Real-time Traffic Signal Research Articles

Efficient traffic signal detection with tiny YOLOv4: enhancing road safety through computer vision

As decades go by, technology advances and everything around us becomes smarter, such as televisions, mobile phones, robots, and so on. Artificial intelligence (AI) is applied in these technologies where AI assists the computer in making judgments like humans, and this intelligence is artificially fed to the model. The self-driving technique is a developing technology. Autonomous driving has been a broad and fast-expanding technology over the last decade. This model is carried out using the tiny you only look once (YOLO) algorithm. YOLO is mainly used for object detection classification. Tiny YOLO model is explored for the traffic signal detection. ROBI FLOW dataset is used for object detection which contains 2000+ image data to train the tiny YOLO model for traffic signal detection in real time. This model gives an improved accuracy and lightweight implementation compared to other models. Tiny YOLO is fast and accurate model for real-time traffic signal detection.

Integrating vehicle trajectory planning and arterial traffic management to facilitate eco-approach and departure deployment

Eco-approach and departure (EAD) enable continuous vehicle motion in urban signalized corridors. Since such a motion can extend to the EAD vehicles’ followers, it makes EAD a promising technology to benefit the traffic flow where automated vehicles and conventional vehicles coexist. Most existing EAD studies envision an ideal setting that neglects real-world operational conditions such as lane changes, multi-movement intersection configuration, partially automated fleet, and/or limited traffic state awareness. This study aims to fill the gap by designing an EAD algorithm considering real-world traffic operation constraints. The proposed algorithm uses a model predictive controller to minimize vehicle speed reduction and variation based on the real-time traffic signal control plan and measured queues at the intersection. The required inputs are readily available at many modern intersections. We observed that the proposed controller’s performance might degrade because of lane-changing maneuvers and lead-left turn traffic signals. These observations motivated our development of a lane change management strategy and a signal control implementation strategy to facilitate the EAD implementation. The lane change management strategies separate the EAD operations and lane-changing maneuvers in time and space. The signal control implementation strategy applies lag-left turn signals to enable EAD operation for both the through and left-turn vehicles. Compared to the non-EAD case, our EAD approach produces 2.5% to 7.8% energy savings while keeping similar intersection mobility. Notably, this approach brings about 2.5% to 3.6% energy savings in a 2% CAV case. This result demonstrates the feasibility of deploying EAD at low connected automated vehicle penetration rates.

Live Intersection Data Acquisition for Traffic Simulators (LIDATS).

Real-time traffic signal acquisition and network transmission are essential components of intelligent transportation systems, facilitating real-time traffic monitoring, management, and analysis in urban environments. In this paper, we introduce a comprehensive system that incorporates live traffic signal acquisition, real-time data processing, and secure network transmission through a combination of hardware and software modules, called LIDATS. LIDATS stands for Live Intersection Data Acquisition for Traffic Simulators. The design and implementation of our system are detailed, encompassing signal acquisition hardware as well as a software platform that is used specifically for real-time data processing. The performance evaluation of our system was conducted by simulation in the lab, demonstrating its capability to reliably capture and transmit data in real time, and to effectively extract the relevant information from noisy and complex traffic data. Supporting a variety of intelligent transportation applications, such as real-time traffic flow management, intelligent traffic signal control, and predictive traffic analysis, our system enables remote data analysis and decisionmaking, providing valuable insights and enhancing the traffic efficiency while reducing the congestion in urban environments.

Synchronization and implementation of real-time traffic signal optimization regulator

Synchronization and implementation of real-time traffic signal optimization regulator

A survey on deep reinforcement learning approaches for traffic signal control

In the domain of complex urban traffic networks, real-time Traffic Signal Control (TSC) serves as a pivotal strategy for mitigating congestion. Coordinating signal control across multiple intersections involves considerable complexity. Deep Reinforcement Learning (DRL) has emerged as a robust solution. In recent years, there has been rapid advancement in TSC methods, with numerous researchers employing various novel DRL methodologies. Yet, existing surveys lack timeliness and universality in capturing the latest research. There is a notable gap in current research surveys with respect to the latest developments in TSC. Therefore, the focus of this paper lies in analyzing the most recent papers from the past five years, with the aim to provide a comprehensive and multi-dimensional review of the evolution of DRL in TSC. The survey categorizes current research based on model setups, utilized algorithms, and application scenarios. Finally, this paper highlights potential directions for future TSC research.

A Comprehensive Review on Real-Time Traffic Signal Coordination Using Machine Learning: Optimizing Urban Traffic Flow for Efficiency and Sustainability

Abstract: Cities globally grapple with escalating traffic congestion stemming from urban expansion, aginginfrastructure, suboptimal traffic signal coordination, and a deficiency in real-time data. INRIX quantified the resulting toll at $305 billion for U.S. commuters in 2020, underscoring the pressing need for cities to adopt innovative strategies within the constraints of building new roads. This challenge is further complicated by disparate data collection times at various junctions, creating a mosaic of information from different periods. Some junctions provide limited or sporadic data, necessitating careful consideration for future projections. To address these challenges, cities must embrace cutting-edge approaches that integrate real-time data, efficient traffic management systems, and forward-thinking projections, enabling them to navigate the intricacies of contemporary urban mobility and pioneer sustainable solutions for the future.

Ant Colony Optimization With Look-Ahead Mechanism for Dynamic Traffic Signal Control of IoV Systems

In the era of Internet of Vehicle (IoV), traffic data are obtained by wireless sensor networks (WSNs), which provides convenience for real-time traffic signal control and is expected to further improve the efficiency of the whole system. Traffic signal controllers designed for IoV systems usually fully depend on data transmitted from the IoV system and are limited to the penetration rate of connected vehicles. This work proposes a dynamic ant colony optimization algorithm with the look-ahead mechanism (DALM) with eight no-conflict phases. The needed traffic information in the DALM algorithm is small and can be obtained independently by the system, which makes this method does not fully rely on the vehicle trajectories data and can better protect customer privacy and deal with cyber attack. With the look-ahead mechanism, the forthcoming traffic flow together with the current waiting queue are involved to predict the length of waiting vehicles and the optimal phase sequence is dynamically selected according to shortest green-light duration of candidate phase sequences obtained form the collected real-time traffic data. Experiments conducted under five scenarios with different kinds of traffic flow on SUMO platform show that compared with other four methods, both the total waiting time and the total carbon emissions of vehicles at the intersection are greatly reduced by using the DALM algorithm, especially in high traffic flow situations. The proposed algorithm can greatly improve the efficiency of the intersection in high traffic flow situation and is predictable to benefit the real-time traffic signal control in IoV system.

Intelligent Traffic Management System with Adaptive Signal Control and Emergency Vehicle Prioritization

Abstract: Efficient traffic management is essential for ensuring smooth flow and minimizing congestion on roadways. In this project, we propose an Arduino-based solution that utilizes IR sensors for real-time traffic monitoring and signal timing adjustment, coupled with a Bluetooth module to prioritize emergency vehicles during critical situations. The IR sensors are deployed at intersections to monitor vehicle presence, enabling real-time traffic monitoring. The Arduino Uno microcontroller processes the sensor data and dynamically adjusts signal timings to ensure smooth traffic flow. Additionally, a Bluetooth module facilitates communication between the traffic management unit and ambulance vehicles, triggering an alert signal to clear the way for emergencies. The system's adaptability allows for easy integration into existing infrastructure. Results indicate improved traffic efficiency, reduced congestion, and enhanced emergency vehicle response times

Study on the Operational Effect of Real-time Traffic Signal Control Using the Data from Smart Instersections

Study on the Operational Effect of Real-time Traffic Signal Control Using the Data from Smart Instersections

A Review of Urban Real-time Traffic Signal Control

A Review of Urban Real-time Traffic Signal Control

Optimal Traffic Signal Control Using Priority Metric Based on Real-Time Measured Traffic Information

Optimizing traffic control systems at traffic intersections can reduce network-wide fuel consumption as well as improve traffic flow. While traffic signals have conventionally been controlled based on predetermined schedules, various adaptive control systems have been developed recently using advanced sensors such as cameras, radars, and LiDARs. By utilizing rich traffic information enabled by the advanced sensors, more efficient or optimal traffic signal control is possible in response to varying traffic conditions. This paper proposes an optimal traffic signal control method to minimize network-wide fuel consumption utilizing real-time traffic information provided by advanced sensors. This new method employs a priority metric calculated by a weighted sum of various factors, including the total number of vehicles, total vehicle speed, vehicle waiting time, and road preference. Genetic Algorithm (GA) is used as a global optimization method to determine the optimal weights in the priority metric. In order to evaluate the effectiveness of the proposed method, a traffic simulation model is developed in a high-fidelity traffic simulation environment called SUMO, based on a real-world traffic network. The traffic flow within this model is simulated using actual measured traffic data from the traffic network, enabling a comprehensive assessment of the novel optimal traffic signal control method in realistic conditions. The simulation results show that the proposed priority metric-based real-time traffic signal control algorithm can significantly reduce network-wide fuel consumption compared to the conventional fixed-time control and coordinated actuated control methods that are currently used in the modeled network. Additionally, incorporating truck priority in the priority metric leads to further improvements in fuel consumption reduction.

Based on MOPSO Algorithm of Real-Time Traffic Signal Optimization Control for Intelligent Transportation Intersections

In order to improve the traffic efficiency of intersections, this paper establishes a multi-objective optimization model with the minimum real-time delay time and maximum capacity as the optimization objectives by applying multi-objective particle swarm optimization algorithm, and simulates the actual intersections. By optimizing the multi-objective model, a set of Pareto optimal solution sets can be obtained, and the optimal timing scheme can be determined by using multi-attribute decision-making algorithm and real-time update of signal timing configuration parameters to achieve dynamic optimization of signal timing. The simulation model of city road intersection is established by SUMO software. By using multi-objective particle swarm optimization algorithm to optimize the intersection simulation model, the traffic capacity of the simulated intersection has been greatly improved, with the average traffic capacity increasing by about 3.69% and the average vehicle delay time decreasing by about 21.35%.

Investigation of the discharge flow rate patterns at real-time traffic signal control intersections

This study investigates the effects of variable queue lengths and green times on discharge flow rates at real-time managed intersections in Mersin, Turkey. For this purpose, traffic flow data were collected at two different signalized intersections during morning peak hours for two days. The traffic data including the time headways, queue lengths for each cycle were derived from video records via MATLAB coding while the signal timing data were obtained from Mersin Metropolitan Municipality. The impact of variable queue lengths and green times on discharge flow rate were evaluated separately via analysis of variance (ANOVA) tests. The results indicated that time headways of the first vehicles in the queue were statistically larger than the time headways of the remaining vehicles in the queue (p-value<0.05). On the other hand, the time headways of the remaining vehicles in the queue were not found statistically different at 95 confidence level (p-value>0.05). Furthermore, the effect of the variable green time on discharge flow rate revealed that the significant difference was only observed for the first twelve seconds of the green time.

Traffic Signal Self-Organizing Control With Road Capacity Constraints

This paper aims at the problem of intersection traffic oversaturation and the spillover of queuing vehicles caused by random fluctuations of traffic flow. A self-organizing control model with road capacity constraints and taking minimum expected traffic flow delay as the goal is established to realize real-time adaptive control of traffic signal phase duration. Taking the local intersection and its adjacent intersections as the basic unit of traffic signal control, defining the constraints of road space in queuing state and the equations of traffic signal control basic unit are established. According to the equations, an online method for solving the optimal phase duration of traffic signal is established to efficiently allocate intersection space resources, by which a rule-based real-time traffic signal control model is established. Through the interaction between adjacent intersections, the traffic inflow rate of oversaturated phase is automatically restricted to alleviate the queuing spillover problem. Computer simulation shows that the control effect is significantly better than that of SCATS under the same boundary inflow conditions. Compared with the traditional centralized coordinated control, the rule-based real-time traffic signal control model can better adapt to the fluctuation of traffic flow and effectively relief the problem of vehicle queuing spillover caused by fluctuations of traffic flow.

The Implementation of Smart Mobility for Smart Cities: A Case Study in Qatar

This paper contributes to building a systematic view of the mobility characteristics of smart cities by reviewing the lessons learned from the best practices implemented around the world. The main features of smart cities, such as smart homes, smart infrastructure, smart operations, smart services, smart utilities, smart energy, smart governance, smart lifestyle, smart business, and smart mobility in North America, Asia, and Europe are briefly reviewed. The study predominantly focuses on smart mobility features and their implications in newly built smart cities. As a case study, the modern city of Lusail located in the north of Doha, Qatar is considered. The provision of car park management and guidance, real-time traffic signal control, traffic information system, active-modes arrangement in promenade and busy urban avenues, LRT, buses, taxis, and water taxis information system, and multimodal journey planning facilities in the Lusail smart city is discussed in this study. Consequently, the implications of smart mobility features on adopting Intelligent Transportation Systems (ITS) will be studied. The study demonstrates that the implementation of Information and Communication Technologies (ICT) when supported by Intelligent Transportation Systems (ITS), could result in making the most efficient use of existing transportation infrastructure and consequently improve the safety and security, mobility, and the environment in urban areas. The findings of this study could be considered an initial step in the implementation of Mobility-as-a-Service (MaaS) in cities with advanced public transportation such as Doha, the capital of Qatar. Doi: 10.28991/CEJ-2022-08-10-09 Full Text: PDF

A Rolling Optimization Algorithm for Real-Time Traffic Control With Delay Minimization

This article presents a rolling optimization algorithm (ROA) for minimizing total vehicle delay for real-time traffic signal control in urban road networks, where the vehicle delay minimization problem is formulated as a quadratically constrained quadratic programming problem, which is NP-hard. The programming problem is relaxed and resolved using the ROA in a distributed manner. In particular, we introduce network partition to a large-scale urban road network and then present a regional ROA to eliminate the low efficiency caused by the large number of decision variables. Numerical experiments are performed on one of Beijing’s district road networks to validate the efficiency of the proposed method in benchmark against several typical techniques.

A Person-Based Adaptive Traffic Signal Control Method with Cooperative Transit Signal Priority

Real-time traffic signal control has long been a critical way to improve traffic congestion. Transit Signal Priority (TSP) is seen as a cost-effective way to reduce travel time variability. Most of the previous studies develop real-time signal control systems on a vehicle basis, which is unable to efficiently provide preferential treatment on transit vehicles. Person-based signal control systems, which transform traffic delay computation units from vehicle to passenger, have been proposed to try to address this limitation. However, their models, optimizing signal plan cycle-by-cycle, cannot rapidly respond to traffic variations. This study proposes a Person-based Adaptive traffic signal control method with Cooperative Transit signal priority (PACT). In PACT, not only do Road-Side Units (RSUs) perform signal optimization, but also On-Board Units (OBUs) provide in-vehicle speed advisory to reduce delays. The interaction between RSU and OBU is conducted second-by-second, which has high adaptability to traffic variations. Experiments are performed based on real traffic data via traffic simulation platform SUMO. The results indicate that PACT can efficiently reduce delays of both bus passengers and auto passengers at a signalized intersection. Compared to preoptimized signal plans, the results show that each passenger on transit vehicles experiences 33%–70% decreases in delays, and each auto passenger experiences 3%–29% decreases in delays. PACT can reduce 80%–98% in delays when the occupancy weight factor is relatively large, showing the potential of extending PACT on performing signal preemption.

State-of-Art Review of Traffic Light Synchronization for Intelligent Vehicles: Current Status, Challenges, and Emerging Trends

The effective control and management of traffic at intersections is a challenging issue in the transportation system. Various traffic signal management systems have been developed to improve the real-time traffic flow at junctions, but none of them have resulted in a smooth and continuous traffic flow for dealing with congestion at road intersections. Notwithstanding, the procedure of synchronizing traffic signals at nearby intersections is complicated due to numerous borders. In traditional systems, the direction of movement of vehicles, the variation in automobile traffic over time, accidents, the passing of emergency vehicles, and pedestrian crossings are not considered. Therefore, synchronizing the signals over the specific route cannot be addressed. This article explores the key role of real-time traffic signal control (TSC) technology in managing congestion at road junctions within smart cities. In addition, this article provides an insightful discussion on several traffic light synchronization research papers to highlight the practicability of networking of traffic signals of an area. It examines the benefits of synchronizing the traffic signals on various busy routes for the smooth flow of traffic at intersections.

A Novel Real-Time Traffic Signal Control Strategy for Emergency Vehicles

Intelligent traffic signal control strategies for emergency vehicles (EV) is a research hotspot in intelligent transportation systems. Its research results will promote the shortening of the travel time of EVs, which is vital to saving lives and reducing property losses. This paper proposes a novel real-time traffic signal control strategy. The first step is called on-demand signal timing for reducing road saturation, which makes it possible that ordinary vehicles(OV) give way to EVs and make EVs travel faster, according to three indicators: the emergency response level(ERL), the congestion level of the road section(CLRS), and the time urgency level(TUL). The second step is called novel signal preemption, which combines non-intrusive preemption and intrusive preemption. This step provides green indicates and makes EVs pass through intersections quickly without stopping. The final step is called the recovery cycle strategy, which restores the road network to the normal situation as soon as possible by using linear programming(LP) to find the shortest green time in each phase after an EV passes the intersection. This paper conducts simulation experiments on an urban traffic simulator SUMO. The experimental results show that our novel strategy can shorten the travel time and reduce the traffic impact on the road network caused by prioritizing the EVs significantly. Compared with the fixed-time control method (FTCM), Min’s flexible signal preemption method(FSPM), and Qin’s intrusive signal preemption method (ISPM), our method can optimize travel time by up to 62.85%, 50.83%, and 11.62%, respectively.

A Particle Swarm Optimisation with Linearly Decreasing Weight for Real-Time Traffic Signal Control

Nowadays, traffic congestion has become a significant challenge in urban areas and densely populated cities. Real-time traffic signal control is an effective method to reduce traffic jams. This paper proposes a particle swarm optimisation with linearly decreasing weight (LDW-PSO) to tackle the signal intersection control problem, where a finite-interval model and an objective function are built to minimise spoilage time. The performance was evaluated in real-time simulation imitating a crowded intersection in Dalian city (in China) via the SUMO traffic simulator. The simulation results showed that the LDW-PSO outperformed the classical algorithms in this research, where queue length can be reduced by up to 20.4% and average waiting time can be reduced by up to 17.9%.