Connected Vehicle–Based Adaptive Signal Control and Applications

Control methodologies of traffic signals have significantly improved during the recent past along with advancements in technology. Adaptive traffic signal control is the most recent and advanced control type of traffic signals. Adaptive control is able to efficiently relieve traffic congestion by continuously adjusting signal timings according to real-time traffic conditions. Conventional optimization methods such as integer programming, hill climbing, or descent gradient searching have been gradually overshadowed by genetic algorithms in many areas including traffic signal operation. The research presented in this article is distinct from previous studies in that it focuses on real-time adaptive signal optimization using genetic algorithms. The proposed adaptive signal system provides acyclic signal operation based on a rolling horizon real-time control approach. The algorithm was tested using microsimulation for on-line evaluation and comparison to fixed-time plans generated from the latest TRANSYT-7F version 9.7, which has a genetic optimization feature. The developed signal system consists of three major components including a genetic algorithm optimization module, an internal traffic simulation module, and a database management system all working in cooperation to optimize signal timings in real time. Using the pseudo on-line simulation platform, three testing scenarios for high, medium, and low level of traffic demands were conducted focusing on evaluating several important features of the proposed adaptive signal control system. The test results indicated that real-time genetic control outperformed fixed-signal timing plan in all scenarios based on total vehicle delay.

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.

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.

Current practice of dilemma zone protection relies on traditional detection technology such as loop, video, or radar detectors, the performance of which is sensitive to installation and constricted by the limited detection range. This paper proposes and evaluates a dilemma zone protection framework via vehicle-to-infrastructure communications. The proposed framework utilizes the communications data received at roadside equipment for actuated signal operations and dilemma zone protection. To examine the effectiveness of the proposed framework, a simulation test bed was built in VISSIM. Three simulation scenarios were designed to investigate the effectiveness of the proposed framework. Results indicated that, at full market penetration, the proposed framework provided the best dilemma zone protection. Delay on conflicting movements was also reduced significantly compared with the traditional green extension system. Sensitivity analyses on market penetration showed that the performance of dilemma zone protection degraded to that of the green extension system at 40% or lower penetration levels. Higher variation in the waiting period was found for conflicting movements at lower penetration rates. Generally, at 20% penetration rate, the standard deviation doubled from that at the full market penetration.

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.

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

With the development of connected vehicles (CVs) technology, it has become a new research topic to capture the dynamic traffic system by using CVs data. The traditional vehicle arrival prediction models are limited to the fixed detectors, which can only recognize the passing information of vehicles, but cannot identify the state of vehicles. This paper proposed a new vehicle arrival prediction model of traffic signal control in a connected vehicle environment. First, the vehicle's identification number (ID), position, velocity, and acceleration were obtained in a CV environment. Second, a new vehicle arrival prediction model was developed by the joint probability distribution calibrated based on CV data. Then, the simulation experiment was designed for analyzing influence factors on model performance. The results show that the average relative error of each factor is less than 10%. Finally, the reliability of the proposed model was verified based on an adaptive signal control algorithm. Therefore, the proposed model can be used in the adaptive signal control system.

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.

Traffic signal control is a complex problem and it is difficult to determine an optimal strategy to control multi-directional traffic at multiple intersections. Recent years have witnessed numerous successes of deep learning neural networks in the fields of artificial intelligence. Motivated by the dominant performance of neural networks, this study attempts to develop a novel adaptive signal control approach by fusing deep learning (DL) and reinforcement learning (RL), i.e., deep reinforcement learning (DRL), for arterial signal coordination. DRL can considerably improve the ability to deal with large amounts of data processing, systematic perception and expression, which is key to coordinated control of arterial intersections. The proposed algorithm is implemented by utilizing real-time traffic detection data and aims to optimize the hybrid global and local reward functions. The experimental results obtained by traffic simulation software SUMO demonstrate the advantage of the proposed approach, as well as its efficiency and effectiveness compared with fixed-time and actual signal control methods.

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.

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.

Automated intersection delay estimation using the input–output principle and turning movement data

Network traffic instability with automated driving and cooperative merging

Adaptive signal control is a method that dynamically adjusts signal phases based on real-time traffic conditions, aiming to improve the efficiency of intersection light control. However, in practical applications, obtaining complete traffic spatiotemporal states at intersections is challenging given the limited data sources. To address this issue, this paper presents a novel deep reinforcement learning model, namely 3DQN-PSTER, which combines Dueling Deep Q Network, Double Deep Q Network, and Priority SumTree Experience Replay technology. This model effectively extracts traffic states from sampled floating car data (FCD) and achieves optimal timing schemes. To evaluate its performance, the proposed model is tested in a simulated intersection environment using actual intersection data. The model interacts with the environment by adjusting signal phases, updating traffic states, and obtaining optimal timing schemes. Simulation results demonstrate that the proposed model exhibits higher convergence efficiency and outperforms benchmark models. When applied, the reinforcement learning single control (RLSC) based on the 3DQN-PSTER model outperforms fixed-time signal control schemes and actuated signal control (ASC) schemes. Even in scenarios with low penetration ratios of FCD, RLSC still significantly outperforms ASC. Therefore, the signal timing optimization method proposed in this paper could greatly enhance intersection operation efficiency in both ideal overall data environments and partial states.

Urban traffic congestion necessitates a transition from reactive signal control toward proactive, prediction-driven traffic management strategies.This study proposes a multivariate forecasting framework based on Long Short-Term Memory (LSTM) neural networks to support short-term adaptive signal control at urban intersections.Using high-resolution traffic data collected at 20-second intervals, three independent yet structurally consistent LSTM models were developed to predict vehicle count, traffic density, and adaptive green time.The models exploit temporal dependencies through sequence-based learning and are trained using a supervised multivariate formulation.Experimental results demonstrate stable convergence and strong generalization, with validation loss values below 0.093 across all targets.Additional evaluation using RMSE, MAE, and MAPE confirms robust predictive accuracy under heterogeneous traffic conditions.Twenty-stepahead forecasts (approximately 6-7 minutes) reveal coherent temporal behavior, characterized by increasing and stabilizing traffic demand alongside converging green time allocations, indicating that the models capture key nonlinear interactions between congestion and control logic.Compared with conventional statistical and machine learning approaches, the proposed framework achieves competitive accuracy with lower computational complexity.The findings highlight the potential of LSTM-based forecasting to enable anticipatory traffic signal control, improve intersection performance, and reduce congestion-related environmental impacts.