incorporating ATSC and speed advisories to optimize safety in real-time. By applying a rule-based approach in conjunction with a Soft-Actor Critic RL framework, the system assigns speed advisories to platoons of vehicles on each approach and extends the current signal time accordingly. Dynamic traffic parameters are collected in real-time and are used to estimate the current conflict rate at the intersection, which is then used both as an input to the model and to evaluate performance. The system was tested on two different intersections modeled using real-world data through the simulation platform VISSIM. Traffic conflicts were reduced by 41–55%, and vehicle delay was reduced by 21–24%. The results also show that the system functions at lower levels of market penetration, with diminishing returns beyond 50% MPR. The proposed system presents an SVCC framework that is both effective and low in computational intensity to optimize safety at signalized intersections.

Grey models for short-term queue length predictions for adaptive traffic signal control

Traffic congestion at a signalized intersection greatly reduces the travel time reliability in urban areas. Adaptive signal control system (ASCS) is the most advanced traffic signal technology that regulates the signal phasing and timings considering the traffic patterns in real-time to reduce traffic congestion. Real-time prediction of traffic queue length can be used to adjust the signal phasing and timings for different traffic movements at a signalized intersection with ASCS. The accuracy of the queue length prediction model varies based on many factors, such as the stochastic nature of the vehicle arrival rates at an intersection, time of the day, weather, and driver characteristics. In addition, accurate queue length prediction for multilane, undersaturated and saturated traffic scenarios at signalized intersections is challenging. Thus, the objective of this study is to develop short-term queue length prediction models for signalized intersections that can be leveraged by adaptive traffic signal control systems using six variations of Grey systems: (i) the first-order single variable Grey model (GM(1,1)); (ii) GM(1,1) with Fourier error corrections (EGM); (iii) the Grey Verhulst model (GVM), (iv) GVM with Fourier error corrections (EGVM), (v) the Grey model with cosine term (GM(1,1|cos(ωt) or GMC), and (vi) GMC with Fourier error corrections (EGMC). The efficacy of the Grey models is that they facilitate fast processing; as these models do not require a large amount of data; as would be needed in artificial intelligence models; and they can adapt to stochastic changes, unlike statistical models. We have conducted a case study using queue length data from five intersections with adaptive traffic signal control on a calibrated roadway network in Lexington, South Carolina. Grey models were compared with linear, nonlinear time series models, Adaboost (ADA), Bagging (BAG), Gradient Boost (GB), Neural Network (NN), Random Forest (RF), Support Vector Regression (SVR), and Long Short Term Memory (LSTM) models. Based on our analyses, we found that GMC can produce better or competing performance compared to more complex (i.e., LSTM and NN models) in predicting average and maximum queue lengths in terms of root mean squared and mean absolute errors. GMC model needs only 4 data values and can be used in real-time as the computational time to generate a prediction in less than 0.001 s.

DQN-BASED TRAFFIC SIGNAL CONTROL SYSTEMS

Real-time adaptive traffic control is an important problem in modern world. Historically, various optimization methods have been used to build adaptive traffic signal control systems. Recently, reinforcement learning has been advanced, and various papers showed efficiency of Deep-Q-Learning (DQN) in solving traffic control problems and providing real-time adaptive control for traffic, decreasing traffic pressure and lowering average travel time for drivers. In this paper we consider the problem of traffic signal control, present the basics of reinforcement learning and review the latest results in this area.

Effects of Connectivity and Traffic Observability on an Adaptive Traffic Signal Control System

The effectiveness of adaptive signal control strategies depends on the level of traffic observability, which is defined as the ability of a signal controller to estimate traffic state from connected vehicle (CV), loop detector data, or both. This paper aims to quantify the effects of traffic observability on network-level performance, traffic progression, and travel time reliability, and to quantify those effects for vehicle classes and major and minor directions in an arterial corridor. Specifically, we incorporated loop detector and CV data into an adaptive signal controller and measured several mobility- and event-based performance metrics under different degrees of traffic observability (i.e., detector-only, CV-only, and CV and loop detector data) with various CV market penetration rates. A real-world arterial street of 10 intersections in Seattle, Washington was simulated in Vissim under peak hour traffic demand level with transit vehicles. The results showed that a 40% CV market share was required for the adaptive signal controller using only CV data to outperform signal control with only loop detector data. At the same market penetration rate, signal control with CV-only data resulted in the same traffic performance, progression quality, and travel time reliability as the signal control with CV and loop detector data. Therefore, the inclusion of loop detector data did not further improve traffic operations when the CV market share reached 40%. Integrating 10% of CV data with loop detector data in the adaptive signal control improved traffic performance and travel time reliability.

Vehicular Crowdsourcing for Congestion Support in Smart Cities

Under present-day practices, the vehicles on our roadways and city streets are mere spectators that witness traffic-related events without being able to participate in the mitigation of their effect. This paper lays the theoretical foundations of a framework for harnessing the on-board computational resources in vehicles stuck in urban congestion in order to assist transportation agencies with preventing or dissipating congestion through large-scale signal re-timing. Our framework is called VACCS: Vehicular Crowdsourcing for Congestion Support in Smart Cities. What makes this framework unique is that we suggest that in such situations the vehicles have the potential to cooperate with various transportation authorities to solve problems that otherwise would either take an inordinate amount of time to solve or cannot be solved for lack for adequate municipal resources. VACCS offers direct benefits to both the driving public and the Smart City. By developing timing plans that respond to current traffic conditions, overall traffic flow will improve, carbon emissions will be reduced, and economic impacts of congestion on citizens and businesses will be lessened. It is expected that drivers will be willing to donate under-utilized on-board computing resources in their vehicles to develop improved signal timing plans in return for the direct benefits of time savings and reduced fuel consumption costs. VACCS allows the Smart City to dynamically respond to traffic conditions while simultaneously reducing investments in the computational resources that would be required for traditional adaptive traffic signal control systems.

Adaptive and multi-path progression signal control under connected vehicle environment

Through wireless communications, enriched information from connected vehicles (CVs) can describe traffic information near an intersection and would supplement a data source for an effective signal control. This paper proposes an adaptive traffic signal control system in a CV environment. At the intersection level, an adaptive control model is developed to assign optimal green times by minimizing the total vehicle delay. When the CV penetration rate is low, a method depending on limited CV data is presented to estimate the vehicle arrival information. At the corridor level, a real-time optimization model is formulated to design the dynamic progression plan for critical paths (i.e. paths with high flows). These two optimization models are solved by the dynamic programming technique. A real-world arterial is modeled in VISSIM to evaluate the effectiveness and efficiency of the proposed traffic signal control system. Simulations with various CV penetration rates and demand levels are conducted to compare the proposed system with fixed coordination and adaptive signal control systems. Results indicate that the proposed system outperforms both base systems by reducing 15.67% and 13.81% of the average delay, respectively, when the CV penetration rate is relatively high. Moreover, the proposed system can improve the arterial performance under all tested scenarios with various CV penetration rates and demand levels. Since the tested demand scenarios can reflect the real traffic fluctuations, it can be proved that the proposed control system could be applied in the field to boost the overall traffic efficiency along the arterial.

Adaptive traffic signal control system using composite reward architecture based deep reinforcement learning

The increasing traffic congestion problem can be solved by an adaptive traffic signal control (ATSC) system as it utilises real-time traffic information to control traffic signals. Recently, deep reinforcement learning (DRL) has shown its potential in solving the traffic signal timing. However, one of the main challenges of DRL is to design a proper reward function and special attention needs for a multi-objective reward design. Since the feedback to the agent depends on the reward function, a proper design of reward function is needed for fast and stable learning. In this study, the authors proposed a new reward architecture called composite reward architecture (CRA) for multi-objective ATSC to optimise multiple objectives. It calculates multiple rewards in parallel for each action and applies the majority voting method to choose the desired action. Since the traffic signal of one intersection affects the adjacent intersections, a new coordination approach is proposed to get the overall smooth traffic flow. The proposed reward architecture CRA is compared with several existing reward functions used in the literature for different traffic scenarios. The new coordinated approach is compared with the non-coordinated approach. The authors demonstrated that the proposed approaches outperform the others concerning waiting time, halting the number of vehicles, and so on.

Hybrid Radial Basis Function Neural Networks for Urban Traffic Signal Control

In this study, a real-world isolated signalized intersection with a fixed-time signal control system is considered. The signal timing plans are arranged regardless of the traffic density, and these plans cause delays in vehicle queues. To increase the efficiency of the intersection, an adaptive traffic signal control system is proposed to manage the intersection. To find the appropriate adaptive green times for each lane, simulations are performed by traffic simulation software using vehicle arrivals and other information about vehicle movements gathered from the real-world intersection. Then, a hybrid radial basis function neural network is developed to forecast the adaptive green times, which is trained and tested with historical arrivals and simulation results. The performance of the proposed network is compared with well-known data mining classification methods, such as support vector regression, k-nearest neighbors, decision tree, random forest, and multilayer perceptron methods, by different evaluation parameters. The comparison results provide that the developed radial basis function neural network outperforms other classification methods and can be successfully used for forecasting adaptive green times as an alternative to complex unsupervised classification methods.

Multi-Objective reinforcement learning approach for improving safety at intersections with adaptive traffic signal control

Adaptive traffic signal control (ATSC) systems improve traffic efficiency, but their impacts on traffic safety vary among different implementations. To improve the traffic safety pro-actively, this study proposes a safety-oriented ATSC algorithm to optimize traffic efficiency and safety simultaneously. A multi-objective deep reinforcement learning framework is utilized as the backend algorithm. The proposed algorithm was trained and evaluated on a simulated isolated intersection built based on real-world traffic data. A real-time crash prediction model was calibrated to provide the safety measure. The performance of the algorithm was evaluated by the real-world signal timing provided by the local jurisdiction. The results showed that the algorithm improves both traffic efficiency and safety compared with the benchmark. A control policy analysis of the proposed ATSC revealed that the abstracted control rules could help the traditional signal controllers to improve traffic safety, which might be beneficial if the infrastructure is not ready to adopt ATSCs. A hybrid controller is also proposed to provide further traffic safety improvement if necessary. To the best of the authors’ knowledge, the proposed algorithm is the first successful attempt in developing adaptive traffic signal system optimizing traffic safety.

Crash modification factors for adaptive traffic signal control: An Empirical Bayes before-after study

Adaptive traffic signal control (ATSC) is a novel traffic management system that is often deployed at high-volume intersections in order to mitigate traffic congestion and improve travel time reliability. While past studies have demonstrated its operational effectiveness, relatively few have focused on safety performance. Those that have tend to suffer from limitations including small sample sizes, insufficient study designs, or the lack of consideration of potential temporal and corridor effects after ATSC installation. Furthermore, results from previous studies are mixed: while many studies point to a safety improvement, more recent studies seem to indicate that ATSC systems might increase crash frequency. In light of this, a comprehensive Empirical Bayes (EB) before-after observational study was conducted using ATSC data collected throughout Pennsylvania. Crash modification factors (CMFs) were estimated based on the following different case scenarios: crash severity levels and crash types (total, fatal and injury, rear-end, and angle crashes); intersection locations (all intersections and intersections along corridors only); and, intersection configurations (3-leg and 4-leg). Temporal trends for intersection-level CMFs were examined using annual crash data in the after period. Corridor-level CMFs were also developed to quantify changes in safety performance along corridors with ATSC installed. The results suggest that ATSC is associated with a nominal increase in total and angle crashes, and an expected decrease in fatal plus injury crashes and rear-end crashes. However, the results were not statistically significant. The safety effect estimates are similar when considering intersection locations and configurations. In addition, the temporal trend analysis indicates that the safety effectiveness does not vary annually in the after period, suggesting no obvious novelty effect associated with ATSC. Finally, the magnitude of the corridor-level CMFs are slightly lower than the intersection-level CMFs, except for rear-end crashes.

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.

Decentralized network level adaptive signal control by multi-agent deep reinforcement learning

Adaptive traffic signal control systems are deployed to accommodate real-time traffic conditions. Yet travel demand and behavior of the individual vehicles might be overseen by their model-based control algorithms and aggregated input data. Recent development of artificial intelligence, especially the success of deep learning, makes it possible to utilize information of individual vehicles to control the traffic signals. Several pioneering studies developed model-free control algorithms using deep reinforcement learning. However, those studies are limited to isolated intersections and their effectiveness was only evaluated in ideal simulated traffic conditions by hypothetical benchmarks. To fill the gap, this study proposes a network-level decentralized adaptive signal control algorithm using one of the famous deep reinforcement methods, double dueling deep Q network in the multi-agent reinforcement learning framework. The proposed algorithm was evaluated by the real-world coordinated actuated signals in a simulated suburban traffic corridor which emulates the real-field traffic condition. The evaluation results showed that the proposed deep-reinforcement-learning-based algorithm outperforms the benchmark. It is able to reduce 10.27% of the travel time and 46.46% of the total delay.

An Adaptive Signal Control Method with Optimal Detector Locations

The adaptive traffic signal control system is a key component of intelligent transportation systems and has a primary role in effectively reducing traffic congestion. The high costs of implementation and maintenance limit the applicability of the adaptive traffic signal control system, especially in developing countries. This paper proposes a low-cost adaptive signal control method that is easy to implement. Two detectors are installed in each vehicle lane at an optimal location determined by the proposed method to detect green and red redundancy time, based on which the original signal timing is adjusted through a signal controller. The proposed method is evaluated through case studies with low and high volume-to-capacity ratio intersections. The results show that the proposed adaptive signal control method can significantly reduce total traffic delay at intersections.