A New System for Road Traffic Optimisation Using the Virtual Traffic Light Technology

Large cities suffer from traffic congestion,particularly at intersections,due to a large number of vehicles,which leads to the loss of time by increasing carbon emissions,including fuel consumption.Therefore,the need for optimising the flow of vehicles at different intersections and reducing the waiting time is a critical challenge.Conventional traffic lights have been used to control traffic flow at different intersections and have been improved to become more efficient by using different algorithms,sensors and cameras.However,they also face some challenges,such as high-cost installation,operation,and maintenance issues.This paper develops a new system based on the Virtual Traffic Light(VTL)technology to improve traffic flow at different intersections and reduce the encountered loss of time and vehicles’travel time.Additionally,it reduces the costs of installation,maintenance and operation over various conventional traffic light systems.Consequently,the system proposes algorithms for traffic scheduling and lane identification by using vehicle ID,priority and time of arrival.To evaluate the system,four scenarios were presented where each scenario uses a different number of vehicles consisting of three types(emergency vehicles,public buses and private vehicles),each given a different priority.The proposed system is evaluated by integrating two simulators,namely,(OMNeT++)and(SUMO),and two frameworks,namely,(VEINS)and(INET)to prepare an appropriate working environment.the results prove that an improvement in the average travel time for several vehicles reaches 44.43%–49.76%compared with conventional traffic lights.Further,it is proven from the obtained results that the average waiting time for emergency vehicles is enhanced by 96.63%–97.63%,while the average waiting time for public buses is improved by 94.81%–97.23%.On the other hand,the waiting time for private vehicles‘improved by 87.14%to 89.71%’.

Intelligent 3-Way Priority-Driven Traffic Light Control System for Emergency Vehicles

The problem of traffic congestion is a significant phenomenon that has had a substantial impact on the transportation system within the country. This phenomenon has given rise to numerous intricacies, particularly in instances where emergency situations occur at traffic light intersections that are consistently congested with a high volume of vehicles. This implementation of a traffic light controller system is designed with the intention of addressing this problem. The purpose of the system was to facilitate the operation of a 3-way traffic control light and provide priority to emergency vehicles using a Radio Frequency Identification (RFID) sensor and Reduced Instruction Set Computing (RISC) Architecture Based Microcontroller. This research work involved designing a system to mitigate the occurrence of accidents commonly observed at traffic light intersections, where vehicles often need to maneuver in order to make way for emergency vehicles following a designated route. The research effectively achieved the analysis, simulation and implementation of wireless communication devices for traffic light control. The implemented prototype utilizes RFID transmission, operates in conjunction with the sequential mode of traffic lights to alter the traffic light sequence accordingly and reverts the traffic lights back to their normal sequence after the emergency vehicle has passed the traffic lights.

Traffic light detection and recognition in intersections based on intelligent vehicle

To ensure revulsive driving of intelligent vehicles at intersections, a method is presented to detect and recognize the traffic lights. First, the stabling siding at intersections is detected by applying Hough transformation. Then, the colors of traffic lights are detected with color space transformation. Finally, self-associative memory is used to recognize the countdown characters of the traffic lights. Test results at 20 real intersections show that the ratio of correct stabling siding recognition reaches up to 90%;and the ratios of recognition of traffic lights and divided characters are 85% and 97%, respectively. The research proves that the method is efficient for the detection of stabling siding and is robust enough to recognize the characters from images with noise and broken edges.

Deep Reinforcement Learning for Addressing Disruptions in Traffic Light Control

This paper investigates the use of multi-agent deep Q-network(MADQN)to address the curse of dimensionality issue occurred in the traditional multi-agent reinforcement learning(MARL)approach.The proposed MADQN is applied to traffic light controllers at multiple intersections with busy traffic and traffic disruptions,particularly rainfall.MADQN is based on deep Q-network(DQN),which is an integration of the traditional reinforcement learning(RL)and the newly emerging deep learning(DL)approaches.MADQN enables traffic light controllers to learn,exchange knowledge with neighboring agents,and select optimal joint actions in a collaborative manner.A case study based on a real traffic network is conducted as part of a sustainable urban city project in the Sunway City of Kuala Lumpur in Malaysia.Investigation is also performed using a grid traffic network(GTN)to understand that the proposed scheme is effective in a traditional traffic network.Our proposed scheme is evaluated using two simulation tools,namely Matlab and Simulation of Urban Mobility(SUMO).Our proposed scheme has shown that the cumulative delay of vehicles can be reduced by up to 30%in the simulations.

Optimal traffic light control method for a single intersecti on basedon hybrid systems

A single intersection of two phases is selected as a model to put forward a new optimal time-planning scheme for traffic light based on the model of hybrid automata for single intersection. A method of optimization is proposed for hybrid systems, and the average queue length over all queues is used as an objective function to find an optimal switching scheme for traffic light. It is illustrated that traffic light control for single intersection is a typical hybrid system, and the optimal planning-time scheme can be obtained using the optimal hybrid systems control based on the two stages method.

Effectiveness of traffic light system on Brazilian consumers perception of food healthfulness

Front-of-package(FOP)nutrition labelling schemes were developed to improve consumer’s comprehension about the food nutrients associated with non-communicable diseases(NCDs).Several FOPs have already been developed,and Brazil is in the process of evaluating a scheme to introduce in the products.The aim of this study was to investigate the impact of TLS,the scheme proposed by the food industry,on the product healthfulness perception.A study with 141 participants was carried out.A conjoint task was designed considering three categories and levels:types of dairy products(light yogurt,prato cheese,and chocolate flavoured milk),Traffic Light System(yes vs.no)and brand(well-known vs.unknown).The effect of TLS on perceived healthfulness was evaluated using a 9-point scales(1:not healthy;9:very healthy).Results showed that the inclusion of TLS did not influence the perceived healthfulness of the products by consumers,even among consumers with higher interest in healthy eating.These findings suggest that the proposal supported by the food industry does not seem to be the most appropriate,being recommended the development of further studies to compare the efficacy of TLS and other FOP schemes.

Advanced Leader Election for Virtual Traffic Lights

We examine the network performance of algorithms for self-organized traffic management. In particular, we focus on wireless network- ing between cars. One of many technologies that make road traffic safer and more efficient is the Virtual Traffic Light （VTL） system, which is able to coordinate the traffic flow at intersections without the need for physical lights. VTL takes a leading vehicle at an inter- section and uses it to control the traffic lights. We developed algorithms for leader election and traffic light computation in realistic ve- hicular networking scenarios. Our key contribution is the extension of this algorithm to support arbitrary intersection layouts. We in- vestigated the proposal in synthetic and realistic scenarios. The results show that, overall, VTLs use network resources efficiently and positively influences driving experience. It performs better than stationary traffic lights for a low to medium network load. We also identify potential optimizations to deal with high network load and to improve fairness.

Design of Traffic Light Based on Field Programmable Gate Array

The use of fixed-time traffic lights for road traffic control has the disadvantage of low traffic efficiency. In order to optimize the vehicle traffic at the intersection, this paper proposes a design scheme of a real-time control system for road intelligent traffic lights based on FPGA. The system adopts the polling control model, the vehicle detector detects the arrival rate of vehicles, and obtains the corresponding traffic light green time length according to the traffic rules and polling model theory. Using Altera’s Cyclone IV series EP4CE15E22C8 chip as the development platform, a specific design plan is given. The circuit mainly includes program-controlled amplifier module, AD acquisition module, cross-correlation calculation module, serial port transmission and Lab-VIEW module. The system can realize the intelligent adjustment of traffic lights. Different vehicle arrival rates are detected at different times, so that the corresponding traffic light configuration time length changes accordingly. This intelligent adjustment controls road traffic and makes the main and branch roads coordinate and cooperate, thereby improving the traffic efficiency of the intersection.

TWO-DIMENSIONAL CELLULAR AUTOMATON TRAFFIC MODELWITH RANDOMLY SWITCHING TRAFFIC LIGHTS

Cellular automation traffic models can include various factors in traffic systems and the corresponding computational simulations are rather simple and effective. The Biham-Middleton-Levine model (BML model) facilitates the simulation of two-dimensional traffic flow problems via the cellular automaton models. In this paper, the BML model is improved by removing its limitation of synchronized change of traffic lights. In the new model, the traffic light at each crossing could arbitrarily change its starting time and tempo of variation, and hence the model could more realistically describe the influence of traffic lights on the performance of traffic systems. Some new effects appearing in the new model are also elucidated.

Simulation of traffic flow with traffic light strategies via a modified cellular automaton model

Traffic flows controlled by traffic light strategies were investigated via a cellular automaton model with anticipation, which is suitable for describing urban traffic. Three kinds of strategies, i. e., synchronized, green-wave and random switching lights, were designed, simulated and compared with each other. It is shown that the green-wave strategy is only valid at lower density and there is not an effective way with the three strategies to improve the efficiency of traffic flow at high density.

A deep learning method for traffic light status recognition

Real-time and accurate traffic light status recognition can provide reliable data support for autonomous vehicle decision-making and control systems.To address potential problems such as the minor component of traffic lights in the perceptual domain of visual sensors and the complexity of recognition scenarios,we propose an end-to-end traffic light status recognition method,ResNeSt50-CBAM-DINO(RC-DINO).First,we performed data cleaning on the Tsinghua-Tencent traffic lights(TTTL)and fused it with the Shanghai Jiao Tong University’s traffic light dataset(S2TLD)to form a Chinese urban traffic light dataset(CUTLD).Second,we combined residual network with split-attention module-50(ResNeSt50)and the convolutional block attention module(CBAM)to extract more significant traffic light features.Finally,the proposed RC-DINO and mainstream recognition algorithms were trained and analyzed using CUTLD.The experimental results show that,compared to the original DINO,RC-DINO improved the average precision(AP),AP at intersection over union(IOU)=0.5(AP50),AP for small objects(APs),average recall(AR),and balanced F score(F1-Score)by 3.1%,1.6%,3.4%,0.9%,and 0.9%,respectively,and had a certain capability to recognize the partially covered traffic light status.The above results indicate that the proposed RC-DINO improved recognition performance and robustness,making it more suitable for traffic light status recognition tasks.

A Workable Solution for Reducing the Large Number of Vehicle and Pedestrian Accidents Occurring on a Yellow Light

Traffic intersections are incredibly dangerous for drivers and pedestrians. Statistics from both Canada and the U.S. show a high number of fatalities and serious injuries related to crashes at intersections. In Canada, during 2019, the National Collision Database shows that 28% of traffic fatalities and 42% of serious injuries occurred at intersections. Likewise, the U.S. National Highway Traffic Administration (NHTSA) found that about 40% of the estimated 5,811,000 accidents in the U.S. during the year studied were intersection-related crashes. In fact, a major survey by the car insurance industry found that nearly 85% of drivers could not identify the correct action to take when approaching a yellow traffic light at an intersection. One major reason for these accidents is the “yellow light dilemma,” the ambiguous situation where a driver should stop or proceed forward when unexpectedly faced with a yellow light. This situation is even further exacerbated by the tendency of aggressive drivers to inappropriately speed up on the yellow just to get through the traffic light. A survey of Canadian drivers conducted by the Traffic Injury Research Foundation found that 9% of drivers admitted to speeding up to get through a traffic light. Another reason for these accidents is the increased danger of making a left-hand turn on yellow. According to the National Highway Traffic Safety Association (NHTSA), left turns occur in approximately 22.2% of collisions—as opposed to just 1.2% for right turns. Moreover, a study by CNN found left turns are three times as likely to kill pedestrians than right turns. The reason left turns are so much more likely to cause an accident is because they take a driver against traffic and in the path of oncoming cars. Additionally, most of these left turns occur at the driver’s discretion—as opposed to the distressingly brief left-hand arrow at busy intersections. Drive Safe Now proposes a workable solution for reducing the number of accidents occurring during a yellow light at intersections. We believe this fairly simple solution will save lives, prevent injuries, reduce damage to public and private property, and decrease insurance costs.

Traffic light detection and recognition for autonomous vehicles

Traffic light detection and recognition is essential for autonomous driving in urban environments. A camera based algorithm for real-time robust traffic light detection and recognition was proposed, and especially designed for autonomous vehicles. Although the current reliable traffic light recognition algorithms operate well under way, most of them are mainly designed for detection at a fixed position and the effect on autonomous vehicles under real-world conditions is still limited. Some methods achieve high accuracy on autonomous vehicle, but they can＇t work normally without the aid of high-precision priori map. The authors presented a camera-based algorithm for the problem. The image processing flow can be divided into three steps, including pre-processing, detection and recognition. Firstly, red-green-blue （RGB） color space is converted to hue-saturation-value （HSV） as main content of pre-processing. In detection step, the transcendental color threshold method is used for initial filterings, meanwhile, the prior knowledge is performed to scan the scene in order to quickly establish candidate regions. For recognition, this article use histogram of oriented gradients （HOG） features and support vector machine （SVM） as well to recognize the state of traffic light. The proposed system on our autonomous vehicle was evaluated. With voting schemes, the proposed can provide a sufficient accuracy for autonomous vehicles in urban enviroment.

Optimization management of hybrid energy source of fuel cell truck based on model predictive control using traffic light information

Energy optimization management can make fuel cell truck(FCT)power system more efficient,so as to improve vehicle fuel economy.When the structure of power source system and the torque distribution strategy are determined,the essence is to find the reasonable distribution of electric power between the fuel cell and other energy sources.The paper simulates the assistance of the intelligent transport system(ITS)and carries out the eco-velocity planning using the traffic signal light.On this basis,in order to further improve the energy efficiency of FCT,a model predictive control(MPC)-based energy source optimization management strategy is innovatively developed,which uses Dijkstra algorithm to achieve the minimization of equivalent hydrogen consumption.Under the scenarios of signalized intersections,based on the planned eco-velocity,the off-line simulation results show that the proposed MPC-based energy source management strategy(ESMS)can reduce hydrogen consumption of fuel cell up to 7%compared with the existing rule-based ESMS.Finally,the Hardware-in-the-Loop(HiL)simulation test is carried out to verify the effectiveness and real-time performance of the proposed MPC-based energy source optimization management strategy for the FCT based on eco-velocity planning with the assistance of traffic light information.

Design and Analysis of Traffic Information Transmission System Based on Visible Light Communication

Visible Light Communication( VLC) based on LED is a new wireless communication technology with high response rate and good modulation characteristics in the wavelengths of 380- 780 nm. Compared with conventional methods,the waveband of VLC is harmless to human and safe to communication because of no magnetism radiation. An audio information transmission system using LED traffic lights is presented based on VLC technology. The system is consisted of transmitting terminal,receiving terminal and communication channel. Some experiments were made under real communication environment. The experimental results showed that the traffic information transmission system works steadily with good communication quality and achieves the purpose of transmitting audio information through LED traffic lights,with a data transfer rate up to 250 kbps over a distance of 5 meters.

Range-rate tradeoffs in the communication between LED traffic lights and vehicles

Visible light communication between light emitting diode(LED) traffic lights and vehicles with a receiving photodiode front-end is developed for intelligent transportation systems. In this letter, the communication data rates for different ranges are evaluated. The data rates are based on real scenarios of the background noise and path losses and are experimentally obtained with a testing system built upon commercial offthe-shelf components. Comparisons of range-rate performance for different average noise levels are also conducted with the use of red/yellow/green LED lights. Results show that achieving the data rates of kilobits per second at a communication range of hundred meters is possible under the ordinary noise scenario, a finding that is highly significant for practical applications.

Design and development of an improved traffic light control system using hybrid lighting system

The deployment of light emitting diodes （LEDs） based traffic system control created the problem of dim displays when ambient light is similar to traffic lights. It causes some drivers＇ disability of seeing and obeying traffic signs. This makes drivers violate traffic rules. In this paper, an attempt to use hybrid lighting technology to mitigate this problem was developed. Incandescent lightings with deployed halogen bulbs provided an instan- taneous source of highly efficacious illumination which is brighter than the drivers＇ ambient lights （both daylight, electrical lights and their reflections）, which can help drivers get access to enough warning and help them initiate traffic safety warning as necessary. The halogen lightings also offered the required high current draw needed in electrical circuitry to help brighten the LED displays. The problem of heat generated was eliminated by aerating the T-junction traffic light control unit designed for this technology. The result of hybrid lighting system design was found to be high luminosity and capability of gaining driver attention in real-time. It also allowed enhanced sign＇s image detection and pro- cessing for smart based technologies by providing the ＂light punch＂ needed for a wide range of visual concems.

Multi-Objective Optimization of Traffic Signal Timing at Typical Junctions Based on Genetic Algorithms

With the rapid development of urban road traffic and the increasing number of vehicles,how to alleviate traffic congestion is one of the hot issues that need to be urgently addressed in building smart cities.Therefore,in this paper,a nonlinear multi-objective optimization model of urban intersection signal timing based on a Genetic Algorithm was constructed.Specifically,a typical urban intersection was selected as the research object,and drivers’acceleration habits were taken into account.What’s more,the shortest average delay time,the least average number of stops,and the maximum capacity of the intersection were regarded as the optimization objectives.The optimization results show that compared with the Webster method when the vehicle speed is 60 km/h and the acceleration is 2.5 m/s^(2),the signal intersection timing scheme based on the proposed Genetic Algorithm multi-objective optimization reduces the intersection signal cycle time by 14.6%,the average vehicle delay time by 12.9%,the capacity by 16.2%,and the average number of vehicles stop by 0.4%.To verify the simulation results,the authors imported the optimized timing scheme into the constructed Simulation of the Urban Mobility model.The experimental results show that the authors optimized timing scheme is superior to Webster’s in terms of vehicle average loss time reduction,carbon monoxide emission,particulate matter emission,and vehicle fuel consumption.The research in this paper provides a basis for Genetic algorithms in traffic signal control.

Basic Limit Theorems for Light Traffic Queues &Their Applications

In this paper, we study some basic limit theorems characterizing the stationary behavior of light traffic queuing systems. Beginning with limit theorems for the simple M/M/1 queuing system, we demonstrate the methodology for applying these theorems for the benefit of service systems. The limit theorems studied here are dominant in the literature. Our contribution is primarily on the analysis leading to the application of these theorems in various problem situations for better operations. Relevant Examples are included to aid the application of the results studied in this work.