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In recent years, the problem of traffic congestion has become increasingly serious, and research on traffic system control has become a new hotspot. Studying the bifurcation characteristics of traffic flow systems and designing control schemes for unstable support points can alleviate traffic congestion from a new perspective. This article improves the full speed differential model considering strong wind models from the perspective of bifurcation control to adjust traffic flow. This article theoretically proves the existence conditions of Hopf bifurcation and saddle node bifurcation in the model and finds the stability mutation point of the transportation system stability. A nonlinear system feedback controller was designed for unstable bifurcation points using Chebyshev polynomial approximation and random feedback control methods. Without changing the system equilibrium point, the advance, delay, and elimination of Hopf bifurcation were achieved, and the abrupt behavior of the transportation system was controlled, thereby alleviating traffic congestion. This article explains the changes in the stability of complex transportation systems from the perspective of bifurcation analysis, which can better capture the characteristics of traffic flow. By adjusting the control parameters in the feedback controller, the influence of boundary conditions on the stability of the transportation system is fully described, and the influence of unstable focal points and saddle points on the system is suppressed, thereby slowing down the traffic flow. In addition, unstable bifurcation points can be eliminated, and the Hopf bifurcation can be controlled to advance, delay, and disappear, thereby achieving control over the stable behavior of the transportation system. This helps alleviate traffic congestion and also helps describe actual traffic phenomena. [ABSTRACT FROM AUTHOR]

Now-a-days since number of vehicles are growing day by day on the roads in urban and metropolitan area, hence traffic jam is becoming very common problem which everyone is facing time to time. So it becomes very crucial to forecast traffic to avoid the regular traffic congestion situation in daily life. In today's intelligent traffic system, reliable and precise traffic forecasting in metropolitan road networks is critical. Many models based on classical methods have been presented to address this; however, they lag in certain circumstances, failing to incorporate spatial and temporal dependency in the environment. As a result, this research aims to solve the geographical and temporal dependency problem that plagues most traffic forecasting models. Graph Convolution Network (GCN) and Gated Recurrent Unit (GRU) are employed in conjunction with an attention-based model for more accurate traffic forecasts in cities. A combined model called Spatio-Temporal Attention-based Model with GCN and GRU (ST AGG) is employed to anticipate traffic. ST-AGG model mainly focuses on extracting the relevant information from all the input given to the attention-based model. The spatial dependency is determined by using GCN and the temporal dependency is determined by using GRU, used in the ST-AGG model. It assists in short-term traffic forecasting as well as long-term traffic forecasting. The results demonstrate that the suggested ST AGG has properly predicted the volume of traffic on city roadways in real time with greater accuracy. The model depicts how traffic is influenced by geography and time. The Shen Zhen (SZ) car dataset from China and the Los Angeles (LAS) dataset from California are used to test the suggested model. And the result shows that Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) have reduced by up to 2.7% less, and accuracy has increased by 2.7% more compared to the previous model ST GCN for the SZ vehicle dataset. Similarly, although the accuracy has not changed significantly for the Loss Angeles data set, RMSE and MAE have reduced by 3.71% less as compared to the previous ST GCN model. The proposed model is more effective than the prior models like GCN, GRU, and ST GCN. [ABSTRACT FROM AUTHOR]

Accurate traffic prediction is indispensable for relieving traffic congestion and people's daily trips. Nevertheless, accurate traffic flow prediction is still challenging due to the traffic network's complex and dynamic spatial and temporal dependencies. Most existing methods usually ignore the dynamicity of spatial dependencies or have limitations, as using the self-attention mechanism for capturing dynamic spatial dependencies is computation forbidden in large networks. In addition, there are both short- and long-range dynamic temporal dependencies, which are not well captured. To overcome these limitations, we propose an intelligent dynamic spatial and temporal graph convolutional system for traffic flow prediction. First, we propose a dynamic spatial block to capture the complex and dynamic spatial dependencies, which is computation-friendly. Next, we propose a dynamic temporal block to capture the complex and dynamic temporal dependencies, which well balances the short- and long-range dynamic temporal dependencies. We validate and analyze the performance of the proposed method through extensive experiments on two traffic datasets. Analysis of results demonstrates that our proposed model has better prediction performance than the state-of-art baselines. Compared with the best contrast methods, the proposed method improves by 2.28% and 8.01% in terms of the mean absolute error on PEMS04 and PEMS08 datasets. [ABSTRACT FROM AUTHOR]

In metropolitan regions, the quick increase in vehicles has caused gridlock, pollutants, and problems in the transportation of products. IoT is a breakthrough advancing the development of intelligent management systems and automated processes throughout the universe. For automatic and intelligent cultures, this is a crucial contribution. Effective management and prevention of traffic congestion are reliable strategies that contribute to conserving numerous valuable resources. Autonomous vehicles and smart devices are equipped with an IoT-based ITM system of sensors that can understand, collect, and transfer information. Another method to enhance the transportation system is deep learning (DL). Traffic jams, delays, and a high death rate result from the numerous problems of conventional transportation management systems. An adaptive traffic management system (ATM) built on DL and IoT is designed and implemented in this study's efforts. The suggested system's architecture is built on the automobile, the framework, and the events. To account for all potential problems with the transportation system, the design uses a variety of situations. The proposed methodology uses a Quantum Optical Communication (QOC) system for Traffic monitoring and enhanced data communication. The suggested adaptive traffic management-based QOC model continuously modifies the timing of traffic signals based on traffic volume and anticipated movements from neighbouring junctions. By progressively allowing cars to cross green lights, it considerably reduces the duration of travel. It also reduces congestion in traffic by creating a smoother transition within 38 s, which is less than all other techniques. [ABSTRACT FROM AUTHOR]

In this study, a traffic queue model is employed to simulate traffic parameters for optimization purposes. The primary objective is to enhance the average queue length, cumulative delay, and cumulative reward for varying numbers of vehicles generated using an innovative approach. The proposed methodology integrates deep Reinforcement Q-Learning techniques implemented through a realistic simulator, Simulation of Urban MObility, based on OpenStreetMap. This simulator is used to balance the flow of traffic by monitoring traffic conditions and adjusting signals (Red/Yellow/Green) accordingly. The research extensively evaluates simulation outcomes across different scenarios involving 50, 100, 500, and 1000 vehicles, with episodes 10, 50 and 100. The results provide valuable insights into training times, simulation durations, and total rewards obtained, indicating improvements in mitigating traffic congestion. The introduction of a traffic control strategy enhances model effectiveness through the utilization of four-layer deep neural network architectures with 400 neurons in hidden layers. However, the study also highlights persistent challenges related to real-time implementation and complexities in state-action-agent relationships. The progress highlights improvements in reducing congestion and enhancing efficiency at junctions by dynamically adjusting signal phases while leveraging historical data. The results highlight the potential of dynamic traffic management strategies in effectively addressing urban traffic challenges. [ABSTRACT FROM AUTHOR]

The alleviation of traffic congestion relies on efficient traffic control and traffic guidance, which are based on real-time short-term traffic flow prediction. In this paper, the stacked autoencoder (SAE) deep learning model with powerful feature learning capability is selected to predict the traffic flow on road sections. The process of training SAE includes the pre-training phase and the fine-tuning phase, which mainly apply the BP algorithm. However, the process of training SAE is time-consuming and cannot meet the real-time performance of modern application systems. This paper proposes a parallel training strategy for the SAE prediction model based on data parallel mode. The gradient solution process in our algorithm satisfies the conditions of parallel computing, so the training process can be designed in a parallel manner. The original dataset is distributed to some computing nodes, which are work nodes. The work node is responsible for gradient calculation using the local data. The task of the sole master node is to synthesize the gradient calculation results and then broadcast the updated gradient to each work node. The simulation results show that the SAE-based prediction model achieves better results than the traditional model, and the parallel algorithm reduces the running time of training processes. [ABSTRACT FROM AUTHOR]

In traffic flow, flyovers play a vital role in reducing traffic congestion, advancing commute timing, and helping to prevent collision scenarios. Inspired by the real-life applications of flyover, we model a coupled two-lane transport system where the lanes are divided into three segments in which the particle lane switching mechanism executes only in the middle segment of the lane, and particle inclusion and expulsion kinetics are performed in first and last segment of the lane to embody the infrastructure of flyover. The nature of the system characteristics is analyzed for diverse values of particle inclusion, expulsion, and coupling rates through graphical diagrams of phase planes, density profiles, shock dynamics, and phase transitions. The time-invariant behavior of the system is inspected numerically by utilizing the well-known method of mean-field theory, and it is discovered that the biased dynamics of particle inclusion, expulsion, and fully asymmetric coupling condition yield the phase diagram to disclose more unanticipated mixed stationary phases. Besides, the system experiences the fruitful novel incident of the double shock phase when the possibility of particle inclusion occurs relatively higher than the expulsion event. Also, it is noticed that the system experiences the fascinating phenomenon of shock propagation by traveling in either vertical/horizontal direction in the shock region of the phase diagram under the symmetric coupling circumstance, such as the position of the shock suddenly transits from the right to left segment without crossing the middle segment. The calculated numerical results are in good agreement with Monte Carlo simulation. [ABSTRACT FROM AUTHOR]

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]

To explore the mechanism of the end-to-end transmission delay of the communication network on the collaborative driving process for traffic flow in the vehicle-to-vehicle communication environment, based on the idea of the car-following model, this paper introduces characteristic parameters characterizing the end-to-end transmission delay of the network into Newell's following model and proposes a CD and OV model by considering the time delay characteristics of the collaborative driving process from information transmission to control decision and then to physical execution. To determine the cooperative driving system's stability criterion, the stability analysis of the new model is examined. By using the reductive perturbation approach, the spatiotemporal evolution mechanism of the traffic flow around the critical stability point under the influence of various transmission delays is analyzed. The resulting modified Korteweg-de Vries (mKdV) equations and density wave solutions are derived. The results show that the end-to-end transmission delay of the network has a significant shock effect on the stability of the vehicle-vehicle cooperative driving system, and the stability of the traffic flow and the ability to suppress traffic congestion becomes worse with the increase in the end-to-end transmission delay. [ABSTRACT FROM AUTHOR]

Safety at the gore areas near diverging ramps is very crucial during planning and implementation of highway safety improvement programs. Limited research has been conducted on safety at the gore areas on arterial roads. This study aims at investigating the impact of improving a sharp gore area in Lincoln, Nebraska by performing a micro simulation before-and-after study with respect to its underlying state of safety and congestion. Data on travel times and traffic volumes for peak hours are incorporated after successful calibration to find out how a geometric intervention can decrease mobility issues as well as the likelihood of crash involvement. This study has utilized VISSIM software package to run the simulation however, to perform safety analysis, Surrogate Safety Assessment Model (SSAM) is used which is developed by the Federal Highway Administration (FHWA). During the model calibration, custom driving behaviors are created to represent driving tendencies of familiar drivers. The simulation results indicated that by adding an auxiliary lane near the gore area, the mobility issues such as bottle necks, lane changing dilemmas and queue lengths are substantially decreased. However, geometric interventions such as provision of a separate lane, increasing ramp spacing, nose spacing, deceleration area and queue storage area considerably reduced the likelihood of rear-end and lane changing crashes. Surrogate safety assessment in diverging ramps, particularly for sharp gores, has not previously been studied, and this study can serve as a primary footmark for future research on ramp-gores safety. [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]

Many highways are acquiring smart transportation systems to improve traffic efficiency, safety and management. Intelligent transportation systems can monitor traffic congestion by providing estimated time of arrival or suggesting a diversion route. However, due to the chaotic complexity of the highway traffic road network and the short-term mobility of the population, traffic flow prediction is affected by many complex factors, and the development of an effective traffic flow forecasting system is very challenging. This paper establishes a novel framework to enhance the prediction of highway traffic flow based on the integration of Complementary Ensemble Empirical Mode Decomposition and Gaussian Mixture Model (CEEMD-GMM). Firstly, the complementary ensemble empirical mode decomposition is used to decompose and reduce the noise of traffic flow data. The empirical modal components are combined by calculating the sample entropy (SE), which makes the time series stable and reduces the time cost of forecasting. Secondly, the histogram of oriented gradient (HoG) is computed to extract the distinct features of various traffic flows and enhance the traffic flow prediction. Furthermore, the highway traffic data is classified using Gaussian mixture model to realize the feature extraction of different types of highway traffics. The performance of the model is evaluated in terms of mean absolute error (MAE), root mean square error (RMSE), mean absolute percent error (MAPE), and average correlation. The model is extensively verified using traffic flow data under individual weather conditions and different traffic flow datasets. Compared with the traditional support vector machine (SVM), artificial neural network (ANN), random forest (RF) and Adaboost algorithms, the proposed model shows 2.90% MAE, 6.07% RMSE, 0.38% MAPE and average correlation coefficient of 0.74 for traffic flow detection. It is concluded that the model can predict the highways traffic flow with high accuracy as compared to other contemporary models. [ABSTRACT FROM AUTHOR]

Accurately estimated highway traffic flow info plays a decisive role in dynamic and real-time road management, planning, and preventing frequent/recurring traffic jams, traffic rule violations, and chain/fatal traffic accidents. Traffic flow information is extracted by processing raw camera images via vehicle detection and tracking algorithms. Object detectors including the Yolo, single-shot detector, and EfficientNet algorithms are used for vehicle detection; however, You only look once version 5 (Yolov5) has a clear advantage in terms of real-time performance. Due to this reason, the pre-trained Yolov5 models were utilized in the vehicle detection part, and in the vehicle tracking module, a novel tracker algorithm was developed using vehicle detection features. The performance of the proposed approach was measured by comparing it to the Kalman filter-based tracker. The evaluation results show that the proposed tracking approach outperformed the Kalman filter-based tracker with 5.82% (Buses), 2.24% (Cars), 36.50% (Trucks), and overall 2.58% better traffic counting accuracy for the 12 nighttime case study videos captured from the highways with different horizontal and vertical angle-of-views. [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]

A macroscopic traffic flow model considering the effects of curves, ramps, and adverse weather is proposed, and nonlinear bifurcation theory is used to describe and predict nonlinear traffic phenomena on highways from the perspective of global stability of the traffic system. Firstly, the stability conditions of the model shock wave were investigated using the linear stability analysis method. Then, the long-wave mode at the coarse-grained scale is considered, and the model is analyzed using the reduced perturbation method to obtain the Korteweg-de Vries (KdV) equation of the model in the sub-stable region. In addition, the type of equilibrium points and their stability are discussed by using bifurcation analysis, and a theoretical derivation proves the existence of Hopf bifurcation and saddle-knot bifurcation in the model. Finally, the simulation density spatio-temporal and phase plane diagrams verify that the model can describe traffic phenomena such as traffic congestion and stop-and-go traffic in real traffic, providing a theoretical basis for the prevention of traffic congestion. [ABSTRACT FROM AUTHOR]

In recent years, traffic flow prediction has been extensively explored in Intelligent Transportation Systems, which is beneficial for reducing traffic jams and accidents as well as optimizing traffic network resources. Most of the previous methods divide cities into equal-sized grids and predict flows within one grid. However, we believe that each area is not independent, and there are interactions between areas. And the interaction between areas belonging to different attributes is more regular. Therefore, we propose a Multi-Attribute Graph Convolutional Network (MAGCN) for regional traffic flow prediction. Based on the attributes to which the areas belong, we divide cities into unequal-sized grids, and then a matrix is constructed using the flow of Functional area-based Origin-Destination pairs. GCN and dilated causal convolution allow the model to capture the spatial correlation and temporal dependence between functional regions while overcoming the under-fitting of local peaks. Extensive experimental results and evaluation metrics on two real-world datasets show that the MAGCN outperforms the baselines and has a higher accuracy for traffic flow prediction. [ABSTRACT FROM AUTHOR]

As a critical node and major bottleneck of the urban traffic networks, the control of traffic signals at road intersections has an essential impact on road traffic flow and congestion. Deep reinforcement learning algorithms have shown excellent control effects on traffic signal timing optimization. Still, the diversity of actual road control scenarios and real-time control requirements have put forward higher requirements on the adaptiveness of the algorithms. This paper proposes an Adaptive Weighted Averaged Double Deep Q Network (AWA-DDQN) based traffic signal optimal control method. Firstly, the formula is used to calculate the double estimator weight for updating the network model. Then, the mean value of the action evaluation is calculated by the network history parameters as the target value. Based on this, a certain number of adjacent action evaluation values are used to generate hyperparameters for weight calculation through the fully connected layer, and the number of action values for mean calculation is gradually reduced to enhance the stability of model training. Finally, simulation experiments were conducted using the traffic simulation software Vissim. The results show that the AWA-DDQN-based signal control method effectively reduces the average delay time, the average queue length and the average number of stops of vehicles compared with existing methods, and significantly improves traffic flow efficiency at intersections. [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]

Urban road stormwater reuse is one of the most effective ways to mitigate water resource shortage. However, due to a diversity of human activities such as traffic, various toxic pollutants can be deposited on road surfaces during dry periods and washed off during wet periods, threatening stormwater reuse safety. Among these pollutants, polycyclic aromatic hydrocarbons (PAHs) have been widely found in road stormwater. This study selected twelve road sites in Shenzhen, China, and investigated PAHs deposited on urban roads and their influential factors (traffic characteristics, land use and road surface condition). The research outcomes showed that high-molecular-weight PAH species (5–6 benzene rings) had higher concentrations and variability on spatial distributions than light-molecular-weight ones (2–4 benzene rings). Additionally, more PAHs were attached to dusts with small particle sizes (< 150 µm), and among influential factors, commercial land use showed a stronger correlation with PAHs distributions, regardless of particle sizes. Furthermore, it is noteworthy that traffic volume did not have an important influence on PAH generations on roads, while the source tracking results did indicate that traffic activities were the main contributor of PAHs. This implies that other traffic characteristics such as frequent go-and-stop activities might also contribute PAHs on roads. This means that areas with frequent traffic congestions could be the "hot spot" areas of PAHs, although the traffic volume might be not high. These research outcomes can provide useful insight into effective stormwater management and ensuring their reuse safety. [ABSTRACT FROM AUTHOR]

Due to the continuous development of economy and society, the pace of life is also accelerated, so people pay more and more attention to the time cost, and the transportation time cost is also a very important part of it. The traffic system is an important carrier to realize the traffic operation. The increase of automobile ownership requires the traffic system to be higher and higher. Moreover, vehicles in congested traffic flow inevitably start and stop operations with high frequency, which undoubtedly increases vehicle exhaust emissions, environmental pollution, noise pollution and other problems. For today's traffic system, vehicle dynamics information is also an important factor which affects the traffic system. Therefore, adding throttle, vehicle dynamics information, to the macro-traffic flow modeling research in this paper is a supplement and improvement to the current traffic flow theory research. By analyzing the equilibrium point, this paper proves the conditions for the existence of Hopf branch and saddle junction branch. Finally, numerical simulation is carried out, and the space–time diagram of density and phase plane are obtained through simulation, which can be used to describe the actual traffic phenomenon. Through numerical simulation, it is found that this model can better describe the congestion phenomenon of the actual traffic system, and provide scientific theoretical support for macroscopic traffic flow state analysis. [ABSTRACT FROM AUTHOR]

Cooperation between intersections in large-scale road networks is critical in traffic congestion. Currently, most traffic signals cooperate via pre-defined timing phases, which is extremely inefficient in real-time traffic scenarios. Most existing studies on multi-agent reinforcement learning (MARL) traffic signal control have focused on designing efficient communication methods, but have ignored the importance of how agents interact in cooperative communication. To achieve more efficient cooperation among traffic signals and alleviate urban traffic congestion, this study constructs a Graph Cooperation Q-learning Network Traffic Signal Control (GCQN-TSC) model, which is a graph cooperation network with an embedded self-attention mechanism that enables agents to adjust their attention in real time according to the dynamic traffic flow information, perceive the traffic environment quickly and effectively in a larger range, and help agents achieve more effective collaboration. Moreover, the Deep Graph Q-learning (DGQ) algorithm is proposed in this model to optimize the traffic signal control strategy according to the spatio-temporal characteristics of different traffic scenes and provide the optimal signal phase for each intersection. This study also integrates the ecological traffic concept into MARL traffic signal control, which aims to reduce traffic exhaust emissions. Finally, the proposed GCQN-TSC is experimentally validated both in a synthetic traffic grid and a real-world traffic network using the SUMO simulator. The experimental results show that GCQN-TSC outperforms other traffic signal control methods in almost all performance metrics, including average queue length and waiting time, as it can aggregate information acquired from collaborative agents and make network-level signal optimization decisions. [ABSTRACT FROM AUTHOR]

The science of cities aims to model urban phenomena as aggregate properties that are functions of a system's variables. Following this line of research, this study seeks to combine two well-known approaches in network and transportation science: (i) The macroscopic fundamental diagram (MFD), which examines the characteristics of urban traffic flow at the network level, including the relationship between flow, density, and speed. (ii) Percolation theory, which investigates the topological and dynamical aspects of complex networks, including traffic networks. Combining these two approaches, we find that the maximum number of congested clusters and the maximum MFD flow occur at the same moment, precluding network percolation (i.e. traffic collapse). These insights describe the transition of the average network flow from the uncongested phase to the congested phase in parallel with the percolation transition from sporadic congested links to a large, congested cluster of links. These results can help to better understand network resilience and the mechanisms behind the propagation of traffic congestion and the resulting traffic collapse. This study analyzes the way car traffic networks collapse by connecting classical traffic flow descriptors with percolation theory: The maximum average car traffic flow in a network and the maximum number of congested clusters occur at the same moment, precluding the network percolation. [ABSTRACT FROM AUTHOR]

The performance of end-to-end wireless link congestion control algorithm in the vehicular internet of things network is plagued by the inherent limitations of spurious rate control initiation, slow convergence time, and fairness disparity. In this article, the delay assisted rate tuning (DART) approach is proposed for the vehicular network that implements two algorithms, utilization assisted reduction (UAR) and super linear convergence (SLC), to overcome the transmission control protocol (TCP) limitations. The UAR algorithm is responsible for initiating the proportionate rate control process based on the bottleneck prediction parameter, thereby regulating the needless rate control during non-congested losses. In the congestion recovery mode, the SLC algorithm executes a dynamic rate update mechanism that enhances the flow rate and minimizes bandwidth sharing disparity among TCP flows. An analytical model was developed to study the DART convergence rate and fairness performance against the existing algorithm. The vehicular simulation outcome also confirms significant enhancement in average transmission rate, average message latency, and average bandwidth sharing performances of the DART algorithms against the RFC 6582, TCP-LoRaD, and CERL + congestion avoidance algorithms under varying traffic flows and node movement scenarios. [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]

With traffic network becoming increasingly complicated, traffic flow prediction has important practical significance for the management of traffic roads and public safety. For example, an accurate taxi demand prediction can help to improve efficiency of vehicle scheduling and reduce traffic congestion. The main issue of flow prediction is how to extract the information of complex spatio-temporal dependencies and interactions between arrival and departure. To solve these problems, we develop a deep learning method based on time-dependent attention convolutional LSTM (TDAConvLSTM) in which a time-dependent attention mechanism is designed to learn similarities of historical traffic flows among different time intervals and a fusion mechanism is introduced to aggregate the feature information produced by convolutional LSTM and attention module. And then, the result of the feature aggregation is fed to a multi-layer deconvolutional network to gain the results of flow prediction. Experimental studies on two real-life datasets indicate that TDAConvLSTM achieves better results than the compared models. The source code of our proposed method is available at the URL1. [ABSTRACT FROM AUTHOR]

In order to solve the problem of traffic congestion in a certain area, this paper develops a set of traffic optimization decision system. For analyzing the actual traffic conditions and calculating the traffic volume, density and traffic speed, a traffic prediction model is established and updated iteratively to modify the prediction model parameters. Based on this model, the congestion degree is estimated at the current road section, thus, an intelligent decision-making and the coordinated optimization methods are proposed. Moreover, this paper implements some application experiments on the isometric road of a three-intersection and obtains better prediction results of traffic density and traffic speed on the three-section highway. At the same time, compared with other existing prediction methods, the prediction model presented in this paper not only has higher accuracy, shorter prediction time and stronger anti-interference ability, but also has better effect on vehicle diversion. In addition, it also greatly relieves the traffic pressure on the road, maximizes the complementary advantages between intersections, and balances the good cooperation between each intersection. [ABSTRACT FROM AUTHOR]

Accurate traffic flow prediction plays a significant role in urban traffic management, including traffic congestion control and public travel route planning. Recently, several approaches have been put forward to learn the patterns from historical traffic data. However, there exist some limitations resulting from the use of the static learning method to explore the dynamical characteristics of the road network. Besides, the dynamic global temporal and spatial properties are not considered in these models. These drawbacks lead to a low prediction performance and make applying to a more extensive road network challenging. To address these issues, from the inspiration of Chebyshev polynomial, we proposed an improved dynamic Chebyshev graph convolution neural network model called iDCGCN. In the proposed approach, a novel updating method for the Laplacian matrix, which approximately constructs features from different period data, is proposed based on the attention mechanism. In addition, a novel feature construction method is proposed to integrate long-short temporal and local-global spatial features for complex traffic flow representation. Experimental results have shown that iDCGCN outperforms the state-of-the-art GCN-based methods on four real-world highway traffic datasets. [ABSTRACT FROM AUTHOR]

Traffic congestion, accidents, and pollution are becoming a challenge for researchers. It is essential to develop new ideas to solve these problems, either by improving the infrastructure or applying the latest technology to use the existing infrastructure better. This research paper presents a high-resolution dataset that will help the research community to apply AI techniques to classify any emergency vehicle from traffic and road noises. Demand for such datasets is high as they can control traffic flow and reduce traffic congestion. It also improves emergency response time, especially for fire and health events. This work collects audio data using different methods, and pre-processed them to develop a high-quality and clean dataset. The dataset is divided into two labelled classes one for emergency vehicle sirens and one for traffic noises. The developed dataset offers high quality and range of real-world traffic sounds and emergency vehicle sirens. The technical validity of the dataset is also established. Measurement(s) Ambulance Siren and Road Noises. (Recordings) Technology Type(s) Microphone Sample Characteristic - Environment Roads Sample Characteristic - Location Pakistan [ABSTRACT FROM AUTHOR]

Accurate traffic flow prediction is crucial for the development of intelligent transportation. It can not only effectively avoid traffic congestion and other traffic problems, but also provide a data basis for other complex tasks. The rapid development of social technology and the increasingly complex traffic environment lead to the emergence of massive traffic data. Traffic flow prediction as a spatial-temporal prediction problem has been widely concerned, but the traditional forecasting methods often ignore the spatial-temporal dependence, difficult to meet the prediction requirements. Therefore, this paper proposes a novel spatial-temporal model based on an attention one-dimension convolutional neural network (1D-CNN) and a gated interpretable framework, which models historical traffic data from the perspectives of time and space respectively. The core of the model proposed in this paper is to construct spatial-temporal blocks. First, a 1D-CNN based on channel attention mechanism and "inception" structure is proposed to extract temporal correlation. Then, considering the complexity of the actual traffic network, an interpretable multi-graph gated graph convolution framework is proposed to extract the spatial correlation. Finally, extensive experiments are carried out on real data sets, which prove the effectiveness of the proposed model, and it is very competitive compared with some state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Increasing traffic congestion is a major obstacle to the development of cities. The prediction of traffic flow is very important to city planning and dredging. A good model of flow is able to accurately predict future flow by learning historical flow data. Traffic flow is usually affected by macro and micro factors. At the macro level, the whole city can be divided into different subregions according to the similarity in the traffic flow patterns. At the micro-level, there is a temporal and spatial correlation between the traffic flow of different road sections at di fferent times. In this paper, we propose a multi-mode traffic flow prediction method with Clustering based Attention Convolution LSTM (CACLSTM) to model spatial-temporal data of traffic flow. The framework includes three modules: a convolution LSTM encoding-decoding layer which is used to predict the traffic flow of the next time slice by encoding the historical traffic information, a clustering based attention layer which is able to extract different temporal features by clustering based attention, and an additional factors layer which can integrate weather, wind speed, holidays and other factors to improve the prediction accuracy. The experimental results on Beijing taxis data show that the CACLSTM method performs more effective than the six well-known compared methods. [ABSTRACT FROM AUTHOR]

The increasing urbanization in the last decades results in significant growth in urban traffic congestion around the world. This leads to enormous time people spent on roads and thus significant money waste and air pollution. Here, we present a novel methodology for identification, cost evaluation, and thus, prioritization of congestion origins, i.e., their bottlenecks. The presented work is based on network analysis of the entire road network from a global point of view. We identify and prioritize traffic bottlenecks based on big data of traffic speed retrieved in near-real-time. Our approach highlights the bottlenecks that have the most significant effect on the global urban traffic flow. We follow the evolution of every traffic congestion in the entire urban network and rank all the congestions, based on the cost they cause (in Vehicle Hours units). We show that the macro-stability that represents the seeming regularity of traffic load both in time and space, overshadows the existence of meso-dynamics, where the bottlenecks that create these congestions usually do not reappear on different days or hours. Thus, our method enables to identify in near-real-time both recurrent and nonrecurrent congestions and their sources. [ABSTRACT FROM AUTHOR]

Accurate traffic flow forecasting (TFF) is significant for mitigating traffic congestion. To address the existing issues of calculation and storage in dealing with big traffic flow data using the traditional centralized models on a single machine, this paper presents a Spark-based Weighted Bidirectional Long Short-Term M emory (SW-BiLSTM) model to improve the robustness and accuracy of TFF. Specifically, the resilient distributed dataset (RDD) and the Kalman filter (KF) are utilized to preprocess large-scale trajectory data (e.g., GPS trajectories of taxicabs). Next, a distributed SW-BiLSTM model on Spark is put forward to enhance the accuracy and efficiency of TFF, combined with the normal distribution for weighing the influence degree of the interaction between adjacent road segments and the time window for implementing the optimization of BiLSTM. Finally, the experimental results on an empirical study with the real-world taxi GPS trajectory data indicate that, compared with ARIMA, LR, GNB, CNN, GRU, SAEs, BP, LSTM, and WND-LSTM (LSTM with a time window and a normal distribution), the MAPE value of SW-BiLSTM is decreased by 65.62%, 17.78%, 87.29%, 69.10%, 3.52%, 21.09%, 59.66%, 42.86%, and 1.22%, respectively. In particular, SW-BiLSTM is superior to BiLSTM with 15.83% accuracy improvement on average. [ABSTRACT FROM AUTHOR]

Traffic density estimation plays a positive role in improving the traffic efficiency of contemporary cities. Accurate estimation of traffic flow density can provide effective information for traffic dispatching command and effectively alleviate road traffic congestion. However, due to the problems of perspective distortion, scale change, serious occlusion and background interference in traffic video images, it brings great challenges to traffic density estimation. To solve the above problems, this paper constructs a traffic density estimation network based on multi-level fusion network (MFNet). Firstly, the low-level feature map and high-level feature map after Depthwide convolution Block (DCB) are combined to fuse features of different scales, which solves the problems of perspective distortion and scale change in the image; Then, the fused feature map is sent to the channel attention mechanism module to realize the smooth transition between pixels; Finally, by restoring the location information of the vehicle space on the density map and combining with the estimated number of vehicles, the traffic density is calculated quantitatively. In addition, our network also uses multiple superimposed dilated convolutions to obtain high-quality density map. Experimental results show that the Gride Average Mean absolute Error (GAME) metric of the proposed method is reduced to 14.32 on the TRANCOS dataset. Compared with the existing traffic density estimation methods, the estimation accuracy is significantly improved, especially in the case of serious perspective distortion and vehicle height overlap. [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]

In recent years, a new paradigm of Content-Centric Networks has emerged, which employs the prefix content name for addressing in the context of Named Data Networking (NDN). In conventional networks, subscribers send interest packets, and publishers generate related content and publish it to the requesters. In NDN, Caching Routers (CRs), as well as publishers, can also propagate data packets toward the requesters. The NDN network flow has two opposite sides: (1) the interest packet flow, known as interest forwarding on the upstream side, (2) the content flow, known as data publishing, on the downstream side. High traffic volume can be created by the increasing number of requested packets and associated data that are leading to bottlenecks in some parts of the networks. Therefore, congestion control and avoidance are significant issues in NDN. A considerable number of congestion control methods employ forwarding interest rate adjustment coming through the forwarding side. Nevertheless, the congestion can be prevented on the data publishing side using an efficient cache placement method. Congestion avoidance is a process for controlling congestion and balancing traffic loads to make the network effective. In this paper, a Dynamic Cache Placement (DCP) method is proposed to provide congestion avoidance by dynamically relocating the content of the CRs according to the traffic volume pattern and the link capacity. The DCP method distributes the popular data to the network regions with less traffic load and more accessible routers to balance the congested routers' traffic load. The DCP is implemented in the ndnSIM network simulator, and its performance is compared to the conventional method. Simulation results show that DCP is a fair and robust cache placement design, which successfully avoids congestion inflows with highly varying traffic demand. The proposed techniques employed in the DCP method improve the network performance in dynamic circumstances of the network. [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]

In the contemporary sustainable urban set up, one of the critical issues adversely affecting the quality of life in urban areas and inflicting immense costs on cities is traffic congestion. Traffic congestion is an outgrowth of increased traffic flow in certain locations of large cities. Recently, urban decision-makers and transportation planners resort to one-way traffic system as an effective traffic management strategy, which has a profound effect on reducing traffic congestion and improving traffic flow, leading to urban sustainability. In the present paper, the authors endeavored to develop a novel methodological framework based on optimization techniques in order to mitigate traffic congestion through one-way traffic network reconfiguration. To test the efficacy of the proposed methodological framework, two cases were analyzed and evaluated: the Sioux Falls transportation network as a medium-sized one and a real-world large-scale transportation network of the city of Isfahan in Iran. Through the first instance, it can be seen that the proposed method can effectively reduce the total travel time in the area of interest by approximately 9%. The numerical results for the transportation network of Isfahan, justified the practical value of the model and solution method through converting proper links to one-way. As the calculation results of two cases have demonstrated, the solution can effectively reduce the total travel time of travelers in a certain congested urban area. [ABSTRACT FROM AUTHOR]

The development of the Internet of Things (IoT) has produced new innovative solutions, such as smart cities, which enable humans to have a more efficient, convenient and smarter way of life. The Intelligent Transportation System (ITS) is part of several smart city applications where it enhances the processes of transportation and commutation. ITS aims to solve traffic problems, mainly traffic congestion. In recent years, new models and frameworks for predicting traffic flow have been rapidly developed to enhance the performance of traffic flow prediction, alongside the implementation of Artificial Intelligence (AI) methods such as machine learning (ML). To better understand how ML implementations can enhance traffic flow prediction, it is important to inclusively know the current research that has been conducted. The objective of this paper is to present a comprehensive and systematic review of the literature involving 39 articles published from 2016 onwards and extracted from four main databases: Scopus, ScienceDirect, SpringerLink and Taylor & Francis. The extracted information includes the gaps, approaches, evaluation methods, variables, datasets and results of each reviewed study based on the methodology and algorithms used for the purpose of predicting traffic flow. Based on our findings, the common and frequent machine learning techniques that have been applied for traffic flow prediction are Convolutional Neural Network and Long-Short Term Memory. The performance of their proposed techniques was compared with existing baseline models to determine their effectiveness. This paper is limited to certain literature pertaining to common databases. Through this limitation, the discussion is more focused on (and limited to) the techniques found on the list of reviewed articles. The aim of this paper is to provide a comprehensive understanding of the application of ML and DL techniques for improving traffic flow prediction, contributing to the betterment of ITS in smart cities. For future endeavours, experimental studies that apply the most used techniques in the articles reviewed in this study (such as CNN, LSTM or a combination of both techniques) can be accomplished to enhance traffic flow prediction. The results can be compared with baseline studies to determine the accuracy of these techniques. [ABSTRACT FROM AUTHOR]

The Nagel–Schreckenberg vehicular traffic model is extended to analyse a system of vehicles moving on a scale-free network to study extreme events in them. The dependence of the free-flow and congestion states on the density of vehicles is determined. In particular, a free-flow state at low vehicular density is observed and it gradually transitions to a congested state at higher density. Using detrended fluctuation analysis, it is shown that the flux of vehicles are long range correlated in a regime of low density. At higher densities, the flux becomes uncorrelated. We study the recurrence interval distribution for extreme events and the probability for its occurrence at low and high vehicular density regimes. It is shown that the occurrence probability for extreme events is independent of the degree of the node or the threshold used for defining extreme events [ABSTRACT FROM AUTHOR]

In this paper, we consider the interaction of elementary waves for the Aw–Rascle (AR) traffic flow model with variable lane width. The model is established based on the conservation laws where the lane width is considered, and the variable spaces ahead of the vehicles can be viewed as a source term which is incorporated into the model. Stationary waves are involved as the ingredient of the elementary waves. We then discuss the interactions of stationary wave with rarefaction wave, shock wave and contact discontinuity respectively, and construct the solutions globally in the phase plane. The results may contribute to dealing with traffic jam problems in the future study. [ABSTRACT FROM AUTHOR]

Traffic congestion is a major problem in the last few decades. Though the Government is taking various measures like installing more traffic signals, constructing fly-overs, one-ways, etc., the problem could not be addressed effectively on the heavily congested areas. The increasing number of vehicles, the dynamic population of traffic, the narrowness of roads and the absence of alternative roads or other routes are the major obstacles to reduce traffic congestion. Researchers of various streams have been working on predicting traffic volume to solve this problem. In this paper, a new method named Intuitionistic Fuzzy Grey-Markov Model(IFGMM) is introduced, which is an extension of Grey-Markov to Intuitionistic fuzzy set. The model is distinctive when compared with other models as it has a high level of accuracy performance. The forecasting result of this model is almost in proximity with the actual result of the traffic volume. In addition, the relative errors shown in this method are lesser than that of other models, due to its prediction accuracy is higher. Since the data collected in this method are taken from a heavily congested road from a metropolitan city, the proposed approach will be suitable for any congested area of the world. [ABSTRACT FROM AUTHOR]

In recent times, congestion in traffic is bit cost effective and several traffic management methods are utilized to balance the traffic as per the capacity of roads. However, many methods fails in pre-analyzing the network events to regulate the traffic flow. Also, the dynamic movement of vehicles fails in adapting the vehicle units to follow the changing topology. Hence, it is necessary for packet forwarding between source and destination nodes in a precise manner, where the packet loss and communication overhead should be in control. In this paper, we utilize the concept of Connected Dominating Set (CDS) approach in traffic management for reducing the flow of traffic streaming in main routes. The CDS approach dynamically adapts itself with the changing topology and acts as a virtual backbone to reduce traffic overhead. It restricts the increased flow of vehicles to the dominators, since traffic congestion in such areas increases. The CDS forwarding (CDSF) finds the dominating or high congestion routes and diverts the traffic to other areas based on the level of traffic in sub-routes. The CDSF method works on an assumption that edges on road segments has multiple vehicles, where it increases the network efficiency. However, we make careful consideration of choosing the other road segments to traverse the required packets from source to destination with reduced packet loss. The construction of a virtual backbone network with series of neighboring backbone nodes traverses efficiently the packets. Further, choosing vehicles with low velocity improves the probability of connectivity in a specific area and increases the network throughput. This improves the re-routing flexibility and makes the packets to reach the destination without traffic congestion in VANETs. The CDSF method and control strategies are tested in NS2 and SUMO simulator under different traffic conditions. The results of simulation show that CDSF traffic management system reduces significantly the traffic congestion and overall traffic cost that includes CDF, average latency, average end-to-end delay, Hop count, packet error rate, percentage delivery ratio, data delivery rate, throughput. The result shows that the proposed method 0.345 times improved packet delivery ratio, 0.35 times improved throughput and 0.7 times reduced delay than the existing methods. Further, the CDSF method controls the traffic density by reducing the maximum queue length and traffic controlling time. [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]

Undoubtedly, one of the greatest issues nowadays is congestion. To face such problem, forecasting of traffic is required. Bayesian combined neural network (BCNN) is applied to four different locations in Kuwait (Cairo Street, Riyadh Street, Maghreb Road and Istiqlal Road) to predict the short-term traffic volume at the middle section due to traffic flow from adjacent intersections. All data were collected for a period of 1 week over 15-min observation intervals using loop detectors. In addition to time-series responses and regression plots, mean square error (MSE) has been used to validate the network performance after data normalization. In comparison with MSE and R values, both values were slightly less precise during weekdays compared to weekends. After standardizing, the average MSE during weekdays was 0.003468 and regression (R) was 0.98113 for the four streets. For weekends model, the average MSE was 0.003563 and regression (R) was 0.97374 for the four streets. Istiqlal Street weekday model was the best model that fits the information among all the four models; as it has the smallest MSE value equivalent to 0.0010087 and the highest R value of 0.9959. BCNN model has achieved outstanding prediction performance with great potential to be generalized for various locations at different times of the day. These results can allow transportation planners to forecast traffic congestions and take prior measures to avoid them. Further modeling can assist in studying factors causing intersection congestions. [ABSTRACT FROM AUTHOR]

If not restricted by tolls, private decisions to drive on a highway result in inefficiently high usage which leads to traffic jams. When traffic demand is high, traffic jams can occur simply because of the interaction of vehicle drivers on the road, a phenomenon called phantom jam. The probability of phantom jams occurring increases with traffic flow. Unpriced externalities lead to inefficiently high road usage. We offer a method for quantifying traffic jam externalities and identifying and isolating the phantom jam externality. We examine the method by applying it to a specific highway section in Germany. The maximal congestion externality for the analyzed highway section is about 38 cents per vehicle and kilometer. Congestion charges that are calculated ignoring phantom jam externalities, can only internalize two-thirds of the true externality. [ABSTRACT FROM AUTHOR]

Traffic congestion is a common phenomenon in road transportation networks, especially during peak hours. More accurate prediction of dynamic traffic flows is very important for traffic control and management. However, disturbances caused by the time-varying origin-destination matrix, dynamic route choices, and disruptions make the modelling of traffic flows difficult. Therefore, this study focuses on modelling the dynamic evolution processes of traffic flows under disturbances and estimating dynamic travel times for arbitrary moment. A revised Lighthill–Whitham–Richards (RLWR) model with non-equilibrium states is presented to describe the dynamic traffic states on individual roads, and the ripple-spreading model (RSM) is integrated to investigate the interactions among several shockwaves from multiple roads. We propose a hybrid RLWR–RSM to model the congestion and congestion-recovery propagations in an entire transportation network. After predicting the dynamic traffic flows by the RLWR–RSM, the road travel times for arbitrary moment were estimated. Theoretical analyses indicated that (1) the RLWR–RSM inherits the advantages of macroscopic traffic flow models and integrates the characteristics of both low- and high-order continuum models, and (2) the RLWR–RSM considers multiple disturbances. From numerical experiments with various inputs, the variation in travel times under disturbances was investigated, and this further demonstrated that (1) the modelled dynamic traffic flows have four basic properties, and (2) the experimental results validate the theoretical analyses. In addition, the RLWR–RSM can explain several distinct traffic phenomena. Finally, the estimated travel times can provide decision supports for vehicle navigation. [ABSTRACT FROM AUTHOR]