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Abstract The emergence of connected and automated vehicles (CAV) indicates improved traffic mobility in future traffic transportation systems. This study addresses the research gap in macroscopic traffic modeling of mixed traffic networks where CAV and human-driven vehicles coexist. CAV behavior is explicitly included in the proposed traffic network model, and the vehicle number non-conservation problem is overcome by describing the approaching and departure vehicle number in discrete time. The proposed model is verified in typical CAV cooperation scenarios. The performance of CAV coordination is analyzed in road, intersection and network scenario. Total travel time of the vehicles in the network is proved to be reduced when coordination is applied. Simulation results validate the accuracy of the proposed model and the effectiveness of the proposed algorithm.

The emergence of connected and automated vehicles (CAV) indicates improved traffic mobility in future traffic transportation systems. This study addresses the research gap in macroscopic traffic modeling of mixed traffic networks where CAV and human-driven vehicles coexist. CAV behavior is explicitly included in the proposed traffic network model, and the vehicle number non-conservation problem is overcome by describing the approaching and departure vehicle number in discrete time. The proposed model is verified in typical CAV cooperation scenarios. The performance of CAV coordination is analyzed in road, intersection and network scenario. Total travel time of the vehicles in the network is proved to be reduced when coordination is applied. Simulation results validate the accuracy of the proposed model and the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Nonlocal calculus-based macroscopic traffic models overcome the limitations of classical local models in accurately capturing traffic flow dynamics. These models incorporate “nonlocal” elements by considering the speed as a weighted mean of downstream traffic density, aligning it more closely with realistic driving behaviors. The primary contributions of this research are manifold. Firstly, we choose a nonlocal LWR model and Greenshields fundamental diagram and prove that this traffic flow model satisfies the well-posed conditions. Furthermore, we prove that the chosen model maintains bounded states, laying the groundwork for developing numerically stable schemes. Subsequently, the efficacy of the proposed nonlocal model is evaluated through extensive field validation using real traffic data from the NGSIM dataset and developing a stable numerical scheme. These validation results highlight the superiority of the nonlocal model in capturing traffic characteristics compared to its local counterpart and establish its enhanced accuracy in reproducing complex traffic behavior. Therefore, this research expands both the theoretical constructs within the field and substantiates its practical applicability.

To help mitigate road congestion caused by the unrelenting growth of traffic demand, many transportation authorities have implemented managed lane policies, which restrict certain freeway lanes to certain types of vehicles. It was originally thought that managed lanes would improve the use of existing infrastructure through demand-management behaviors like carpooling, but implementations have often been characterized by unpredicted phenomena that are sometimes detrimental to system performance. The development of traffic models that can capture these sorts of behaviors is a key step for helping managed lanes deliver on their promised gains. Towards this goal, this paper presents an approach for solving for driver behavior of entering and exiting managed lanes at the macroscopic (i.e., fluid approximation of traffic) scale. Our method is inspired by recent work in extending a dynamic-system-based modeling framework from traffic behaviors on individual roads, to models at junctions, and can be considered a further extension of this dynamic-system paradigm to the route/lane choice problem. Unlike traditional route choice models that are often based on discrete-choice methods and often rely on computing and comparing drivers' estimated travel times from taking different routes, our method is agnostic to the particular choice of physical traffic model and is suited specifically towards making decisions at these interfaces using only local information. These features make it a natural drop-in component to extend existing dynamic traffic modeling methods.

The existence of different types of Commercial Vehicles (CVs) in the shared roadway affects traffic flow characteristics differently from other vehicles. Owing to the uncertain placement and movement of these CVs in both longitudinal and lateral directions, the opportunities for lane changing and overtaking by other vehicles with lower maneuverability decrease, resulting in the formation of platoons. The study's primary aim is to assess the effect of mixed traffic platoons formed by three different classes of CVs on highway traffic speed, flow, and density under two different traffic regimes (regimes A and B). In this study, regime A represents the non-platooning condition, and regime B represents the platooning condition. Bi-directional traffic data was collected from the highway sections in India using an Infra-Red sensor-based device. The critical leading time headway is determined for the different CVs (platoon leaders) based on the mean absolute relative speed of platoons. The speed-flow-density plots are established using the macroscopic fundamental diagrams for the highway sections under regimes A and B to quantify the platooning impacts of CVs on the traffic characteristics. The study findings reveal that the speed at capacity, density at capacity, and traffic capacity decreased significantly due to CVs' influence on the general traffic mix during the mixed traffic platooning conditions. However, this effect was found to be relatively higher during the Heavy Commercial Vehicle operation as a platoon leader compared to Medium Commercial Vehicle and Light Commercial Vehicle as a platoon leader. [ABSTRACT FROM AUTHOR]

In the foreseeable future, the coexistence of Connected and Automated Vehicles (CAVs) and Human-Driven Vehicles (HVs) will continue to be a feature of the traffic landscape for an extended period. The accurate estimation of mixed traffic conditions becomes essential for effectively managing traffic flow and ensuring road safety. In this research, we introduce a discrete, macroscopic, and second-order traffic flow model designed to capture the complexities of the mixed traffic environment. Our approach builds upon a microscopic traffic model and considers the interactions between CAVs and HVs, as well as the distinctive driving behaviors exhibited by each vehicle type. In particular, acknowledging that CAVs behave differently from HVs, we conduct a thorough analysis of the car-following and lane-changing behaviors of HVs in the presence of CAVs. To evaluate the effectiveness of our proposed model, we carried out extensive numerical simulations on a freeway segment with variable speed controls. The simulation results highlight two key findings: First, the proposed model achieves accurate estimations of mixed traffic states, including both flow and speed parameters. Second, there is a marked improvement in performance with increasing differences in speed control between CAVs and HVs. Our research contributes to the advancement of traffic management strategies and facilitates improved traffic flow in mixed traffic conditions. Additionally, the proposed model has significant potential to aid in the design and implementation of future transportation systems that incorporate connected automated vehicles while seamlessly accommodating human-driven vehicles. • CAVs & HVs coexist, requiring accurate traffic flow & safety management. • New model captures mixed traffic complexities between CAVs & HVs. • Analyzes HVs' behaviors around CAVs, focusing on car-following & lane-changing. • Simulations show model accurately estimates traffic flow & speed. • Model aids future traffic systems, blending CAVs with HVs effectively. [ABSTRACT FROM AUTHOR]

Abstract Traffic flow will harmonize to forward conditions. The time and distance required for harmonization can have a significant effect on the traffic density behavior. The flow can evolve into clusters of vehicles or become uniform depending on parameters such as safe time headway and safe distance headway. In this paper, a new model is presented to provide a realistic characterization of traffic behavior during the harmonization period. Results are presented for a discontinuous density distribution on a circular road which shows that this model produces more realistic traffic behavior than other models in the literature.

Forecasting future driving conditions such as acceleration, velocity, and driver behaviors can greatly contribute to safety, mobility, and sustainability issues in the development of new energy vehicles (NEVs). In this brief, a review of existing velocity prediction techniques is studied from the perspective of traffic flow and vehicle lateral dynamics for the first time. A classification framework for velocity prediction in NEVs is presented where various state-of-the-art approaches are put forward. Firstly, we investigate road traffic flow models, under which a driving-scenario-based assessment is introduced. Secondly, vehicle speed prediction methods for NEVs are given where an extensive discussion on traffic flow model classification based on traffic big data and artificial intelligence is carried out. Thirdly, the influence of vehicle lateral dynamics and correlation control methods for vehicle speed prediction are reviewed. Suitable applications of each approach are presented according to their characteristics. Future trends and questions in the development of NEVs from different angles are discussed. Finally, different from existing review papers, we introduce application examples, demonstrating the potential applications of the highlighted concepts in next-generation intelligent transportation systems. To sum up, this review not only gives the first comprehensive analysis and review of road traffic network, vehicle handling stability, and velocity prediction strategies, but also indicates possible applications of each method to prospective designers, where researchers and scholars can better choose the right method on velocity prediction in the development of NEVs.

Monitoring vehicle traffic flow plays a central role in enhancing traffic management, transportation safety and cost savings. In this paper, we propose an innovative approach for detection of traffic congestion. Specifically, we combine the flexibility and simplicity of a piecewise switched linear (PWSL) macroscopic traffic model and the greater capacity of the exponentially-weighted moving average (EWMA) monitoring chart. Macroscopic models, which have few, easily calibrated parameters, are employed to describe a free traffic flow at the macroscopic level. Then, we apply the EWMA monitoring chart to the uncorrelated residuals obtained from the constructed PWSL model to detect congested situations. In this strategy, wavelet-based multiscale filtering of data has been used before the application of the EWMA scheme to improve further the robustness of this method to measurement noise and reduce the false alarms due to modeling errors. The performance of the PWSL-EWMA approach is successfully tested on traffic data from the three lane highway portion of the Interstate 210 (I-210) highway of the west of California and the four lane highway portion of the State Route 60 (SR60) highway from the east of California, provided by the Caltrans Performance Measurement System (PeMS). Results show the ability of the PWSL-EWMA approach to monitor vehicle traffic, confirming the promising application of this statistical tool to the supervision of traffic flow congestion. [ABSTRACT FROM AUTHOR]

Automatic transportation systems nowadays play a key role in decreasing human errors and accelerating traffic flow. To implement controllers aiming at optimizing commute in terms of comfort and safety demands a rigorous modeling of the system. An accurate full-scale model will result in a more precise and reliable simulation. On the other hand, the growing number of vehicles and consequent rise in accidents associated with lack of driver attention highlights the need for driver assistant systems whereby more driver convenience, reduced accidents, safety and comfort could be provided. In the present study, a complete nonlinear model of longitudinal vehicle dynamics has been chosen in order to make the model more compliant with reality and to minimize simulation and control uses errors. In the control section, a novel approach to developing an adaptive cruise control system is proposed in which the host vehicle acceleration is not only influenced by target car motion but also by the macroscopic motion of the traffic flow. The results indicate that the pile up resulted from sudden braking could be avoided by using a predictive control over vehicle acceleration which takes account of the motion of both front car and traffic jam. In the low level control section, a fuzzy control based on tracking error minimization is employed to maintain desired acceleration through calculating throttle angle and brake pedal. Such control command is then applied to the longitudinal model so as to appraise the selected model performance in the driver assistant system. [ABSTRACT FROM AUTHOR]

A macroscopic model is presented that simultaneously estimates route flows and trip matrices for congested road networks using data on link densities instead of link flows. The advantage of this approach is that it avoids errors that may occur in the individual links' flow-cost relationships when congestion is heavy. Under the proposed methodology, both the flows and the matrices are estimated by the model using an image of the network such as an aerial photograph in which the number of vehicles on each link can be identified. The model itself is formulated as a maximum entropy optimization problem subject to linear constraints given by vehicle densities on the links, and is validated using analytic examples and traffic microsimulations. The results demonstrate the superiority of the link-density approach over the traditional flow-based method. [ABSTRACT FROM AUTHOR]

The work presented in this paper deals with freeway ramp metering using a differential flatness concept. Such an approach is deployed in the cases when the traffic data provided by loop detectors (or any measurements device), are partially unknown or missed, and/or the downstream measurement station, located at the vicinity of the controlled ramp, is faulty. The proposed solution rests on the estimation of the main variables using the 'revised' method of numerical differentiation, i.e., estimation of the derivative of noisy time signals. Such an algebraic approach, which does not need any integration of differential equations, turns out to be quite robust with respect to perturbations inputs and measurements noises. Convincing numerical simulations are provided using a modified second-order continuum macroscopic model. Furthermore, a comparative study using the Extended Kalman Filter (EKF), which is the most used in the area of traffic state estimators, is proposed in order to demonstrate the effectiveness of the strategy. [ABSTRACT FROM AUTHOR]

In this paper we present two low cost, airborne sensor systems capable of long-term vehicle tracking. Based on the properties of the sensors, a method for automatic real-time, long-term tracking of individual vehicles is presented. This combines the detection and tracking of the vehicle in low frame rate image sequences and applies the lagged Cell Transmission Model (CTM) to handle longer tracking outages occurring in complex traffic situations, e.g. tunnels. The CTM model uses the traffic conditions in the proximities of the target vehicle and estimates its motion to predict the position where it reappears. The method is validated on an airborne image sequence acquired from a helicopter. Several reference vehicles are tracked within a range of 500m in a complex urban traffic situation. An artificial tracking outage of 240m is simulated, which is handled by the CTM. For this, all the vehicles in the close proximity are automatically detected and tracked to estimate the basic density-flow relations of the CTM model. Finally, the real and simulated trajectories of the reference vehicles in the outage are compared showing good correspondence also in congested traffic situations. [ABSTRACT FROM AUTHOR]

While agent-based modelling of traffic demand is gaining attention, a macroscopic dynamic network loading model may be beneficial, particularly in large-scale applications. We investigate the implications of coupling such models, with inclusion of en-route choices, for the modelling of links and the determination of turning fractions, yielding useful recommendations to help select an appropriate solution scheme of the macroscopic traffic flow theory and overcome other practical challenges specifically associated with the coupling of agent-based traffic demand and macroscopic traffic propagation. [ABSTRACT FROM AUTHOR]

This research quantifies the relationship between the cost of earliness and lateness by empirically observing commute trips from two different sources. The first empirical analysis uses individual level travel survey data from six metropolitan regions while the second analysis uses traffic data from the Twin Cities freeway network. The analysis conducted in this research provides a method to estimate the ratio of the costs of earliness to lateness for different datasets. This can be a useful tool for traffic engineers and planners, to assist them in the development and implementation of improved control strategies for congested cities. The results also corroborates the hypothesis of earliness being less expensive than lateness and show that the finding holds steady over time and across different regions and levels. [ABSTRACT FROM AUTHOR]

Abstract: Similarly to the treatment of diluted gases, kinetic methods are formulated for the study of unidirectional freeway traffic. From these it is possible to construct fluid-dynamic equations which in comparison with heuristic fluid-dynamic models have the advantage of just some adjustable parameters although they have some other restrictions. In this work the comparison between two macroscopic models which are based on a kinetic traffic equation is shown. On the other hand, there will be presented some advances to attempt to generalize some restrictions of the kinetic formulation in order to study the synchronization phenomena, which is a very interesting transitory phase between free flow and traffic jams. [ABSTRACT FROM AUTHOR]

Abstract: The balanced vehicular traffic model is a macroscopic model for vehicular traffic flow. We use this model to study the traffic dynamics at highway bottlenecks either caused by the restriction of the number of lanes or by on-ramps or off-ramps. The coupling conditions for the Riemann problem of the system are applied in order to treat the interface between different road sections consistently. Our numerical simulations show the appearance of synchronized flow at highway bottlenecks. [Copyright &y& Elsevier]

Abstract: The kinetic theory for traffic flow equations can be approached using the Grad’s method. This method, which is derived from the kinetic gas theory, was developed for the Paveri-Fontana equation when a special desired velocity model is assumed. A closure relation for the set of macroscopic equations is found when the density, the average velocity and the velocity variance are the relevant variables chosen to describe the system. Simulation results are also shown and a qualitative comparison with other models in the literature is presented. [Copyright &y& Elsevier]

We present a new fluid-dynamical model of traffic flow. This model generalizes the model of Aw and Rascle [SIAM J. Appl. Math., 60 (2000), pp. 916-938] and Greenberg [SIAM J. Appl. Math., 62 (2001), pp. 729-745] by prescribing a more general source term to the velocity equation. This source term can be physically motivated by experimental data, when taking into account relaxation and reaction time. In particular, the new model has a (linearly) unstable regime as observed in traffic dynamics. We develop a numerical code that solves the corresponding system of balance laws. Applying our code to a wide variety of initial data, we find the observed inverse-λ shape of the fundamental diagram of traffic flow. [ABSTRACT FROM AUTHOR]

The modeling of traffic flow is a key tool to simulate and predict the behavior of traffic systems. Macroscopic traffic simulation models are based on advection dominated coupled non-linear partial differential equations. The solution of such advection dominated equations with the method of finite elements is leading to the development of stabilization techniques. The choice of suitable stabilization parameters is often application-dependent. A stabilized finite element procedure on the basis of a Galerkin/least-square approximation is presented for systems of transient advection-dominated equations. A general rule for computing suitable element stabilization parameters is outlined which uses the spectral radius of the differential operators and the specific element expansion. The application of this approximation to a macroscopic traffic model shows the applicability of this approach. Simulation results of typical phenomena of jam formation in freeway traffic are presented. [ABSTRACT FROM AUTHOR]

Forecasting future driving conditions such as acceleration, velocity, and driver behaviors can greatly contribute to safety, mobility, and sustainability issues in the development of new energy vehicles (NEVs). In this brief, a review of existing velocity prediction techniques is studied from the perspective of traffic flow and vehicle lateral dynamics for the first time. A classification framework for velocity prediction in NEVs is presented where various state-of-the-art approaches are put forward. Firstly, we investigate road traffic flow models, under which a driving-scenario-based assessment is introduced. Secondly, vehicle speed prediction methods for NEVs are given where an extensive discussion on traffic flow model classification based on traffic big data and artificial intelligence is carried out. Thirdly, the influence of vehicle lateral dynamics and correlation control methods for vehicle speed prediction are reviewed. Suitable applications of each approach are presented according to their characteristics. Future trends and questions in the development of NEVs from different angles are discussed. Finally, different from existing review papers, we introduce application examples, demonstrating the potential applications of the highlighted concepts in next-generation intelligent transportation systems. To sum up, this review not only gives the first comprehensive analysis and review of road traffic network, vehicle handling stability, and velocity prediction strategies, but also indicates possible applications of each method to prospective designers, where researchers and scholars can better choose the right method on velocity prediction in the development of NEVs. [ABSTRACT FROM AUTHOR]

• The multi-class S model describes the heterogeneous flow of vehicles. • The performance of multi-class S model is compared with the single-class S model using microsimulation as a reference. • The computation time of the macroscopic models does not depend on the number of vehicles. In this paper a new multi-class urban traffic model is proposed based on the features of a single-class urban traffic model and the characteristics of a multi-class freeway traffic model. The heterogeneous traffic flow is represented using the concept of Passenger Car Equivalent (PCE) for congestion and free-flow regimes separately. The proposed multi-class urban traffic model is intended for model-based control applications. The single-class model and the proposed multi-class traffic model are compared with microscopic simulation data obtained using the SUMO (Simulation of Urban MObility) open-source simulator. The two models are calibrated through optimal parameter estimation and their performance is evaluated and compared by taking into account the error index between the models and the simulation data. Simulation results show that the multi-class model gives a significantly better fit. [ABSTRACT FROM AUTHOR]

Traffic flow will harmonize to forward conditions. The time and distance required for harmonization can have a significant effect on the traffic density behavior. The flow can evolve into clusters of vehicles or become uniform depending on parameters such as safe time headway and safe distance headway. In this paper, a new model is presented to provide a realistic characterization of traffic behavior during the harmonization period. Results are presented for a discontinuous density distribution on a circular road which shows that this model produces more realistic traffic behavior than other models in the literature. [ABSTRACT FROM AUTHOR]