Microscopic Traffic Flow Models Research Articles

Multi-lane traffic flow model based on cellular automaton fine-scale under cooperative vehicle infrastructure system

Microscopic traffic flow modeling is crucial for predicting future traffic demand and optimizing the design of transportation systems. At the microscopic level, a well-crafted model is capable of describing the interactions and dynamic variations among vehicles with greater accuracy. The cell scale in the classical cellular automaton (CA) multi-lane traffic flow model is a critical parameter to accurately express the location relationship of vehicles. In this paper, a new method based on Symmetric Two-lane Cellular Automaton (STCA), entitled STCA-X, is proposed for the cell scale selection. Firstly, the position, velocity, acceleration, and interaction of vehicle operation in the urban multi-lane environment are analyzed, and a feature model is built based on CA. One important drawback of the existing models is that they do not match the vehicle movement in the actual lane. To handle this problem, a fine-scale CA multi-lane model is developed. Secondly, a new traffic flow model is created by redefining several key behaviors such as road following, lane changing, and blocking in the STCA model. The experimental results are analyzed and compared with several of the state-of-the-art methods. Analysis of the simulation results proves that STCA-X improves vehicle lane change frequency and lane utilization efficiency. However, the enhancements in the model have resulted in increased computational complexity. Consequently, future research will delve into novel approaches to boost computational efficiency, thereby propelling the progress of traffic flow modeling technology.

Impact of Aggressive HGV Platoons and Human-Driven Heavy Goods Vehicles on Signalized Intersections Performance

This paper presents a study on the performance and environmental impacts of aggressive heavy goods vehicle (HGV) platoons in comparison to human-driven HGVs at a random signalized intersection under varying traffic volumes between 500 and 1500 HGVs. A total of 12 scenarios have been developed, 6 for each of the vehicular behaviors, to quantify the emissions (CO, NOX, VOC) and fuel consumption, travel time, and delays. The analysis is implemented using the PTV VISSIM microscopic traffic flow model. Realization that the majority of the differences in the performance of the two vehicular controls seems to be different as the traffic volume increases was the realization. In most cases, aggressive HGV platoons were found to have lower emissions, in comparison to fuel consumption, while the flow and delay of aggressive HGV platoons were comparatively better against the case with human-driven HGVs. The results have thus provided new avenues for the incorporation of aggressive HGV platoons into urban traffic systems, more so in scenarios that involve a high level of traffic at intersections, as a potentially effective tool for augmenting the efficiency of an intersection and cutting down its environmental impacts. The present study strongly recommends the advancement of traffic management strategies that can capture the dynamics between the two heterogenous traffic flows induced by autonomous and semi-autonomous vehicle technologies.

Physical models of traffic safety at crossing streams

Traffic safety at intersecting streams is studied quantitatively using methods from Statistical Mechanics on the basis of simple microscopic traffic flow models. In order to determine a relationship between traffic flow and the number of crashes, the modeling focus is on the building block of any road network, namely the crossing of two streams. In this paper, it is shown that the number of crossing conflicts is proportional to the product of the two traffic flows from which a simple model is developed. This model substantiates known empirical findings. Since real crash data are obtained by an involved process from such building blocks, there is a difference between the theoretical and empirical results. This process is modeled here as well and narrows the mismatch between theory and observation.

Intelligent driving model considering vehicular dynamics and heterogeneous road environments

ABSTRACT Understanding vehicles’ movement in complex environments becomes cruical with the fast development of connected automated vehicles (CAVs). Current microscopic traffic flow models lack consideration for vehicle dynamics and complex road topologies. This study develops a model addressing these issues, retrieving vehicles’ maneuvers and predicting vehicles’ two-dimensional motion. It introduces a two-dimensional intelligent driving model utilizing steering angle and acceleration as control inputs. Intricate road topologies are represented with potential fields and a virtual boundary to capture the heterogeneous environment’s complexity. A driving potential field model is also developed for off-ramp areas. Model parameters are optimized with the dynamic time warping (DTW) and particle swarm optimization (PSO) algorithm. Furthermore, model predictive control (MPC) enhances the realism of the model’s output. Field validation results demonstrate that the proposed models can accurately describe vehicles’ two-dimensional movement in intricate environments, offering valuable support for research on heterogeneous traffic flows for CAVs and CVs.

A microscopic traffic flow model for explaining nonlinear traffic phenomena: Modeling, stability analysis and validation

As the core of reproducing the real nonlinear phenomena of traffic congestion, the investigation of car-following models, which take into account human factors, has consistently assumed a central role within the domain of transportation science. Nevertheless, it is noteworthy that prior studies have invariably employed a fixed value for driver reaction time to stimuli, whereas it is imperative to recognize the dynamic nature of this parameter, closely associated with the instantaneous vehicle speed. To address this issue, this paper constructs a dynamic sensitivity coefficient (DSC), and further develops a nonlinear human factoring car-following model to reveal the relation between the reaction time and the current speed. Firstly, the linear critical stability condition and the mKdV equation of the model are derived by the linear stability analysis and kink–antikink solution analytic method, respectively. Then, the numerical experiments are conducted to demonstrate that the proposed model is more practical and can reproduce the real driving behavior and phenomena. Finally, calibration and validation results exhibit the actual vehicle trajectory can be simulated well.

Cooperative control of dynamic CAV dedicated lanes and vehicle active lane changing in expressway bottleneck areas

Bottleneck areas on expressways plague the operational efficiency of entire road systems. In mixed traffic flow environments consisting of connected and autonomous vehicles (CAVs) and connected human-driven vehicles (CHVs), it is believed that road capacity can be improved to relieve traffic congestion in bottleneck areas by setting CAV dedicated lanes (CDLs) on expressways. Existing static CDL setup methods provide fixed exclusive right-of-way for CAVs but cannot accommodate dynamic changes in traffic demand. To address this issue, utilizing lane control signals and CAV active lane-changing technology, a cooperative method involving dynamic CAV dedicated lane control (CDLC) and active lane-changing control (LCC) in the bottleneck areas of expressways is proposed in this paper. First, a fundamental diagram of traffic flow with CDLs is established based on the microscopic car-following model of mixed traffic flow, which can be used to determine the conditional thresholds for setting the CDLs of multilane expressways. Second, an expressway traffic model with CDLs is established by improving the lane-level cell transmission model. Finally, cooperative control of dynamic CAV dedicated lanes and active lane changing (CDLLC) is proposed based on the model predictive control (MPC) framework for solving the congestion problem at expressway bottlenecks in a mixed driving environment. The simulation results show that the proposed CDLLC strategy can effectively reduce the total vehicle travel time and decrease the risk of congestion propagation at expressway bottlenecks when compared to the LCC-only strategy.

Effect of speed humps on instantaneous traffic emissions in a microscopic model with limited deceleration capacity

As a common transportation facility, speed humps can control the speed of vehicles on special road sections to reduce traffic risks. At the same time, they also cause instantaneous traffic emissions. Based on the classic instantaneous traffic emission model and the limited deceleration capacity microscopic traffic flow model with slow-to-start rules, this paper has investigated the impact of speed humps on traffic flow and the instantaneous emissions of vehicle pollutants in a single lane situation. The numerical simulation results have shown that speed humps have significant effects on traffic flow and traffic emissions. In a free-flow region, the increase of speed humps leads to the continuous rise of CO2, NO X and PM emissions. Within some density ranges, one finds that these pollutant emissions can evolve into some higher values under some random seeds. Under other random seeds, they can evolve into some lower values. In a wide moving jam region, the emission values of these pollutants sometimes appear as continuous or intermittent phenomenon. Compared to the refined NaSch model, the present model has lower instantaneous emissions such as CO2, NO X and PM and higher volatile organic components (VOC) emissions. Compared to the limited deceleration capacity model without slow-to-start rules, the present model also has lower instantaneous emissions such as CO2, NO X and PM and higher VOC emissions in a wide moving jam region. These results can also be confirmed or explained by the statistical values of vehicle velocity and acceleration.

Model of driver overacceleration causing breakdown in vehicular traffic.

We introduce a mathematical approach for the description of driver overacceleration in a microscopic traffic flow model. The model, in which no driver overreaction occurs, explains the empirical nucleation nature of traffic breakdown.

Integrated self-consistent macro-micro traffic flow modeling and calibration framework based on trajectory data

Calibrating microscopic car-following (CF) models is crucial in traffic flow theory as it allows for accurate reproduction and investigation of traffic behavior and phenomena. Typically, the calibration procedure is a complicated, non-convex optimization issue. When the traffic state is in equilibrium, the macroscopic flow model can be derived analytically from the corresponding CF model. In contrast to the microscopic CF model, calibrated based on trajectory data, the macroscopic representation of the fundamental diagram (FD) primarily adopts loop detector data for calibration. The different calibration approaches at the macro- and microscopic levels may lead to misaligned parameters with identical practical meanings in both macro- and micro-traffic models. This inconsistency arises from the difference between the parameter calibration processes used in macro- and microscopic traffic flow models. Hence, this study proposes an integrated multiresolution traffic flow modeling framework using the same trajectory data for parameter calibration based on the self-consistency concept. This framework incorporates multiple objective functions in the macro- and micro-dimensions. To expeditiously execute the proposed framework, an improved metaheuristic multi-objective optimization algorithm is presented that employs multiple enhancement strategies. Additionally, a deep learning technique based on attention mechanisms was used to extract stationary-state traffic data for the macroscopic calibration process, instead of directly using the entire aggregated data. We conducted experiments using real-world and synthetic trajectory data to validate our self-consistent calibration framework.

A Microscopic Traffic Model Incorporating Vehicle Vibrations Due to Pavement Condition

A microscopic traffic flow model is developed that incorporates vehicle vibrations due to pavement condition. The Intelligent Driver (ID) model employs a fixed exponent so traffic behavior is the same regardless of the road condition. Thus, it ignores the underlying physics. To address this limitation, the proposed model employs the Pavement Condition Index (PCI) in describing traffic behavior. The performance of both models is evaluated on a 3000 m circular road using the Euler numerical discretization technique. The results show that the performance of the proposed model varies with the pavement condition (PCI), as expected. Furthermore, the traffic flow increases with vehicle speed. The oscillations in speed and density with the proposed model decrease as the PCI increases, and are larger when the speed is higher. Consequently, the results with the proposed model align more closely with reality as they are based on the PCI, and so are a more accurate representation of traffic behavior.

FollowNet: A Comprehensive Benchmark for Car-Following Behavior Modeling

Car-following is a control process in which a following vehicle adjusts its acceleration to keep a safe distance from the lead vehicle. Recently, there has been a booming of data-driven models that enable more accurate modeling of car-following through real-world driving datasets. Although there are several public datasets available, their formats are not always consistent, making it challenging to determine the state-of-the-art models and how well a new model performs compared to existing ones. To address this gap and promote the development of microscopic traffic flow modeling, we establish the first public benchmark dataset for car-following behavior modeling. This benchmark consists of more than 80 K car-following events extracted from five public driving datasets under the same criteria. To give an overview of current progress in car-following modeling, we implemented and tested representative baseline models within the benchmark. The established benchmark provides researchers with consistent data formats and metrics for cross-comparing different car-following models, coming with open datasets and codes.

Analyzing differences of highway lane-changing behavior using vehicle trajectory data

Analyzing fine-resolution lane-changing behavior is of great significance in the traffic flow modeling and safety assessment. The fact that lane-changing behavior varies over different environment and operation factors poses great challenges for lane-changing behavior modeling and analysis. To explore the differences of highway lane-changing behavior in complicated scenarios, the analysis of this study stems from a large volume of lane-changing trajectories (i.e., 6506 lane-changing trajectories) collected from the Highway Drone (HighD) dataset. In real-world scenarios, to identify and extract lane-changing start and end points of lane-changing behavior from trajectories, we present a fusion method by using frame interval and lateral displacement variation in trajectory data, which can be calibrated by the visualization. The empirical results show that: (a) the relative error between the extracted lane-changing time and real one is less than 4%; (b) the lane-changing time is ranged from 2.88 s to 7.32 s, while the lane-changing speed is ranged from 18.83 m/s to 43.30 m/s. For the difference analysis, we use the tests of statistical significance and heatmaps to analyze the differences of lane-changing behavior under complicated scenarios that involve different lane numbers, lane-changing directions, vehicle types, and speed limit condition. The results demonstrate that the lane-changing behavior varies over different scenarios significantly. Overall, the systematical analysis of lane-changing behavior in this study provides insight into the microscopic traffic flow modeling.

Research on microscopic traffic flow modeling and energy characteristics in the energy-saving driving environment

Research on microscopic traffic flow modeling and energy characteristics in the energy-saving driving environment

Physics-Aware Learning-Based Vehicle Trajectory Prediction of Congested Traffic in a Connected Vehicle Environment

The physics of shockwaves is a fundamental traffic characteristic that is useful for microscopic traffic flow modeling. Thanks to connected vehicle technologies, it is possible to capture shockwaves by collecting the information of multiple downstream vehicles. Numerous classical physics-based models have utilized the physics of shockwaves to predict vehicle trajectory dynamics, yet their predictability is often limited due to the volatile and complex nature of highway traffic composed of human-driven vehicles. Recent learning-based trajectory prediction models utilize historical trajectories of surrounding vehicles to improve predictability. However, those learning-based models are purely data-driven, thus lacking interpretability and physical insights, or even missing opportunities for further improving model predictability. To leverage the advantages of both learning-based and physics-based models, this study proposes a physics-aware learning-based model for a trajectory prediction of congested traffic in a connected vehicle environment. A newly collected highway trajectory dataset is adopted for training and validation. Experiment results show that the proposed hybrid model yields better predictability, compared with the learning-based models (e.g., long short-term memory neural networks and convolutional neural networks), with an 8.7% predictability reduction of position errors and a 6.5% reduction of speed errors, which further verify the positive impacts of adopting physics of shockwaves in hybrid learning models. Moreover, result analysis shows that predictability improves as the market penetration rate increases, and the proposed hybrid model is better than the learning-based models with 3-10% improvements in predictability.

Motion Planning for Dynamic Scenario Vehicles in Autonomous-Driving Simulations

Scenario vehicles are an important part of the dynamic environment utilized in autonomous-driving simulations. They are required to meet the demands of traffic-scenario diversity and form a larger coverage scale in the road network. However, the current motion planning of scenario vehicles either adheres to the classical microscopic traffic-flow model or follows a predefined path; thus, interacting with the vehicle under test in a dynamic bidirectional fashion is difficult. This study researches a motion-planning method for a broader category of unmanned vehicles and proposes a motion-planning method for scenario vehicles based on Pontryagin’s minimal principle, used in optimal control theory and the closed-form solution of the minimum snap method. The study reclassifies actions received from the behavior layer according to the boundary conditions and final times and derives an analytical solution for each of them. The analytical solution is then experimentally verified. The proposed method not only accomplishes efficient motion planning but also exhibits variant driving styles, which provides a practical solution for the motion planning of scenario vehicles in simulations.

A Microscopic Traffic Flow Model Characterization for Weather Conditions

Road surfaces are affected by rain, snow, and ice, which influence traffic flow. In this paper, a microscopic traffic flow model based on weather conditions is proposed. This model characterizes traffic based on the weather severity index. The Intelligent Driver (ID) model characterizes traffic behavior based on a constant acceleration exponent resulting in similar traffic behavior regardless of the conditions, which is unrealistic. The ID and proposed models are evaluated over a circular road of length 800 m. The results obtained indicate that the proposed model characterizes the velocity and density better than the ID model. Further, variations in the traffic flow with the proposed model are smaller during adverse weather, as expected. It is also shown that traffic is stable with the proposed model, even during adverse weather.

A Systematic Solution of Human Driving Behavior Modeling and Simulation for Automated Vehicle Studies

Though automated vehicles (AVs) are believed to play a crucial role in future transport, human driving vehicles will share the road with automated vehicles for a relatively long period. So, we need to enable automated vehicles to run along with human drivers especially when they may have conflicts in the right of way. One key problem is how to appropriately model human driving behaviors and quickly simulate their actions when training/testing automated vehicles. Many existing models were originally built for traffic flow studies and may not be suitable for automated vehicles studies. In this paper, we propose a set of new principles of human driving behaviors modeling and simulations. Then, we propose a Data-Driven Simulator (D2Sim) model for human behavior learning, description, and vehicle interaction simulation. In contrast to conventional microscopic traffic flow models, the D2Sim is a trajectory generation model that accepts rich driving environment information (e.g., lane geometry, crosswalks, traffic signals, surrounding vehicles, etc.). Different from many empirical trajectory records replay models, we can arbitrarily set the long-term intentions of the simulated vehicles and intentionally design the corner cases that had not been observed in practice. In addition, the D2Sim adopts adversarial learning to comprehend complex yet stochastic human driving behaviors from empirical data. Testing results show that the proposed model can quickly generate high-resolution trajectory data for training and testing.

A dynamical traffic flow model for a cognitive drivers' sensitivity in Lagrangian scope

A new microscopic traffic flow model is established based on heterogeneous driver's sensitivity; in this new model, the driver's sensitivity is defined as being dependent on the headway distances to the preceding vehicle, similar to Bando’s optimal velocity function. We introduce the formulation of this cognitive driver's sensitivity utilizing a modified form of Bando’s optimal velocity function. A simple methodology, which is used for improving Bando’s optimal velocity function, has been implemented for developing the cognitive driver’s sensitivity function, which establishes a correlation between the flow field’s density and human drivers' responses. The model is highly advanced for introducing a human-driven traffic flow field considering the driver’s mental behavioral activity. Using the linear stability condition, we elucidate a neutral stability condition. A series of numerical simulations indicates how the present model describes dynamics that differ from the conventional model, which assumes a constant driver's sensitivity.

Microscopic state evolution model of mixed traffic flow based on potential field theory

To investigate the microscopic-state evolution mechanism of mixed traffic flow composed of Connected and Automated Vehicles (CAVs) and Human Driven Vehicles (HDVs) in the intelligent and connected environment, this paper proposed a safety potential field-NaSch (SPF-NS) model for mixed traffic flow based on potential field theory. The model introduces the safety potential field theory into the cellular automata model, reformulates the cellular automata rules and realizes the discretization of the potential field. In addition, a comparative numerical simulation experiment between NaSch model and SPF-NS model was designed. The results show that SPF-NS model can realize the real-time change of vehicle’s acceleration according to the potential field distribution, which is more precise for the actual car-following state description than NaSch model. At the same time, the road traffic flow stability under SPF-NS model is higher and the traffic capacity is increased. Besides, the microscopic-state evolution process of mixed traffic flow is emphatically studied, and the simulation experiments of different CAV penetration conditions are designed. The simulation results show that the stability of mixed traffic flow increases with the increase of CAV penetration. Compared with the HDV environment, when the CAV penetration of mixed traffic flow reaches 100%, The maximum traffic capacity of the road was increased by 2.2 times, and the congestion ratio was reduced by 96.60%. Therefore, this model can reflect the driving risk faced by vehicles in the process of car-following and simulate the microscopic-state evolution process of mixed traffic flow. The research results can provide theoretical support for future research on vehicle lane changing behavior, mixed traffic flow management and control, macroscopic state prediction in traffic flow and so on.

A stochastic two-dimensional intelligent driver car-following model with vehicular dynamics

The law of vehicle movement has long been studied under the umbrella of microscopic traffic flow models, especially the car-following (CF) models. These models of the movement of vehicles serve as the backbone of traffic flow analysis, simulation, autonomous vehicle development, etc. Two-dimensional (2D) vehicular movement is basically stochastic and is the result of interactions between a driver’s behavior and a vehicle’s characteristics. Current microscopic models either neglect 2D noise, or overlook vehicle dynamics. The modeling capabilities, thus, are limited, so that stochastic lateral movement cannot be reproduced. The present research extends an intelligent driver model (IDM) by explicitly considering both vehicle dynamics and 2D noises to formulate a stochastic 2D IDM model, with vehicle dynamics based on the stochastic differential equation (SDE) theory. Control inputs from the vehicle include the steer rate and longitudinal acceleration, both of which are developed based on an idea from a traditional intelligent driver model. The stochastic stability condition is analyzed on the basis of Lyapunov theory. Numerical analysis is used to assess the two cases: (i) when a vehicle accelerates from a standstill and (ii) when a platoon of vehicles follow a leader with a stop-and-go speed profile, the formation of congestion and subsequent dispersion are simulated. The results show that the model can reproduce the stochastic 2D trajectories of the vehicle and the marginal distribution of lateral movement. The proposed model can be used in both a simulation platform and a behavioral analysis of a human driver in traffic flow.