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Spatial and temporal behaviors of the rock–paper–scissors (RPS) game is key to understanding not only biodiversity but also a variety of cyclic systems. It has been demonstrated that, in the stochastic cellular automaton of RPS game, three species cannot survive on one-dimensional (1-d) lattice; only a single species survives. Previous studies have shown that three species are able to coexist if the migration of species is considered. However, their definitions of migration are the swapping of two species or the random walk of species, which rarely occurs in nature. Here, we investigate the effect of migration by using the 1-d lattice traffic model in which species can move rightward if the site ahead is empty. Computer simulations reveal that three species can survive at the same time within the wide range of parameter values. At low densities, all species can coexist. In contrast, the extinction of two species occurs if the density exceeds the critical limit of the jamming transition. This dynamical phase transition between the coexistence and single (non-coexistence) phase clearly separates due to the self-organized pattern: condensation and rarefaction in the stripe-pattern of three species. [ABSTRACT FROM AUTHOR]

Vehicles are getting equipped in technology, communication between vehicle and the user is getting better. The vehicular network is an emerging technology to provide mobile users with the flexibility to use various services such as entertainment and navigation on wheels. The users are served with data related to their travel in terms of road map, weather updates, traffic congestions, radio services, social network applications and place of interest. The data communication takes place with the help of Smart On-board Unit (SOBU) present on the vehicle. There is a requirement of compound data computations with firm latency. Vehicle Edge computing (VEC) is the emerging technology that has serves at the edge in the neighbourhood of vehicle that enables data offloading. There will be much of energy consumption and latency resulting from offloading and computations. In this paper smart offloading scheme is proposed that will efficiently harvest the energy and reduce the energy consumption problem. As a performance statistic, the execution cost is used, which accounts for both execution delay and task failure. And also, the offloading scheme is analyzed for single user and multiuser by simulations and the results are compared graphically for battery energy level, average execution cost and channel mode select parameters respectively. The factors affecting both Single-user and multi-user computation are identified from the results. Experimental results show that offloading schemes proposed for single user and multi user work better than the other state-of-the-art algorithms for vehicular networks. This single-user analysis will be a benchmark for developing auto pilot vehicle for people with special needs and multi-user analysis will help in developing to all kinds of Electric Vehicle communication. [ABSTRACT FROM AUTHOR]

Background: Nowadays, industrialization, using cars and consequently traffic jams are part of human life which grows every day. Along with the expansion of communication and transportation speed, the number and severity of traffic accidents increases. According to the World Health Organization, traffic accidents are now recognized as the ninth cause of death worldwide. In Iran, traffic accidents after cardiovascular diseases are the second leading cause of death. Materials and Methods: This qualitative study was designed and implemented to determine driver's views and opinions in two cities of Warsaw, Poland, and Tabriz, Iran, about driver's high-risk behaviors. This study was conducted using in-depth interviews with 27 drivers. The study results were divided into four main questions about driving behaviors, reasons for driving abnormal behaviors, the prevalence and causes of abnormal behaviors, and suggested measures to correct these behaviors. Results: The study results were divided into six main themes of driving norms, individual factors, social factors, external factors, driving skills, and driving laws. Our findings indicate that drivers are more likely to rely on individual skills in driving in unacceptable conditions. In other words, they ignore the rules and regulations. But, when driving in high standards conditions and good facilities, drivers accept the rules and prioritize them. Conclusion: Internal control should be considered a helpful complement to external control, and that external control provides the highest efficiency when it comes with internal control. To internalize norms and observe driving laws and regulations, the authors suggest removing obstacles such as distrust among drivers regarding the effectiveness of driving laws, unawareness of breaking the laws, lack of job satisfaction, low level of participation, and structural barriers. Also, roads and vehicle safety must be improved along with a better track of the drivers' behavior. [ABSTRACT FROM AUTHOR]

Increasing display process complexity, Automated Guided Vehicle becomes more important and the delivery time constraint is getting shorter. The complicated process causes traffic congestion and deadlocks between AGVs. Preventing these issues, this paper suggests digital twin technology that allows AGV to choose the best route among various paths. This research validates the AGV simulation for the display plant. The result shows AGV average delivery time was shortened by 14.1% compared to the traditional algorithm. In addition, the AGV capacity rate can be reduced by 21.5% and the number of AGVs can be reduced by 10.1%. [ABSTRACT FROM AUTHOR]

The world is experiencing an unprecedented rate of urbanisation, with 70% of its population expected to live in urban cities by 2050 (United Nations, 2018). This is driving demand for smart city development projects, that address city related issues such as traffic congestion, inadequate housing, excessive crime rates, strains on utilities like energy and water, amongst others. The volume and analysis of data associated with these activities, call for a mechanism for managing data in smart cities.This paper is aimed at developing a data governance framework for managing data generated by smart cities, and improving the way they operate as well as the quality of life of their citizens. The paper will highlight the characteristics of smart cities, the importance for cities to become smart, how the smartness of cities can be measured, and which cities around the world are considered smart. It will also discuss why data is important for the success of smart cities, the types and sources of data typically present in smart city settings and the data management challenges that plague smart cities globally.A sequential mixed methods research approach incorporating qualitative and quantitative data collection and analysis methods was employed, following a multi-step approach starting with analysis of existing frameworks and interviews with subject matter experts. This was followed by a survey of industry professionals, for insights on key framework components, and the use of case studies and questionnaires for framework evaluation.Throughout the research process, the focus of smart cities on people rather than technology was prominent. As such, the most important theme of the framework was identified as "People", particularly in their roles as data stewards and data owners. The remaining five themes of the recommended data governance framework are, Process, Technology, Data, Protection and Valuable Insights. [ABSTRACT FROM AUTHOR]

Carpooling has gained significant attention as an effective solution to mitigate traffic congestion, reduce carbon emissions, and optimize transportation resources. Traditional carpooling platforms typically rely on centralized intermediaries to match drivers with passengers, leading to concerns regarding privacy, trust, and operational inefficiencies. To address these challenges, this paper proposes a novel approach to peer-to-peer carpooling utilizing blockchain technology.The application of blockchain in carpooling enables the establishment of a decentralized platform that facilitates direct interactions between drivers and passengers. By leveraging the immutability, transparency, and security features of blockchain, this peer-to-peer carpooling system enhances trust, privacy, and efficiency in ride-sharing arrangements.The proposed system utilizes smart contracts, a self-executing and tamper-proof code stored on the blockchain, to automate various carpooling processes such as ride requests, matching, payment settlements, and reviews. These smart contracts ensure that transactions are securely recorded and executed without the need for intermediaries, reducing costs and potential conflicts.Additionally, the use of blockchain technology allows for the implementation of reputation systems, where participants can build their reputation based on past interactions. This reputation mechanism enhances trust among users, fostering a safe and reliable carpooling environment. In conclusion, this paper presents an innovative approach to peer-to-peer carpooling using blockchain technology. By leveraging the decentralized nature of blockchain, smart contracts, and reputation systems, this system offers a secure, efficient, and trust-based platform for carpooling, contributing to the development of sustainable and intelligent transportation systems. [ABSTRACT FROM AUTHOR]

This paper proposes a Smart Electric Bus (e-Bus) Battery SUbstitution Scheme called SBUS in a cloud-based e-Bus system for the efficient battery replacement during e-Bus services. A smart e-Bus system using cloud-based management is considered as an alternative public transportation system. The battery of an e-Bus often needs to replaced, which can be one of the reasons for vehicle traffic congestion. When the battery of an e-Bus was not enough to run itself on its service route, it needs to replace its battery during the running. By this reason, road traffic congestion can be caused by this routine battery exchange. In order to solve this problem, SBUS is proposed to reduce road traffic congestion. To our knowledge, this paper presents the first attempt to investigate how to effectively assign an appropriate e-Bus station for battery replacement e-Bus station for battery replacement to each e-Bus to reduce road traffic congestion. As a result, the proposed scheme can assign an appropriate e-Bus station for battery replacement to each e-Bus to reduce road traffic congestion. To show the effectiveness of the proposed SBUS, we compare it with baseline schemes through simulation. The results of the experiment show that SBUS is more efficient than the baseline schemes in terms of the shorter average waiting time for battery replacement and the shorter end-to-end travel delay for e-Bus services. [ABSTRACT FROM AUTHOR]

Starting in the 1960s, traffic congestion became a staple part of Manila's history. Rapid urbanization, century-old thoroughfares, and large-scale migrations to Manila are just some of the reasons for the worsening traffic conditions. During the Marcos Era, the academe, practitioners, government agencies, and various organizations produced plans, position papers, and transport documents that sought to understand, assess, and perhaps provide possible solutions. Using data available in the libraries of University of the Philippines Diliman, this research note seeks to provide an initial survey and preliminary review of transport materials that documented and proposed solutions to the traffic congestion in the city of Manila from the 1960s until the 1980s. [ABSTRACT FROM AUTHOR]

The rapid increase in the number of vehicles has led to increasing traffic congestion, traffic accidents, and motor vehicle crime rates. The management of various parking lots has also become increasingly challenging. Vehicle-type recognition technology can reduce the workload of humans in vehicle management operations. Therefore, the application of image technology for vehicle-type recognition is of great significance for integrated traffic management. In this paper, an improved faster region with convolutional neural network features (Faster R-CNN) model was proposed for vehicle-type recognition. Firstly, the output features of different convolution layers were combined to improve the recognition accuracy. Then, the average precision (AP) of the recognition model was improved through the contextual features of the original image and the object bounding box optimization strategy. Finally, the comparison experiment used the vehicle image dataset of three vehicle types, including cars, sports utility vehicles (SUVs), and vans. The experimental results show that the improved recognition model can effectively identify vehicle types in the images. The AP of the three vehicle types is 83.2%, 79.2%, and 78.4%, respectively, and the mean average precision (mAP) is 1.7% higher than that of the traditional Faster R-CNN model. [ABSTRACT FROM AUTHOR]

Automated vehicles (AVs) hold the potential to reduce road accidents, mitigate traffic congestion, and improve travel experience. However, the possible countervailing impacts from the changes in underserved populations' vehicle travel demand tend to be overlooked. To determine the vehicle travel demand changes that resulted from underserved populations aged between 6 and 80, this paper explores the latent effect of AVs on vehicle kilometers traveled (VKT) in a fully AV environment using person trip survey data from the cities of Sanya, Shijiazhuang, and Shenzhen in China. This paper uses the natural decline hypothesis of travel demand and proposes a regression model to investigate the difference among the cities' latent vehicle travel demand. Results show that the average VKT of the overall population in Sanya, Shijiazhuang, and Shenzhen increased by 33.4%, 47.0%, and 46.8%, respectively. The analysis of the regression model confirms that the current travel behavior of individuals can affect the degree of increase in their average VKT. Integrating AVs into public transport, increasing the acceptance of automated shared mobility options, transforming road space use type, and prototyping AV designs with various features and needs are potential methods to cope with the countervailing impacts. The total VKT of the overall population increased by approximately 10%e25% depending on the city. The conclusions of this paper provide informative insights into the evaluation of VKT for underserved populations and contribute to the deployment of AVs to address equity and inclusion issues. [ABSTRACT FROM AUTHOR]

The increased usage of navigation technologies has caused conflicts in local traffic management, resulting in congested residential areas among other challenges for residents. This paper uses content analysis to investigate such negative social externalities within local communities and neighbourhoods. Through a corpus of 90 news articles about traffic incidents caused by navigation technologies, we identified negative traffic and safety-related externalities, including congestion, damage, pollution, and accidents. We also report on countermeasures by local communities and governments, including street closures, speed limit reduction, and turn bans. Based on our results, we discuss the implications for designing mobile navigation technologies that reduce negative social externalities. [ABSTRACT FROM AUTHOR]

Under the context of rapid development of the Internet of vehicles and vehicle-road collaboration system, active traffic management (ATM) becoming the mainstream means of road traffic control and developing toward refinement. In this paper, to study the high-precision lane-level dynamic induction control strategy in different scenarios, based on the NaSch model of cellular automata and combined with the characteristics of the failure section area, a fuzzy lane-changing bypass vehicle-following model considering lane-changing pressure in multi-lane failure scenarios was built. The simulation results show that (i) if the lane failure occurs on the middle lane, the lane should be induced in advance, and the induced lane change effect is the best at about 100 m. When the lane failure occurs in the left lane and right lane, the prompt is best at about 250 m. (ii) The induced distance should be based on actual traffic conditions, free combination of different early warning distances between 100 and 300 m can save about 20–30 s congestion time. (iii) The lane-level dynamic coordinated guidance control measures can effectively improve the road traffic efficiency compared with the static unified control measures, improve the traffic efficiency of road performance, and alleviate traffic congestion time. The conclusion of this paper can provide some reference for dynamic active control management and achieve higher accuracy of traffic flow lane-level control. [ABSTRACT FROM AUTHOR]

Massive amounts of real-time streaming network data are generated quickly because of the exponential growth of applications. Analyzing patterns in generated flow traffic streaming offers benefits in reducing traffic congestion, enhancing network management, and improving the quality of service management. Processing massive volumes of generated traffic poses more challenges when data traffic encryption is raised. Classifying encrypted network traffic in real-time with deep learning networks has received attention because of their excellent performance. The substantial volume of incoming packets, characterized by high speed and wide variety, puts real-time traffic classification within the domain of big data problems. Classifying traffic with high speed and accuracy is a significant challenge in the era of big data. The real-time nature of traffic intensifies deep learning networks, necessitating a considerable number of parameters, layers, and resources for optimal network training. Until now, various datasets have been employed to evaluate the effectiveness of previous methods for classifying encrypted traffic. The primary objective has been to enhance accuracy, precision, and F1-measure. Presently, encrypted traffic classification performance depends on pre-existing datasets. The learning and testing phases are done offline, and more research is needed to investigate the feasibility of these methods in real-world scenarios. This paper examines the possibility of a tradeoff between evaluating the model's effectiveness, execution time, and utilization of processing resources when processing stream-based input data for traffic classification. We aim to explore the feasibility of establishing a tradeoff between these factors and determining optimal parameter settings. This paper used the ISCX VPN-Non VPN 2016 public dataset to evaluate the proposed method. All packets from the dataset were streamed continuously through Apache Kafka to the classification framework. Numerous experiments have been designed to demonstrate the efficacy of the proposed method. The experimental results show that the proposed method outperforms the baseline methods by 11% in the F1-measure when the number of workers is two and by 25% when the number of workers is equal to 32. [ABSTRACT FROM AUTHOR]

With the continuous advancement of the economy and technology, the number of cars continues to increase, and the traffic congestion problem on some key roads is becoming increasingly serious. This paper proposes a new vehicle information feature map (VIFM) method and a multi-branch convolutional neural network (MBCNN) model and applies it to the problem of traffic congestion detection based on camera image data. The aim of this study is to build a deep learning model with traffic images as input and congestion detection results as output. It aims to provide a new method for automatic detection of traffic congestion. The deep learning-based method in this article can effectively utilize the existing massive camera network in the transportation system without requiring too much investment in hardware. This study first uses an object detection model to identify vehicles in images. Then, a method for extracting a VIFM is proposed. Finally, a traffic congestion detection model based on MBCNN is constructed. This paper verifies the application effect of this method in the Chinese City Traffic Image Database (CCTRIB). Compared to other convolutional neural networks, other deep learning models, and baseline models, the method proposed in this paper yields superior results. The method in this article obtained an F1 score of 98.61% and an accuracy of 98.62%. Experimental results show that this method effectively solves the problem of traffic congestion detection and provides a powerful tool for traffic management. [ABSTRACT FROM AUTHOR]

In this paper if any vehicle violating the traffic rules for example, break the signal, wrong parking and over speeding etc. our Paper will automatically detect a vehicle's number plate and allow authority to scan his/her data that is stored in their database and send them the challan inform of email and inform them to pay their challan within respective day. The main reason of doing this because the traffic is increasing on daily basis in our country, it gets more and more difficult to find out the culprits of traffic violations, including signal violation, over speeding, and illegal car parking which can result in either accident or traffic jam. When this process done by system number of vehicles detected it check in our database, the database will be saved in our system in every vehicle which will be registered in our country, the system will check number if that number matches than checks honour emails and then send a violation challan at the email of honour to pay nearest police station our system will be connected by internet. The system is made by using 40% hardware and 60% python programming hardware consist of raspberry pi, SGB night vision camera, servo motor and other necessary material. In this Paper, we have designed a hardware using Raspberry Pi which does all of that work of finding out the culprit without the help of human being. This Paper is made using the PIR sensor to detect motion when the signal is red, IR sensor to detect if a car is over speeding, and another IR sensor to check if the car is illegally parked. [ABSTRACT FROM AUTHOR]

Real‐time and accurate short‐term traffic flow prediction can provide a scientific basis for decision making by travellers and traffic management, and alleviate traffic congestion to a certain extent. The existing traffic flow prediction methods often encounter limitations in real‐time performance and accuracy due to the post‐processing required to rectify anomaly detection results during data pre‐processing. This paper presents a novel traffic flow prediction model, termed the With‐Anomaly Detection Probability (ADP) Attention‐Bidirectional Long‐Short Term Memory (BiLSTM) model. This model takes the probability of anomaly detection into consideration, integrating the anomaly detection outcomes as an inherent parameter into the traffic flow prediction framework. Additionally, the model incorporates an attention mechanism within the long‐short term memory network. Through a comprehensive simulation study utilizing actual measured traffic flow data from the Shanghai‐Chongqing Expressway, the effectiveness of the proposed model is rigorously evaluated. The prediction results show that the model proposed in this paper is a real‐time and accurate short‐time traffic flow prediction model compared with the basic models. [ABSTRACT FROM AUTHOR]

Various kinds of smart sensors are being developed and released with the growth of sensor technology and Internet of Things (IoT) technology. IoT smart service can provide convenience in our daily lives with these smart sensors. However, the increasing number of mobile sensors and amount of data may increase latency. It may cause network congestion on a particular network. Therefore, we propose a Smart Edge Broker (SEB) to intelligently transmit data traffic generated by a smart city in this paper. SEB can prevent the traffic congestion from being transmitted or bypassed to a location where traffic is not necessary. In addition, SEB can prevent overload in a specific area between services through a location-based transfer. Plus, SEB is suitable to operate it as a fog computing model by placing it at the edge of a smart city network. We conducted a latency measurement experiment and load measurement experiment to evaluate the effectiveness of the proposed Smart Edge Broker. As a result, we found that the latency was reduced by 72%, and the CPU usage was reduced by 63% compared to when the Smart Edge Broker was not used. [ABSTRACT FROM AUTHOR]

With the development of automated vehicles, researches related to automated vehicle platoon (AVP) have received more and more attention. AVP is considered one of the effective means to alleviate traffic congestion and reduce vehicle energy consumption. This paper studies a three‐layer method of avoiding obstacle vehicles in traffic by switching the formation for the automated heavy vehicle platoon. In the decision‐making layer, a decision‐making system based on the finite‐state machine is established for formation switching. In the second layer, the lane‐changing trajectory is optimized based on the quantic polynomial curve fitting for vehicles that need to change lanes. In terms of vehicle control layer, each vehicle has a longitudinal controller based on sliding mode control and a lateral controller based on model predictive control to track the planned trajectory to complete the target formation. Finally, the proposed method is simulated in MATLAB/TruckSim. The simulation results show that the proposed method could effectively avoid the obstacle vehicles by switching the formation and has a small average value of errors in speed tracking and trajectory tracking. [ABSTRACT FROM AUTHOR]

As urbanization accelerates, traffic congestion and frequent accidents have become prominent issues, prompting the development of intelligent transportation systems. This paper focuses on the research of vehicle and pedestrian detection algorithms to improve detection accuracy in complex traffic environments. Considering the limitations of traditional object detection algorithms in complex situations, this study adopts the deep learning-based YOLOv8 algorithm and introduces the Coordinate Attention (CA) module to enhance the model's feature extraction and localization capabilities. Experimental results show that the improved YOLOv8 network achieves a 1.1% increase in detection accuracy while maintaining its original speed. Furthermore, this paper constructs a vehicle and pedestrian dataset suitable for Chinese traffic scenes, providing an effective solution for autonomous driving assistance systems. Overall, this study holds significant reference value for vehicle and pedestrian detection in the field of intelligent transportation. [ABSTRACT FROM AUTHOR]

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Traffic congestion occurs when there is a mismatch between the demand for road use and the available capacity. The volume-delay function (VDF) can quantify the relationship between travel time and the volume of traffic on a particular link, and also provide insight into the state of a traffic system, such as whether it is congested or uncongested. In this paper, we present a VDF model that is based on the fundamental diagram and has two main components: (1) an improved VDF with fewer parameters that can handle both congested and uncongested traffic conditions, based on a fundamental diagram, and (2) a model-based VDF practical calibration framework for practical traffic applications that can determine key parameters for a link in a corridor. Our experiments using corridors in Los Angeles and Beijing demonstrate that our proposed analytical methods effectively calculate road impedance under congested conditions. The results indicate that the proposed model is superior to other existing models in terms of the root mean squared error (RMSE) and mean absolute error (MAE). In addition, our calibrated results indicate that the travel time index (TTI) in Los Angeles is 2.12, in Beijing is 1.74. The model proposed in this paper provides a useful calibration tool for enhancing model performance and improving the accuracy of travel time and speed estimates in traffic assignment. [ABSTRACT FROM AUTHOR]

The consequences of sustainable and greener mobility have been demonstrated previous year by the pandemic situation. One significant benefit was to reduce the level of noise, dust and air pollution from the traffic in many European countries due to reduced mobility most of the population. Positive phenomena was also recorded in the Slovak Republic. Today's urban mobility offers new, more sustainable ways of transport for residents, especially app-based and shared-ride services, which have become increasingly widespread and very popular. Undoubtedly offer a level of convenience unseen before in the urban mobility systems throughout the world. Although new forms of transport are developed in most Slovak cities, their operation is not very rational and therefore individual car transport still dominates at the expense of sustainable ways of transport. In our paper, we analyze the traffic situation in the city of Nitra, as the large number of trips in the course of the peak-hours often results in severe traffic congestion. Multimodality is one of the solutions to improve the current traffic situation, as it allows urban residents to choose from a range of alternative travel options. The main aim of this paper is to determinate the possibilities of multimodal urban mobility in the city of Nitra in addition to analyze how the potential change in the variability of choice of means of transport affects the urban mobility behaviour. In order to meet stated objectives, a marketing research was conducted. The research findings point to the fact that the current urban infrastructure and overall possibilities are limited, the consequence of which is that travel behaviour of citizens does not demonstrate the features of sustainable urban mobility. Our results point to major differences in attitudes of Nitra´s residents from different urban and sub-urban areas. [ABSTRACT FROM AUTHOR]

With the escalating density of vehicles converging at road intersections, the surge in road accidents, traffic conflicts, and traffic congestion has emerged as a pressing concern. This research paper addresses these challenges by employing MC (manual control) techniques to mitigate encroachment issues at three selected intersections. These intersections were identified through a comprehensive analysis of the Ranking-based Instance Selection (RIS), enabling the design of suitable measures to facilitate smooth traffic flow and minimize the occurrence of crashes. In order to gather pertinent data, the study incorporates various parameters such as traffic volume, peak flow rate (PFR), traffic conflicts, accidents, and intersection inventory. Through the implementation of our proposed approaches, which involve both MC techniques and signalized operation, a supreme level of service (LOS) is attainable. Notably, our findings demonstrate a remarkable reduction in the volume-to-capacity ratio (v/c ratio) of up to 0.62. This paper thus serves as a significant contribution to the field of traffic management, offering practical insights for optimizing intersection design and effectively addressing the challenges posed by increased traffic density. [ABSTRACT FROM AUTHOR]

Time-sensitive networking (TSN) is considered an ideal solution to meet the transmission needs of existing industrial production methods. The traffic scheduling problem of TSN is an NP-hard problem. The traditional traffic scheduling algorithms can lead to issues such as significant computational time consumption and traffic congestion, which are not conducive to the rapid and high-quality deployment of TSN. To simplify the complexity of TSN schedule table generation, the paper studies the scheduling problem of mixed critical traffic in TSN. Using a combinatorial packing traffic scheduling algorithm based on unoccupied space (CPTSA-US), a scheduling table for TSN traffic transmission is generated, proving the feasibility of transforming the TSN traffic scheduling problem into a packing problem. In addition, the initial packing algorithm and traditional traffic scheduling algorithm can cause traffic accumulation, seriously affecting network performance. This paper proposes a mixed-critical traffic scheduling algorithm based on free time domain (MCTSA-FTD), which further partitions the packing space transformed by the time domain. And performs multiple packing of traffic based on the partitioned packing space and generates the TSN schedule table according to the reverse transformation of the packing results. The simulation results show that compared to the CPTSA-US and the traditional traffic scheduling solution algorithm SMT (Satisfiability Modulo Theory), the schedule table generated by the MCTSA-FTD significantly improves the delay, jitter, and packet loss of BE flows in the network while ensuring the transmission requirements of TT flows. This can effectively enhance the transmission performance of the network. [ABSTRACT FROM AUTHOR]

The current traffic signal light timing scheme cannot effectively solve the problem of traffic congestion. This paper studied the intelligent control of signal lights based on infrared sensors. Firstly, the traffic flow was detected by infrared sensors; then, the future traffic flow was predicted using a back-propagation neural network; an intelligent control algorithm was designed based on the prediction results, and data were collected at the actual intersection for simulation experiments. It was found that the maximum and minimum root-mean-square error (RMSE) value of the BPNN prediction were 72.33 and 45.77, respectively, i.e., BPNN could predict the traffic flow accurately. Then, in the comparison of timing schemes, the maximum average queuing delay time of the BPNN was 0.55 s, and the maximum average queuing length was 92 m in the case of large traffic flow, both of which were better than the traditional fixed timing scheme. The results prove the reliability of the proposed signal light intelligent control algorithm. The algorithm can be further applied in practice. [ABSTRACT FROM AUTHOR]