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Congestion on roadways is an issue in many cities, especially at peak times, which causes air and noise pollution and cause pressure on citizens. So, the implementation of intelligent transportation systems (ITSs) is a very important part of smart cities. As a result, the importance of making accurate short-term predictions of traffic flow has significantly increased in recent years. However, the current methods for predicting short-term traffic flow are incapable of effectively capturing the complex non-linearity of traffic flow that affects prediction accuracy. To overcome this problem, this study introduces two novel models. The first model uses two long-short term memory (LSTM) units that can extract the traffic flow temporal features followed by four dense layers to perform the traffic flow prediction. The second model uses two gated recurrent unit (GRU) units that can extract the traffic flow temporal features followed by three dense layers to perform the traffic flow prediction. The two proposed models give promising results on performance measurement system (PEMS), traffic and congestions (TRANCOS) dataset that is firstly used as metadata. So, the two models can do this in specific cases and are able to suddenly capture trend changes.

The adaptive neurofuzzy inference system (ANFIS) is a structured multioutput learning machine that has been successfully adopted in learning problems without noise or outliers. However, it does not work well for learning problems with noise or outliers. High-accuracy real-time forecasting of traffic flow is extremely difficult due to the effect of noise or outliers from complex traffic conditions. In this study, a novel probabilistic learning system, probabilistic regularized extreme learning machine combined with ANFIS (probabilistic R-ELANFIS), is proposed to capture the correlations among traffic flow data and, thereby, improve the accuracy of traffic flow forecasting. The new learning system adopts a fantastic objective function that minimizes both the mean and the variance of the model bias. The results from an experiment based on real-world traffic flow data showed that, compared with some kernel-based approaches, neural network approaches, and conventional ANFIS learning systems, the proposed probabilistic R-ELANFIS achieves competitive performance in terms of forecasting ability and generalizability.

Traffic congestion is a major problem in developing and developed countries vehicle traffic management systems. Traffic control system works based on the idea of removing instabilities and avoid accidents in order to minimize the traffic and maximize the vehicle flow. To control the congestion need to predict the upcoming traffic flow and it will be useful for Advanced Traffic Information Systems (ATIS), Advanced Traffic Management Systems (ATMS) and traffic analytics. Non–linear historical data and uncertain factors influence the vehicle congestion at peak hours which cannot be considered in existing algorithms. This study proposes hybrid neural network algorithms such as Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) network for short term traffic flow prediction based on multivariate analysis. Widely referred datasets Performance Measurement Systems (PEMS) and Mesowest have been used to evaluate this model. Experiment results shows that CNN-LSTM Hybrid prediction model achieves high accuracy compared with other models.

Transportation infrastructure plays a significant role in the economic growth of developing countries where heavy vehicles act as the backbone in transporting goods and people via highways. However, presence of heavy vehicles in traffic stream lowers the overall operating speed and flow in rural highways especially at the vicinity of intersections. This research aims at developing an empirical model for estimating and predicting capacity of highway roundabouts considering both geometric and traffic parameters. Based on data collected from six roundabouts along the national highways in Bangladesh, multivariate regression method has been used to develop an empirical model as a function of entry width, rotary lane width, distance between entry and nearest exit, central island diameter, and circulating traffic flow to estimate and predict the capacity of rural highway roundabouts. For incorporating the presence of heavy vehicles in the model, all vehicle types are converted to passenger car equivalent units. A comparative study with established methods (i.e., HCM 2016, TRRL, IRC and German method) proves the superiority of the developed model in terms of predictive capacity. Furthermore, a Python based program called PyNomo, has been executed with the help of several add-on packages (e.g., numpy, scipy, pyx) to generate a compound parallel scale nomograph consisting of multiple variables using the developed empirical model. Considering the impact of heavy vehicles in the socio-economic development of a country, the nomograph will aid the practitioners and engineers as a quick and easily interpretable graphical tool for planning and designing roundabouts in rural highways.

In the present paper, we derive and solve the space-fractional traffic flow model which is considered as a generalization of the transport density equation. Based on the fundamental physical principles on finite-length highway where the number of vehicles is conserved, without entrances or exits, we construct a fractional continuity equation. As a limitation of the classical calculus, the continuity equation is constructed based on truncating after the first order of Taylor expansion, which means that the change in the number of vehicles is linear over the finite-length highway. However, in fractional calculus, we prove that nonlinear flow is a result of truncating the fractional Taylor polynomial after the second term with zero error. Therefore, the new fractional traffic flow model is free from being linear, and the space now is described by the fractional powers of coordinates, provided with a single variable measure. Further, some exact solutions of the fractional model are generated by the method of characteristics. Remarkably, these solutions have significant physical implications to help to make the proper decisions for constructing traffic signals in a smart city.

Traffic forecasting has attracted considerable attention due to its importance in proactive urban traffic control and management. Scholars and engineers have exerted considerable efforts in improving the performance of traffic forecasting algorithms in terms of accuracy, reliability, and efficiency. Spatial feature representation of traffic flow is a core component that greatly influences traffic forecasting performance. In previous studies, several spatial attributes of traffic flow are ignored due to the following issues: a) traffic flow propagation does not comply with the road network, b) the spatial pattern of traffic flow varies over time, and c) single adjacent matrix cannot handle the complex and hierarchical urban traffic flow. To address the abovementioned issues, this study proposes a novel traffic forecasting algorithm called traffic transformer, which achieves great success in natural language processing. The multihead attention mechanism and stacking layers enable the transformer to learn dynamic and hierarchical features in sequential data. Two components, namely, global encoder and global-local decoder, are proposed to extract and fuse the spatial patterns globally and locally. Experimental results indicate that the proposed traffic transformer outperforms state-of-the-art methods. The learned dynamic and hierarchical features of traffic flow can help achieve a better understanding of spatial dependency of traffic flow for effective and efficient traffic control and management strategies.

Accurate traffic forecasting is critical in improving safety, stability, and efficiency of intelligent transportation systems. Despite years of studies, accurate traffic prediction still faces the following challenges, including modeling the dynamics of trafc data along both temporal and spatial dimensions, and capturing the periodicity and the spatial heterogeneity of trafc data, and the problem is more difficult for long-term forecast. In this paper, we propose an Attention based Spatial-Temporal Graph Neural Network (ASTGNN) for traffic forecasting. Specifically, in the temporal dimension, we design a novel self-attention mechanism that is capable of utilizing the local context, which is specialized for numerical sequence representation transformation. It enables our prediction model to capture the temporal dynamics of traffic data and to enjoy global receptive elds that is beneficial for long-term forecast. In the spatial dimension, we develop a dynamic graph convolution module, employing self-attention to capture the spatial correlations in a dynamic manner. Furthermore, we explicitly model the periodicity and capture the spatial heterogeneity through embedding modules. Experiments on five real-world traffic flow datasets demonstrate that ASTGNN outperforms the state-of-the-art baselines.

The evolution of traffic-related accidents caused by long, short, and medium trucks at signalized road intersections have been underemphasized in the last few years. Far, little attention has been paid to the modelling of trucks traffic flow using an artificial neural network model and evaluating the stability analysis of trucks depending on the heterogenous optimal velocity. This research evaluates the effect of trucks on some specific traffic flow features. Over the years, it has been deduced that trucks, irrespective of their sizes, significantly impact their surrounding traffic flow due to their body sizes and operational features. In this study, we focused on modelling the traffic flow of trucks at signalized road intersections using traffic flow variables such as speed, traffic volume, traffic density, and time as our inputs and outputs. The truck traffic data was collected using up-to-date equipment such as video cameras and inductive loop detectors from the South Africa transportation network. During the ANN modelling of the truck traffic flow, we used 956 traffic datasets divided into 70% for training and 15% each for testing and validation. The ANN model results show testing regression values of R2 (0.99901). This shows that the inputs and output are well correlated and the ANN model’s superiority in predicting truck traffic flow at signalized road intersections. Based on the HEOV model results, the result of the research indicates that in the mixed traffic flow of trucks in real-life scenarios, the proportion of different trucks on the signalized road intersections rather than the proportions of types of trucks can be used in the determination of traffic flow stability of each truck. This research extends our knowledge of truck traffic flow modelling and provides a blueprint for examining the stability analysis of long, short, and medium trucks in their immediate driving environment.

In intelligent transportation system (ITS), accurate and real-time prediction of short-term multi-lane traffic flow with existing traffic data is an important part of urban traffic planning, traffic management and control. The data generated in the process of vehicle driving has the cooperative characteristics of multi-source, space-time and dynamic. Combining the data with high-performance computing or cloud computing, a new space-time information framework is designed, which is of great significance for the analysis and prediction of traffic flow, as well as intelligent traffic management, service and decision-making. This paper analyzes the statistical characteristics of urban road traffic flow from the two dimensions of time and space through the spatial-temporal correlation between multi-lane short term traffic flow in single observation point and multi observation points. We constructed a data fusion powered bi-directional long short term memory (DFBD-LSTM) model for individual lane and aggregate traffic flow, then used this model to predict multi-lane short term traffic flow. By taking individual lane traffic flow and aggregate traffic flow as different variables, the model produces more accurate predictions, which can better guide people to travel, alleviate the congestion of urban road traffic network to a certain extent, and improve the utilization rate and transportation efficiency of traffic road.

Spatiotemporal models that incorporate information about the relationships between traffic flows over space and time have recently become the mainstream of urban traffic forecasting. Many algorithms have been developed to learn the structure of spatiotemporal relationships - the travel time-based, cross-correlation, graphical lasso, mutual information, and random forest methods, among several others. In this paper, we address two issues of spatiotemporal relationship learning. First, we suggest the application of ensemble techniques (ranking- and voting-based) that combine the advantages of individual algorithms and allow more robust structures to be obtained for learning spatiotemporal relationships. Second, we propose strategies that enable discovery of their dynamics and provide extensive information on traffic flows for the responsive learning of spatiotemporal relationships. In addition to the widely used forecasting performance (estimated for the spatially regularised vector autoregression model, the spatiotemporal k-nearest neighbour algorithm, and the high-order graph convolutional artificial neural network), we compare the estimated spatiotemporal relationship structures using their complexity and stability metrics and demonstrate the importance of these characteristics. A multitude of numerical experiments using a large citywide traffic data set are conducted, and the results demonstrate the preferences of ensemble learning and the strengths of the proposed learning strategies.

The object of this study is a regulated intersection of city streets. The main task being solved is the reduction of queue lengths on approaches to such intersections by organizational measures. As a result of the research, patterns of change in queue lengths in front of the stop line were revealed, taking into consideration the specialization of traffic lanes and the distribution of traffic flows by directions. It was established that with an increase in the share of left-turning vehicles in the lane by 15% and above, there is an increase in the length of the queue before the intersection from 45 to 80m. The results indicate that when choosing a traffic pattern at the intersection, it is necessary to take into consideration not only the intensity of the flow itself but also the proportion of vehicles performing certain maneuvers (in this work, turning left is considered). A feature of the results is that they are obtained using simulation tools. This research method has made it possible to derive the value of the length of the queue in front of the intersection, changing the load on the approach from 200 to 1000vehicle/h and the share of left-turning vehicles from 5 to 15%. In addition, using a simulation model, it was checked how the intersection functions under various traffic management schemes on it when the input parameters of traffic flows change. The research results are recommended to be used in the development of traffic management schemes at regulated intersections. When determining the permitted directions of movement in the lanes, it is necessary to take into consideration the patterns of change in the length of queues before the intersection since under certain circumstances they may exceed critical values.

Measurement of traffic flow stability and resilience is a critical step toward evaluating the performance of transportation systems and implementing appropriate management strategies. Quantifying changes in the stability and resilience of transportation systems, however, is hampered by the complexity of real traffic dynamics and the diversity of infrastructures. Here, we demonstrate that changes in traffic flow stability and resilience are signaled by generic features, known as early warning signals in the theory of critical slowing down, observed before traffic instabilities occur. This finding is incorporated in an operational data-driven algorithm to evaluate the risk of traffic jams on highways. Theoretical findings and tests on simulated and empirical case studies support the premise of this approach and identify candidate statistical measures that are sensitive to changes in the stability and resilience of transportation systems. Our use of universal measures advances the monitoring capability, prediction and control of complex transportation systems.

Traffic signal control is a critical method that ensures the efficiency of traffic flow in cities across the world. There are massive studies that focus on generating optimal signal timing plans. Most of the these studies are model-based, where the signal plan is determined by optimization models with fixed parameters. Reinforcement learning (RL) is a model-free method that learns the optimal control policy over time which avoids limitations of traditional methods. In the past a few years, deep RL methods become particularly attractive since deep neural networks can provide scalable learning capability given the complex traffic condition in real life. In this manuscript, we develop a deep actor-critic method that can provide efficient traffic signal plans. First, a novel deep neural network model is applied to mine the recent traffic condition information as a sequence of temporally sequential image representations of the intersection, which improves from relying solely on limited traffic information such as queue length; then the deep neural network model is integrated with an actor-critic model which avoids drawbacks from the value-based or the policy-based methods. Simulation experiments illustrate that the deep actor-critic method outperforms the classic model-based method and several existing deep reinforcement learning methods according to different measures such as queue length, average delay time, and throughput.

A cyclist is a vulnerable road user whose interaction with the road infrastructure depends on several factors, including variable environmental conditions of lighting and meteorological road surface. This paper is concerned with nanoscopic crash modelling under the riskiest environmental conditions. There are very few works in the literature dealing with such modelling. An intelligent methodological framework consisting of the data collection unit and a knowledge processing unit (KPU) is proposed. In the knowledge processing unit, a combination of a) Statistical, b) Data learning and c) Casual inference methods are applied for investigating crashes on the study area of Tyne and Wear county in North-East of England. Three predictive nanoscopic road safety models are constructed (with 86% accuracy) using a) Spatial, b) Personal, and c) Infrastructure input variables. The importance of each of the identified input variable is estimated by deep learning and statistically validated through chi-square test and Cramer's V statistic. It is found that unsafeness of interaction between rider and infrastructure depends on lighting and road surface meteorological conditions. Different environmental conditions present a varying degree of risk to different types of infrastructure. The riskiest environment conditions are significantly affected by rider's gender and age, traffic flow regime, specific riding manoeuvre, and the road hierarchy difference. The increase in the number of variables, a rider encounters during his entire trip, imparts risky riding behaviour, affecting its safe interaction with the infrastructure. A novel infrastructure variable, i.e. `functional road hierarchy level and direction' introduced in this work, is found to be a critical road safety variable. A shift in road safety analysis towards nanoscopic modelling can help achieve zero-vision road traffic fatality. The study reinforces the need to plan and design infrastructure to move towards a more holistic approach while considering this vulnerable road user's limitations.

Accurate speed prediction is practically critical to eco-safe driving for intelligent vehicles. Existing research only makes vehicles adapt to the dynamic driving environment while rarely considering the influence of human driving preferences. This paper proposes a learning-based model to leverage human driving preferences into speed prediction. We first designed an Oriented Hidden Semi-Markov Model (Oriented-HSMM) to learn and predict the driver's driving preference sequences while considering traffic flow influence. Then, we developed an optimal speed prediction algorithm to retrieve the smooth speed trajectories with maximal likelihood based on the estimated driving preferences. Finally, we evaluated the proposed model using the Next Generation Simulation (NGSIM) data compared to its counterparts that do not consider driving preferences. Experimental results demonstrate that our proposed Oriented-HSMM method reaches the best results and achieves a satisfying performance with a low mean absolute error (4.16 km/h) and root mean square error (5.08 km/h) at a 200 m prediction horizon.

With the advent of the next-generation traffic monitoring systems, there has been a significant increase in the spatial-temporal resolution of vehicle mobility data in many cities. Effective analysis and visualization of such data can provide transportation planners with data-driven insights, which can facilitate the understanding of multiscale traffic dynamics. In this paper, we present a web-based traffic emulator for emulating and visualizing near-real-time and historical traffic flows on highways using data from road-side sensors. To construct a continuous traffic flow, the emulator adopts an analytical pipeline that can (a) integrate traffic data collected from discrete road-side radar detection sensors, (b) interpolate traffic conditions (vehicle speed and volume) on unmeasured road segments based on traffic flow theory, and (c) generate lane-specific vehicle trajectories and movements using a mathematically optimized representation of the road network. Our app also provides an integrated visual workflow that allows users to explore the interconnected traffic dynamics using an appropriate traffic flow visualization selected based on the level of detail. We devise two innovative geo-visualization techniques that utilize an animated strips-network representation and a lane usage matrix to visualize lane performances. To ensure a smooth emulation of large-scale traffic flow in an easy-to-access web environment, we implement the emulator using client-side GPU-accelerated techniques. Finally, we close with a case study that visualizes traffic dynamics of two scenarios - an afternoon peak hour and a traffic accident - in Chattanooga, Tennessee. Our app visualizes the responses of traffic dynamics during different traffic conditions, and to the presence of the traffic accident at different spatial scales.

Connected and automated vehicles (CAVs) have the potential to improve traffic throughput and achieve a more efficient utilization of the available roadway infrastructure. They also have the potential to reduce energy consumption through traffic motion harmonization, even when operating in mixed traffic with other human-driven vehicles. The key to realizing these potentials are coordinated control schemes that can be implemented in a distributed manner with the CAVs. In this paper, we propose a distributed predictive control framework that features a two-dimensional maneuver planner incorporating explicit coordination constraints between connected vehicles operating in mixed traffic at various penetration levels. The framework includes a distributed implementation of a reference speed assigner that estimates local traffic speed from on-board measurements and communicated information. We present an extensive evaluation of the proposed framework in traffic micro-simulations at various CAV penetrations from traffic flow, energy use, and lane utilization points of view. Results are compared to a baseline scenario with no CAVs, as well as, a benchmark one-dimensional planner.

As well as offering tremendous opportunities for existing transportation systems, autonomous driving presents challenges for traffic management, particularly in mixed traffic flow. The design of driving strategies for autonomous vehicles (AVs) considers both safety and efficiency to promote smooth traffic flow with no collisions. However, the complex traffic conditions at freeway bottlenecks mean that the traffic impacts of AVs in such scenarios remain unclear. Therefore, this study employs several driving strategies utilizing deep reinforcement learning (RL) to form a mixed-autonomy traffic flow on a one-way two-lane freeway with on- and off-ramps and weaving sections in SUMO traffic microsimulations. The study also examines the overall traffic impacts in terms of congested patterns, safety, efficiency, and passenger comfort. Multiple experiments are conducted with different AV driving strategies, penetration rates, and key traffic conditions. The results show that AVs with a deep-RL driving strategy significantly mitigate traffic-congested patterns and prevent the propagation of shockwaves, but at the expense of speed at low penetration rates. Moreover, the proposed driving strategy is conducive to a safer capability in vehicle dynamics. However, AVs under a deep-RL strategy lead to poorer passenger comfort at bottlenecks. Therefore, it is crucial to design a driving strategy with high robustness and scalability so that deep RL-based AVs can adapt to more complex scenarios and traffic events. Overall, the present research improves the understandings of AV driving mechanisms and provides insights into corresponding future traffic-management measures.

Although considerable methods have been developed for the performance evaluation of signalized intersections using sampled vehicle trajectories, most of them aim at estimating a single parameter and cannot describe the entire arrival-departure process of the traffic flow. This significantly constrains the application of these methods for a comprehensive evaluation and efficient optimization of signalized intersections. In this paper, we propose a cumulative flow diagram (CFD) estimation and prediction method using sampled vehicle trajectories. It can be used to calculate multiple performance measures--traffic volume, queue length, average delay, and total delay--based on the estimated CFD for the current signal timing plan. Concurrently, it can be further employed for signal control optimization based on the predicted CFDs for candidate signal timing plans. The core idea of the proposed method is to generate the cumulative arrival curve based on the arrival characteristics of the sampled vehicles, and then fit the queue leaving points to obtain the cumulative departure curve. Thereby, given the current or any candidate signal timing plan, we can estimate or predict the CFDs by updating the sampled vehicle arrivals. The proposed method is evaluated using both simulation and empirical data. The simulation results yield that the average estimation error of the four performance measures is 10.3% under a real-world level penetration rate of 10%. Meanwhile, similar accuracies are achieved for the CFD prediction. The empirical results show that under a penetration rate of 8.6%, the estimation errors of the traffic volume and queue length are 2.7% and 3.3%, respectively.

Eco-driving incorporating multiple signalized intersections simultaneously has been proven to substantially benefit Connected Vehicles (CVs) in energy performance. However, ignoring the dynamic variation of waiting queues before downstream intersections may prevent CVs from following the obtained speed profile on security grounds. In this paper, the dynamic variation of waiting queue is modelled and predicted based on shockwave theory and data-driven based traffic flow prediction. To formulate the waiting queues as additional time-varying constraints for optimization problem, an extended traffic signal model is constructed based on the prediction. Furthermore, a hierarchical optimization framework is proposed, under which the hybrid optimization problem is decomposed into a discrete problem and a continuous one. Monte Carlo simulation demonstrates that if the proposed eco-driving approach is implemented, failure to follow the reference speed profile decreases by 79.4%. Also, the fuel consumption can be saved by over 4% compared with approaches ignoring the waiting queue.

This paper aims to design speed advisory profile for connected vehicles (CVs) at an isolated signalized intersection in a mixed traffic flow of CVs and human driven vehicles (HDVs). To consider the uncertainty of HDVs, we propose the concept of α -percentile trajectory of HDVs. We calculate the advisory speed of a CV that follows a HDV, assuming that the HDV moves along its α -percentile trajectory. We use dynamic programming to solve the optimization problem. Simulation experiments show that, roughly speaking, the benefits of fuel consumption and travel time would increase with the increase of inflow rate and market penetration rate (MPR) of CVs. The maximum benefits would be achieved at the smallest value of α when the traffic flow is undersaturated. However, when the traffic flow is oversaturated, the maximum benefits would be achieved at intermediate value of α provided the MPR of CVs is not large. With the further increase of the MPR of CVs, the benefits would become not so sensitive to α in the range α le 40%, but significantly decrease with the increase of α when α >40%. Finally, the impact of driver heterogeneity, inflow heterogeneity, tracking error and delay has been investigated.

The fast development of information and communication technologies has engendered a massive generation of mobile data with great potential for extracting mobility patterns and sensing urban dynamics. The data, itself, has unique characteristics depending on the technology and the network coverage design for different zones (urban-rural). Therefore, many research directions investigated the potential of such data to extract some important features of traffic information, such as travel time estimation, traffic flow, etc. From this perspective, our paper focuses on highlighting the advancements done during the last twenty years concerning the usage of mobile phone network data to estimate vehicles' travel time in different geographical conditions. Furthermore, it discusses and covers the existing literature methods and the challenges they face due to the nature of the data, its quality, and its complexity. Finally, we reveal the main trends and directions for future work.

Accurate spatio-temporal traffic forecasting serves as the basis of dynamic strategy and applications for intelligent transportation systems, which is of great practical significance for improving traffic safety and mitigating road congestion. Recently, deep learning methods such as convolutional neural networks (CNN) have been applied to traffic flow forecasting, which exhibits better performance than conventional methods. However, these CNN-based methods typically learn traffic as images to model spatial correlation, which is only applicable to Euclidean grid map data rather than non-Euclidean multi-sensor data. To address this problem, we propose a graph-based temporal attention framework GTA, which considers both spatial and temporal correlation, to forecast traffic flow based on data collected from multiple sensors. More specifically, GTA can better capture spatial dependencies leveraging graph embedding techniques on sensor networks because it preserves more details in the algorithms. We also introduce an attention mechanism to adaptively identify the relations among temporal submodules. Spatio-temporal dependencies are more effectively and comprehensively integrated due to the full use of the topological properties of transportation networks. We evaluate GTA with a large-scale traffic dataset from England and enhance it with topology information. The experimental results show that our approach outperforms several state-of-the-art baselines.

With the development of the economy and the acceleration of urbanization, traffic congestion has become a worldwide problem. Advances in mobile Internet and sensor technologies have increased real-time data sharing, providing a new opportunity for urban route planning. However, due to the difficulty of handling complex global information, making correct decisions in large-scale and complex traffic environments is a problem that urgently needs to be solved. In this paper, a multiple route planning model (multi-route dynamic programming (DP) model) is proposed to solve the urban route planning problem with traffic flow information. In particular, we adopt the DP algorithm in this model, design a reward function suitable for urban path planning problems, and generate multiple routes based on the Q values. In addition, we design different scenarios using real-world road networks to test our model. Through the experiments, we demonstrate that our model has the potential to yield optimal results under large-scale scenarios with high efficiency. The advantages of integrating the distance contribution index (DCI) in the reward function are also elaborated. Moreover, our model can provide alternative routes to divert traffic from the optimal route, thus mitigating the congestion drift problem.

The prediction of lane changes can reduce traffic accidents and improve traffic flow. In this paper two classifiers, Echo State Network and a recurrent neural network with Long Short Term Memory cells, were compared to predict lane changes using the input variables steering angle and indicator. The input variables were extracted from a data set which was generated from a naturalistic driving study in the urban area of Chemnitz, Germany. Both classifiers predicted left and right lane changes successfully. They achieved high true positive rates and low false positive rates. The Echo State Network predicted left and the recurrent neural network predicted right lane changes better.

This paper presents a distributed synchronization strategy for connected and automated vehicles in traffic networks. The strategy considers vehicles traveling from one intersection to the next as waves. The phase angle and frequency of each wave map to its position and velocity, respectively. The goal is to synchronize traffic such that intersecting traffic waves are out of phase at every intersection. This ensures the safe collective navigation of intersections. Vehicles share their phase angles through the V2X infrastructure, and synchronize these angles using the Kuramoto equation. This is a classical model for the self-synchronization of coupled oscillators. The mapping between phase and location for vehicles on different roads is designed such that Kuramoto synchronization ensures safe intersection navigation. Each vehicle uses a constrained optimal control policy to achieve its desired target Kuramoto phase at the upcoming intersection. The overall outcome is a distributed traffic synchronization algorithm that simultaneously tackles two challenges traditionally addressed independently, namely: coordinating crossing at an individual intersection, and harmonizing traffic flow between adjacent intersections. Simulation studies highlight the positive impact of this strategy on fuel consumption and traffic delay time, compared to a network with traditional traffic light timing., 12 pages, 5 figures, submitted to IEEE Transactions on Intelligent Transportation Systems

The purposes are to investigate the personalized situation adaptive Human-Computer Interaction (HCI) in the COVID-19 context, achieve accurate predictions for HCI in different urban transportation situations, and solve the urban intelligent transportation problems. Problems of Human-Vehicles-Interaction (HVI) in context awareness are analyzed. Historical traffic flow in three different situations, including novice user situation, mid user situation, and expert user situation, are taken as the data sources. The HVI data are preprocessed afterward. Next, Dilated Convolution (DC) and Long-Short Term Memory (LSTM) are integrated (DC-LSTM) to build an HVI model based on situation adaptive. The proposed model is simulated to analyze its performance. Simulation experiments suggest that the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) of the proposed model are 4.64%, 5.34%, and 7.82%, respectively. Although these three metrics increase under the mid user and expert user situations, the proposed model can still provide a higher accuracy than LTSM, Convolutional Neural Network (CNN), Simple Recurrent Network (SRN), Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP). Besides, the prediction velocity can maintain about 60 Frame-Per-Second (FPS) under all three user situations. Regarding the path guidance performance, the proposed model can suppress the traffic congestion and dredge the congested sections effectively. Hence, the HVI model based on situational adaptation constructed has high prediction accuracy and traffic congestion evacuation performance, which can provide an experimental basis for the later intelligent transportation field and improving situational self-adaptability. IEEE

With the rapid development of sensor technologies, time series data collected by multiple and spatially distributed sensors have been widely used in different research fields. Examples of such data include geo-tagged temperature data collected by temperature sensors, air pollutant monitoring data, and traffic data collected by road traffic sensors. Due to sensor failure, communication errors and storage loss, etc., data collected by sensors inevitably includes missing data. However, models commonly used in the analysis of such large-scale data often rely on complete data sets. This paper proposes a model for the imputation of missing data of traffic flow, which combines a self-attention mechanism, an auto-encoder, and a generative adversarial network, into a self-attention generative adversarial imputation net (SA-GAIN). The introduction of the self-attention mechanism can help the proposed model to effectively capture correlations between spatially-distributed sensors at different time points. Adversarial training through two neural networks, called generators and discriminators, allows the proposed model to generate imputed data close to the real data. In comparison with different imputation models, the proposed model shows the best performance in imputing missing data.

In this paper, a novel lane keeping control method for automated vehicles based on human-simulated intelligent control (HSIC) is proposed, which is inspired by human expert drivers' steering characteristics including good foresight, precise execution and notable intermittency. The novelty of the paper is to introduce the HSIC concept into lateral control of vehicles, which is a multi-mode control scheme implemented by the combination of the feedforward control for curve tracking and act-and-wait control for intermittent error correction. Theoretically, the stabilization problem of the HSIC method is investigated based on the switched system related method. Experiments on the joint simulation platform of PreScan and CarSim show that the newly presented HSIC scheme has better matching performance to the expert driver and good robustness. For automated lane keeping systems, the HSIC method could provide human-like qualities, which may be one of the essential points to determine whether the driver is comfortable or not when the driver hands over the steering authority, improve the transition smoothness in the scenario of human vehicle co-piloting, and eliminate the potential conflicts between manual driving and automated driving vehicles in the future mixed traffic flow.

When dealing with traffic big data under the background of Internet of Things (IoT), traffic control under the single-machine computing environment is difficult to adapt to the massive and rapid analysis and decision-making. To tackle this problem, we propose a parallel computing approach of traffic network flow control based on the mechanism of model predictive control (MPC). A non-analytical rule-based traffic flow model is developed to forecast vehicle movements in the prediction horizon according to the real-time feedback information of traffic flow, and evaluate the performances of candidate control strategies. Furthermore, to accelerate the solution process of obtaining the optimal control schemes in the prediction horizon, a two-level hierarchical parallel genetic algorithm (HPGA) based on Spark cloud computing is designed. Through the parallel computing architecture, the computationally intensive optimization tasks are decomposed into multiple parallel sub-tasks with the aid of resilient distributed datasets (RDDs), which improves the computational efficiency. The simulation results demonstrate the validity of the proposed methodology for traffic network flow predictive control with respect to unsaturated and oversaturated traffic scenarios. The Spark-based parallel optimization approach has the potential to satisfy the computing requirements of online optimization when dealing with the big data of traffic network flow control while keeps favorable control performances.

With the deterioration of the transportation ecosystem and traffic congestion, traffic graph representations become more complex and lack intelligibility. The deep learning method provides a new way for traffic prediction by mining the spatiotemporal relations of historical states in traffic network. Recurrent neural networks based on gate control can avoid the gradient vanishing and graph convolutional networks provide theoretical support for spatial feature extraction of traffic graph. However, in the prediction of traffic evolution behaviors, the statuses of units and groups distributed in the traffic network have strong and continuous spatiotemporal correlations. Modeling only based on the temporal or spatial perspective usually lacks comprehensiveness and semantic relevance which results in poor prediction performance. In this paper, we propose a spatiotemporal hierarchical propagation graph convolutional network(SHPGCN) to get the whole spatiotemporal evolution features of traffic graph, which can be used to predict the propagation effects and state changes of traffic flow. Considered the inadequacy of graph convolution in the learning of hierarchical features, we construct a low-dimensional propagation graph representation that projects complex node relationships into first-order neighborhoods to capture dynamic changes at different spatial and temporal scales. The SHPGCN uses graph convolution to encode the traffic propagation features and embeds them into a bidirectional recurrent sequence for traffic prediction. The experiment results show that SHPGCN can provide good prediction accuracy and robustness for the traffic evolution behaviors.

Previous work has shown that Adaptive Cruise Control (ACC) can improve traffic flow by raising the critical vehicular density at which congestion first appears. However, these works also indicate that traffic with medium-to-high penetration of ACC-equipped vehicles is more susceptible to the formation of self-organized phantom traffic jams induced by perturbations in vehicular demographics. In this work, we propose a congestion-aware Cooperative Adaptive Cruise Control (CACC) algorithm as an alternative to address the trade-off between competing goals of raising the critical density, and reducing susceptibility to congestion observed at higher penetration rates of ACC-equipped vehicles. The congestion-aware CACC algorithm is modeled after the General Motor's car-following models, wherein the driver sensitivity is altered based on the prevailing congestion state (or traffic jam size) downstream of the connected vehicle. The dynamics of the self-organized traffic jam are modeled using a master equation. Results indicate that the congestion-aware CACC algorithm can increase the effective critical density leading to higher traffic flows, while also reducing the susceptibility to perturbations in vehicle demographics in the density range that adversely affects ACC-equipped vehicles.

This paper introduces Sentinel, an onboard system for intelligent vehicles that guides their lane changing behavior during a freeway incident with the goal of reducing traffic congestion, capacity drop, and delay. When an incident blocking the lanes ahead is detected, Sentinel calculates the probability of leaving the blocked lane(s) before reaching the incident point at each time step. It advises the vehicle to leave the blocked lane(s) when that probability drops below a certain threshold, as the vehicle nears the congestion boundary. By doing this, Sentinel reduces the number of late-stage lane changes of vehicles in the blocked lane(s) trying to move to other lanes, and distributes those maneuvers upstream of the incident point. A simulation case study is conducted in which one lane of a four-lane section of the I-66 interstate highway in the U.S. is temporarily blocked due to an incident, to understand how Sentinel impacts traffic flow and how different parameters - traffic flow, system penetration rate, and incident duration - affect Sentinel's performance. The results show that Sentinel has a positive impact on traffic flow, reducing average delay by up to 37%, particularly when it has a considerable penetration rate. Working alongside Traffic Incident Management Systems (TIMS), Sentinel can be a valuable asset for reducing traffic delay and potentially saving billions of dollars annually in costs associated with congestion caused by freeway incidents., Comment: $\copyright$ 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Predicting urban traffic flow is a challenging task, due to the complicated spatio-temporal dependencies on traffic networks. Urban traffic flow usually has both short-term neighboring and long-term periodic temporal dependencies. It is also noticed that the spatial correlations over different traffic nodes are both local and non-local. What's more, the traffic flow is affected by various external factors. To capture the non-local spatial correlations, we propose a Dilated Attentional Graph Convolution (DAGC). The DAGC utilizes a dilated graph convolution kernel to expand the nodes' receptive field and exploit multi-order neighborhood. Technically, the lower-order neighborhood corresponds to local spatial dependencies, while the higher-order neighborhood corresponds to non-local spatial dependencies between nodes. Based on DAGC, a Multi-Source Spatio-Temporal Network (MS-Net) is designed, which suffices to integrate long-range historical traffic data as well as multi-modal external information. MS-Net consists of four components: a spatial feature extraction module, a temporal feature fusion module, an external factors embedding module, and a multi-source data fusion module. Extensive experiments on three real traffic datasets demonstrates that the proposed model performs well on both the public transportation networks, road networks, and can handle large-scale traffic networks in particular the Beijing bus network which has more than 4,000 traffic nodes.

With the increase of private cars, traditional traffic signal control methods cannot alleviate the traffic congestion problem. Reinforcement learning (RL) is increasingly used in adaptive traffic light control. As urban traffic becomes more complex, reinforcement learning algorithms solely based on value or policy are not suitable for such scenarios. Moreover, the centralized method does not show a good effect on the multi-intersection, in particular, when the traffic flow is in the high-dimensional continuous state space. In this article, we propose a decentralized framework based on the advantage actor-critic (A2C) algorithm by assigning global control to each local RL agent or intersection. A2C algorithm involved in this article is a product of combining policy function and value function, which has good convergence ability and can be applied to continuous state space. The decentralized methods may put a new challenge: from the perspective of each local agent, the environment becomes partially observable. We overcome this problem by putting forward a region-aware cooperative strategy (RACS) based on graph attention network (GAT), which can incorporate the spatial information of the surrounding agents. We carry out experiments on synthetic traffic grid and real-world traffic network of Monaco city to compare with the existing A2C and Q-learning algorithms. Experimental results confirm that our RACS method has a shorter queue length and less waiting time than the two existing algorithms, and can reduce the total vehicle travel time.

As one of the most widely used applications of the Internet of Things (IoT), intelligent transportation system (ITS) is of great significance for urban traffic planning, traffic control and traffic guidance. However, widespread traffic congestion occurs with the increased number of vehicles. Traffic flow prediction is a good idea for traffic congestion. Therefore, many schemes have been proposed for accurate and real-time traffic flow prediction, but there still exist many issues including low accuracy, weak adaptability and inferior real-time. Meanwhile, the complex spatial and temporal dependencies in traffic flow are still challenging. To address the above issues, we propose a novel spatial-temporal Chebyshev graph neural network model (ST-ChebNet) for traffic flow prediction to capture the spatial-temporal features, which can ensure accurate traffic flow prediction. Concretely, we firstly add a fully connected layer to fuse the features of traffic data into a new feature to generate a matrix, and then the long short-term memory (LSTM) model is adopted to learn traffic state changes for capturing the temporal dependencies. Then, we use the Chebyshev graph neural network (ChebNet) to learn the complex topological structures in the traffic network for capturing the spatial dependencies. Eventually, the spatial features and the temporal features are fused to guarantee the traffic flow prediction. The experiments show that ST-ChebNet can make accurate and real-time traffic flow prediction compared with other 8 baseline methods on real-world traffic datasets PeMS.

Traffic flow prediction is an important part of the intelligent transportation system. Accurate multi-step traffic flow prediction plays an important role in improving the operational efficiency of the traffic network. Since traffic flow data has complex spatio-temporal correlation and non-linearity, existing prediction methods are mainly accomplished through a combination of a Graph Convolutional Network (GCN) and a recurrent neural network. The combination strategy has an excellent performance in traffic prediction tasks. However, multi-step prediction error accumulates with the predicted step size. Some scholars use multiple sampling sequences to achieve more accurate prediction results. But it requires high hardware conditions and multiplied training time. Considering the spatiotemporal correlation of traffic flow and influence of external factors, we propose an Attention Based Spatio-Temporal Graph Convolutional Network considering External Factors (ABSTGCN-EF) for multi-step traffic flow prediction. This model models the traffic flow as diffusion on a digraph and extracts the spatial characteristics of traffic flow through GCN. We add meaningful time-slots attention to the encoder-decoder to form an Attention Encoder Network (AEN) to handle temporal correlation. The attention vector is used as a competitive choice to draw the correlation between predicted states and historical states. We considered the impact of three external factors (daytime, weekdays, and traffic accident markers) on the traffic flow prediction tasks. Experiments on two public data sets show that it makes sense to consider external factors. The prediction performance of our ABSTGCN-EF model achieves 7.2%–8.7% higher than the state-of-the-art baselines.

Although there have been assorted car-following (CF) models for connected vehicles (CVs), studying their impacts in mixed traffic flow of human-driven vehicles (HVs) and CVs remains a challenge. Considering the multiple front vehicles' optimal speed changes with memory, this study proposed a new CF model (MVCM model) implemented through vehicle-to-everything (V2X) technology in CVs environment in terms of OVCM (Optimal Velocity Changes with Driving Memory) model. The stability condition of MVCM model was derived through linear stability analysis. Then, the disturbance propagation of MVCM model was compared with that of both classical FVD (Full Velocity Difference) model and MHOVA (Multiple Headway Optimal Velocity and Acceleration) model. Finally, a case study was conducted in VISSIM to analyze the impact of CV (with MVCM model) rates on traffic characteristics, including the average speed, delay time and travel time. Results show that 1) considering more front vehicles' optimal speed strengthens the stability of traffic flow and the optimal considered vehicle number is 4; 2) MVCM model shows better resistance to disturbance than FVD model; 3) and obtain the same stability with MHOVA model, but with fewer front vehicles considered (only 3); 4) larger rate of CVs leads to higher average speed, smaller average travel time and the delay time of CVs and HVs; and 5) CVs' positive effects reaches stable when CV rate approaches 0.6.

The energy consumption of a plug-in hybrid electric vehicle (PHEV) is directly related to its velocity. Considering the influence of road condition information such as traffic velocity, road speed limit and slope on the energy economy of PHEV, the economic velocity planning algorithm is designed in this study. Firstly, based on the actual vehicle test data, the polynomial fitting energy consumption models with the input parameters of vehicle speed and acceleration for charging of depleting mode and charging of sustaining mode are developed. The economic velocity planning model based on traffic flow velocity and road speed limit on flat roads is established adopting the Pontryagin's minimum principle (PMP). Afterwards, the road gradient information is introduced into the economic velocity planning algorithm based on the equivalent acceleration. Finally, the planned economic velocity and traffic flow speed are compared by the hardware-in-the-loop energy consumption tests. The results show that compared with the traffic flow speed, the economic velocity improves the vehicle energy economy by more than 4%, and the output torques of the engine and driving motor are more stable, which are more conducive to the energy economy of the vehicle. The designed economic velocity planning algorithm based on road information for PHEV improves the adaptability of PHEV control strategy to actual road conditions, and expands the optimization dimension of energy-saving driving. Thus, it has high practical application value.

This paper presents an innovative method to adaptively optimize traffic signal plans based on the estimation of traffic situation achieved from the information of various penetration rates of Connected Vehicles (CVs). The network-wide signal control problem is formulated as a linear optimization problem. Moreover, we develop a Kalman filter (KF) and Neural Network (NN) algorithms to predict and update the traffic situation under mixed non-connected and connected vehicles environment. To capture the dynamic of the traffic flow, we employ the cell transmission model synched with the Vissim traffic simulator. The methodology is tested using a challenging network of six intersections. We test our model for various Penetration Rates (PR) of the CV to provide a comparative analysis. The performance of the method is also compared with a conventional actuated-coordinated traffic signal plan. The results show that with a bare minimum PR (say more than 30%), our proposed methodology outperforms the actuated traffic signal plan. (note that the minimum PR is subject to further ongoing research in the literature, to the extent that lower PRs might be plausible). Though a 100% PR is highly desirable, our method can fetch the maximum benefit just by 60% PR.

In recent years, the adaptive cruise control (ACC) system has become widely adopted. The vehicle-positioning system plays an important role in automotive platoon driving and can provide vehicle-position information through vehicle-to-vehicle communication. However, the acquired vehicle-position information often presents uncertainties given the existence of variations in the traffic environment and of noise in the vehicle-positioning system. These uncertainties, in turn, influence the car-following performance of a vehicle platoon. In this study, we employ the desired safety margin (DSM) model, an ACC control strategy, to investigate the influence of the uncertainty level of vehicle-position information (ULVPI) on string stability and car-following safety. The stability criterion of the DSM model with ULVPI is derived through linear stability theory. Theoretical analysis results are verified by numerical simulations. Analytical results indicate that a negative ULVPI value can expand the stable region and improve string stability and that a positive ULVPI value can reduce delay time. Moreover, a negative ULVPI value can improve car-following safety during the stopping and evolution processes, whereas a positive ULVPI values can increase the safety margins of vehicles during the starting process. In the car-following process, a negative ULVPI value can improve car-following safety and reduce rear-end collision risk. The variation in ULVPI values can improve string stability and reduce the risk of rear-end collisions when mean and standard deviation are reasonable. Therefore, the improvement of a vehicle's dynamic performance, string stability, and safety relies on the effect of ULVPI in different traffic scenarios. These results are useful in designing different control strategies that stabilize traffic flow and improve traffic safety for vehicles with ACC systems.

On-ramp merging scenario has a great impact on traffic efficiency and fuel economy. At present, most research on-ramp merging focuses on the optimization of merging sequence in the single main lane scenario, which fails to give full play to the capacity of multi-lane roads. To overcome this problem, an efficient on-ramp merging strategy (ORMS) is proposed to coordinate vehicle merging in multi-lane traffic. First, we built a model of the unevenness of traffic flow between lanes. Based on this model, we established a lane selection model by reinforcement learning for the coordination of vehicles in multi-lane traffic. Before vehicles enter the merging zone, the decision of lane selection is made by analyzing the unevenness of traffic flow between lanes to relieve local congestion in the outside lane that may be caused by ramp vehicle inflow. Then, we adopted a vehicle motion planning algorithm based on the time-energy optimal control, so that all vehicles travel according to the optimal trajectory to reach the merging zone. The simulation results show that the traffic efficiency and fuel economy of the proposed on-ramp merging strategy are significantly improved compared with the existing optimal control algorithm.

Short-term traffic prediction is of great importance to the management of traffic congestion, a pervasive and difficult-to-solve problem in many metropolises all over the world. However, existing studies on traffic prediction contain rough traffic information at the carriageway level that ignore the distinction between different turns in one intersection. With the aim of predicting traffic at road intersections from big trace data on a finer scale, this study proposes a novel method, the fine-grained traffic prediction method (FTPG) with a graph attention network (GAT), which predicts traffic information, including traffic flow speeds, traffic states, and average queue lengths, at the turn level. In the FTPG, a method for estimation of the queue starting point is proposed to improve the accuracy of traffic information detection. Furthermore, the topology is constructed under turn-level conditions, and a GAT-based method, the spatio-temporal residual graph attention network (ST-RGAN), is proposed to improve the prediction accuracy. Experiments are performed using taxi GPS trace data collected in the city of Wuhan and show that the proposed FTPG method can make predictions with fine-grained traffic information for road intersections accurately and robustly.

This study proposes a short-term traffic flow prediction framework. The vector autoregression (VAR) model based on econometric theory and the CNN-LSTM hybrid neural network model based on deep learning are employed in the analysis. An intrinsic association among traffic variables is first evaluated using the VAR model, and the predictable relationship of these variables is determined. Then the multi-features speed prediction for one spatial location using the CNN-LSTM hybrid neural network model is conducted, the prediction results prove that prediction with multi-feature is better than that with a single feature. Subsequently, several popular deep learning models and other shallow predicted models are proposed to be compared with the constructed CNN-LSTM network model, and the comparison illustrates that the model performance of the developed CNN-LSTM network model is superior to other models in forecasting the short-term traffic flow. Then the multi-feature speed predictions for a group of spatial locations are further conducted using the CNN-LSTM model. The result demonstrates the predictive accuracies are associated with the spatial correlation of traffic flow. Finally, a heatmap is produced to visualize the predicted speed, from which the spatial-temporal traffic condition can be presented clearly. The research results have the potential to be applied to the travel information releasing and traffic congestion management.

The existing conventional traffic flow models aims to simulate human-driven following vehicles in real world. In this era of emerging transport solutions, controlling or intervening traffic flow to achieve high fuel efficiency along with good driving safety and travel efficiency becomes a reality. As such, it is worth exploring the possibility of developing eco-driving models to optimise vehicle movements for fuel consumption minimisation, while maintaining safety and efficiency. In this study, we propose a modified genetic algorithm (GA) based calibration method that enables the calibrated parametric traffic flow (car following) models to simulate or control vehicles in an eco-driving manner. By developing a novel objective function for the GA method based on the widely-used VT-Micro fuel consumption model, the proposed method can calibrate model parameters towards improving fuel efficiency. Besides, by subtly using heavy fuel consumptions as a surrogate index to represent low travel efficiency or dangerous driving strategies, the modified GA method with the novel objective function can guide the calibrated model towards achieving complete eco-driving requirements. Experimental simulation results further indicate that traffic flow models calibrated by the modified GA-based method can also alleviate traffic disturbances and oscillations in a more effective manner.

The technological advancements involving information and communication technologies (ICT), such as Connected and Automated Vehicles (CAVs) and the Intelligent Transport Systems (ITS), have enabled new efficient traffic control and management strategies to mitigate traffic congestion. Specifically, the combined traffic flow-speed advisory systems based on CAVs and ITS technologies could provide the individual vehicle with the optimal speed to reduce the fuel consumption, the number of stops, simultaneously reduce the network-wide traffic congestion and improve road safety. This article develops a novel bi-level control framework underpinned by the mutual interaction between a system optimal traffic flow control strategy at a network level and a speed control policy for an individual vehicle at a link level within a connected traffic environment. Our framework proposes the novel group-based method to guarantee the consistency and interaction between the macroscopic and microscopic models. To this end, it efficiently optimizes vehicular trajectories while meeting the network-wide objectives which have not been investigated previously in the literature. We propose an efficient algorithm for this problem that iteratively solves mixed-integer linear programming (MILP) models for each upper and lower level. Numerical results indicate the effectiveness of the proposed speed advisory method in vehicular emission reduction, favorable network queue formation, and its positive influence on traffic flow patterns over the network.

Speed limit of freeway tunnel restricts car driving behaviors. To explore the tunnel effect of travel time, a double lane traffic model is put forward, and a compensative function of mean travel time is simply introduced in this paper. The model describes the net lane-changing rate using the ratio of characteristic lane changing time to traffic relaxation time, assumes that the cars on both lanes of the tunnel have the same equilibrium speed, but the speeds on both lanes of normal section are different. It is assumed that the road length is 100 km, and the tunnel length is 7.5 km except for the case of seeking tunnel length effects. The model is used to build a simulation platform. Numerical results indicated that when the initial road density normalized by jam density is above 0.2, the tunnel causes a congestion region just upstream the tunnel inlet, provides a smooth region just downstream the tunnel outlet, can affect the traffic flow pattern significantly. But the tunnel effect of travel time is small, as the tunnel can merely slightly increase the mean travel time. The on/off ramp flows play a crucial role in the formation of traffic flow pattern.