Real-Time Optimization for Adaptive Traffic Signal Control Using Genetic Algorithms

Traffic congestion and delays at signalized intersections remain a challenge in urban mobility. To minimize congestion, adaptive traffic signal control systems have been adopted across the United States. However, for these systems to function at their best, systems require an accurate estimation of traffic states, such as traffic density and queue length. This paper implements a relatively novel method, Physics-Informed Neural Networks (PINNs), for real-time traffic density estimation to enhance adaptive intersection signal control. The methodology leverages PINNs, which integrate both data-driven learning and physical constraints imposed by the Lighthill-Whitham-Richards (LWR) macroscopic traffic flow model. A simulation of a four-way intersection was used to generate realistic spatio-temporal traffic data to evaluate the performance of the model. Separate PINNs models, one trained for each incoming approach using simulation-based spatio-temporal density data, provide segment-level density predictions. These real-time density estimates are then employed within an adaptive traffic signal control algorithm designed to adjust green light phasing based on detected demand. The proposed PINNs control strategy was evaluated through simulation, comparing its performance against the Webster traffic timing method using key metrics such as average vehicle delay at the intersection and average vehicle travel time. The findings show an approximate 18% decrease in average vehicle delay and more than a 6% reduction in average travel time when utilizing adaptive traffic control based on the PINNs method for determining density values. These findings highlight the potential of leveraging PINNs for accurate, real-time traffic state estimation, which can effectively inform and improve the performance of adaptive traffic signal control systems, leading to reduced congestion and enhanced mobility at intersections.

CAV-enabled data analytics for enhancing adaptive signal control safety environment

Traffic congestion can be alleviated by infrastructure expansions, however, improving the existing infrastructure using traffic control is more plausible due to the obvious constraints on financial resources and physical space. Independent applications of Adaptive Traffic Signal Control (ATSC) and Ramp Metering (RM) have shown strong potential to effectively alleviate urban traffic congestion by adjusting the signal timings in real-time in response to traffic fluctuations to achieve desirable objectives (e.g., minimize delay at intersections or minimize travel time along freeways). This paper presents the problem formulation and the framework for addressing traffic control problem using an integrated solution combining ATSC and RM. According to that problem definition, an optimal closed-loop approach for ATSC and RM can be designed using a multi-agent reinforcement learning (MARL) approach. Authors' previous studies of MARL on a large-scale simulated network of 59 intersections in the lower downtown core of the City of Toronto for the morning rush hour have shown unprecedented reduction in the average intersection delay up to 39% at the network level, and travel time savings of up 2to 26% along the busiest routes in downtown Toronto. Additionally, authors' studies of MARL applied to a calibrated microsimulation model of the Gardiner Expressway in Toronto, Canada resulted in 50% reduction in total travel time compared with the base case scenario.

This paper presents an adaptive traffic signal controller algorithm that allows real time traffic flow data to control traffic signal timing for an isolated intersection. The proposed design allows greater flexibility and control of signal timings while keeping in view multiple criterions. It is based on simple rules that allow traffic flows on all intersection branches to control the timing of current signal phase without causing unrealistic delays on low volume side streets. The algorithm is also adjusted for capacity of different links. Detector data has been used to assess congestion situation and respond accordingly. A priority scheme for priority vehicles is also proposed. As a proof of concept, the proposed algorithm has been implemented on FPGA as a parameterized VERILOG model allowing adjustment to any intersection size and topology. An evaluator circuit was designed to assess the performance as compared to actuated signal controller. Improvement was observed in total stopped delay, number of stops and queue lengths.

An adaptive traffic signal controller (ATSC) combined with a connected vehicle (CV) concept uses real-time vehicle trajectory data to regulate green time and has the ability to improve travel time in a signalized corridor. This study introduces an innovative "slow poisoning" cyberattack for a waiting-time-based ATSC algorithm and a corresponding detection strategy. The objectives of this paper are to (1) develop a "slow poisoning" attack generation strategy for an ATSC and (2) develop a prediction-based "slow poisoning" attack detection strategy using a recurrent neural network—i.e., long short-term memory model. We have generated a "slow poisoning" attack modeling strategy using a microscopic traffic simulator—Simulation of Urban Mobility (SUMO)—and used generated data from the simulation to develop both the attack model and detection model. Our analyses revealed that the attack strategy is effective in creating a congestion in an approach and that our detection strategy can capably flag the attack.

As with the advances in location-based technology that emerged from ecommerce and wireless communication, individual vehicular information is becoming available instantaneously and could be used for traffic signal control. A new paradigm of a signal control algorithm might be needed to accommodate such abundant location-based information. This preliminary study investigates the benefit of adaptive signal control under such conditions. A simple event-based simulation program along with a genetic algorithm-based signal optimization program developed in this study evaluates various scenarios including both demand and market penetration levels. A test bed used in the study consists of two one-way streets with single-lane approaches. This study provides a ceiling benchmark of adaptive signal control under perfect knowledge in vehicle arrivals. The results indicate that adaptive signal control outperforms pretimed signal control for all cases considered, but the marginal benefit of adaptive signal control increases up to a certain volume level and then decreases. The results also indicate that the use of imperfect knowledge improves performance of adaptive signal timing plans over pretimed timing plans.

Population is steadily increasing worldwide, resulting in intractable traffic congestion in dense urban areas. Adaptive traffic signal control (ATSC) has shown strong potential to effectively alleviate urban traffic congestion by adjusting signal timing plans in real time in response to traffic fluctuations to achieve desirable objectives (e.g., minimize delay). Efficient and robust ATSC can be designed using a multiagent reinforcement learning (MARL) approach in which each controller (agent) is responsible for the control of traffic lights around a single traffic junction. Applying MARL approaches to the ATSC problem is associated with a few challenges as agents typically react to changes in the environment at the individual level, but the overall behavior of all agents may not be optimal. This paper presents the development and evaluation of a novel system of multiagent reinforcement learning for integrated network of adaptive traffic signal controllers (MARLIN-ATSC). MARLIN-ATSC offers two possible modes: 1) independent mode, where each intersection controller works independently of other agents; and 2) integrated mode, where each controller coordinates signal control actions with neighboring intersections. MARLIN-ATSC is tested on a large-scale simulated network of 59 intersections in the lower downtown core of the City of Toronto, ON, Canada, for the morning rush hour. The results show unprecedented reduction in the average intersection delay ranging from 27% in mode 1 to 39% in mode 2 at the network level and travel-time savings of 15% in mode 1 and 26% in mode 2, along the busiest routes in Downtown Toronto.

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.

To reduce the traffic congestion, ensuring intersection control strategy efficiency is a priority in urban transportation system. Reinforcement Learning (RL)-based method provides a new possibility for solving this complex problem. In this paper, a novel region adaptive traffic signal control (RA-TSC) strategy is proposed. As an intelligent agent, the traffic signal light can learn the optimal control policy via deep reinforcement learning algorithm, namely Double Dueling Deep Q Network (3DQN). Different from the previous researches, the proposed method takes traffic characteristics into account, and two approaches are proposed to improve the performance of intersection control: trust region state (TRS) and standardized reward (SR). Besides, prioritized experience replay is adopted to guarantee algorithm stability and optimize data exploitation. Simulation results show that the proposed RA-TSC algorithm can reduce vehicle waiting length by 20% and 15.1% respectively when compared with fixed time and original 3DQN control algorithms.

Distributed and adaptive traffic signal control within a realistic traffic simulation

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.

The authors describe a computer vision application to adaptive traffic signal control. Adaptive traffic signal control systems allocate green time at an intersection dynamically in response to sensed incoming traffic flows to continually optimize overall throughput. Existing video-based systems are limited to the use of vehicle presence and volume (count) data, and since it is not possible to distinguish between different types of vehicles, optimization opportunities can be missed. The authors propose to detect specific types of vehicles, such as buses, so that they can be assigned higher priority when appropriate and the overall effectiveness of the adaptive signal system can be improved. The authors base the authors system design on current visual recognition technology (HOG SVM) and exploit configuration constraints specific to this application, such as knowledge about the anticipated scale of the vehicles. The challenge is to be robust to varying illumination and weather conditions, occlusions from other vehicles, and large variations in scale while producing recognition results in real-time. The authors show results on challenging data from traffic cameras under different observation conditions and at varying ranges.

Adaptive traffic signal control (ATSC) based on deep reinforcement learning (DRL) has shown promising prospects to reduce traffic congestion. Most existing methods keeping traffic signal phases fixed adopt two agent actions to match a four-phase suffering unstable performance and undesirable operation in a four-phase signalized intersection. In this paper, a Double Deep Q-Network (DDQN) with a dual-agent algorithm is proposed to obtain a stable traffic signal control policy. Specifically, two agents are denoted by two different states and shift the control of green lights to make the phase sequence fixed and control process stable. State representations and reward functions are presented by improving the observability and reducing the leaning difficulty of two agents. To enhance the feasibility and reliability of two agents in the traffic control of the four-phase signalized intersection, a network structure incorporating DDQN is proposed to map states to rewards. Experiments under Simulation of Urban Mobility (SUMO) are carried out, and results show that the proposed traffic signal control algorithm is effective in improving traffic capacity.

Adaptive traffic signal controllers offer better signal time management especially when the traffic flow pattern is not uniform on all approaches. Traditional adaptive traffic controller use upstream or advance vehicle detection which works well in situations where traffic follows good lane discipline. However, when the spacing between intersections increases or in the case of complex geometry these systems may not be efficient. This is primarily because of the inability of traffic flow models to accurately estimate the traffic demand from the upstream detectors. Using stop-line detector information is best suited in such traffic conditions as they do not require any explicit prediction models. Furthermore, there are many intersections which works using stop-line detectors with preset maximum green timings as vehicle actuated controllers. These controllers can be easily converted into truly adaptive by changing their maximum green timings continuously with respect to changing traffic flow pattern. Hence, this paper proposes an adaptive traffic control model which uses stop-line detector information instead of upstream detector. The model aims at real-time allocation of green time through reinforcement learning; an approach originated from the machine learning community. This approach has the ability to learn relationships between signal control actions and their effect on the queue while pursuing the goal of maximizing throughput which is a distinct improvement over the traditional vehicle actuated system. To demonstrate the performance of the proposed model a typical four-way intersection with four-phase scheme is evaluated for various flow conditions with the proposed model as well as with the traditional vehicle actuated system. The results show improvement over traditional system, especially when the flow is near the capacity.

Abstract- With the rapid growth of urban populations and the increasing number of vehicles on the road, traffic congestion has become a significant problem in metropolitan areas. Conventional traffic control systems, which rely on pre-set signal timers, often fail to address real-time traffic conditions effectively, leading to inefficient traffic flow, increased fuel consumption, and environmental degradation. The paper “Smart Traffic Control System Based on Traffic Density Using YOLO” introduces a novel approach to address this issue by integrating artificial intelligence and computer vision into traffic management. The proposed system utilizes the YOLO (You Only Look Once) object detection algorithm to detect and classify vehicles in real-time from live video feeds captured at intersections. YOLO, known for its speed and accuracy in object detection tasks, calculates the vehicle density in each direction. Based on the detected density, the system dynamically adjusts the green light duration for each lane, ensuring that the direction with higher traffic receives longer green light periods. This method aims to optimize signal switching and minimize overall vehicle wait time. The paper outlines the complete architecture of the system, which includes modules for video input processing, vehicle detection using YOLO, traffic density computation, and adaptive signal timing control. By leveraging deep learning, the system enhances decision-making capabilities without relying on costly hardware like inductive loops or infrared sensors. This review evaluates the effectiveness and innovation of the proposed system, comparing it with traditional and sensor-based models. It highlights the advantages of using YOLO, such as real-time processing, high accuracy, and scalability. Additionally, the review discusses the system’s limitations, such as dependency on video clarity and environmental conditions, and proposes areas for future enhancement, including integration with cloud computing and edge AI for broader deployment. Keywords- Smart Traffic Control System, YOLO Object Detection, Traffic Density Estimation, Real-Time Traffic Monitoring, Adaptive Traffic Signal Control, Vehicle Classification ( Cars, Bikes, Buses, Trucks, Rikshaws), Intelligent Transportation System.