Traffic Control Algorithm Research Articles

Assessing the performance of a hybrid max‐weight traffic signal control algorithm in the presence of noisy queue information: An evaluation of the environmental impacts

Assessing the performance of a hybrid max‐weight traffic signal control algorithm in the presence of noisy queue information: An evaluation of the environmental impacts

Traffic Light Control in Vehicular Network Systems using Fuzzy Logic

Vehicular concentration is getting worse in big and developed cities as we approach the year 2020, creating a challenge. Accurate traffic signal management is a key to efficient free traffic movement and increasing transport security levels. It is thus important to have traffic lights that change with the traffic intensity information that is collected in real-time. Thus, the objective of this study is to design a viable traffic signal control algorithm that would cause minimal delay to drivers. Building on the basis of fuzzy logic, the approach sets input attributes like the volume of the street and the queue of vehicles to identify the right traffic light settings. Performance criteria for evaluation include the average waiting time of the vehicles, and the findings of simulations in this regard show 21.7 seconds, which is faster than the benchmark formula of 25 seconds. This improvement is attributed to the fact that the fuzzy control system is adjustable.

Dynamic Traffic Light Algorithm Incorporating Post-Intersection Space Allocation

This research presents a novel dynamic traffic light algorithm designed to optimize traffic flow and reduce traffic congestion by dynamically allocating green time based on post-intersection space availability. The algorithm employs a three-stage process: input generation, processing, and output. The input stage involves capturing traffic images using cameras strategically placed at intersections, which are then processed using background subtraction, edge detection, and object counting techniques. The processing phase includes vehicle counting using the YOLOv8 algorithm and open space calculation based on the maximum capacity of each road section. The output phase involves dynamically allocating green time to roads based on available post-intersection space and occupancy rates. The algorithm is designed to adapt to changing traffic conditions by continuously monitoring the post- intersection space and adjusting green times accordingly. It also incorporates a reset timer to ensure the algorithm loops back to the initial stage of gathering and processing traffic images. Simulation experiments using a physical model with toy vehicles and a camera setup demonstrated the benefits of this approach. Compared to the density-based approaches[1], this algorithm reduced average vehicle delay by 20-30%, increased overall intersection throughput by 15-25%, and decreased maximum queue lengths in each lane by 25-35%. It also adapted more effectively to fluctuations in traffic conditions, improving performance metrics by 20-30%. These results highlight the potential of incorporating downstream space considerations into traffic light control algorithms to enhance intersection efficiency, reduce traffic congestion, and enable more adaptive and fair traffic management.

A Dynamic Traffic Light Control Algorithm to Mitigate Traffic Congestion in Metropolitan Areas.

This paper proposes a convolutional neural network (CNN) model of the signal distribution control algorithm (SDCA) to maximize the dynamic vehicular traffic signal flow for each junction phase. The aim of the proposed algorithm is to determine the reward value and new state. It deconstructs the routing components of the current multi-directional queuing system (MDQS) architecture to identify optimal policies for every traffic scenario. Initially, the state value is divided into a function value and a parameter value. Combining these two scenarios updates the resulting optimized state value. Ultimately, an analogous criterion is developed for the current dataset. Next, the error or loss value for the present scenario is computed. Furthermore, utilizing the Deep Q-learning methodology with a quad agent enhances previous study discoveries. The recommended method outperforms all other traditional approaches in effectively optimizing traffic signal timing.

N-MP: A network-state-based Max Pressure algorithm incorporating regional perimeter control

The Max Pressure (MP) framework has been shown to be an effective real-time decentralized traffic signal control algorithm. However, despite its superior performance and desirable features – such as the maximum stability property – it may still suffer from deterioration in network mobility due to the rise of congestion within specific regions of an urban traffic network. To address this drawback and further improve the performance of MP control in urban networks, this paper proposes a novel MP algorithm that incorporates regional traffic states into the MP framework. The proposed model – called N-MP – simultaneously integrates perimeter metering control at the boundary of regions of a network to be protected with traditional local intersection control. The proposed model is the first to incorporate perimeter metering control fully within a decentralized signal control environment and inherits the maximum stability property. In addition, it does not require extra traffic state measurements compared to the original MP algorithms, beyond a measure of congestion within the protected region of the network. Microscopic traffic simulation results demonstrate that the proposed model can outperform two baseline perimeter control models – Bang–Bang control and feedback gating – under various traffic conditions. More interestingly, this superiority is maintained in both fully and partially connected environments.

Data-driven traffic signal adaptive control algorithm integrating vehicle perception and traffic flow data

Due to the equipment error, environmental interference and data transmission delay of vehicle flow detection, the accuracy and real-time performance of vehicle perception and traffic flow data will be affected to some extent, resulting in poor traffic signal control effect. Therefore, a data-driven traffic signal adaptive control algorithm is designed by integrating vehicle perception and traffic flow data. To complete the modeling of urban traffic, the discrete distribution and continuous distribution of traffic are obtained. Based on this research environment, the DV-hop localization algorithm is improved to sense the vehicle position. Based on the phase space reconstruction of traffic flow time series and vehicle location information, traffic flow data is predicted. Based on the driving of traffic data, the vehicle types are divided into small, medium and large three categories, and the impact weights are assigned respectively, and the weight values affecting the final allocation of green time are obtained to realize the allocation of green time. The experimental results show that: The research algorithm can not only predict the traffic flow intensity effectively, but also the predicted results are highly coincident with the actual traffic flow intensity. Vehicle arrival rates are higher, vehicle delays are shorter, and vehicles stop fewer times on average.

A Survey on Challenges and Solutions of Traffic Surveillance Systems in IoT-Enabled Eco System

A Survey on Challenges and Solutions of Traffic Surveillance Systems in IoT-Enabled Eco System

The shortest-path and bee colony optimization algorithms for traffic control at single intersection with NetworkX application

Abstract In this article, we study the application of NetworkX, a Python library for dealing with traffic networks, to the problem of signal optimization at a single intersection. We use the shortest-path algorithms such as Bellman-Ford (Dynamic Programming), A star (A*), and Dijkstra’s algorithm to compute an optimal solution to the problem. We consider both undersaturated and oversaturated traffic conditions. The results show that we find optimal results with short Central Processor Unit (CPU) time using all the applied algorithms, where Dijkstra’s algorithm slightly outperformed others. Moreover, we show that bee colony optimization can find the optimal solution for all tested problems with different degrees of computational complexity for less CPU time, which is a new contribution to knowledge in this field.

HumanLight: Incentivizing ridesharing via human-centric deep reinforcement learning in traffic signal control

Single occupancy vehicles are the most attractive transportation alternative for many commuters, leading to increased traffic congestion and air pollution. Advancements in information technologies create opportunities for smart solutions that incentivize ridesharing and mode shift to higher occupancy vehicles (HOVs) to achieve the car lighter vision of cities. In this study, we present HumanLight, a novel decentralized adaptive traffic signal control algorithm designed to optimize people throughput at intersections. Our proposed controller is founded on reinforcement learning with the reward function embedding the transportation-inspired concept of pressure at the person-level. By rewarding HOV commuters with travel time savings for their efforts to merge into a single ride, HumanLight achieves equitable allocation of green times. Apart from adopting FRAP, a state-of-the-art (SOTA) base model, HumanLight introduces the concept of active vehicles, loosely defined as vehicles in proximity to the intersection within the action interval window. The proposed algorithm showcases significant headroom and scalability in different network configurations considering multimodal vehicle splits at various scenarios of HOV adoption. Improvements in person delays and queues range from 15% to over 55% compared to vehicle-level SOTA controllers. We quantify the impact of incorporating active vehicles in the formulation of our RL model for different network structures. HumanLight also enables regulation of the aggressiveness of the HOV prioritization. The impact of parameter setting on the generated phase profile is investigated as a key component of acyclic signal controllers affecting pedestrian waiting times. HumanLight’s scalable, decentralized design can reshape the resolution of traffic management to be more human-centric and empower policies that incentivize ridesharing and public transit systems.

A large-scale traffic signal control algorithm based on multi-layer graph deep reinforcement learning

Due to its capability in handling complex urban intersection environments, deep reinforcement learning (DRL) has been widely applied in Adaptive Traffic Signal Control (ATSC). However, most existing algorithms are designed for specific road networks or traffic conditions, making it difficult to transfer them to new environments. Moreover, current graph-based algorithms do not fully capture the geometric and spatial features of intersections, leading to incomplete embedding of the agent's environment depiction. Additionally, the actions adopted by these algorithms are inherently based on fixed-cycle phases, limiting the flexibility of traffic signal control. To address the aforementioned issues, this paper proposes a Multi-layer Graph Mask Q-Learning (MGMQ) algorithm for multi-intersection ATSC to optimize traffic and reduce delay. Unlike previous graph-based algorithms, this paper introduces a method for computing multi-layer graphs, dividing the traffic environment into upper-level traffic network-layer graphs and lower-level intersection-layer graphs, and employs the graph attention algorithm and an improved GraphSAGE algorithm for computation. This method not only enables the generation of embedded state for intersections that include geometric and spatial features, but also allows the algorithm to adapt to different traffic conditions and road networks. Additionally, we introduce an action masking mechanism, allowing this algorithm can be adapted to different action spaces. As a result, the algorithm uses arbitrary signal phases as actions to achieve flexible traffic flow control, and can be directly applied to intersections with arbitrary geometry. The final test results demonstrate that a model trained solely on synthetic road networks can be directly transferred to other synthetic network configurations or real-world urban road networks, outperforming current state-of-the-art algorithms.

TRAFFIC CONTROL AT PEDESTRIAN PRIORITY CROSSINGS WITH GUARANTEED LANE THROUGHPUT CAPACITY AS EXEMPLIFIED BY THE LEGAL REGULATIONS APPLICABLE IN POLAND

The article describes the problem of excessive traffic concentration at signalized pedestrian crossings. The main drawback of such crossings is the inappropriate duration of green signals for vehicles. Too long green times for vehicular traffic significantly increase time lost by pedestrians, while too short ones cause short-term congestion, resulting in long queues of vehicles and a deteriorated level of service. The negative effects of both of these situations also include dangerous behavior of road users, such as red light running or crossing. Recent (June 2021) changes in the road traffic law in Poland have privileged unprotected road users at pedestrian crossings. New models and technical and design standards proposed in this country (March 2021) recommend the installation of pedestrian priority crossings at particularly dangerous spots and in urban areas with a high proportion of pedestrian traffic. All this is done mainly for traffic-calming purposes. However, previously these measures were not a common practice in Poland. Therefore, the objective of this article was to develop a simple method of green signal timing, ensuring, firstly, priority to pedestrian traffic at such places, and, secondly, sustained capacity of vehicular traffic lanes. The developed models are practical, universal and easy to adapt in other countries. They ensure a very good or good quality of vehicle traffic service, with average lost time not exceeding 25 s in a vast majority of analyzed traffic level cases. The article also describes a traffic control algorithm dependent on traffic detection.

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.

The Impact of Priority in Coordinated Traffic Lights on Tram Energy Consumption

Traffic signal priority issues have been a research subject for several decades in Poland and worldwide. Traffic control algorithms have evolved considerably during this period and have become increasingly advanced. Most of them operate within coordinated street sequences, which adds to their complexity. Tramway priority affects traffic conditions for other road users, so many aspects must be taken into account when choosing a priority solution. Typically, one of the main criteria for evaluating the effectiveness of priority is reducing travel time for the priority vehicle while ensuring that the travel times of other traffic participants through the intersection are maintained or slightly deteriorated. However, the energy aspects are often overlooked. This publication aims to investigate how local priority for tramways in traffic signals of coordinated streets affects energy consumption for tramway traction needs. The study was conducted using a microscopic modeling method with PTV Vissim software (ver. 2021). The models were built for coordinated sequences with different levels of priority. Real traffic control algorithms with priority were implemented into the model on the sequence of Marymoncka Street and Grochowska Street in Warsaw. Then, by introducing changes to the parameters of the algorithms, their effect on traffic characteristics, including estimated power consumption, was studied. The results obtained from the computer simulation were statistically processed using R software (ver. 4.3.2). The analysis results prove the effectiveness of tramway priority operation, show its impact on electricity consumption, and allow us to determine the limits of its effective application. Thus, they complement the knowledge of the impact of tramway priority on traffic. The research results also have practical value, as they help us to make rational decisions in the process of designing traffic control algorithms at intersections with a multi-criteria approach.

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.

Enhancing the Robustness of Traffic Signal Control with StageLight: A Multiscale Learning Approach

The continuous evolution of artificial intelligence and cyber–physical systems has presented promising opportunities for optimizing traffic signal control in densely populated urban areas, with the aim of alleviating traffic congestion. One area that has garnered significant interest from both researchers and practitioners is the application of deep reinforcement learning (DRL) in traffic signal control. However, DRL-based algorithms often suffer from instability due to the dynamic nature of traffic flows. Discrepancies between the environments used for training and those encountered during deployment often lead to operational failures. Moreover, conventional DRL-based traffic signal control algorithms tend to reveal vulnerabilities when faced with unforeseen events, such as sensor failure. These challenges highlight the need for innovative solutions to enhance the robustness and adaptability of such systems. To address these pertinent issues, this paper introduces StageLight, a novel two-stage multiscale learning approach, which involves learning optimal timings on a coarse time scale in stage 1, while finetuning them on a finer time scale in stage 2. Our experimental results demonstrate StageLight’s remarkable capability to generalize across diverse traffic conditions and its robustness to various sensor-failure scenarios.

DERLight: A Deep Reinforcement Learning Traffic Light Control Algorithm with Dual Experience Replay

<p>In recent years, with the increasingly severe traffic environment, most cities are facing various traffic congestion problems, and the demand for intelligent regulation of traffic signals is also increasing. In this study, we propose a new intelligent traffic light control algorithm, dual experience replay light (DERLight), which innovatively and efficiently designs a dual experience replay training mechanism based on the classic deep Q network (DQN) framework and considers the dynamic epoch function. As results show that compared with some state-of-the-art algorithms, DERLight can shorten the average travel time of vehicles, increase the throughput at intersections, and also speed up the convergence of the network. In addition, the design of this algorithm framework is not only limited to the field of intelligent transportation, but also has transferability for some other fields.</p> <p> </p>

Reinforcement Learning Based Urban Traffic Signal Control and Its Impact Assessment on Environmental Pollution

To address the growing complexity of urban traffic congestion and its associated environmental impacts, this study presents a pioneering application of the Gaussian plume model to investigate the carbon dioxide emission reduction efficacy of various reinforcement learning algorithms within a traffic signal control framework. By employing an insightful fusion of the traditional environmental science tool with contemporary reinforcement learning strategies - specifically Independent Partially Observable Policy Optimization (IPPO), Independent Delay Q-Network (IDQN), and MPLight - this research marks a novel intersection of methodologies. By quantitatively simulating and analyzing the diffusion dynamics of carbon dioxide pollutants under different traffic signal control scenarios, the study not only highlights the innovative use of the Gaussian plume model to assess the environmental impact of traffic signal control, but also provides critical insights into the selection and optimization of traffic signal control algorithms for improved urban environmental sustainability.

Research on intelligent vehicle Traffic Flow control algorithm based on data mining

Traffic Congestion (TC) is increasing due to urban growth and vehicle numbers, rendering the development of cities and people's well-being difficult. Traffic Prediction (TP) and control systems have been required to improve Traffic Flow (TF) and reduce TC because standard methods are unsuitable. The paper proposes an innovative method for traffic control using the Dynamic Zone Segmentation Algorithm (DZSA) to solve this significant issue. The algorithm uses real-time data and road conditions to partition city traffic into manageable units, enhancing the adaptability and accuracy of Traffic Prediction (TP) performance. Applying DZSA, the recommended Long Short-Term Memory + Bayesian Structural Time Series (LSTM + BSTS) learning model optimizes TP by integrating the best features of conventional and Machine Learning (ML) methods. The model optimized quality performance when experimentally tested against other benchmark models using metrics like Mean Absolute Error, Mean Absolute Scaled Error, Accuracy Percent, Root Mean Squared Error, and Mean Absolute Percent Error. The recommended model, LSTM + BSTS, shows a minimal error rate of 6.68%, indicating its success.

Max-Pressure Control utilizing Total Movement Delay

Traffic signal control algorithms utilizing max-pressure principles can rely on feedback obtained from either queue lengths or travel times. This paper considers max-pressure algorithms based on travel times, aiming to address traffic-related challenges, including (i) unbounded link capacity, (ii) mitigating the “the Last Packet effect”, and (iii) enhancing queue modeling to account for spatial queue distribution. Hence, we propose a new max-pressure algorithm based on total movement delays, which requires local information only to determine appropriate phase activation, and aims at minimizing delays. First, we introduce a new traffic model based in queuing theory. Unlike previous approaches that rely on the store-and-forward model, our model accurately depicts vehicle travel time delays. Second, based on the developed model, we introduce a new traffic oriented max-pressure algorithm. The objective of maximizing throughput is compared to minimizing travel time delays, through the comparison of different a-cyclic time-based max-pressure control schemes with existing a-cyclic queue-based algorithms control schemes.

CAV Traffic Control to Mitigate the Impact of Congestion from Bottlenecks: A Linear Quadratic Regulator Approach and Microsimulation Study

This work investigates traffic control via controlled connected and automated vehicles (CAVs) using novel controllers derived from the linear-quadratic regulator (LQR) theory. CAV-platoons are modeled as moving bottlenecks impacting the surrounding traffic with their speeds as control inputs. An iterative controller algorithm based on the LQR theory is proposed along with a variant that allows for penalizing abrupt changes in platoon speeds. The controllers use the Lighthill-Whitham-Richards (LWR) model implemented using an extended cell transmission model (CTM) which considers the capacity drop phenomenon for a realistic representation of traffic in congestion. The impact of various parameters of the proposed controller on the control performance is analyzed. The effectiveness of the proposed traffic control algorithms is tested using a traffic control example and compared with existing proportional-integral (PI) and model predictive control (MPC) controllers from the literature. A case study using the TransModeler traffic microsimulation software is conducted to test the usability of the proposed controller as well as existing controllers in a realistic setting and derive qualitative insights. It is observed that the proposed controller works well in both settings to mitigate the impact of the jam caused by a fixed bottleneck. The computation time required by the controller is also small making it suitable for real-time control.