Traffic Signal Optimization Research Articles

Traffic Signal Optimization using Computer Vision

Traffic Signal Optimization using Computer Vision

An adversarial diverse deep ensemble approach for surrogate‐based traffic signal optimization

AbstractSurrogate‐based traffic signal optimization (TSO) is a computationally efficient alternative to simulation‐based TSO. By replacing the simulation‐based objective function, a surrogate model can quickly identify solutions by searching for extreme points on its response surface. As a popular surrogate model, the ensemble of multiple diverse deep learning models can approximate complicated systems with a strong generalizability. However, existing ensemble methods barely focus on strengthening the prediction of extreme points, which we found can be realized by further diversifying base learners in the ensemble. The study proposes an adversarial diverse ensemble (ADE) method for online TSO with limited computational resources, comprising two stages: In the offline stage, base extractors are diversified with unlabeled data by a designed adversarial diversity training algorithm; in the online stage, base predictors are trained in parallel with limited labeled data, and the ensemble then serves as the surrogate model to search for solutions iteratively for TSO. First, it is demonstrated that the prediction accuracy on extreme points, and associated solution quality, can be constantly improved with base learners’ diversity enhanced by ADE. Case studies of TSO conducted on a four‐intersection arterial further demonstrate the superior solution quality and computational efficiency of the ADE surrogate model in a wide range of traffic scenarios. Moreover, a large‐scale online TSO experiment under dynamic traffic demand proves ADE's effectiveness in practical applications.

Intelligent Traffic Control Decision-Making Based on Type-2 Fuzzy and Reinforcement Learning

Intelligent traffic control decision-making has long been a crucial issue for improving the efficiency and safety of the intelligent transportation system. The deficiencies of the Type-1 fuzzy traffic control system in dealing with uncertainty have led to a reduced ability to address traffic congestion. Therefore, this paper proposes a Type-2 fuzzy controller for a single intersection. Based on real-time traffic flow information, the green timing of each phase is dynamically determined to achieve the minimum average vehicle delay. Additionally, in traffic light control, various factors (such as vehicle delay and queue length) need to be balanced to define the appropriate reward. Improper reward design may fail to guide the Deep Q-Network algorithm to learn the optimal strategy. To address these issues, this paper proposes a deep reinforcement learning traffic control strategy combined with Type-2 fuzzy control. The output action of the Type-2 fuzzy control system replaces the action of selecting the maximum output Q-value of the target network in the DQN algorithm, reducing the error caused by the use of the max operation of the target network. This approach improves the online learning rate of the agent and increases the reward value of the signal control action. The simulation results using the Simulation of Urban MObility platform show that the traffic signal optimization control proposed in this paper has achieved significant improvement in traffic flow optimization and congestion alleviation, which can effectively improve the traffic efficiency in front of the signal light and improve the overall operation level of traffic flow.

Real-time application of grey system theory in intelligent traffic signal optimization

Real-time application of grey system theory in intelligent traffic signal optimization

Two‐stage algorithm for traffic signal optimization and web‐service system development

AbstractEfficient control of traffic signals for vehicles and pedestrians at intersections is critical for relieving traffic congestion. Considering the unique characteristics of intersections, such as the number of roads, the presence or absence of crosswalks, road geometric shapes, and traffic demand patterns, an appropriate phase sequence and duration for traffic signals must be established at each intersection. This paper proposes a simulation‐based two‐stage algorithm comprising integer‐constrained Adam (ICA) and tabu search (TS) to optimize the phase sequence and duration for arbitrary intersections with arbitrary traffic‐demand patterns. The ICA promptly identifies a promising region in which a global optimal solution is likely to be obtained, whereas TS determines the best solution near the region. The performance of the proposed algorithm that optimizes phase durations with fixed phase sequence is evaluated against several baseline methods using 24 instances across six actual intersections. Experimental results show that the proposed algorithm reduces the average travel time by 20.2% compared with existing traffic signals within a computation time of 4 min, thus providing a near‐optimal solution eight times faster than commonly used population‐based metaheuristics. Furthermore, the algorithm demonstrates robust performance across heterogeneous vehicles and recommends the best phase sequence that effectively alleviates congestion in current traffic signal systems. The optimized phase sequence with best phase durations further reduces the average travel time by approximately 11.3% compared with the existing phase sequence with best phase durations at an actual intersection. To facilitate its widespread use, a free, open web‐service system named “Smart Intersection for Traffic Efficiency” is developed, which enables users to optimize traffic signal systems without requiring optimization background or simulation knowledge.

Regional traffic congestion coordination control based on critical links

Critical links have the characteristics of carrying large traffic flow and quickly affecting the operational performance of the road network. Existing regional traffic congestion coordination control methods do not consider the impact of critical links. To solve this issue, this paper proposes a regional traffic congestion coordination control method based on critical links. First, the median, gravitational index, and traffic sharing ratio are selected as the identification indexes of critical links in urban networks. Second, the road network vulnerability is assessed, and road link criticality is calculated based on joint entropy theory and multi-scale factor synthesis of multi-metric identification results. Then, the regional signal control range is determined, and a multi-stage signal control optimization model is developed. Finally, the optimal traffic signals for intersections are obtained by minimizing the pressure at the maximum phase of the road link and maximizing the capacity of critical links. To demonstrate the effectiveness of the proposed methods, two benchmarks (i.e., the MAXBAND method and existing traffic signals) are compared. The results show that (1) in terms of boundary control, the average vehicle delay at the boundary intersection of the road network is 34.62 s, an increase of 7.49 s and 5.03 s compared to the benchmark methods, respectively. (2) In terms of internal control, the average delay of the road network based on the proposed method is 87.54 s, which is reduced by 16.38 % and 28.73 % compared with the benchmark methods, and the queue lengths of the critical links are reduced by 21.47 % and 34.15 %, respectively. (3) The control process of the proposed method enables the number of vehicles within the road network to be maintained near the optimal carrying capacity, which gives the road network an efficient operating efficiency.

First steps towards real-world traffic signal control optimisation by reinforcement learning

ABSTRACT Enhancing traffic signal optimisation has the potential to improve urban traffic flow without the need for expensive infrastructure modifications. While reinforcement learning (RL) techniques have demonstrated their effectiveness in simulations, their real-world implementation is still a challenge. Real-world systems need to be developed that guarantee a deployable action definition for real traffic systems while prioritising safety constraints and robust policies. This paper introduces a method to overcome this challenge by introducing a novel action definition that optimises parameter-level control programmes designed by traffic engineers. The complete proposed framework consists of a traffic situation estimation, a feature extractor, and a system that enables training on estimates of real-world traffic situations. Further multimodal optimisation, scalability, and continuous training after deployment could be achieved. The first simulative tests using this action definition show an average improvement of more than 20% in traffic flow compared to the baseline – the corresponding pre-optimised real-world control.

An adaptive coupled control method based on vehicles platooning for intersection controller and vehicle trajectories in mixed traffic

AbstractConnected and autonomous driving technologies offer a novel solution for intersection control optimization. Connected and autonomous vehicles (CAVs) can access signal plans and optimize trajectories to minimize delays and reduce fuel consumption. However, optimizing trajectories for individual vehicles significantly increases complexity, especially for joint optimization of traffic signals and vehicle trajectories. Given the current technical, regulatory, and policy constraints, a superior intersection management approach is necessary before fully automated driving is achieved. This paper introduces an adaptive coupling control (ACC) method based on vehicle platooning to optimize signal timings and vehicle trajectories in mixed traffic. Initially, vehicle platoon segmentation is conducted, led by CAVs. The study then proposes a single‐layer coupled optimization model based on vehicle platoons, simplifying the joint optimization model. To address logistic constraint difficulties, a linearization of the coupled model (LCM) method is developed. Numerical experiments demonstrate that the ACC method significantly reduces vehicle delay and fuel consumption. At high CAV penetration rates (0.8 < R <1) and high traffic volumes (over 900 pcu/h), vehicle platoon control delivers excellent performance, with delays and fuel consumption even lower than in a fully automated environment (R = 1). This surprising result suggests that the mixed platoon system (ACC method) positively impacts mixed traffic.

Cooperative Optimization of Traffic Signals and Vehicle Speed Using a Novel Multi-Agent Deep Reinforcement Learning

Cooperative Optimization of Traffic Signals and Vehicle Speed Using a Novel Multi-Agent Deep Reinforcement Learning

Synchronization and implementation of real-time traffic signal optimization regulator

Synchronization and implementation of real-time traffic signal optimization regulator

Enhanced-GA supports traffic signal optimizations and protects the urban environment

Much current research on traffic signal optimization often neglects the impact of environmental factors in urban areas. This can result in suboptimal solutions that do not consider the effects of traffic on air pollution and the overall urban environment. To address this issue, this article proposes a solution that combines Enhanced GA with a comprehensive framework for considering environmental factors in traffic signal optimization. By optimizing traffic signal timings and minimizing emissions, the proposed solution aims to reduce congestion and improve urban transportation networks' efficiency while protecting the environment. The proposed approach uses a set of optimization algorithms and assumptions to generate a comprehensive framework for traffic signal optimization. These algorithms and assumptions consider environmental factors such as air quality and the impact of traffic on the local ecosystem. Moreover, this article provided the enhanced genetic algorithm operators and suggested model formulation that could be applied in other research on traffic signal optimization directly to reduce calculation times and increase the efficiency of the novel suggested models

A Study of Initial Individual Generation in Search for the Optimal Traffic Signal Offset Using Genetic Algorithm

A Study of Initial Individual Generation in Search for the Optimal Traffic Signal Offset Using Genetic Algorithm

Traffic signal optimization framework using interpretable machine learning technique under heterogeneous-autonomy traffic environment

Traffic signal optimization framework using interpretable machine learning technique under heterogeneous-autonomy traffic environment

Integration of Decentralized Graph-Based Multi-Agent Reinforcement Learning with Digital Twin for Traffic Signal Optimization

Machine learning (ML) methods, particularly Reinforcement Learning (RL), have gained widespread attention for optimizing traffic signal control in intelligent transportation systems. However, existing ML approaches often exhibit limitations in scalability and adaptability, particularly within large traffic networks. This paper introduces an innovative solution by integrating decentralized graph-based multi-agent reinforcement learning (DGMARL) with a Digital Twin to enhance traffic signal optimization, targeting the reduction of traffic congestion and network-wide fuel consumption associated with vehicle stops and stop delays. In this approach, DGMARL agents are employed to learn traffic state patterns and make informed decisions regarding traffic signal control. The integration with a Digital Twin module further facilitates this process by simulating and replicating the real-time asymmetric traffic behaviors of a complex traffic network. The evaluation of this proposed methodology utilized PTV-Vissim, a traffic simulation software, which also serves as the simulation engine for the Digital Twin. The study focused on the Martin Luther King (MLK) Smart Corridor in Chattanooga, Tennessee, USA, by considering symmetric and asymmetric road layouts and traffic conditions. Comparative analysis against an actuated signal control baseline approach revealed significant improvements. Experiment results demonstrate a remarkable 55.38% reduction in Eco_PI, a developed performance measure capturing the cumulative impact of stops and penalized stop delays on fuel consumption, over a 24 h scenario. In a PM-peak-hour scenario, the average reduction in Eco_PI reached 38.94%, indicating the substantial improvement achieved in optimizing traffic flow and reducing fuel consumption during high-demand periods. These findings underscore the effectiveness of the integrated DGMARL and Digital Twin approach in optimizing traffic signals, contributing to a more sustainable and efficient traffic management system.

A synchronization-constraints-based dual bands method of traffic signal optimization

This paper presents a synchronization-constraints-based dual bands method of traffic signal optimization for multiple traffic flows. The synchronization coefficient, traffic volume ratio, synchronization benefit, and their thresholds of traffic flow pair are proposed to select synchronized traffic flow pairs for progression. Then, progression bands of multiple traffic flows can be achieved by merging time windows, combining equivalent phase, and optimizing benefits evaluation index of the synchronized traffic flow pair. The proposed method adopts step-by-step optimization processing, and the combinatorial optimization of signal cycle, phase sequence, and offset is decomposed by performing synchronization and progression. A real-world case is presented to illustrate the application of the proposed method with VISSIM simulations. The results show that compared with the MULTIBAND model and TRANSYT, this method significantly decreased the average delay and average stops along the arterial of the entire traffic system composed of buses and cars.

Distributed Traffic Signal Optimization at V2X Intersections

This paper presents our research on a traffic signal control system (TSCS) at V2X intersections. The overall objective of the study is to create an implementable TSCS. The specific objective of this paper is to investigate a distributed system towards implementation. The objective function of minimizing queue delay is formulated as the integral of queue lengths. The discrete queueing estimation is mixed with macro and micro traffic flow models. The novel proposed architecture alleviates the communication network bandwidth constraint by processing BSMs and computing queue lengths at the local intersection. In addition, a two-stage distributed system is designed to optimize offsets, splits, and cycle length simultaneously and in real time. The paper advances TSCS theories by contributing a novel analytic formulation of delay functions and their first degree of derivatives for a two-stage optimization model. The open-source traffic simulation engine Enhanced Transportation Flow Open-Source Microscopic Model (ETFOMM version 1.2) was selected as a simulation environment to develop, debug, and evaluate the models and the system. The control delay of the major direction, minor direction, and the total network were collected to assess the system performance. Compared with the optimized TSCS timing plan by the Virginia Department of Transportation, the system generated a 21% control delay reduction in the major direction and a 7% control delay reduction in the minor direction at just a 10% penetration rate of connected vehicles. Finally, the proposed distributed and centralized systems present similar performances in the case study.

Traffic light control with reinforcement learning

Urban traffic signal optimization is important for alleviating congestion in urban transportation systems. This study proposes a real-time traffic light control algorithm based on deep Q learning with a reward function that accounts for queue lengths, delays, travel times, and throughput. The model dynamically decides phase changes based on current traffic conditions. The training of the deep Q network involves an offline stage from pre-generated data with fixed signal timing and an online stage using real-time traffic data. A deep Q network structure with aphase gate component is used to simplify the model's learning task under different phases. Amemory palace" mechanism is used to address sample imbalance during the training process. Both synthetic and real-world traffic flow data are used to validate our approach under an urban road intersection scenario in Hangzhou, China. Results demonstrate significant performance improvements of the proposed method in reducing vehicle waiting time (57.1% to 100%), queue lengths (40.9% to 100%), and total travel time (16.8% to 68.0%) compared to traditional fixed signal timing plans.

Efficient derivation of optimal signal schedules for multimodal intersections

Tramways decrease time, cost, and environmental impact of urban transport, while requiring multimodal intersections where trams arriving with nominal periodic timetables may have right of way over road vehicles. Quantitative evaluation of stochastic models enables early exploration and online adaptation of design choices, identifying operational parameters that mitigate impact on road transport performance.We present an efficient analytical approach for offline scheduling of traffic signals at multimodal intersections among road traffic flows and tram lines with right of way, minimizing the maximum expected percentage of queued vehicles of each flow with respect to sequence and duration of phases. To this end, we compute the expected queue size over time of each vehicle flow through a compositional approach, decoupling analyses of tram and road traffic. On the one hand, we define microscopic models of tram traffic, capturing periodic tram departures, bounded delays, and travel times with general (i.e., non-Exponential) distribution with bounded support, open to represent arrival and travel processes estimated from operational data. On the other hand, we define macroscopic models of road transport flows as finite-capacity vacation queues, with general vacation times determined by the transient probability that the intersection is available for vehicles, efficiently evaluating the exact expected queue size over time. We show that the distribution of the expected queue size of each flow at multiples of the hyperperiod, resulting from temporization of nominal tram arrivals and vehicle traffic signals, reaches a steady state within few hyper-periods. Therefore, transient analysis starting from this steady-state distribution and lasting for the hyper-period duration turns out to be sufficient to characterize road transport behavior over time intervals of arbitrary duration.We implemented the proposed approach in the novel OMNIBUS Java library, and we compared against Simulation of Urban MObility (SUMO). Experimental results on case studies of real complexity with time-varying parameters show the approach effectiveness at identifying optimal traffic signal schedules, notably exploring in few minutes hundreds of schedules requiring tens of hours in SUMO.

Enhancing Urban Intersection Efficiency: Visible Light Communication and Learning-Based Control for Traffic Signal Optimization and Vehicle Management

This paper introduces a novel approach, Visible Light Communication (VLC), to optimize urban intersections by integrating VLC localization services with learning-based traffic signal control. The system enhances communication between connected vehicles and infrastructure using headlights, streetlights, and traffic signals to transmit information. Through Vehicle-to-Vehicle (V2V) and Infrastructure-to-Vehicle (I2V) interactions, joint data transmission and collection occur via mobile optical receivers. The goal is to reduce waiting times for pedestrians and vehicles, enhancing overall traffic safety by employing flexible and adaptive measures accommodating diverse traffic movements. VLC cooperative mechanisms, transmission range, relative pose concepts, and queue/request/response interactions help balance traffic flow and improve road network performance. Evaluation in the SUMO urban mobility simulator demonstrates advantages, reducing waiting and travel times for both vehicles and pedestrians. The system employs a reinforcement learning scheme for effective traffic signal scheduling, utilizing VLC-ready vehicles to communicate positions, destinations, and routes. Agents at intersections calculate optimal strategies, communicating to optimize overall traffic flow. The proposed decentralized and scalable approach, especially suitable for multi-intersection scenarios, showcases the feasibility of applying reinforcement learning in real-world traffic scenarios.

Traffic signal optimization using multiobjective linear programming for oversaturated traffic conditions

Traffic signal optimization using multiobjective linear programming for oversaturated traffic conditions