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The research of traffic congestion control has been developed rapidly in recent years. Researching the bifurcation characteristics of traffic flow and designing control schemes for unstable bifurcation points can provide some new methods for relieving the traffic congestion. In this paper, we study the bifurcation characteristics and nonlinear control of traffic flow based on the continuum model considering the new energy vehicles effect. First, a macroscopic traffic flow model considering the number of new energy vehicles and their range is proposed. Then, we transform it into a traffic flow stability model suitable for branching analysis using the traveling wave transformation. Therefore, we can transform the traffic flow problem into the stability analysis problem of systems and reflect unstable traffic characteristics such as congestion. Based on the stability model, the existence conditions of the Hopf bifurcation are proved and some bifurcation points of the traffic system are found. Second, we study the congestion and stability abrupt change behaviors near the equilibrium point and branching point to seek for the formation mechanism of traffic congestion. Finally, for the unstable bifurcation point, the control scheme is designed by Chebyshev polynomial approximation, stochastic feedback control, and other methods to delay or disappear the unstable bifurcation point and alleviate traffic congestion. It has important theoretical significance and application value. [ABSTRACT FROM AUTHOR]

Traffic congestion not only has a great impact on people's travel, but also increases energy consumption and air pollution. The control analysis of the macroscopic traffic flow model considering the vehicle braking effect is particularly important, reflecting the impact on the actual traffic flow density wave, so as to better solve the actual traffic problems. In this paper, based on a speed difference optimization speed model, the micro–macro-variables are transformed into a high-order continuous traffic flow model. Then, a random function considering the physical correlation of random components is added to the high-order continuous traffic flow model to establish a random traffic flow model that can reflect the uncertain behavior of traffic flow acceleration or deceleration. Based on this stochastic traffic model, the existence of Hopf bifurcation and bifurcation control of the traffic flow system model considering stochastic characteristics are derived by using Hopf bifurcation theorem. By Chebyshev polynomial approximation method, the stochastic problem of the system is transformed into the bifurcation control problem of its equivalent deterministic system. A feedback controller is designed to delay the occurrence of Hopf bifurcation and control the amplitude of the limit cycle. Without changing the equilibrium point of the system, the complete elimination of Hopf bifurcation can be achieved by controlling the amplitude of the limit cycle. That is, the feedback controller is used to modify the bifurcation characteristics of the system, such as the bifurcation appearing at the equilibrium point in the control system moves forward, moves backward or disappears, so as to achieve the effect of preventing or alleviating traffic congestion. [ABSTRACT FROM AUTHOR]

In the process of collecting traffic information, traditional traffic flow control methods have some problems, such as high loss rate of sensing and detection information and long average queue length, which lead to the unsatisfactory effect of traffic control and evacuation in main roads. Therefore, based on multi-sensor information fusion technology, this study designs a new intelligent control method of traffic flow on main road. According to the multi-sensor information, the fusion algorithm and measurement equation are designed to obtain the main road traffic sensor position, vehicle linear velocity and other parameters, and update the fusion results in real time. Then, the short-term characteristics of traffic flow are used to control the appearance time of target section. Based on the ordinary differential equation, the main road traffic network is represented by intelligent directed graph, and the intelligent dredging model of urban main road traffic flow is constructed. The simulation results show that in five experimental roads, the average delay time of this method is less than 35 s, the maximum average number of stops is only 8.8, the average queue length is less than 30 m, the average travel speed is more than 25 km/h, the traffic flow per unit time is more than 250veh/10 min, and the traffic congestion index of main road is always more than 5.5. The above indexes of this method are better than those of traditional methods, which verifies that this method has better application performance. [ABSTRACT FROM AUTHOR]

Traffic congestion is a major concern in urban centers, as it can affect society, the environment, and the economy. There are many studies on the use of computational intelligence (CI) to improve mobility in urban centers. Some of these researches focus on developing strategies for traffic light programming, since traffic coordination is complex due to its many parameters, variables, and dynamic behavior, and also an inefficient traffic control plan can lead to increased delays and contribute to traffic congestion. Although there are many works in the literature on strategies for traffic control, there are still some contributions and gaps to be filled, especially because some studies do not consider the automatic optimization of traffic signals in real time, that is, according to the demand of vehicles on the roads, considering multiple objectives and the use of a network of intersections in their experiments. In addition, some of the proposed models are not independent of simulation to evaluate the solutions of CI algorithms, resulting in a more complex deployment in real situations. In this context, this paper presents a new method to optimize traffic light plan in a network of intersections and in real time, called Active Control of Traffic Signals (ACTS) associated with the Non-Dominated Sorting Genetic Algorithm, considering multiple objectives in the optimization process (minimizing the average delay time and the number of vehicles stops per cycle). To test the applicability of the model, a real dataset of vehicle demand collected by the Company of Transport and Traffic of Belo Horizonte (BHTrans) is loaded into the AIMSUN simulator, then the method is applied and compared with the current traffic control plan used by BHTrans. The results show that the ACTS method reduces the average vehicle delay by almost half compared to the results obtained with the current solution used by BHTrans. In real life, this means less time spent in traffic, which promotes faster traffic flow, reducing traffic congestions. [ABSTRACT FROM AUTHOR]

Smart cities can effectively improve urban life quality. But, with the rise in population size, there is an increase in the use of vehicles for transportation. In smart city development, traffic control and route planning are the primary tasks to address the problem of travel time estimation. It has become complex due to concerns of wrapped spatio-temporal data on dynamic real-time traffic conditions. The existing methods failed to estimate travel time efficiently due to not considering the spatio-temporal features and lack of computing resources. There is a need for a system like intelligent transportation system to monitor and accurately predict traffic flow to avoid traffic congestion and reduce the impact on the ecological system. This paper developed a novel hybrid deep learning model to estimate optimized travel time and possible trajectories. In this work, U-Net used to reduce the number of feature points for each temporal data and GNN is used to build the graph with connectivity between vehicular nodes. This hybrid model helps us predict traffic flow and aims to estimate accurate travel time from one location (node) to another. The model's performance is evaluated on the standard benchmark datasets Q-Traffic, TaxiBJ, and Chengdu. The experimental results show that the proposed framework significantly improves performance in extracting travel patterns and provides an optimal route with an estimated travel time than existing methods with RMSE 4%, MAE 20.49%, and MAPE 18%. The proposed model has accurately predicted the estimation of travel time of the vehicle for the given urban traffic data. [ABSTRACT FROM AUTHOR]

The rapid development of urbanization in China has contributed to traffic events, such as traffic accidents and delays. It is difficult to detect and resolve highway traffic congestion in a timely manner using traditional methods because they are slow, require a large number of workers, and require the installation of a large amount of monitoring equipment. Therefore, it is imperative to introduce advanced technology to address these challenges. Recently, deep learning technology has made significant breakthroughs and has been widely applied to various fields with satisfactory results. Deep learning is among the most important technologies for the detection and evaluation of traffic congestion, enabling the accurate detection of the state of the expressway network's traffic congestion, the evaluation of traffic congestion, and the prediction of possible traffic congestion. This enables management to formulate traffic dredging strategies in advance to prevent the negative impact of traffic congestion on normal traffic flow. This paper proposes a framework based on deep learning for next-generation highway traffic management. This framework selects traffic congestion indicators to construct an index model, and then constructs a deep learning model based on self-coding. It predicts and classifies highway traffic environment data and excavates sample data based on the characteristics of traffic parameters. As soon as traffic data were classified, a prediction model based on SoftMax was established to detect and predict highway traffic congestion. We conducted a traffic congestion analysis of the Shanghai expressway network based on the speed performance data obtained from the China Traffic Management Bureau. As a result of their research, we developed an index to measure highway traffic congestion. For traffic control and management organizations to function effectively, it is crucial to have an accurate and clear picture of traffic network operations. We evaluated the proposed framework using data gathered from highway monitoring scenes, and the results indicated that 98.6% of the data could be correctly detected. Using the prediction model based on SoftMax for expressway vehicles during peak hours, the accuracy was 92%, and the misjudgment rate was 8%. This study demonstrates that detecting and evaluating the state of highway traffic environments using deep learning has high accuracy, can be applied to actual highway traffic systems, and is extremely useful for detecting highway congestion. This framework is a promising solution for next-generation highway traffic management and provides accurate and timely traffic congestion detection and evaluation. [ABSTRACT FROM AUTHOR]

Accurate traffic flow forecasting (TFF) is a prerequisite for urban traffic control and guidance, which has become the key to avoiding traffic congestion and improving traffic management in intelligent transportation systems. To precisely characterize the spatial structure of road networks and discover temporal and spatial characteristics, we propose an attention-based spatial–temporal adaptive dual-graph convolutional network (ASTA-DGCN) for TFF in this paper. Specifically, we employ a spatial–temporal attention module to explore the hidden temporal correlation information of traffic data and the implicit influence of weights among road network nodes and to further capture the dynamic influence of different spatial–temporal positions on the current spatial–temporal position. Then, we utilize an adaptive graph modeling module to automatically extract the one-way relationship between variables and integrate external knowledge into the module. The FastDTW algorithm is exploited to measure the similarity of road network nodes, and the non-Euclidean pairwise association between regions is encoded into graphs to discover the hidden temporal pattern similarity effectively. Furthermore, temporal and spatial correlations are explicitly modeled using dual-graph convolution and sequential convolution based on the obtained graphs to mine the spatial–temporal patterns in dynamic traffic flow, and the final prediction result is produced based on the weighted fusion of the output values of the recent, daily, and weekly components. Finally, the ASTA-DGCN algorithm is successfully applied to TFF on two real-world traffic datasets. The experimental results indicate that our ASTA-DGCN algorithm outperforms ARIMA, VAR, FNN, GCN, GAT, GWNet, STGCN, ASTGCN, and STSGCN. [ABSTRACT FROM AUTHOR]

Accurate assessment of road conditions can effectively alleviate traffic congestion and guide people's travel plans, traffic control decisions of transportation departments, and formulation of traffic-related laws and regulations. This paper proposes a quantum particle swarm optimization (QPSO) and adaptive hybrid exponential smoothing with residual correction (AHES-RC) for the nonlinearity and randomness of traffic flow. In the proposed algorithm, the single–double-exponential smoothing method is mixed with adaptive weights to form AHES, a new method of mixing weights according to real-time traffic trend changes. After the residual correction of AHES is calculated by the extreme learning machine algorithm, the parameters of AHES-RC are optimized using QPSO to improve the prediction accuracy further. This paper comprehensively compares the QPSO-AHES-RC algorithm with other benchmark models through testing on 26 real-world data sets. The results show that the proposed algorithm can adaptive forecasting under various traffic conditions, yielding the best forecasting performance in terms of various forecast error metrics. Compared with advanced machine learning algorithms such as XGBoost and CatBoost, the mean RMSE and mean MAPE have been improved by over 20% on average. In addition, it is revealed that the real-time dynamic capture of traffic flow can effectively improve prediction accuracy. [ABSTRACT FROM AUTHOR]

Intelligent traffic light control (ITLC) algorithms are very efficient for relieving traffic congestion. Recently, many decentralized multi-agent traffic light control algorithms are proposed. These researches mainly focus on improving reinforcement learning method and coordination method. But, as all the agents need to communicate while coordinating with each other, the communication details should be improved as well. To guarantee communication effectiveness, two aspect should be considered. Firstly, a traffic condition description method need to be designed. By using this method, traffic condition can be described simply and clearly. Secondly, synchronization should be considered. As different intersections have different cycle lengths and message sending event happens at the end of each traffic signal cycle, every agent will receive messages of other agents at different time. So it is hard for an agent to decide which message is the latest one and the most valuable. Apart from communication details, reinforcement learning algorithm used for traffic signal timing should also be improved. In the traditional reinforcement learning based ITLC algorithms, either queue length of congested cars or waiting time of these cars is considered while calculating reward value. But, both of them are very important. So a new reward calculation method is needed. To solve all these problems, in this paper, a new ITLC algorithm is proposed. To improve communication efficiency, this algorithm adopts a new message sending and processing method. Besides, to measure traffic congestion in a more reasonable way, a new reward calculation method is proposed and used. This method takes both waiting time and queue length into consideration. [ABSTRACT FROM AUTHOR]

With the rapid development of internet technology and mobile devices, massive streaming data of spatiotemporal information is available for real-time data mining. Outlier detection is playing as one of the most important analysis tasks for trajectory stream processing, such as traffic control and urban planning. Existing work about this issue can be divided into two main categories: individual outlier detection (IOD) and group outlier detection (GOD). IOD focuses on detecting an individual trajectory outlier generated by a single moving object (e.g., an over-speeding car), while GOD aims to detect a group outlier generated by various moving objects (e.g., a group of cars influenced by a traffic jam). However, existing studies only support one of them and cannot comprehensively understand the traffic conditions by combining both of them. To this end, we propose a novel framework for real-time urban traffic outlier detection (RUTOD) in this paper. RUTOD contains IOD based on current traffic conditions and GOD according to historical data. In addition, we adopt a street-based trajectory division to accelerate investigation. Experimental results show that our proposal is not only effective but also efficient for detecting both individual outliers and group outliers in real-time scenario. [ABSTRACT FROM AUTHOR]

Bifurcation of traffic flow involves complex dynamic characteristics of the system. In order to understand the complex traffic phenomenon, this work designed a macro traffic model considering the driver's memory which plays an important role in the traffic flow. Based on this model, we investigate the effects of the driver's memory and wave velocity on the stability of the traffic flow. By means of one and two parameter bifurcation analysis, we explore how these parameters affect the bifurcation structure of the system, and further investigate the dynamic mechanisms of traffic flow. We explain various traffic phenomena related to the different types of equilibrium points and limit cycles by phase plane analysis. We also study how the initial density and bifurcation structure affect the energy consumption in the system. The results show that the driver's memory and wave velocity play an important role in the stability of the traffic flow. By considering the change of bifurcation structure, we can better understand the source of traffic congestion, and further predict and control the possible traffic congestion. [ABSTRACT FROM AUTHOR]

Traffic congestion is one of the biggest challenges around the world, resulting in multiple harmful consequences such as air pollution, road fatalities, and traffic jams. Thus, it is a vital need to develop preventive mechanisms that allow better traffic management and alleviate the burden put on the transport network. Vehicle-to-anything (V2X) communication is gaining massive research interest as a promising solution to mobility challenges. This technology will enable vehicles and the infrastructure to form a distributed network constantly exchanging traffic information in real-time. The availability of timely information can help road users make optimal choices and enable effective autonomous traffic control. Therefore, to avoid traffic congestion, mobility models need to be established to study vehicular dynamics and to forecast future traffic conditions. The main goal of this study is to develop a traffic model based on Markov chain to tackle the congestion issue in a highway environment. Based on traffic data collected from vehicles through V2X technology, the model studies the evolution of traffic flow along a multiple lane divided highway and locally calculates estimates of the expected number of vehicles traveling on a highway segment. Performance measures are then inferred to detect possible congestion and then prevent it from happening. The numerical results presented in this study validate the model accuracy and show its ability to reproduce the fundamental mobility aspects in a highway environment. [ABSTRACT FROM AUTHOR]

Traffic congestion is becoming a serious problem with the large number of vehicle on the roads. In the traditional traffic control system, the timing of the green light is adjusted regardless of the average traffic rate at the junction. Many strategies have been introduced to solve and improve vehicle management. However, in order to handle road traffic issues, an intelligent traffic management solution is required. This article represents a self adaptive real-time traffic light control algorithm based on the traffic flow. We present a machine learning approach coupled with image processing to manage the traffic clearance at the signal junction. The proposed system utilizes single image processing via neural network and You Only Look Once (YOLOv3) framework to establish traffic clearance at the signal. We employed YOLO architectures because it is accurate in terms of mean average precision (mAP), interaction over union (IOU) values and fast in object detection tasks as well. It runs significantly faster than other detection methods with comparable performance. The average processing time of single image was estimated to be 1.3 s. Further based on the input from YOLO we estimated the 'on' time period green light for effective traffic clearance. Several real time parameters like number of vehicles (two wheelers, four wheelers), road width and junction crossing time are considered to estimate the 'on'time of green light. Moreover, we used the real traffic images to test the performance and trained the system with different dataset. Our experiments investigation reveals that the predicted vehicle counts were well matched with the actual vehicle count and proposed method apprehended an average accuracy of 81.1%. The reported strategy is self adaptive, highly accurate, fast and has the potential to be implemented in the traffic clearance at the junctions. [ABSTRACT FROM AUTHOR]

Traffic lights are an important controlling factor in traffic flows, and good policies will facilitate traffic congestion. A car's waiting time is highly related to the period in which the traffic lights are green or red; thus, a proper controlling policy reduces the waiting time and speeds up the car movement. Emergency vehicles always have a higher priority than other cars and need to reach their destination faster. In the last decade, a lot of research has been done to solve the traffic light scheduling issue using artificial intelligence techniques. Reinforcement learning with simulating human learning has the outstanding performance to solve complex problems, such as traffic lights control. In this study, a deep reinforcement learning traffic light control was implemented at a crossroad using real-time traffic information with an emphasis on emergency vehicles. The agent learns to adjust its policies to prioritize the emergency cars over the other cars. The results show less waiting time and speed up the cars' movement. It also indicates the emergency vehicles will cross the intersection with the least delay. With 200 to 600 cars and one emergency vehicle, the average delay decreases 2–16% in total using reinforcement learning. Considering more emergency vehicles, there is a 27–40% decrease in average delay for emergency vehicles. [ABSTRACT FROM AUTHOR]

Because of the population increasing so high, and traffic density remaining the same, traffic prediction has become a great challenge today. Creating a higher degree of communication in automobiles results in the time wastage, fuel wastage, environmental damage, and even death caused by citizens being trapped in the middle of traffic. Only a few researchers work in traffic congestion prediction and control systems, but it may provide less accuracy. So, this paper proposed an efficient IoT-based traffic prediction using OWENN algorithm and traffic signal control system using Intel 80,286 microprocessor for a smart city. The proposed system consists of '5' phases, namely IoT data collection, feature extraction, classification, optimized traffic IoT values, and traffic signal control system. Initially, the IoT traffic data are collected from the dataset. After that, traffic, weather, and direction information are extracted, and these extracted features are given as input to the OWENN classifier, which classifies which place has more traffic. Suppose one direction of the place has more traffic, it optimizes the IoT values by using IBSO, and finally, the traffic is controlled by using Intel 80,286 microprocessor. An efficient OWENN algorithm for traffic prediction and traffic signal control using a Intel 80,286 microprocessor for a smart city. After extracting the features, the classification is performed in this step. Hereabout, the classification is done by using the optimized weight Elman neural network (OWENN) algorithm that classifies which places have more traffic. OWENN attains 98.23% accuracy than existing model also its achieved 96.69% F-score than existing model. The experimental results show that the proposed system outperforms state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Traffic congestion and smog are hot topics in recent years. This study analyzes the impacts of road traffic characteristic parameters on urban air quality quantitatively based on aerosol optical thickness (AOD) and geographical weighted regression (GWR) models, including the road network density, road area occupancy, intersection number, and bus network density as main factors. There are some major research findings. Firstly, there exists a strong positive correlation between the peak congestion delay index (PCDI) and air quality, the correlation has R2 values of up to 0.4962 (R 0.70). Secondly, GWR refines the local spatial changes in the AOD and the road parameters, and the correlation R2 based GWR model all above 0.6. The correlation between AOD and the road area occupancy was the highest, and the correlations with the bus network density and the intersections number were higher than that with the road network density. Thus, bus route planning, bus emission reduction, road network planning, and signal timing (at intersections) have a greater impact on air quality than other policy, especially in areas with traffic jams. The results of this study could provide theoretical support for traffic planning and traffic control, and is promising in practice. [ABSTRACT FROM AUTHOR]

As the global economy develops rapidly, traffic congestion has become a major problem for first-tier cities in various countries. In order to address the problem of failed real-time control of the traffic flow data by the traditional traffic light control as well as malicious attack and other security problems faced by the intelligent traffic light (ITL) control system, a multi-agent distributed ITL control method was proposed based on the fog computing platform and the Q learning algorithm used for the reinforcement learning in this study, and the simulation comparison was conducted by using the simulation platform jointly constructed based on the VISSIM-Excel VBA-MATLAB software. Subsequently, on the basis of puzzle difficulty of the computational Diffie–Helleman (CDH) and Hash Collision, the applicable security control scheme of ITL under the fog computing was proposed. The results reveal that the proposed intelligent control system prolongs the time of green light properly when the number of vehicles increases, thereby reducing the delay time and retention rate of vehicles; the security control scheme of ITL based on the puzzle of CDH is less efficient when the vehicle density increases, while that based on the puzzle of Hash collision is very friendly to the fog equipment. In conclusion, the proposed control method of ITL based on the fog computing and Q learning algorithm can alleviate the traffic congestion effectively, so the proposed method has high security. [ABSTRACT FROM AUTHOR]

In recent years, due to the drastic rise in the number of vehicles and the lack of sufficient infrastructure, traffic jams, air pollution, and fuel consumption have increased in cities. The optimization of timing for traffic lights is one of the solutions for the mentioned problems. Many methods have been introduced to deal with these problems, including reinforcement learning. Although a great number of learning-based methods have been used in traffic signal control, they suffer from poor performance and slow learning convergence. In this paper, a transfer learning-based method for traffic signal control has been proposed. Multi-agent system has also been used for modelling the traffic network and transfer learning has been used to make reinforcement learning agents transfer their experience to each other. Furthermore, a classifier has been utilized to classify the transferred experiences. The results show that using the proposed method leads to a significant improvement on average delay time and convergence time of the learning process. [ABSTRACT FROM AUTHOR]

Traffic is a major challenge for electronic cities and coping with it requires analyzing traffic congestion in the city road network. In this paper, the performance index of vehicle speed was estimated to evaluate the conditions of road networks. This study analyzes the traffic density for the main network of Hamedan communication routes based on the collected data of Speed performance of Hamedan traffic control system. According to this analysis, the congestion index and traffic peak hours were determined. Also the relationship between vehicle speed and traffic congestion was predictably predictable by neural network and the genetic algorithm. In this study areas of traffic were identified using Hamedan traffic control center due to the speed of vehicles. [ABSTRACT FROM AUTHOR]

Roundabouts are a form of circular intersections adopted to secure road safety of the vehicles. However, collisions are not prevented entirely even when using roundabouts, since accidents may happen at specific intersection points leading to either traffic congestions or serious accidents. In this paper, a visible light communication (VLC)-based collision avoidance scheme is proposed to coordinate autonomous vehicles in traversing roundabouts, which has high effectiveness in vehicular environments. In particular, roadside units (RSUs) are deployed at the roundabout entrances to coordinate the vehicles in a vehicle-to-infrastructure (V2I) mode. By adopting the synchronization approach, vehicles can pass the roundabout simultaneously if their paths are concurrent with each other. Otherwise, vehicles are prioritized according to their arrival time and reasonable decelerations are applied to waive potential conflicts. Simulation results showed that our proposed approach satisfies the roundabout traffic demands in terms of concurrency, safety, and time utilization as vehicles are strongly recommended to decelerate in only 22% of the cases studied in different scenarios. [ABSTRACT FROM AUTHOR]

Since decades, road traffic congestions have been recognized as an escalating problem in many metropolitan areas worldwide. In addition to causing substantial number of casualties and high pollution rates, these congestions are decelerating economic growth by reducing mobility of people and goods as well as increasing the loss of working hours and fuel consumption. In order to deal with this problem, extensive research works have successively focused on predicting road traffic jams and then predicting their propagations. In spite of their relevance, the proposed solutions to traffic jam propagation have been profoundly dependent on historical data. They have not also used their predictions to intelligently allocate traffic control resources accordingly. We, therefore, propose in this paper a new two-stage traffic resource dispatching solution which is ultimately aiming to implement a self-organizing traffic control system based on Internet of Things. Our solution uses in its first phase a Markov Random Field (MRF) to model and predict the spread of traffic congestions over a road network. According to the obtained predictions, the solution uses Markov Decision Process (MDP) to automatically allocate the road traffic resources. Our simulations are showing satisfactory results in terms of efficient intervention ratios compared to other solutions. [ABSTRACT FROM AUTHOR]

The urban green wave traffic control system is one of the effective means to solve the problems of urban traffic jams and reduce vehicle delay. Aiming at the problems of traditional green wave traffic control in optimization computation, this paper introduces the adaptive mechanism and crossover, mutation operators to artificial fish swarm algorithm (AFSA) in order to adjust the evolution group. Through setting bulletin board measure and retention strategy, the individual status of the optimal artificial fish is recorded. In that way, the adaptive genetic AFSA is established. Finally, the paper applies this algorithm to green wave traffic control in five continuous intersections on Jianning Road, Lanzhou, China, gains one traffic control program better than traditional method through calculation, and verifies the feasibility and effectiveness of adaptive genetic-AFSA to optimize green wave traffic control system. [ABSTRACT FROM AUTHOR]

Mechanistic-empirical pavement design guide (MEPDG) or Pavement ME Design requires traffic inputs in three levels based on the availability of data and the importance of the project. The availability of site-specific (Level 1) traffic data is generally limited due to expensive cost and the accuracy of Level 3 average is unsatisfactory, and therefore the intermedium level (Level 2) traffic data need to be developed. Using multiple years of weigh-in-motion (WIM) and vehicle classification data from Oklahoma, this paper starts with a comprehensive check of the traffic data quality, and generates site-specific traffic inputs that are required in Pavement ME Design. Cluster analysis is then applied to develop homogeneous groups for each traffic input. Subsequently, decision tree model and multinomial regression model are developed for the selection of appropriate traffic clusters under given site design conditions. In addition, a case study is included to evaluate the variations of pavement performance at various levels of traffic inputs. [ABSTRACT FROM AUTHOR]

The rapid increase in the number of vehicles has given rise to the traffic jam, which is a major problem these days. The effect of traffic jam also effects the operation of ambulance. To avoid the traffic jam for the ambulance, a new idea is developed. In this paper we propose a new concept to avoid traffic jam for the ambulance and thus saving the life of an individual. At first the ambulance is detected and the information about the arrival of the ambulance is sent to the next station. The ambulance will make siren only when it carries patient inside, so by detecting both the ambulance siren and the image taken from the acquisition device, the information is sent to the next point which is on the way to hospital. Announcement of the arrival of the ambulance is done. The CCTV camera is monitoring each vehicle on that road. If any vehicle doesn't abide by the announcement done, the image of the vehicle will be captured, and then by using the acquired image the number plate will be tracked and will be detected automatically by image processing. The vehicle information is sent to the police station, to take the necessary action. Thus the traffic control can be done and the way is cleared for the ambulance and life is saved. The processing is done using MATLAB and is verified. [ABSTRACT FROM AUTHOR]

Urban traffic congestion is a common problem that affects many cities around the world. In this paper, an actual urban traffic simulation model (AUTM) for traffic congestion predicting and avoiding is proposed, which includes three key components: the map and transfer (MT) conversion method, the optimized spatial evolution rules, and a congestion-avoidance routing algorithm. Three key techniques are combined in our proposed model: (1) The MT conversion method is proposed to get actual urban cellular spaces, which apply the optimized spatial evolution rules to simulate the vehicular dynamics better. (2) AUTM is proposed for simulating traffic congestion and predicting the effect of adding overpasses and roadblocks. (3) The congestion-avoidance routing algorithm is proposed for vehicles to dynamically update their routes toward their destinations, which can achieve traffic optimization in urban simulations. This paper presents the results of applying this novel model to a large-scale real-world case in different urban traffic congestion situations. Extensive experimental simulations in various actual cities have been carried out. Our results in the extreme case are encouraging: The prediction accuracy of traffic congestions is almost 89%, and the variance of prediction road density is less than 0.15. [ABSTRACT FROM AUTHOR]

In recent years, the number of motor vehicles in the country has risen rapidly. Traffic demand has continued to grow, while land resources, capital and energy have become increasingly tense. The development trend of urban transport system is not optimistic. Especially in the face of major events, vehicle evacuation suddenly increased, which is a serious planning for the evacuation of a serious challenge. Reasonable and accurate implementation of the emergency evacuation plan is to make the evacuation time at least, to minimize the major traffic congestion protection. Among them, route selection and traffic flow distribution are the core contents of emergency evacuation plan. In this paper, traffic emergency evacuation of major activities is taken as the research object, and the state vector of each road is introduced into the navigation system according to the linking analysis algorithm thought. The network model of evacuation route selection is established by using the theory of spatial diversity and the theory of minimum cost maximum flow, and an empirical analysis is made on the road network. Based on the research, the emergency evacuation plan for large-scale activities is proposed, and the theory and method system of emergency evacuation are enriched, which can help with formulating more reasonable and effective traffic management policies according to the characteristics of emergency evacuation, meanwhile we provide decision-making basis for urban emergency evacuation transportation planning and management. An experimental evaluation is also conducted with the real data from city of Beijing, in aspects of effectiveness and efficacy. [ABSTRACT FROM AUTHOR]

Purpose: Freight vehicle parking facilities at large urban freight traffic generators, such as urban retail malls, are often characterized by a high volume of vehicle arrivals and a poor parking supply infrastructure. Recurrent congestion of freight parking facilities generates environmental (e.g. pollution), economic (e.g. delays in deliveries) and social (e.g. traffic) negative externalities. Solutions aimed at either improving or better managing the existing parking infrastructure rely heavily on data and data-driven models to predict their impact and guide their implementation. In the current work, we provide a quantitative study of the parking supply and freight vehicle drivers' parking behaviour at urban retail malls. Methods: We use as case studies two typical urban retail malls located in Singapore, and collect detailed data on freight vehicles delivering or picking up goods at these malls. Insights from this data collection effort are relayed as data stories. We first describe the parking facility at a mall as a queueing system, where freight vehicles are the agents and their decisions are the parking location choice and the parking duration. Results: Using the data collected, we analyse (i) the arrival rates of vehicles at the observed malls, (ii) the empirical distribution of parking durations at the loading bays, (iii) the factors that influence the parking duration, (iv) the empirical distribution of waiting times spent by freight vehicle queueing to access the loading bay, and (v) the driver parking location choices and how this choice is influenced by system congestion. Conclusions: This characterisation of freight driver behaviour and parking facility system performance enables one to understand current challenges, and begin to explore the feasibility of freight parking and loading bay management solutions. [ABSTRACT FROM AUTHOR]

Purpose: Road traffic incidents cause delay, affect public safety and the environment. The CEDR PRIMA project aims to extend practical guidance for traffic managers in pro-active Traffic Incident Management (TIM) techniques to reduce the impacts and associated costs of incidents. Methods: The paper describes modelling methods used in the project for assessing the effect of different management techniques on incident duration and travel delay under various scenarios, including collision, adverse weather, heavy vehicle breakdown and other obstruction, assuming various management strategies and generic impacts of novel technologies. Macroscopic simulations of 178 variations of 13 basic scenarios have been performed using a flexible and computationally efficient macroscopic queue model, results being verified by simulation using a velocity-based Cell Transmission Model (CTM-v). Results: The results of the two modelling methods are broadly consistent. While delays estimated by the two methods can differ by up to 20%, this is small compared to the factor of 30 range of modelled delays caused by incidents, depending on their nature and circumstances, and is not sufficient to affect general conclusions. Under the peak traffic conditions assumed, the most important factor affecting delay is whether running lanes can be kept open, but quick clearance of carriageway is not always feasible. Conclusions: Comparison of two very different modelling methods confirms their consistency within the context of highly scenario-dependent results, giving confidence in the results. Future research and data needs include further validation of the models, potential application to traffic flow and conflict prediction and incident prevention, and more complete and consistent recording of incident timelines and impacts. [ABSTRACT FROM AUTHOR]

Wireless network sensors and their use in traffic monitoring, traffic density determination or vehicle speed detection and classification have recently been the focus of interest for researchers. This article describes how a new sensor circuit was designed to deliver instantaneous, real-time and novel solutions as a vehicle detection system, which is more powerful than the nodes used in other studies, and gives results with smaller error margins due to its serial communication qualification. With the proposed logic algorithm, it was possible to categorise the instantaneous traffic status of a road in four levels: no traffic, mild traffic, heavy traffic and very heavy traffic. Additionally, with the nodes placed at the beginning and the end of the road, the number of vehicles per hour for a day was determined and traffic was analysed. Then, vehicles passing by were classified with a proposed classification algorithm and magnetic signature length (MSL) parameter as cars, minibuses, buses and trucks, and an accuracy rate of 95% was obtained. As the last application, the direction of motion of the vehicle on the x-axis as well as left-to-right or right-to-left directions was determined, and the result was 94% accurate. The simplicity of the proposed algorithms, the absence of any complex mathematical calculations, the low cost of the sensor node and circuit and the low power consumption of the communication system demonstrate the superiority of this system in comparison with other studies. [ABSTRACT FROM AUTHOR]

In order to ensure that traffic capacity is stable, and avoid incident congestion is anabatic, a double-layer ramp-metering model is proposed in this paper to control the traffic flow at each on-ramp, nearby incident congestion. The function of the lower model is to recognize where the incident congestion exists, based on an adaptive neural network, whose inputs are traffic flow, velocity and density. The outputs of the lower model are the section number where the congestion occurs and the ramp number which should be controlled. If there is congestion, the upper model would be activated. The function of the upper model is to calculate the control strategy. The required control ramp number from the lower model would be transmitted to the upper model together with the real-time state of controlled section. The upper model would calculate the ramp-metering ratio for each required ramp by minimizing the main time cost, total average queue length and equity index. In order to describe the traffic state more accurately, a tailored traffic model for urban freeway in China is established in the upper model to get the prediction, which considers the speed-limited value on main roadway, and corrects the calculation method of traffic flow state on origin point and exit ramp. The simulation results show the double model is effective to control the incident congestion induced by aggressive behavior. [ABSTRACT FROM AUTHOR]

The increasing volume of traffic in cities has a significant effect on the road traffic congestions and the time it takes for road users to reach their destinations as well. Adaptive signal control system has shown powerful ability to effectively alleviate urban traffic congestions to achieve desirable objectives (e.g., minimization of delay). The real-time adaptive traffic signal control system can obtain traffic density information by taking advantage of vehicle-to-infrastructure (V2I) communication system to prepare efficient measurement statistics for traffic controllers. In this paper, we present a novel multi-agent based control method for an integrated network of adaptive traffic signal controllers under V2I communication environment which is characterized by the following features: flow model free, area coordinated, co-learning and suitable for parallel processing. Our method has been tested on a large-scale network covering 23 km $$^{2}$$ with 22 intersections in the downtown area of Xiaogan, China. The results show the significant reductions in the average travel time per vehicle, the average delay per vehicle and the average queue length, when compared to the data in the traditional fixed-time method and actuated method. [ABSTRACT FROM AUTHOR]

The traditional vehicle mobility is restricted to the driver habits and the line-of-sight. The new-emerging connected vehicles enable information exchange with each other at vicinity, which will undoubtedly bring a greatly positive effect on the vehicle mobility pattern. In this paper, we propose to modeling and analyzing the vehicle mobility patterns respectively driven by traditional drivers and connected drivers. We perform the extensive simulations to explore the statistical differences between two mobility patterns and the underlying reasons through comparing to the real vehicle trace datasets. The results show that they behave quite diversely at the concerned aspects, e.g. degree distribution, clustering coefficient, topological shortest path, topological coefficient, and vehicle density, and also we find that the connected vehicles are well distributed and contribute a relieved traffic congestion situation. [ABSTRACT FROM AUTHOR]

Congestion avoidance mechanisms are used to prevent the saturation of the gateway which represents a bottleneck of the system. Recently, a new method for the congestion avoidance has been proposed: the smart access point with limited advertised window (SAP-LAW). The main idea is to hijack at the gateway the acknowledge packets in the transmission control protocol (TCP) connections in order to artificially reduce the advertised destination window according to some bandwidth allocation policy. Therefore, the flux control mechanism is artificially exploited to control the congestion at the bottleneck. The advantage of this approach is that it does not drop any packet and does not require any modification in the TCP implementations at the clients. While the most popular congestion avoidance mechanisms are the explicit congestion notification (ECN) and the random early detection (RED). In this paper, we propose stochastic models for the ECN/RED and SAP-LAW mechanisms in order to compare their performances under different scenarios. The models are studied in mean field regime, i.e. under a great number of TCP connections and UDP-based transmissions. Different from previous work for the ECN/RED, in this paper, we consider the presence of UDP traffic with bursts and the case of not greedy TCP connections. The models for SAP-LAW are totally new. The comparison is performed in terms of different performance indices including average queue length, system throughput, and expected waiting time. [ABSTRACT FROM AUTHOR]

Differing from the traditional traffic, connected vehicles enable information sharing between vehicles at vicinity to facilitate cooperative path planning, which may positively affect the congestion propagation process. In this paper, we propose to modeling and simulating traffic congestion propagation in such new situation where the path planning is driven by a temporal or spatial preference with aims at investigating the effects of various factors on traffic congestion, e.g. traffic light, mobility pattern, traffic density and communication radius. Simulations show that the traffic congestion is indeed affected by the concerned factors; however, the traffic congestion fails to be mitigated persistently as the communication radius increases beyond a certain threshold. The result is helpful for understanding the traffic congestion propagation in connected vehicles. [ABSTRACT FROM AUTHOR]

Traffic congestion is a growing problem in many countries around the world. It has been recognized that instead of constructing more roads and freeways to counter this problem it is prudent to improve the utilization of existing road network through a judicious combination of advances in control engineering, communication and information technology. The traffic control architecture proposed in this paper is a combination of communicating Urban Traffic Control Architecture (UTCA) and Freeway Traffic Control Architecture (FTCA). The UTCA combines context-awareness, Cyber-Physical Systems (CPS) principles, and Autonomic Computing System (ACS) principles to optimize traffic congestion and enforce safety in urban traffic network. The UTCA includes a network of adaptive intersection traffic controllers and their immediate supervisory systems, who are also networked. The central piece of each traffic controller is an arbiter, which is a mini CPS. It is aware of the traffic dynamics at the intersection managed by it, by virtue of continuous input from monitoring sensors. Due to this context-awareness ability and its communication ability to exchange traffic information with its neighbors, it can execute policy-based reactions in order to enable safe and efficient traffic throughput at its intersection. Each urban traffic supervisory system is designed with ACS principles in order to minimize downtime caused by environmental emergencies and maximize security of the subsystem under it. A supervisory subsystem will also collect global traffic flow information and contextual constraints from its neighbors. Based on this input it will modify policies and communicate them to its traffic controller for timely adaptation. The urban traffic flowing into freeway traffic will be mediated by Intelligent Ramp Meters (IRM). An IRM interacts with the urban traffic control system and its nearest Intelligent Roadside Unit (IRSU) to regulate the flow of traffic from urban to freeway network. The FTCA consists of a network of mutually interacting IRSUs which monitor traffic flow, communicate with IRMs for providing traffic guidance for freeway drivers. An IRSU will communicate with the vehicles in the zone managed by it in order to provide information on rerouting when road and weather conditions warrant it. It also facilitates exchange of information between vehicles, guide them in lane changes and maintaining safe distance in order to avoid collision. [ABSTRACT FROM AUTHOR]

Route planning and recommendation have received significant attention in recent years. In this light, we study a novel problem of planning unobstructed paths in traffic-aware spatial networks (TAUP queries) to avoid potential traffic congestions. We propose two probabilistic TAUP queries: (1) a time-threshold query like 'what is the path from the check-in desk to the flight SK 1217 with the minimum congestion probability to take at most 45 minutes?', and (2) a probability-threshold query like 'what is the fastest path from the check-in desk to the flight SK 1217 whose congestion probability is less than 20 %?'. These queries are mainly motivated by indoor space applications, but are also applicable in outdoor spaces. We believe that these queries are useful in some popular applications, such as planning unobstructed paths for VIP bags in airports and planning convenient routes for travelers. The TAUP queries are challenged by two difficulties: (1) how to model the traffic awareness in spatial networks practically, and (2) how to compute the TAUP queries efficiently under different query settings. To overcome these challenges, we construct a traffic-aware spatial network G( V, E) by analyzing uncertain trajectories of moving objects. Based on G( V, E), two efficient algorithms are developed to compute the TAUP queries. The performances of TAUP queries are verified by extensive experiments on real and synthetic spatial data. [ABSTRACT FROM AUTHOR]

A novel algorithm capable of estimating free space for vehicle navigation is presented. When a disparity map - dense or sparse - from stereo matching and a longitudinal profile of the road surface on the disparity domain are provided, the free space is estimated precisely. According to the longitudinal profile of the road surface, the disparity map is classified into an obstacle disparity map and a road surface disparity map. After combining these two disparity maps through a score map, a border line separating the road surface and the non-road surface is estimated using dynamic programming on a udisparity representation. The main contribution of the proposed approach is the robust detection of the free space and the distance between stereo cameras and obstacles, whereby the detection is sufficiently rapid for vehicle navigation. The validity of the proposed algorithm is demonstrated by experiments through many outdoor road images from various traffic scenarios. [ABSTRACT FROM AUTHOR]

Evacuations are massive operations that create heavy travel demand on road networks some of which are experiencing major congestions even with regular traffic demand. Congestion in traffic networks during evacuations, can be eased either by supply or demand management actions. This study focuses on modeling demand management strategies of optimal departure time, optimal destination choice and optimal zone evacuation scheduling (also known as staggered evacuation) under a given fixed evacuation time assumption. The analytical models are developed for a system optimal dynamic traffic assignment problem, so that their characteristics can be studied to produce insights to be used for large-scale solution algorithms. While the first two strategies were represented in a linear programming (LP) model, evacuation zone scheduling problem inevitable included integers and resulted in a mixed integer LP (MILP) one. The dual of the LP produced an optimal assignment principle, and the nature of the MILP formulations revealed clues about more efficient heuristics. The discussed properties of the models are also supported via numerical results from a hypothetical network example. [ABSTRACT FROM AUTHOR]

Metropolitan cities in China are commonly confronted with unresolved traffic congestion issues, primarily due to rapidly increasing traffic demand. Group disparity between commuting mode choice and its spatial distribution on road networks has enabled us to examine the factors that give rise to the discrepancies and the fundamental spatial causes of traffic congestion. In recent years, micro-perspective, individual, and behavior-based spatial analysis have mushroomed and been facilitated with effective tools such as temporal geographic information systems (T-GIS). It is difficult to study the interrelations between transport and space on the basis of commuting mode choice since the mode choice data are invisible in a specific space such as a particular road network. Therefore, in the field of transport, the classical origin destination (OD) four-stage model (FSM) is usually employed to calculate data when studying commuting mode choice. Based on the relative principles of T-GIS and the platform of ArcGIS, this paper considers Guangzhou as a case study and develops a spatio-temporal tool to examine the daily activities of residents. Meanwhile, the traffic volume distribution in rush hours, which was analyzed according to commuting modes and how they were reflected in the road network, was scrutinized with data extracted from travel diaries. Moreover, efforts were made to explain the relationship between traffic demand and urban spatial structure. Based on the investigation, this research indicates that traffic volumes in divergent groups and on the road networks is driven by: 1) the socio-economic characteristics of travelers; 2) a jobs-housing imbalance under suburbanization; 3) differences in the spatial supply of transport modes; 4) the remains of the Danwei (work unit) system and market development in China; and 5) the transition of urban spatial structure and other factors. [ABSTRACT FROM AUTHOR]

We present a microscopic model for the dynamics of a transportation flow based on cellular automata with improved lane changing rules. With this model, we study the influence of crossing transportation flows on the throughput of a multilane highway. For a two-lane highway with an exit, we obtain space-time density diagrams for different distributions of exiting cars along the lanes. Our results indicate how important traffic control is for the throughput of automobile roads. [ABSTRACT FROM AUTHOR]

Congestion in transport networks is a topic of theoretical interest and practical importance. In this paper we study the flow of vehicles in urban street networks. In particular, we use a cellular automata model on a complex network to simulate the motion of vehicles along streets, coupled with a congestion-aware routing at street crossings. Such routing makes use of the knowledge of agents about traffic in nearby roads and allows the vehicles to dynamically update the routes towards their destinations. By implementing the model in real urban street patterns of various cities, we show that it is possible to achieve a global traffic optimization based on local agent decisions. [ABSTRACT FROM AUTHOR]

This paper studies a class of transport equations arising from stochastic models in congestion control. This class contains two cases of loss models as particular cases: the rate-independent case where the packet loss rate is independent of the throughput of the flow and the rate-dependent case where it depends on it. This class of equations covers both the case of persistent and of non-persistent flows. For the first time, we give a direct proof of the fact that there is a unique density solving the associated differential equation. This density and its mean value are provided as closed form expressions. [ABSTRACT FROM AUTHOR]

In complex scale-free networks, ranking the individual nodes based upon their importance has useful applications, such as the identification of hubs for epidemic control, or bottlenecks for controlling traffic congestion. However, in most real situations, only limited sub-structures of entire networks are available, and therefore the reliability of the order relationships in sampled networks requires investigation. With a set of randomly sampled nodes from the underlying original networks, we rank individual nodes by three centrality measures: degree, betweenness, and closeness. The higher-ranking nodes from the sampled networks provide a relatively better characterisation of their ranks in the original networks than the lower-ranking nodes. A closeness-based order relationship is more reliable than any other quantity, due to the global nature of the closeness measure. In addition, we show that if access to hubs is limited during the sampling process, an increase in the sampling fraction can in fact decrease the sampling accuracy. Finally, an estimation method for assessing sampling accuracy is suggested. [ABSTRACT FROM AUTHOR]

An extended car following model is proposed by incorporating intelligent transportation system and the backward looking effect under certain condition in traffic flow. The neutral stability condition of this model is obtained by using the linear stability theory. The results show that anticipating the behavior of vehicles preceding and following one vehicle could lead to appreciable stabilization of traffic system. From the simulation of space-time evolution of the vehicle headways, it is shown that the traffic jam could be suppressed efficiently via taking into account the information about the motion of two preceding vehicles and one following vehicle, and the analytical result is consistent with the simulation one. [ABSTRACT FROM AUTHOR]

By introducing relative velocities of arbitrary number of cars ahead into the full velocity difference models (FVDM), we present a forward looking relative velocity model (FLRVM) of cooperative driving control system. To our knowledge, the model is an improvement over the similar extension in the forward looking optimal velocity models (FLOVM), because it is more reasonable and realistic in implement of incorporating intelligent transportation system in traffic. Then the stability criterion is investigated by the linear stability analysis with finding that new consideration theoretically lead to the improvement of the stability of traffic flow, and the validity of our theoretical analysis is confirmed by direct simulations. In addition, nonlinear analysis of the model shows that the three waves: triangular shock wave, soliton wave and kink-antikink wave appear respectively in stable, metastable and unstable regions. These correspond to the solutions of the Burgers equation, Korteweg-de Vries (KdV) equation and modified Korteweg-de Vries (mKdV) equation. [ABSTRACT FROM AUTHOR]

ÀRouting protocols can decide for data packets which route is reachable and co-optimal, and may cause data packets to swarm into certain links, thus causing congestion on those links. General traffic engineering (GTE) technology provides ER-LSP/CR-LSP in MPLS networks to avoid this kind of congestion. However, GTE takes only the current data flow into account and establishes an ER-LSP/CR-LSP for this current data flow in order to guarantee QoS. Although this could resolve the issue raised by routing protocols, it may also waste some resource. In this article we focus on optimization of traffic engineering and propose an automatic traffic balance algorithm based on GTE technology. [ABSTRACT FROM AUTHOR]

In this paper we address the Sensor Location Problem, that is the location of the minimum number of counting sensors, on the nodes of a network, in order to determine the arc flow volume of all the network. Despite the relevance of the problem from a practical point of view, there are very few contributions in the literature and no combinatorial analysis is performed to take into account particular structure of the network. We prove the problem is $$\cal N \cal P$$ -complete in different cases. We analyze special classes of graphs that are particularly interesting from an application point of view, for which we give low order polynomial solution algorithms. [ABSTRACT FROM AUTHOR]

The aim of this paper is to control the vehicle system to move orderly by a feedback signal in the optimal velocity (OV) model under open boundary. Following the description of the OV model and its stability analysis, some problems about the definition of traffic jams in [12] are presented and illustrated by an example. Based on our analysis, a feedback signal acting on the traffic system has been introduced into the OV model. The signal will act in the system only if the state is inhomogeneous, and it will vanish in the homogeneous traffic flow. Theoretically, it is proved that the disorder state in traffic system could be suppressed by using the control method. Two kinds of noises are tested in our computer simulations. The simulation results demonstrate that the traffic system can move into a homogeneous phase by the control. [ABSTRACT FROM AUTHOR]

It is suggested that the question of existence of a jamming phase transition in a broad class of single-lane cellular-automaton traffic models may be studied using a correspondence to the asymmetric chipping model. In models where such correspondence is applicable, jamming phase transition does not take place. Rather, the system exhibits a smooth crossover between free-flow and jammed states, as the car density is increased. [ABSTRACT FROM AUTHOR]

This paper deals with the continuum modeling of traffic dynamics for freeways with two lanes where the faster vehicles are allowed to travel on both lanes while the slower vehicles are allowed to travel on one lane only. The speed gradient-based momentum equation is used to develop the traffic models for each lane. Using the proposed models, some nonequilibrium phenomena such as small disturbance instability and stop-and-go waves, together with results from numerical tests are investigated. The conditions for keeping the models' linear stability are presented. [ABSTRACT FROM AUTHOR]