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The emergence of connected and autonomous vehicles (CAVs) holds promise for fine-grained traffic control. However, due to the longevity of future mixed traffic scenarios, there is a need for an in-depth exploration of integrating the microscopic speed control of CAVs with the macroscopic variable speed limit (VSL) of human-driven vehicles (HDVs). This paper proposes a Cooperative Traffic Optimization with Multi-agent Reinforcement Learning and Evolutionary VSL (CTO-ME) framework, which combines microscopic CAV control with macroscopic VSL control. The framework incorporates a Graph Attention Mechanism (GATs) into the multi-agent reinforcement learning framework for intelligent decision-making by microscopic-level vehicles. Additionally, an evolutionary strategy is developed to design the VSL network architecture, enabling macroscopic level real-time speed limit adjustments based on infrastructure. A multi-objective reward function is proposed to optimize both micro and macro efficiency and safety, accounting for both vehicle behavior and traffic flow. Experiments on the designed Bottleneck traffic scenarios show that the proposed approach, CTO-ME, is able to achieve superior performance and outperforms other baselines in terms of traffic throughput, average speed, and safety. Specifically, CTO-ME enhances average velocity by 37%, increases overall throughput by 309%, and raises arrival ratio by 70% than traditional Intelligent Driver Model (IDM). • Macro–Micro Control Integration: We tackled the challenge of harmonizing macroscopic (Variable Speed Limit - VSL) and microscopic (Connected and Automated Vehicles - CAVs) controls to enhance both granular and overarching traffic efficiency. Our proposed method, the Cooperative Traffic Optimization with Multi-agent Reinforcement Learning and Evolutionary VSL (CTO-ME), cooperates with these control methods, paving the way for synchronized and efficient traffic management. • We integrated the Graph Attention Mechanism (GATs) into the multi-agent reinforcement learning framework to amplify the decision-making capabilities of vehicles and developed an advanced evolutionary strategy for VSL control to facilitate real-time speed limit adjustments. • We proposed a multi-objective reward function, considering both micro and macro efficiency and safety considerations, to promote optimized traffic flow and enhance overall control performance. [ABSTRACT FROM AUTHOR]

In the environment of intelligent connected vehicles, the mixed traffic flow consisting of human-driven vehicles (HDVs), connected and automated vehicles (CAVs), and their platoons will coexist for a long time. Exploring and discovering the operational rules of mixed traffic flow is the foundation for its management and control. This study delves into the distinct characteristics of various vehicles within a mixed traffic environment, particularly focusing on HDVs, CAVs, and CAV platoons. Drawing on the psychological predisposition of HDVs to maintain a prudent following distance, we introduce a discrete motion safety distance model to establish the longitudinal movement rule for HDVs. Furthermore, a longitudinal movement rule for CAVs is established based on their ability to keep a constant headway using Adaptive Cruise Control (ACC) and Cooperative Adaptive Cruise Control (CACC), alongside meeting specific speed and spacing criteria for platoon movement. On this basis, the paper proposes a cellular automaton model for single-lane mixed traffic flow that accounts for CAV platoon length and the decision-time disparity between HDVs and CAVs. The simulation results show that an increased proportion of CAVs can enhance the traffic capacity, elevate average speed, extend average platoon length, and reduce the congestion ratio and CACC degradation rate. Notably, in scenarios where the CAV proportion p > 0.7, the flow-density graph shows a trapezoidal shape, with the majority of CAVs in the traffic flow synchronizing their movements in CACC mode. Even when vehicular density surpasses the optimum threshold, the traffic flow maintains high-speed operation until reaching a critical congestion point, whereupon velocities precipitously decline. Through sensitivity analysis aimed at understanding the impact of maximum platoon length, it has been observed that, for CAV proportions below 0.7, variations in maximum platoon length negligibly impact traffic capacity, velocity, CACC degradation, and congestion levels. Only in scenarios dominated by CAVs does extending platoon length restrictions significantly enhance performance. However, as the maximum platoon length increases, the gains in traffic capacity, speed, CACC degradation rate, and congestion ratio diminish. When the platoon length of CACC exceeds a certain threshold, there will not be a significant improvement in traffic capacity or speed. [ABSTRACT FROM AUTHOR]

With the development of autonomous driving and vehicle-to-infrastructure technologies, connected automated vehicles (CAVs) will gradually replace human-driven vehicles (HDVs). However, during the widespread adoption of CAVs, the roads will experience a new type of mixed traffic flow composed of vehicles with different automation levels for an extended period. To analyze the impact of different automation levels on mixed traffic flow, this paper proposes a stability analysis framework for mixed traffic flow that considers different automation levels. The influence of automation level classification on the composition of mixed traffic flow is analyzed first, and the degradation of CAVs in mixed traffic flow is further explored. Then, a stability analysis framework for mixed traffic flow considering different automation levels is proposed, and the impact of key parameters on the stability is analyzed. Finally, simulation experiments are designed to validate the theoretical analysis results. The research findings indicate that (1) the stability of mixed traffic flow improves with the increase in steady-state speed. When vehicle speed exceeds 20 m/s, mixed traffic flow remains stable. (2) When the CAV penetration rate is low, the stability of mixed traffic flow is poor due to the degradation of CAVs. However, with the increase in the penetration rate and automation level of CAVs, mixed traffic flow rapidly tends toward a stable state. (3) Numerous factors influence the stability of mixed traffic flow, with the order of importance being the CAV penetration rate, automation level, headway, and maximum acceleration. Therefore, this study can provide theoretical support for managing and controlling mixed traffic flow with different automation levels in the future. [ABSTRACT FROM AUTHOR]

Segments with closely spaced entrance and exit ramps are recognized as crash black-pots on freeways. Accurately identifying hazardous traffic states before crash occurrence can help implement targeted control measures, thereby reducing crash risk in such segments. As such, this study developed a structural causal model (SCM) to explore the causality between traffic states and crash risk in segments with closely spaced entrance and exit ramps. Firstly, high-resolution traffic flow data related to three types of lane configurations and three crash types were collected. A deep clustering based on generative adversarial networks (clusterGAN) was developed to classify traffic flow into seven states. Secondly, the causal graph connecting traffic states and crash risk was generated. Backdoor adjustment from the SCM was used to identify seven confounding variables (e.g. traffic volume, ramp volume ratio, speed difference on the inside lanes between the beginning and end of the segment, the shoulder and median width, etc.), which influence both traffic states and crash risk. The inverse-probability-weighted regression adjustment estimator was adopted to estimate the average causal effects of traffic states on crash risk conditional on different lane configurations. The results suggest that hazardous traffic states in each lane configuration are different, even for the same crash types. In addition, the comparison results with the standard logistic models suggest that ignoring confounding effects may cause unreasonable conclusions and biased estimation. These results have the potential to help advanced traffic management systems develop proactive traffic control strategies to improve traffic safety in freeway segments with closely spaced entrance and exit ramps. • Develop an SCM to establish the causality between traffic states and crash risk. • Estimate the causal effects of traffic states on crash risk without confounding bias. • Hazardous traffic states are different across various lane configurations and crash types. • The model incorporating confounding effects improves prediction accuracy. [ABSTRACT FROM AUTHOR]

Mixed traffic flow involving connected and automated vehicles (CAVs) and human-driven vehicles (HDVs) will be a new state in the future traffic pattern. However, the inherent "stop-and-go" mode of existing traffic signal control seriously weakens the advantage of CAV cooperative operation in mixed traffic. To handle this problem, this study proposes a novel traffic signal control scheme that addresses a mixed traffic scenario at a typical intersection. Specifically, the control scheme can precisely match traffic demand and space-time supply. Then, a protocol of white phases is proposed to decrease the crossing time of CAVs. Finally, the traffic signals and vehicle trajectories are optimized in a unified control framework that minimizes the travel delay of all vehicles. The proposed scheme is verified in SUMO-based simulation considering various penetration rates of the CAVs, different input traffic volumes, and different proportions of left-turning traffic. The experimental results show that the proposed scheme is superior to existing actuated signal control regarding stability when facing different input traffic volumes and proportions of left-turning traffic. Moreover, it is observed that the travel delay decreases with the penetration rates of the CAVs, indicating the potential of the proposed method in the near future mixed traffic scenario. • Matching traffic demand and space-time supply improves intersection efficiency. • Reservation-based phase can improve the control stability in dynamic traffic flow. • Average vehicle delay decreases with the increased number of CAVs. • Introducing white phases can decrease the crossing time of CAVs. [ABSTRACT FROM AUTHOR]

Autonomous driving technology is advancing rapidly, and heterogeneous traffic flow consisting of intelligent controlled vehicles (ICV) and human driving vehicles (HDV) is expected to become the dominant mode on roads. In this study, three driving styles of HDV were analyzed using actual data sets, and the Gipps rule was introduced to establish safety conditions for following and lane-changing rules. A two-lane heterogeneous traffic flow cellular automata model was proposed and simulated using Matlab. According to the simulation results, the impact of ICV penetration rate on traffic capacity is significant, exhibiting a 23.4% to 62.5% increase compared to homogeneous HDV flow within the range of 0.2 to 0.8 ICV penetration rate. Homogeneous ICV flow is about 1.5 times higher than homogeneous HDV flow. Moreover, as the ICV penetration rate increases, there is a gradual decrease in both the lane-changing frequency and congestion level. These findings provide valuable insights into the influence of ICV mixed on traffic flow performance and offer a theoretical basis for managing and controlling heterogeneous traffic flow. • A heterogeneous traffic flow model considering driving styles and intelligent controlled vehicles is proposed. • The improved model effectively replicates the phase states of free, synchronous, and congested flow. • The introduction of intelligent controlled vehicles leads to significant improvements in traffic flow capacity. [ABSTRACT FROM AUTHOR]

With the development of autonomous driving technology, designing connected automated vehicles (CAVs) control strategies to mitigate traffic oscillations has become a hot topic. Most current control strategies are mainly oriented to single-lane highway scenarios and do not consider vehicles' lane-changing behaviors. To address the gap, this paper proposes the Follower-Stopper-Platoon (FSP) strategy, which attempts to mitigate traffic oscillation by controlling a platoon of CAVs in a two-lane scenario. Firstly, based on the Follower-Stopper (FS) control and considering CAVs' platoon behaviors, this paper proposes the FSP strategy and compares it with two comparative strategies, the Baseline and FS strategies. Then, a two-lane mixed traffic flow cellular automata model is developed and used to verify the effectiveness of the FSP strategy. Finally, this study demonstrates the efficacy of the FSP strategy by designing simulation experiments and analyzes the effect of CAVs' platoon size on the control effect. The result shows that (1) the FSP strategy can overcome the shortcomings of the FS control and will increase traffic flow speed while mitigating traffic oscillations. (2) In a two-lane scenario, as penetration rates of CAVs and traffic densities increase, the FSP strategy's advantages in mitigating traffic oscillations, reducing energy consumption and pollutant emissions, and improving speed and passenger comfort are gradually apparent. (3) Under the FSP strategy, the larger the maximum size of the CAVs platoon, the better the performance of the mixed traffic flow in terms of traffic efficiency, stability, fuel consumption, and pollutant emission. [ABSTRACT FROM AUTHOR]

Considering the influence of slope sections and random degradation of the communication function of cooperative adaptive cruise control vehicles and the effect of the cooperative adaptive cruise control vehicle group driving, a linear stability criterion for heterogeneous traffic flow composed of cooperative adaptive cruise control vehicles and humanly-driven vehicles on slopes is derived, and a parameter sensitivity analysis is carried out. On this foundation, the safety of traffic flow on two-lane roads up and down hills is simulated while considering the lane-changing behavior of automobiles. The stability study shows increased cooperative adaptive cruise control vehicle penetration and uphill gradient will help improve traffic flow stability. In contrast, the increase in the downhill slope will intensify the transmission amplitude of the disturbance. When vehicle starting sensitivity is 2.5, and the gradient is 60, the minimum cooperative adaptive cruise control vehicle penetration required for mixed traffic flow to maintain stability on a downhill at any initial equilibrium speed is 85%, and the minimum penetration required on an uphill is 73%, and this proportion decreases with the increase in the uphill gradient and increases with the rise in the downhill slope. According to the safety study, increasing cooperative adaptive cruise control vehicle penetration and implementing lane-changing rules will improve traffic flow safety. Compared with the straight section, under the same conditions, the traffic flow safety of the uphill section is the best, and the safety of the downhill section is poor, which is consistent with the stability analysis results. This study is of great significance for future traffic control strategies for mountain roads. [ABSTRACT FROM AUTHOR]

With the surge of traffic flux, the road flux limit method is emerging as an important traffic control measure that can affect the dynamic evolution of traffic. Therefore, a novel lattice hydrodynamic model is provided by integrating the flux limit effect (FLE) in a two-lane system under V2X environment. Linear stable analysis is executed to get the stability condition concerning the FLE on two lanes, which means that the FLE can expand the stable scope under lane change. Furthermore, numerical simulation is carried out to investigate the FLE on the density waves and fuel consumption and pollution emissions in a two-lane system, which implies that the FLE raises traffic stability and cuts down fuel consumption and pollution emissions in the case of lane change. • Novel lattice hydrodynamic model is proposed based on the flux limit effect on two lanes under V2X environment. • The stable condition involving the flux limit effect is deduced via stability analysis. • The stability of traffic flow is improved by the flux limit effect via numerical examples on two lanes. • Numerical examples demonstrate that the flux limit effect reduces energy consumption on two lanes. [ABSTRACT FROM AUTHOR]

Traffic arrival pattern is an indispensable part of the traffic state analysis and optimization of signal control schemes at the urban intersection. In some previous research relevant to the traffic parameters at the intersection, such as the queue length estimation, delay estimation, and vehicle trajectory reconstruction, the vehicle arrival patterns are assumed to obey a Normal or Poisson distribution. However, the actual traffic arrival patterns at the intersection are dynamic and more complex. License plate recognition (LPR) data containing rich vehicle trajectory information are promising in data-driven applications of traffic control and management. We propose a framework based on the LPR data, to infer the dynamic traffic arrival pattern at the intersection. The proposed integrative data-driven framework consists of three main parts, a method for inferring traffic signal cycle parameters, deriving the cycle start time and cycle length from time headway sequences; a link travel time estimation method, extracting the valid travel time at the selected time interval, and an arrival pattern estimation model based on the above modules to perform the second-level estimation at the downstream intersection. The unique characteristics of LPR data are exploited and utilized in this study. To validate the proposed method, the LPR data from two adjacent intersections in the city of Changsha in China are used. Numerical results reveal that the proposed framework can achieve satisfactory estimation under different traffic scenarios. The findings in this study can be extended for supporting efficient traffic control applications. • A statistical framework to infer the fine-grained traffic arrival pattern is proposed. • The signal cycle parameters are inferred by exploiting the features of the time headway sequences. • The valid travel time is extracted at the selected time interval based on the Gaussian mixed model. • The proposed framework could achieve satisfactory performance in different traffic scenarios. [ABSTRACT FROM AUTHOR]

Increasing traffic volumes result in congestion especially at intersections in urban areas. Effective regulation of vehicle flows at intersections has always been an important issue in the traffic control system. This paper presents a multi-objective optimization method for improving traffic performance at intersections. Vehicle conflicts and pedestrian interference are considered in the microscopic simulation of the traffic system. Three categories of the traffic performance are studied including transportation efficiency, energy consumption and road safety. The signal timing and lane assignment are optimized for different traffic flows. The multi-objective optimization problem is solved with the cell mapping method. The optimization results with constant traffic demands show the conflicting nature of different performance objectives. The influence of different traffic variables on the intersection performance is investigated. We further propose a dynamic optimization algorithm to handle the varying traffic demands. It is observed that the proposed optimization method is effective in controlling the traffic at the intersection to meet multiple objectives. Different control demands can be satisfied and optimal performance can be obtained. • Propose a novel multi-objective optimization method for traffic intersections. • Signal setting and lane assignment are optimized at the same time. • Cell mapping method is used to improve multiple traffic performances simultaneously. • Traffic dynamics simulations are implemented to validate the proposed method. • An optimal solution set is obtained, instead of a single solution. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we utilize route flow to propose a network traffic flow model with consideration of interruption factors. The proposed model is then used to study the effects of two typical interruption factors (e.g., bus station and accident) and the divergence effect at the shared node on each link's traffic flow. The model is conducted under the stochastic route choice behavior in a network with an origin–destination (OD) pair and two routes, where the numerical results indicate that the proposed model can qualitatively describe some complex physical phenomena (e.g., shock, rarefaction wave, stop-and-go, jam and route divergence). The above phenomena are related to each route's initial density, where the formation and propagation speed are affected by network structure and route choice behavior. Highlights • A route-based network traffic flow model is proposed. • The impacts of three bus stations on each link's traffic flow are studied in a simple network. • The impacts of an accident on each link's traffic flow are studied under two situations. [ABSTRACT FROM AUTHOR]

Abstract In order to investigate the effect of "backward looking" and flow integral upon traffic flow, an improved lattice hydrodynamic model has been developed. The stability condition is obtained by the use of linear stability analysis. The result of stability analysis demonstrate that both the "backward looking" and flow integral play an important role in enhancing the stability of traffic flow. The mKdV equation is deduced by using the nonlinear theory, which demonstrates that traffic congestion can be described by the solution of mKdV equation. Numerical simulations are explored to study how "backward looking" and flow integral influence the stability of traffic flow. Numerical results verify that the traffic flow stability can be efficiently improved with the consideration of the above two factors. Highlights • A new model is proposed considering the effect of "backward looking" and flow integral. • The stability analysis is carried out. • The mKdV equation is deduced to describe traffic congestion. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we investigate the effect of delayed feedback control on a curved road analytically and numerically. An extended lattice hydrodynamic model is derived on a single-lane road which includes more comprehensive information. The control method is used to obtain the stability of the new model and the critical condition for the linear steady traffic flow is deduced. Moreover the numerical simulations are carried out to verify the advantage of the proposed model with and without the delayed feedback control on a curved road and a straight road. The results are consistent with the theoretical analysis correspondingly. Highlights • An extended lattice hydrodynamic model is proposed considering the effect of delayed feedback control on a curved road. • The stability of this new model is obtained by the control method. • Numerical simulations are carried out to explore the influence of delayed feedback control on a curved road. [ABSTRACT FROM AUTHOR]

Abstract This paper presents an application of a recently proposed methodology for modeling connected and autonomous vehicles (CAVs) in heterogeneous traffic flow, to investigate the impact of setting dedicated lanes for CAVs on traffic flow throughput. A fundamental diagram approach was introduced which reveals the pros and cons of setting dedicated lanes for CAVs under various CAV penetration rates and demand levels. The performance of traffic flow under different number of CAV dedicated lanes is compared with mixed flow situation. Simulation results indicate that at a low CAV penetration rate, setting CAV dedicated lanes deteriorates the performance of the overall traffic flow throughput, particularly under a low density level. When CAVs reach a dominant role in the mixed flow, the merits of setting dedicated lanes also decrease. The benefit of setting CAV dedicated lane can only be obtained within a medium density range. CAV penetration rate and individual CAV performance are significant factors that decide the performance of CAV dedicated lane. The higher level of performance the CAV could achieve, the greater benefit it will attain through the deployment of CAV dedicated lane. Besides, the performance of CAV dedicated lane can be improved through setting a higher speed limit for CAVs on the dedicated lane than vehicles on other normal lanes. This work provides some insights into the impact of the CAV dedicated lane on traffic systems, and helpful in deciding the optimal number of dedicated lanes for CAVs. Highlights • Established a three-lane heterogeneous flow model incorporating CAV dedicated lane policy. • Investigated the impact of CAV dedicated lane policy on traffic flow throughput. • Provide insights into heterogeneous flow dynamics with CAV dedicated lanes. [ABSTRACT FROM AUTHOR]

Abstract In order to suppress traffic jams and make traffic flow smooth, we propose feedback-based control strategy for the coupled map car-following model with time delays on basis of linear discrete-time system. In this new kind of model, time delay is incorporated into dynamic equation of vehicles. The traffic system is analyzed based on Gerschgorin disk theorem and general Nyquist stability criterion. The sufficient condition for the existence of coupled map car-following model with time delays is given. Considering time delays, the proposed control scheme can be competent to alleviate traffic congestion. Numerical examples are given to illustrate the effectiveness of the proposed control method. Highlights • Feedback control of coupled map car-following model with times delay is proposed. • The sufficient condition of stability for the proposed model is given. • The feedback-based control strategy is designed. • The effectiveness of the proposed method is verified by numerical simulation. [ABSTRACT FROM AUTHOR]

Link prediction in incomplete complex networks is an important issue in network science. Recently, various structure-based similarity methods have been proposed. However, most path-dependent methods merely pay attention to the contributions of paths with specific length, which neglects the interactions of paths with different length for performance improvement. Motivated by the resource-traffic flow mechanism on networks, we measure the interaction relationship of paths with a resource receiving process. In this process, each node takes certain initial resources quantified by its H-index, and then the intermediate nodes on paths can receive resources from their neighbours. Based on this process, a local path-based link predictor which emphasizes the effect of the Resources from Short Paths (RSP) is proposed. Experiments on twelve real-world networks demonstrate that the RSP index has better performance than other nine structure-based similarity methods. [ABSTRACT FROM AUTHOR]

Industrial loop detectors (ILDs) are the most common traffic detectors. In Shanghai, most of the ILDs are installed in a single loop way, which can detect various parameters, such as flow, saturation, and so on. However, they cannot detect the speed directly, which is one of the key inputs of intelligent transportation systems (ITS) for identifying the traffic state. Thus, this paper is dedicated to estimate speed accurately. It proposes a new algorithm that multiple kernel support vector regression (MKL-SVR) to complete this goal, which improves the accuracy and robustness of the speed estimation. Extensive experiments have been performed to evaluate the performances of MKL-SVR, compared with polynomial fitting, BP neural networks and SVR. All results indicate that the performances of MKL-SVR are the best and most robust. [ABSTRACT FROM AUTHOR]

We propose three models for the traffic of vehicles within a network formed by sites (cities, car-rental agencies, parking lots, etc.) and connected by two-way arteries (roads, highways), that allow forecasting the vehicular flux in a sequence of n consecutive steps, or units of time. An essential approach consists in using, as an a priori information, previous observations and measurements. The formal tools used in our analysis consists in: (1) associating a digraph to the network where the edges correspond to arteries and the vertices with loops represent the sites. (2) From a distribution of vehicles within the network, we construct a matrix from which we derive a stochastic matrix (SM). This matrix becomes the generator of the evolution of the traffic flow. And (3), we use the Perron–Frobenius theory for a formal analysis. We investigate three models: (a) a closed network with conserved number of vehicles; (b) to this network we add an influx and an outflux of vehicles to picture an open system. And (c), we construct a nonlinear model whose formal structure exhibits the existence of several ( L ) stationary states for the distribution of vehicles at each site, that alternate cyclically with time. Each state represents the traffic for L different moments. These models are hybridized and compared numerically to the effective vehicular traffic in a sector of the city of Tigre, localized in the province of Buenos Aires, Argentina. The empirical data and the traffic modelization are presented in a following paper, referred as Part II. [ABSTRACT FROM AUTHOR]

The Full velocity difference (FVD) model considers the velocity difference, but it cannot capture the acceleration/deceleration asymmetric characteristic. This paper points out that the response of drivers to the velocity difference is influenced by the space gap (which is the space headway minus the vehicle length), and improves the FVD model based on this view. The improved model indicates that the reason why the asymmetric characteristic exists in the car-following behavior is that the response of drivers to the velocity difference is in negative correlation to the space gap. Neutral stability curves of the improved model are asymmetry. And the stability analysis indicates that the visual angle model (one of the improved FVD models) is a special case of the improved model proposed in this paper. Results of numerical computer simulations confirm that the improved model can capture the asymmetric characteristic, and further indicate that the asymmetric characteristic is caused by the influence of space gap to the drivers’ response. And finally, parameters of the improved car-following model are calibrated by real traffic flow data. [ABSTRACT FROM AUTHOR]

Nowadays, the memory effect of drivers’ behavior has been a hot topic in traffic flow research. In this paper, based on the lattice hydrodynamic model a new feedback control model is derived in a single-lane system. The memory effect of flux difference is considered in the new model to suppress the traffic jam. The critical condition of the model is analyzed by control method. The simulations are applied to verify the influence of feedback control signal on alleviating traffic jam. Besides, energy consumption simulation is designed in this paper. All the results demonstrate that the memory effect of flux difference model enhances the stability of traffic flow. [ABSTRACT FROM AUTHOR]

Vehicles in developing countries have widely varying dimensions and speeds, and drivers tend to not follow lane discipline. In this flow state called “mixed traffic”, the interactions between drivers and the resulting maneuvers resemble more that of general disordered self-driven particle systems than that of the orderly lane-based traffic flow of industrialized countries. We propose a general multi particle model for such self-driven “high-speed particles” and show that it reproduces the observed characteristics of mixed traffic. The main idea is to generalize a conventional acceleration-based car-following model to a two-dimensional force field. For in-line following, the model reverts to the underlying car-following model, for very slow speeds, it reverts to an anisotropic social-force model for pedestrians. With additional floor fields at the position of lane markings, the model reverts to an integrated car-following and lane-changing model with continuous lateral dynamics including cooperative aspects such as zip merging. With an adaptive cruise control (ACC) system as underlying car-following model, it becomes a controller for the acceleration and steering of autonomous vehicles in mixed or lane-based traffic. [ABSTRACT FROM AUTHOR]

Realistic modeling traffic flow dynamics in urban traffic network remains a big challenge at present due to the complex nonlinear characteristics of traffic flow. In this paper, a novel and model-free iterative learning control (ILC) strategy for balancing traffic flow in urban traffic network is proposed. To tackle the randomly varying trial lengths in the iteration domain of traffic system, an iterative-average operator is introduced in the proposed ILC law for tracking tasks with non-uniform trial lengths, which thus mitigates the requirement on classic ILC that all trial lengths must be identical. The learning convergence condition of the ILC strategy in iteration-average and expectation is derived through rigorous analysis. The performance and the effectiveness of the ILC strategy are analyzed by simulations on a test road network. The results show that the proposed ILC strategy can homogeneously balance the accumulation in the network and improve the network mobility. [ABSTRACT FROM AUTHOR]

In this letter, we derived an extended continuum model from a car-following model with consideration of the optimal velocity difference called the relative optimal velocity via employing the transformation relation from microscopic variables to macroscopic ones. The stability condition of this continuum traffic model is obtained by performing linear stability analysis. Results show that the optimal velocity difference helps to improve the stability of traffic flow. We make use of the upwind finite difference scheme for simulation. The effects of the optimal velocity difference (the relative optimal velocity) and the velocity difference (the optimal velocity) on local clustering effect and instability are studied and compared each other, respectively. The spatiotemporal evolution patterns of traffic flow for the different initial density, strength of the optimal velocity difference and the velocity-difference are obtained. Their space–time diagrams reveal the local clustering effect induced by the instability of traffic flow and generate the stop&go traffic jams. Numerical simulation result indicates the unstable region is shrunken under the effect of the optimal velocity difference (the relative optimal velocity) and/or the velocity difference (the optimal velocity). [ABSTRACT FROM AUTHOR]

Among traffic flow analysis, there are many methods presented for microscopic and mesoscopic traffic flow in past decades. However, the methods for macroscopic traffic flow analysis are relatively few. That is because the macroscopic traffic flow analysis needs more data and computational resources. In this paper, we focus on macroscopic traffic flow analysis using massive traffic data. A new method, traffic speed cloud maps, is proposed. It can present the traffic states in a large scale, accurately. Also, it can describe the variations of the congestion area. More importantly, with traffic speed cloud maps, people can capture the forming and degrading processes of the congestion and investigate the operation rules of macroscopic traffic flow. In order to evaluate the effectiveness of traffic speed cloud maps, 14 786 073 traffic samples are taken to perform experiments. Experimental results show that traffic speed cloud maps can present the traffic state of large areas accurately, and help us to investigate the variation of macroscopic traffic flow states intuitively and efficiently. [ABSTRACT FROM AUTHOR]

Pedestrian illegal crossings at midblock are very unpredictable and pose a potential tragic conflict with vehicles besides generally disrupting the normal flow of traffic. The drivers need to not only anticipate but also respond to their actions accordingly to avoid potential conflicts. The objective of this paper was to describe the behavior of the drivers under illegal pedestrian crossing circumstances by proposing an extended car-following model, and then analyze the impacts of the illegal pedestrian crossing on the normal traffic flow at the midblock. In this study, three conditions have been considered, namely: no pedestrians, pedestrians present but waiting, and pedestrians crossing. The numerical results illustrate that the disruptive nature of the illegal pedestrian crossing could worsen when the number of pedestrian is large, the pedestrian platoon size is small, and the speed limitation is high. [ABSTRACT FROM AUTHOR]

The study of traffic flow bifurcation control analysis is of great significance for understanding the essential evolution law of traffic flow, controlling traffic flow with practical means and alleviating traffic congestion. This paper describes the influence of lateral distance compensation in the process of lane change, and adjusts the traffic flow from the perspective of bifurcation control, which can capture the characteristics of traffic flow better, so as to describe the practical significance of the actual traffic phenomenon better. For example, the green ratio of traffic lights in the road, the planning and design of traffic signs, and the guidance of traffic condition prediction in ITS. In addition, the traffic flow is introduced and applied to the traffic model from the perspective of random function, and the bifurcation control is carried out according to the bifurcation behavior in traffic. That is, the bifurcation characteristics of the system are modified by the designed feedback controller, and the appearance of the equilibrium point of the system is adjusted to make it move forward, backward or disappear, so as to prevent or alleviate traffic congestion. There are few researches on bifurcation control of traffic flow model with lateral distance compensation. This paper introduces stochastic function based on lateral compensation model to study Hopf bifurcation phenomenon and Hopf bifurcation control. Firstly, the existence condition of Hopf bifurcation at the equilibrium point in the model is proved theoretically. Then, a feedback controller is designed to control the amplitudes of the Hopf bifurcation and the limit cycles formed by the Hopf bifurcation. Finally, the theoretical results are verified by numerical simulation. The research shows that by adjusting the control parameters in the feedback controller, the influence of boundary conditions on the stability of the traffic system is fully described, the effect of unstable focus and saddle point on the system is inhibited, and the traffic flow is slowed down. In addition, the unstable bifurcation points can be eliminated, and the amplitude of the limit cycle formed by Hopf bifurcation can be adjusted, so as to achieve the stability of the control system. • A new continuum model is proposed taking into account the lateral offset compensation. • Introducing the traffic flow into the traffic model from the perspective of the random function. • The designed feedback controller corrects the bifurcation characteristics of the system and adjusts the appearance of the balance point of the system to make it forward, backward or disappear, so as to prevent or reduce traffic jams. [ABSTRACT FROM AUTHOR]

A moving block system (MBS) will contribute meaningfully to the railway. The advanced system can allow trains to operate closely as a platoon and significantly reduce train headway. Any minor speed disturbance of a train may lead to a chain of changes in operating status for some following trains in a platoon. However, any minor speed disturbance of a train may lead to a domino effect of changes in operating status for subsequent trains. This highlights the need for a thorough understanding of rail traffic flow behaviors under the MBS. Unfortunately, few studies have focused on this area. This study develops a micro-simulation model to describe train movements under MBS, and study in detail two typical railway operation scenarios: steep descending grade and arrival. On this basis, congestion phenomenon and bottleneck effect were explained in combination with the macroscopic rail fundamental diagram (RFD). Moreover, the strong controllability of trains and the predictability of shockwaves make it achievable and promising to control the rail traffic flow under the MBS. This study illustrates rail variable speed limit (RVSL) control in the RFD, and it dynamically provides trains with advisory speed limits to resolve delays and maintain a higher flow at bottlenecks. Two congestion scenarios are considered to evaluate its effectiveness, including the temporary excessive demand upstream of a descending grade and the arrival delay. The results demonstrate that the proper imposition of RVSL control reduces the number of affected trains, delay time, and energy consumption in both scenarios. [ABSTRACT FROM AUTHOR]

This article studies adaptive traffic signal control problem of single intersection in dynamic environment. A novel cycle-based signal timing method with traffic flow prediction (CycleRL) is proposed to improve the traffic efficiency under dynamic traffic flow. Firstly, the empirical mode decomposition is applied to denoise the flow data. Then a data-model hybrid driven traffic flow prediction strategy is designed to predict the traffic flow, which combines a model-based Kalman filter and an LSTM network-based predictor and adopts another Kalman filter to fuse both prediction results to improve the prediction precision. Besides, a robust signal cycle timing strategy based on human–machine collaboration is developed to deal with dynamic traffic flow, which firstly designs a rule-based signal cycle scheme according to the predicted flow data as the preliminary scheme, and then finetunes the preliminary scheme based on Soft Actor–Critic (SAC) algorithm according to the real-time traffic dynamics. The experiments in both synthetic scenario and real-world scenario show that the proposed data-model hybrid driven traffic flow prediction algorithm has better prediction performance and the proposed CycleRL method outperforms rule-based methods, flow-based allocation methods and traditional reinforcement learning method. Moreover, it is also shown that the proposed CycleRL method has better transferability to bridge the discrepancy across domains. [ABSTRACT FROM AUTHOR]

With the popularity of computer internet technology in transportation, connected automated vehicles (CAVs) maintain the traffic flow stability by sharing real-time driving information. However, the open-access environment makes CAVs vulnerable to attacks. Hackers transmit erroneous driving information to drivers, which lead to changes in their driving behavior and cause potential traffic hazards. Based on the summary of existing essays, this paper classifies cyberattacks into three types: false messages, repeat/delay and collusion attack. In addition, mixed flow is a common form of traffic. This paper proposed an extended heterogeneous traffic flow model considering cyberattacks, judgments the stability of the CAVs platoon and designs the controller for stability control. Subsequently, the stability of the new model is analyzed theoretically, and the stability condition of the new model is obtained. The simulation results show that the when CAVs platoon attacked by uncertain types and locations, it can still maintain the stability of the team and reduce the time for the team to return to equilibrium. • An extended heterogeneous traffic flow model considering cyber attacks is proposed. • The stability of the new model is analyzed theoretically. • The controller for stability control for the new model is designed. [ABSTRACT FROM AUTHOR]

Urban signal intersection as the key node in the urban road network, its operational efficiency directly affects the overall operational efficiency of the road network traffic. The existing urban road signal intersections cannot be broadened due to the limitation of road size and boundary lines of road. Some signalized intersections adopt straight-right lane design to improve the traffic efficiency of vehicles at intersections. This design can improve the traffic efficiency of straight-through vehicles during the green light phase. However, straight-through vehicles stopping in the straight-right lane during the red light will prevent right-turning vehicles from passing the intersection. This phenomenon causes delays for right-turning vehicles. In order to deal with the above issues, this paper takes the concept of separating straight and right-turning vehicles in spatial and temporal on the dynamic straight-right lane (DSRL). The organization form of vehicles entering DSRL is designed based on signal phase time. The control strategy for DSRL with the interaction of traffic lights and pre-signal is formulated integrated with the intersection design. The delay indexes of straight and right-turning vehicles under DSRL design are quantified using cellular automata model to calculate vehicle delays under the design form of DSRL. Finally, the test platform based on the actual intersection is established with MATLAB to simulate the operation of the vehicles under the DSRL design. The experiment shows that the delay reduction degree of straight-through vehicles is between 1.2% and 4.5%, and the reduction degree of right-turning vehicles delay is more than 66%, that the operation efficiency of intersection is improved. • In terms of design concept, this paper proposes DSRL design scheme, which separates straight-through vehicles and right-turning vehicles from temporal and spatial. Right-turning vehicles are not delayed during the red phase while both straight and right-turning vehicles can successfully pass the intersection during the green phase under DSRL. The traffic efficiency of vehicles at the intersection can be greatly improved. • In terms of control method, the control strategy proposed in this paper is more flexible than the original variable lanes. This paper proposes pre-signal and traffic signal control scheme under DSRL. The calculation method of signal timing parameters and suggesting parameters of signal timing are given according to the ideal operating conditions of the traffic flow. The signal control strategy and DSRL design cooperating together can make traffic flow operate better and fully exploit the lane traffic capacity. • In terms of test results, this paper builds a microscopic model to evaluate the performance of the DSRL. By systematically analyzing key parameters, DSRL design can effectively reduce vehicle delays in different traffic environments. This paper also lays the foundation for optimizing the indicators under DSRL design and provides guidance for the implementation of DSRL design. [ABSTRACT FROM AUTHOR]

Historical cities around the world have serious traffic congestions due to old infrastructure and urbanization. To mitigate traffic problems in such cities, infrastructure investments are channeled to tunnels, bridges, and highways. The solution becomes more complicated as the city centers are expected to become fully pedestrian-friendly as a European Union target for 2050. Any modification to the city transport network will lead to changes in traffic density patterns. Investment decisions become more pronounced as the speed of urbanization increases. However, this decision-making processes in urban transport networks pose a serious risk to city managers as wrong decisions are expensive and will not solve the problem. This paper proposes a practical, cost-effective model to assist decision makers by providing them with estimated changes in the traffic patterns due to the addition of new roads to existing infrastructure. The study adopts the Extreme Gradient Boosting (XGboost) which is a tree-based algorithm that provides 85% accuracy for estimating the traffic patterns in Istanbul, the city with the highest traffic volume in the world. The proposed model is a static model that allows city managers to perform efficient analyses between projects that involves changes to the city's transport network. • The study includes a traffic control model developed for a 6104 km complex network. • The study provides a productivity analysis for long-term road infrastructure investments. • The study provides an important tool for decision makers in pedestrian friendliness studies. • The study provided an important feature selection for a static traffic forecast. • The study created an application for a congested city with the most severe traffic problem. [ABSTRACT FROM AUTHOR]

Abstract Based on the cellular automaton model and the concept of ant pheromone, this paper proposes a new route guidance strategy, called the ant pheromone route guidance strategy, in which the vehicles are regarded as special types of ants and their traffic information is regarded as the ant pheromone. To evaluate its performance, the new route guidance strategy was applied and compared with other three typical route guidance strategies under three different route scenarios, respectively, with open boundary conditions based on the Nagel–Schreckenberg cellular automaton model. First, in a symmetrical two-route scenario with two exits, results showed that the new route guidance strategy and vacancy length route guidance strategy were optimal. They outperformed the other strategies in terms of the value, stability, and balance of vehicle number, and the average speed and average flux on each route. To understand the impact of the strategy on traffic states, flux–density evolution process was studied. In addition, the influence of evaporation rate was also investigated. Second, in an asymmetrical two-route scenario with one exit, the simulation results also proved that ant pheromone route guidance strategy and vacancy length route guidance strategy were the best. Finally, in a symmetrical two-route scenario with a traffic accident, we compared the new route guidance strategy with the vacancy length route guidance strategy. Results indicated that the new route guidance strategy has distinct advantages. In short, in all three route scenarios we investigated, the ant pheromone route guidance strategy performed best. Highlights • This paper proposes the ant pheromone route guidance strategy based on CA model. • The new strategy takes vehicles as ants and vehicles' data as ant pheromones. • Adopting the NS model, this strategy is applied to three two-route scenarios. • Results indicate that the new strategy outperforms other route guidance strategies. • In traffic accident, the new strategy can reduce congestion and save recovery time. [ABSTRACT FROM AUTHOR]

Most of the previous studies on dynamic traffic assignment are based on traditional analytical framework, for instance, the idea of Dynamic User Equilibrium has been widely used in depicting both the route choice and the departure time choice. However, some recent studies have demonstrated that the dynamic traffic flow assignment largely depends on travelers’ rationality degree, travelers’ heterogeneity and what the traffic information the travelers have. In this paper, we develop a new self-adaptive multi agent model to depict travelers’ behavior in Dynamic Traffic Assignment. We use Cumulative Prospect Theory with heterogeneous reference points to illustrate travelers’ bounded rationality. We use reinforcement-learning model to depict travelers’ route and departure time choosing behavior under the condition of imperfect information. We design the evolution rule of travelers’ expected arrival time and the algorithm of traffic flow assignment. Compared with the traditional model, the self-adaptive multi agent model we proposed in this paper can effectively help travelers avoid the rush hour. Finally, we report and analyze the effect of travelers’ group behavior on the transportation system, and give some insights into the relation between travelers’ group behavior and the performance of transportation system. [ABSTRACT FROM AUTHOR]

In this paper, we proposed a lattice hydrodynamic traffic model to study the congested traffic patterns on two-lane with the partial reduced lane via considering the upstream–downstream density difference and lane-changing rule. Using the lattice hydrodynamic model with the partial reduced lane for numerical simulation, various complex congested traffic patterns such as triggered stop-and-go (TSG), pinned localized cluster (PLC), oscillatory congested traffic (OCT), homogeneous congested traffic (HCT), homogeneous synchronized traffic (HST) and moving localized cluster (MLC) can be reproduced. Moreover, it is found that the sensitive coefficient λ of the upstream–downstream density difference plays a main role in generating these congested traffic patterns. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the evening commute behaviors on the social optimum (SO) state in a single-entry traffic corridor with no early departures. Differing from the previous studies on evening commute, the dynamic properties of traffic flow are analyzed with the LWR (Lighthill–Whitham–Richards) model. The properties of optimum cumulative inflow curve with general desired departure time distribution curve are deduced, and then the analytic solutions for common desired departure time in SO are obtained. Three numerical examples are carried out to capture the characteristics of evening commuting behaviors under different values of time. The analytic and numerical results both indicate that the rarefaction wave originating from the first entry point influences the whole or part of the outflow curve. No shock wave exists through the commuting process. In addition, the cost curves show that the trip cost increases and the departure delay cost decreases with departure time, whereas the travel time cost first increases then decreases with departure time under the SO principle. [ABSTRACT FROM AUTHOR]

We consider one-way road deterministic traffic model with N particles. The simplest local control protocol, which reminds physical interaction is considered. We obtain complete phase diagram uniformly in N and study in detail its stable and unstable domains. [ABSTRACT FROM AUTHOR]

To further improve the adaptability of the optimal velocity model (OVM) in the actual traffic flow, this paper proposes a car-following model that considers both the jerk effect and time-delayed feedback control. To study the dynamic behavior of this model, the following analysis is carried out: A stability conditions and equilibrium points controlling OVM are obtained by stability analysis and Hopf bifurcation analysis; Using the intuitive and accurate definite integral stabilization method (DIS), by calculating the number of all unstable eigenvalues of the characteristic equation, we obtain a stable time-delayed interval, and a time-delayed feedback controller is designed to stabilize the traffic system. The results show that the time-delayed feedback controller expands stable time-delayed interval, suppresses the influence of unstable factors on the traffic system, and improves stability of vehicle traffic; Numerical simulations agree with the theoretical results, indicating that the time-delayed feedback controller can effectively suppress traffic congestion and control the managed traffic. • To further improve the control adaptability of the optimal velocity model (OVM), a car-following model with time-delayed feedback control considering the jerk effect is proposed. • The stability condition and equilibrium points controlling OVM are obtained by stability analysis and Hopf bifurcation analysis. Using the intuitive and accurate definite integral stabilization method (DIS), a stable time-delayed interval is derived by calculating the number of all unstable eigenvalues of the characteristic equation. A time-delayed feedback controller is successfully designed to stabilize the traffic system. • Numerical simulations agree with the theoretical analyses, indicating that the time-delayed feedback controller can effectively suppress traffic congestion to realize the control of traffic flow. [ABSTRACT FROM AUTHOR]

In order to explore the influence of electric vehicles on the traffic flow characteristics of traditional fuel vehicles, this paper proposes an improved cellular automata model considering the physical properties of vehicles, such as, the acceleration and deceleration amplitude, relative speed and safety distance etc. Based on the sensitivity analysis of the randomization probability, combined with the actual data, the model parameters are quantified to simulate the electric-fuel vehicle mixed traffic flow. Through numerical simulation, the results show that compared with NS (Nagel–Schreckenberg) model and VE(Velocity Effect) model, the improved model can reproduce rich traffic phenomena and is closer to the measured data. With the increase of electric vehicle permeability, the flow rate and speed are increased, the congestion near the critical density is reduced and the traffic safety is improved. At high density, the congestion shows a trend of decreasing first, then increasing and then decreasing again, and the safety is lower than that of homogeneous traffic flow. The speed fluctuation of fuel vehicles is enlarged, and the speed of the overall traffic flow shows an upward trend. In addition, it is found that the TIT curve can identify the state change of traffic flow, and electric vehicles in high-density mixed flow cause greater disturbance to the speed of traffic flow and reduces traffic safety. The results of this paper can provide a basis for further research of electric-fuel vehicle hybrid traffic flow. • A heterogeneous traffic flow model mixed with EVs considering the amplitude of acceleration and deceleration is proposed. • Under medium and high density, the greater the penetration rate of EVs, the greater the vehicle speed and flow rate. • Near the critical density, the threshold of congestion occured increases with the proportion of electric vehicles. • The speed fluctuation of EVs decreases with the increase of its permeability, while fuel vehicles is the opposite. [ABSTRACT FROM AUTHOR]

In recent years, complex network theory has become an important approach to the study of the structure and dynamics of traffic networks. However, because traffic data is difficult to collect, previous studies have usually focused on the physical topology of subway systems, whereas few studies have considered the characteristics of traffic flows through the network. Therefore, in this paper, we present a multi-layer model to analyze traffic flow patterns in subway networks, based on trip data and an operation timetable obtained from the Beijing Subway System. We characterize the patterns in terms of the spatiotemporal flow size distributions of both the train flow network and the passenger flow network. In addition, we describe the essential interactions between these two networks based on statistical analyses. The results of this study suggest that layered models of transportation systems can elucidate fundamental differences between the coexisting traffic flows and can also clarify the mechanism that causes these differences. [ABSTRACT FROM AUTHOR]

Drivers often change lanes on the road to maintain desired speed and to avoid slow vehicles, pedestrians, obstacles and lane closure. Understanding the effect of lane-changing on the traffic is an important topic in designing optimal traffic control systems. This paper presents a comprehensive study of this topic. We review the theory of microscopic dynamic car-following models and the lane-changing models, propose additional lane-changing rules to deal with moving bottleneck and lane reduction, and investigate the effects of lane-changing on the traffic efficiency, traffic safety and fuel consumption as a function of different variables including the distance of the emergency sign ahead of the lane closure, speed limit, traffic density, etc . Extensive simulations of the traffic system have been carried out in different scenarios. A number of important findings of the effect of various factors on the traffic are reported. These findings provide guidance on the traffic management and are important to the designers and engineers of modern highway or inner city roads to achieve high traffic efficiency and safety with minimum environmental impact. [ABSTRACT FROM AUTHOR]

On freeways, congestion always leads to capacity drop. This means the queue discharge rate is lower than the pre-queue capacity. Our recent research findings indicate that the queue discharge rate increases with the speed in congestion, that is the capacity drop is strongly correlated with the congestion state. Incorporating this varying capacity drop into a kinematic wave model is essential for assessing consequences of control strategies. However, to the best of authors’ knowledge, no such a model exists. This paper fills the research gap by presenting a Lagrangian kinematic wave model. “Lagrangian” denotes that the new model is solved in Lagrangian coordinates. The new model can give capacity drops accompanying both of stop-and-go waves (on homogeneous freeway section) and standing queues (at nodes) in a network. The new model can be applied in a network operation. In this Lagrangian kinematic wave model, the queue discharge rate (or the capacity drop) is a function of vehicular speed in traffic jams. Four case studies on links as well as at lane-drop and on-ramp nodes show that the Lagrangian kinematic wave model can give capacity drops well, consistent with empirical observations. [ABSTRACT FROM AUTHOR]

With the ever-increasing traffic demand, efficient urban traffic operation is facing more and more pressure and challenges. Route guidance and signal control are effective methods to enhance the existing urban traffic capacity and realize the sustainable development of traffic systems. In this paper, a collaborative optimization method of route guidance and intersection signal controlling is proposed under the background of a pre-trip reservation. Vehicles were divided into reserved and non-reserved vehicles, which corresponds to pre-trip reservation and non-reserved travel, respectively. Firstly, route guidance models considering both road network efficiency and vehicle travel time are proposed for different types of vehicles. Then, based on the impact of different travel modes on traffic flow distribution, a collaborative optimization for route guidance and intersection signal control was realized through the proposed dynamic signal optimization method. The experimental results showed that road network capacity was improved by 16% under the condition of high traffic flow by using the proposed optimization method. The proposed method can also alleviate the congestion of some sections without causing the transfer of congestion. The effectiveness of the proposed method was verified by simulation experiments, which can provide a theoretical reference for traffic control and management in the context of pre-trip reservation. • A network control model is established to improve the throughput. • The route guidance and signal control are jointly optimized. • Both reserved and non-reserved travelers are considered. • A validation platform is developed to assess the effectiveness of model. • The travel time of severely congested links can be reduced. [ABSTRACT FROM AUTHOR]

The main motivation of this paper is to analyze the influences of speed guidance strategies on the driving behaviors under four different traffic signalized conditions and to investigate an extended car-following model to explore how the speed guidance affects two different vehicle types that are intelligent vehicles and traditional vehicles during the phase-change periods. The numerical results show that the proposed model can qualitatively describe the impacts of the speed guidance strategies on vehicle’s movement trail including the acceleration strategy, smooth braking strategy, and deceleration strategy. Moreover, the benefits of the speed guidance could be enhanced by lengthening the guiding space range, expanding permitted guiding speed range, and increasing the percentage of the intelligent vehicles. [ABSTRACT FROM AUTHOR]

Lattice hydrodynamic model is a typical continuum traffic flow model, which describes the jamming transition of traffic flow properly. Previous studies in lattice hydrodynamic model have shown that the use of control method has the potential to improve traffic conditions. In this paper, a new control method is applied in lattice hydrodynamic model from a transportation cyber–physical system approach, in which only one lattice site needs to be controlled in this control scheme. The simulation verifies the feasibility and validity of this method, which can ensure the efficient and smooth operation of the traffic flow. [ABSTRACT FROM AUTHOR]

The field car-following data at a signalized intersection of Jinan in China are collected for data mining. An improved car-following model considering the immediately ahead car’s velocity difference on a single-lane road was proposed, calibrated and verified based on full velocity difference model. The results of some numerical simulations indicate that the immediately ahead car’s velocity difference has significant effects on the following car’s motion, that the improved car-following model fits the measured data well and can qualitatively describe the impacts of the immediately ahead car’s velocity difference on traffic flow, and that modeling the car-following behavior considering the immediately ahead car’s velocity difference can improve the stability of the simulated traffic flow. [ABSTRACT FROM AUTHOR]

The driver’s bounded rationality has significant influences on the micro driving behavior and researchers proposed some traffic flow models with the driver’s bounded rationality. However, little effort has been made to explore the effects of the driver’s bounded rationality on the trip cost. In this paper, we use our recently proposed car-following model to study the effects of the driver’s bounded rationality on his running cost and the system’s total cost under three traffic running costs. The numerical results show that considering the driver’s bounded rationality will enhance his each running cost and the system’s total cost under the three traffic running costs. [ABSTRACT FROM AUTHOR]

Transitions between two lanes often have a significant impact on various forms of road traffic. To address this problem, we have developed a two-lane asymmetric simple exclusion process model and two hypothetical traffic control strategies, to simulate a futuristic scenario where the timing and location of transitions between two lanes are highly controlled. Various scenarios were proposed to study the effectiveness of these control strategies. An optimized control strategy, whose parameters were determined through an optimization algorithm, is confirmed to effectively maximize the average traffic flow. Consequently, we may identify suitable road sections and the corresponding timings of transitions to resolve congestion in this model. • Regulated transitions between two lanes can improve traffic flow. • Regulations are simulated using the ASEP model and a hypothetical control strategy. • Parameters of the control strategy are determined with an optimization algorithm. • The control strategy can determine positions and timings suitable for transitions. [ABSTRACT FROM AUTHOR]

Intersection control plays a vital role in influencing transportation efficiency inside urban areas. Connected and Automated Vehicle (CAV) technology enables frequent traffic information sharing through vehicular networks, which emerges as a promising way to reduce vehicle traveling time and improve intersection capacity. Meanwhile, with the gradual deployment of CAVs, the traditional traffic flow consisting of human driven vehicles will evolve into a mixed traffic flow composed of traffic participants with differing intelligence capabilities. Considering the evolution towards the mixed traffic environment, we propose a dynamic capacity-aware traffic signal control method: max-pressure for mixed traffic flow (MPMF) traffic signal controller. Firstly, the lane capacity is modeled and approximated based on the saturation flow rate in mixed traffic flow considering the penetration rate of CAVs. Then the dynamic capacity is involved in calculating pressure in the max-pressure method to indicate the importance of a path in mixed traffic flow. A modification strategy for the max-pressure input value is also proposed. An isolated intersection scenario was simulated first to assess the proposed method locally. A multi-intersection network experiment was also conducted to verify the network-level performance of the proposed MPMF method. Comparative results between the proposed MPMF method, the classic max-pressure control, and the existing fixed time control method demonstrate that the MPMF can effectively improve the performance of intersections and be suitable for the multi-intersection road network. [ABSTRACT FROM AUTHOR]

Improving the traffic efficiency of the existing roads in the city has become an important task for the traffic management department. Timely and accurate traffic prediction is the key to urban traffic control and guidance. The multi-layer automatic feature extraction and expression ability of deep learning is deeply integrated with the non-linearity, multi-modality, and spatiotemporal correlation of traffic flow. A multi-step ahead traffic speed prediction based on gated temporal graph convolution network (GT-GCN) is proposed in this paper. Firstly, a dynamic weighted graph network is constructed according to the traffic road network structure, and then a graph convolutional network is used to process the graph structure data of the traffic road network and capture the spatial characteristics of the traffic flow. Subsequently, a gated temporal convolutional network is applied to capture the short- and long-range temporal correlations of traffic flow. Finally, a multi-step ahead prediction model based on a hybrid deep learning framework GT-GCN is proposed. Experiments using real datasets show that the proposed GT-GCN has a significant multi-step predictive performance compared to the baseline models. • A weighted dynamic graph network is constructed to characterize the topology of the transportation network. • A multi-step traffic prediction GT-GCN is proposed based on graph convolutional network and gated temporal convolutional network. • Extensive experiments on real-world traffic show that the proposed GT-GCN achieves significant predictive performance. [ABSTRACT FROM AUTHOR]