Full Velocity Difference Model Research Articles

Analysis of Traffic Properties of Commuters in a Speed-Limit Corridor with Toll Station under Microscopic Method

AbstractThis study applies the full velocity difference (FVD) model to investigate commuters’ driving process in a speed limit corridor with a toll station and explains how some factors affect the ...

Car-following Behavior Analysis of Left-turn Vehicles at Signalized Intersections

In order to enrich the car-following theory of urban signalized intersections, and reveal the car-following characteristics of left turn at signalized intersections, the car-following behavior of left turn at signalized intersections is studied. The car-following data acquisition test which was based on high precision GPS was designed. And the car-following characteristics of left-turning vehicles at signalized intersections with different turning radii were analyzed. Based on which, the influence of radius on the car-following behavior was explained, and the New Full Velocity Difference (NFVD) model was developed. The genetic algorithm was used to calibrate the parameters of the NFVD model. The stability and accuracy of the calibrated model was further analyzed by using field data. The results showed that the average speed of the following car increases with the turning radius of the signalized intersection; the car-following speed which the highest frequency occurs under different turning radii tends to increase with the enlargement of turning radius; the larger the average headway distance between the car-following vehicles, the more intense of the driver’s response to the deceleration of the front vehicle. These findings could be used in traffic simulation and to make engineering decisions.

A new continuum traffic flow model with two delays

A new continuum version of the car following FVD-Ds model is developed by using a series expansion of the headway in terms of the density. FVD-Ds model is the extension of the full velocity difference (FVD) model with two delays. The stability condition of the new continuum model is derived using the linear stability theory. The Korteweg–de Vries (KdV) equation is derived to describe traffic behavior near the neutral stability line by applying the reductive perturbation method. The corresponding soliton solution of the KdV equation is also obtained. The numerical simulations show that the local clusters appear. It agrees with the theoretical analysis.

An improved car-following model considering forecast speed difference with delay time

It is well known that drivers can adjust their speeds based on forecasted traffic states in driving process, and this forecast behavior affects traffic features. In this paper, an improved car-following model with delay time, by incorporating the behavior of forecast speed difference, is proposed from the full velocity difference model. Its linear steady condition is deduced. Numerical simulations indicate that values of acceleration and deceleration of new model are more reasonable than those of the original model in starting and braking processes, and the kinematic wave speed is proper. Moreover, the behavior of forecast speed difference is found not only to smoothen the traffic fluctuation, but also to reduce the energy consumption in stable and unstable cases.

Car-following Model of Connected Cruise Control Vehicles to Mitigate Traffic Oscillations

With the aim of mitigating traffic oscillations, this paper extends a car-following model for Connected Cruise Control (CCC) systems by considering electronic throttle angles of multiple cars ahead. The linear stability condition of the proposed model is derived and numerical simulations are performed. It has been found that the proposed model is prominently better than the previous model, i.e. full velocity difference model, from the perspective of mitigating traffic oscillations. Additionally, the proposed model can also reduce fuel consumption, emissions, i.e. CO, HC and NOX, safety risk, and improve driving comfort at the same time. Simulation results suggest that the CCC car-following control design should consider the effect of multiple electronic throttle angles from the preceding cars.

An improved car-following model with consideration of multiple preceding and following vehicles in a driver’s view

Common driving experience suggests that a driver’s car-following behavior is often affected by the preceding and following vehicles in the driver’s view. In this study, we propose an improved car-following model based on the full velocity difference (FVD) model by considering two preceding and the immediate following vehicles in a driver’s view. Car-following data extracted from real traffic situation is used to calibrate and verify both of the improved model and the FVD model. Our data analysis shows that the motion status of two preceding vehicles and one following vehicle in a driver’s view can influence traffic flow of following vehicles after the driver’s vehicle. We compare the performance of the improved model and the FVD model by simulating the starting and braking processes of the following vehicles. The numerical simulation study shows that the improved model fits the experimental data better than the FVD model by showing a shorter time of completing the starting and braking processes of the following vehicles.

SV-FVDM: A Synthetic Vision Based Full Velocity Difference Model for Interactive Traffic Simulation

Most methods in traffic simulations cannot deal with emergency circumstances. Inspired by the full velocity difference model (FVDM), we propose an improved approach called the synthetic vision based FVDM. Through numerous simulations, the experimental results demonstrate our SV-FVDM method can effectively generate interactive and realistic traffic simulations.

Stability analysis and the fundamental diagram for mixed connected automated and human-driven vehicles

With the development of connected automated vehicles (CAVs), traffic flow on the road is generally mixed with human-driven vehicles (HDVs) and CAVs. In this paper, the full velocity difference (FVD) model and cooperative adaptive cruise control (CACC) model validated by PATH laboratory of University of California, Berkeley, are used to describe the car-following (CF) driving behavior of HDVs and CAVs, respectively. Firstly, an analytical method for the stability of mixed traffic flow is developed and the stability conditions under different penetration rates of CAVs are obtained. Then, the fundamental diagram model of mixed traffic flow under different penetration rates is derived and the influence factors of the fundamental diagram are analyzed. Finally, a simulation experiment is designed to verify the effectiveness of the proposed model. The simulation results show that there are some differences between the simulation data and the theoretical results under different penetration rates. The simulation data are fluctuating, but they are both on the theoretical curve. The overall trend is consistent with the theoretical results, which proves the validity of the fundamental diagram analysis results.

A Modified Full Velocity Difference Model with Acceleration and Deceleration Confinement: Calibrations, Validations, and Scenario Analyses

The Full Velocity Difference (FVD) model is a well-recognized and widely-used time continuous car following model. Although the model has very good simulation performances in most cases, it is not applicable to some specific traffic scenarios, where it can generate very large or even overshooting accelerations or decelerations that are totally unnecessary and might be far beyond the acceleration/deceleration limits of real vehicles. In this paper, we explore the reason and attempt to correct it by proposing a confined Full Velocity Difference (c-FVD) model in which we limit the accelerations or decelerations generated by the existing FVD models to a reasonable level. The performances of both models are compared from both microscopic and macroscopic perspectives. The ability of the modified model to generate strong but reasonable decelerations to avoid accidents in urgent traffic scenarios is also validated. According to the comparative analyses, both models will have same performances in most cases while the c-FVD model will outperform the existing FVD model in certain scenarios where very large or overshooting accelerations or decelerations are involved.

V2I-based car-following modeling and simulation of signalized intersection

In order to study the influence of V2I (Vehicle-To-Infrastructure) on traffic flow at signalized intersections, a vehicle-following model at signalized intersections based on V2I is established in this paper. On the basis of the FVD (Full Velocity Difference) model, the influence of V2I on the operation of intelligent vehicles at signalized intersections is considered. Through the numerical simulation analysis, it was found that the vehicle following model based on the V2I signalized intersection can better reflect the effect of V2I on the vehicle operation at the intersection. At the same time, with the increase of the influence degree and range of V2I, it can effectively improve the traffic efficiency of the vehicles at the signalized intersection.

An extended car-following model considering driver’s sensory memory and the backward looking effect

On the basis of full velocity difference model (FVDM), an extended car-following model is proposed to account for the effect of driver’s memory and backward looking effect in this paper. The stability conditions of the new model are derived by applying the linear stability analysis. The time-dependent Ginzburg–Lan (TDGL) equation and the modified Korteweg–de Vries (mKdV) equation are derived through nonlinear theory. Moreover, the relationship between the two equations is studied. Numerical simulations show that the stability of traffic flows can be strengthened with the effect of driver’s memory and backward looking effect.

An improved anisotropic continuum model considering the driver’s desire for steady driving

With the consideration of the driver’s desire for steady driving, an improved car-following model is developed by incorporating the difference between the steady and history headways into the full velocity difference model. And then by using the connection method of macro–micro variables, an improved anisotropic continuum model is derived from the improved car-following model. The stability analysis of the improved continuum model is discussed by the linear analysis method. Numerical simulations are carried out to investigate the improved anisotropic continuum traffic model, which explores how the driver’s desire for steady driving affected the shock waves, rarefaction waves, and the stabilities of velocity and density. Numerical results demonstrate that the continuum model presented herein can well describe the developments of shock waves and rarefaction waves, and considering the factor of driver’s desire for steady driving have a positive impact on the dynamic characteristic of macroscopic flow.

Impact of risk illusions on traffic flow in fog weather

In this study, an extended car-following model is proposed to investigate the impacts of driver’s risk illusions on traffic flow stability, velocity, acceleration and headway fluctuation frequency in fog weather. Through stability analysis and numerical simulation, the results indicate that risk illusions weaken the stabilization of traffic system. Furthermore, it is shown that the proposed model is more dangerous than the full velocity difference model from the perspectives of the maximum velocity and acceleration. Additionally, risk illusions increase the headway fluctuation frequency, which means that more adjustments of the headway are needed, increasing driver’s workload. Therefore, the impact of risk illusions on traffic flow is disadvantageous.

Analysis of trip cost allowing late arrival in a traffic corridor with one entry and one exit under car-following model

In this paper, a generalized car-following model is proposed to investigate the trip cost of each commuter in a traffic corridor with one entry and one exit at equilibrium state. Trip cost consists of the travel time cost and the penalty cost of arriving at destination early or late. At equilibrium, all commuters experience identical trip cost regardless of their departure time. We use the full velocity difference (FVD) model to compute the trip cost of a commuter who leaves home at specific time, find a function to fit each commuter’s time headway at the equilibrium state, and verify the fitted function from different perspectives. Finally, we utilize the fitted function of time headway to explore some extension cases (e.g., the number of commuters or the first commuter’s departure time is changed). The paper has revealed some properties of the traffic corridor and proposed some methods to research the traffic corridor in deep from microscopic perspective, which can be easily extended to investigate complex traffic corridor problems and policy making.

An extended macroscopic model for traffic flow on curved road and its numerical simulation

In this paper, we propose a full angular velocity difference model by introducing the angular velocity and displacement on curved road. The relation of transformation of microscopic model in form of angular variables into macroscopic one is deduced. Consequently, a corresponding continuum traffic flow model on curved road is derived. The critical condition for the steady traffic flow is obtained. Meanwhile, the stability condition of this continuum traffic model is compared with one of the microscopic model and lattice hydrodynamic traffic model on curved road. By nonlinear analysis, the KdV–Burgers equation which describes the density wave near the neutral stability line is derived. Furthermore, the numerical simulations are carried out to verify the validity of this macroscopic traffic. Results indicate the radius of curved road have a great impact on the formation of traffic jams. Local clustering effects in the state of instability of traffic flow give rise to the stop&go traffic jams. Numerical simulation also indicates the unstable region is shrunken under the increasing strength of the angular velocity difference.

Traffic Simulation and Visual Verification in Smog

Smog causes low visibility on the road and it can impact the safety of traffic. Modeling traffic in smog will have a significant impact on realistic traffic simulations. Most existing traffic models assume that drivers have optimal vision in the simulations, making these simulations are not suitable for modeling smog weather conditions. In this article, we introduce the Smog Full Velocity Difference Model (SMOG-FVDM) for a realistic simulation of traffic in smog weather conditions. In this model, we present a stadia model for drivers in smog conditions. We introduce it into a car-following traffic model using both psychological force and body force concepts, and then we introduce the SMOG-FVDM. Considering that there are lots of parameters in the SMOG-FVDM, we design a visual verification system based on SMOG-FVDM to arrive at an adequate solution which can show visual simulation results under different road scenarios and different degrees of smog by reconciling the parameters. Experimental results show that our model can give a realistic and efficient traffic simulation of smog weather conditions.

An improved car-following model considering the impact of safety messages

The existing car-following models of connected vehicles commonly lack experimental data as evidence. In this paper, a Gray correlation analysis is conducted to explore the change in driving behavior with safety messages. The data mining analysis shows that the dominant factor of car-following behavior is headway with no safety message, whereas the velocity difference between the leading and following vehicle becomes the dominant factor when warning messages are received. According to this result, an extended car-following model considering the impact of safety messages (IOSM) is proposed based on the full velocity difference (FVD) model. The stability criterion of this new model is then obtained through a linear stability analysis. Finally, numerical simulations are performed to verify the theoretical analysis results. Both analytical and simulation results show that traffic congestion can be suppressed by safety messages. However, the IOSM model is slightly less stable than the FVD model if the average headway in traffic flow is approximately 14–20 m.

Predictive vehicle-following power management for plug-in hybrid electric vehicles

This paper presents a new integrated model predictive control (IMPC) method that combines power management and adaptive velocity control during vehicle-following scenarios in reality, for a plug-in hybrid electric vehicle (PHEV). Innovatively, the IMPC is able to plan the battery state of charge (SOC) and vehicular velocity trajectories, in order to improve fue economy and driving safety. To assess the performance of the IMPC, a comparison is performed with common charge-depleting and charge-sustaining (CDCS) and DP-based energy management strategies, where an improved full velocity difference model (IFVDM) is incorporated to simulate vehicle-following behavior. These solutions are examined using a real-world driving cycle. The results reveal an enormous potential of flexibly tuning the inter-vehicle distance to increase fuel economy. This is distinct from the rigid vehicle-following behavior of the IFVDM just for driving safety. Moreover, the proposed IMPC can ensure the battery charge depletion at the end of the trip. The quantitative results witness that the total cost of the IMPC with a preview horizon of 3s can be reduced by 17.9% and 36.1% for a 70 km city-bus route, compared to IFVDM-based DP and CDCS counterparts, respectively. In addition, the effect of the preview-horizon length on both fuel economy and computational time is examined.

KdV–Burgers equation in the modified continuum model considering the effect of friction and radius on a curved road

Considering the effect of friction and radius on a curved road, we propose an extended continuum model based on the full velocity difference model (FVDM, for short). Through the linear stability theory, the linear stability criterion of the continuum model is gained. By applying the nonlinear analysis, the KdV–Burgers equation is derived to describe the propagating behavior of traffic density wave near the neutral stability line. Numerical simulations show that traffic flow evolves into multiple local cluster, which corresponding to some particular traffic phenomenon with distinctive curved road factors. Numerical results indicate that the effect of friction and radius can suppress the traffic congestion and improve the stability of traffic flow efficiently.

A car-following model considering asymmetric driving behavior based on long short-term memory neural networks

Asymmetric driving behavior is a critical characteristic of human driving behaviors and has a significant impact on traffic flow. In consideration of the asymmetric driving behavior, this paper proposes a long short-term memory (LSTM) neural networks (NN) based car-following (CF) model to capture realistic traffic flow characteristics by incorporating the driving memory. The NGSIM data are used to calibrate and validate the proposed CF model. Meanwhile, three characteristics closely related to the asymmetric driving behavior are investigated: hysteresis, discrete driving, and intensity difference. The simulation results show the good performance of the proposed CF model on reproducing realistic traffic flow features. Moreover, to further demonstrate the superiority of the proposed CF model, two other CF models including recurrent neural network based CF model and asymmetric full velocity difference model, are compared with LSTM-NN model. The results reveal that LSTM-NN model can capture the asymmetric driving behavior well and outperforms other models.