Car-following Parameters Research Articles

On dynamic fundamental diagrams: Implications for automated vehicles

The traffic fundamental diagram (FD) describes the relationships among fundamental traffic variables of flow, density, and speed. FD represents fundamental properties of traffic streams, giving insights into traffic performance. This paper presents a theoretical investigation of dynamic FD properties, derived directly from vehicle car-following (control) models to model traffic hysteresis. Analytical derivation of dynamic FD is enabled by (i) frequency-domain representation of vehicle kinematics (acceleration, speed, and position) to derive vehicle trajectories based on transfer function and (ii) continuum approximation of density and flow, measured along the derived trajectories using Edie's generalized definitions. The formulation is generic: the derivation of dynamic FD is possible with any analytical car-following (control) laws for human-driven vehicles or automated vehicles (AVs). Numerical experiments shed light on the effects of the density-flow measurement region and car-following parameters on the dynamic FD properties for an AV platoon.

Comparing Measured Driver Behavior Distributions to Results from Car-Following Models using SUMO and Real-World Vehicle Trajectories from Radar

In this study, the physical principles governing car-following (CF) behavior and their impact on traffic flow at signalized intersections are investigated. High temporal-resolution radar data is used to provide valuable insights into actual CF behavior, including acceleration, deceleration, and time headway distribution. Demand-calibrated SUMO simulations are run using empirical CF parameter distributions, and three CF models are evaluated: IDM, EIDM, and Krauss. By emulating radar data in SUMO and processing simulated vehicle traces, discrepancies between empirical and simulated parameter distributions are identified. Further analysis includes comparisons with default SUMO CF model parameters. The findings reveal that measured accelerations differ from CF model parameter accelerations and using the empirical value ($\mu = 0.89m/s^2$) leads to unrealistic simulations that fail volume-based calibration. Default parameters for all three models reasonably approximate the mean and median of measured parameters, but fail to capture the true distribution shape, partly due to homogeneity when using default parameters. The results show that it is more effective to simulate with the default parameters provided by SUMO rather than using measurements of real-world distributions without additional calibration. Future work will investigate closing the loop between the measured real-world and SUMO distributions using traditional calibration tactics, as well as assess the impact of calibrated vs. default CF parameters on simulation outputs like fuel consumption.

Consistency Analysis of Drivers’ Car-Following Behaviors

Car-following models are calibrated to account for various driver behaviors such as speed and space headway. Because drivers do not all drive the same way, they are typically classified based on their level, or profile, of aggressiveness. This approach to model calibration assumes that a single set of car-following parameters applies to an individual driver consistently, that is, during the entire driving period a study considers. The purpose of this research is to challenge that assumption by analyzing the heterogeneity and inconsistency of driver behaviors in different traffic conditions. A total of 3,262 urban expressway car-following periods from 51 drivers were extracted from the Shanghai Naturalistic Driving Study database. The intelligent driver model (IDM) was selected as this study’s car-following model for its favorable performance in highly dynamic situations; its parameters, that is, desired speed, desired time headway, maximum acceleration, comfortable deceleration, acceleration exponent, and minimum spacing, were calibrated. To reflect the effect of traffic conditions, the car-following periods were categorized into three regimes from low-speed to high-speed. Two-way analysis of variance was used to evaluate heterogeneity and inconsistency in the car-following parameters. The findings of this study show that (1) the main significant variable that distinguished driver profile was desired time headway, while the comfortable acceleration was commonly affected by both driver profile and speed regime; (2) variability of individual driver parameter estimates greatly depended on the traffic regime (low-, medium-, and high-speed), showing that driver behavior was highly influenced by traffic conditions; and (3) speed information was demonstrated to be a useful tool by which to divide car-following periods for modeling, as the car-following parameters exhibited distinct differences among the regimes in the varied traffic environment.

Assessing Impacts of Shared Bike-Bus Lanes on Bus Operations

A shared bike-bus lane is a potential alternative to exclusive bike lanes in streets with limited scope for extension. A shared bike-bus lane allows the bikes to share the right of way with buses only (safer compared to traditional shared lanes). Though a shared bike-bus lane will separate bicycles from the motorized traffic stream, it may negatively impact bus operations—delay and headways. Also, these effects will vary due to heterogenous interactions between bikes and buses. This study investigates: (i) the impact of introducing bike-bus lanes on bus performances (mobility and emissions), (ii) the effect of shifted and induced demand due to changes in bus headways, speed, and ridership, and (iii) the impacts of different driving behavior (modeled using car-following parameters). We used a microsimulation tool PTV Vissim to simulate hypothetical scenarios on a real-world network from New York City, NY. Later we used the trajectories from Vissim to estimate emissions using EPA MOVES through Link-Driving-Schedules. The results showed that sharing lanes with bikes will increase bus delays and speed compared to the base case—an exclusive bus lane without bikes. However, the emissions might be lower under specific conditions regarding bus headway distribution, speed, and demand for bicycles.

Impact of Variability of Car-Following Parameters on Road Capacity: The Simple Case of Newell’s Model

This paper contributes to the vehicle-level analysis of two macroscopic features of the road traffic: capacity variability and capacity drop. This paper focuses only on car-following (CF) behavior and leaves the part related to lane-change maneuvers for future research. In particular, a simplistic CF model (Newell’s with bounded acceleration) for a single-lane scenario is studied. In this work, by introducing a speed limitation across a zone, a bottleneck with variable nominal capacity has been created. A continuous event-based numerical resolution method is used. Consequently, it is possible to vary the three Newell’s model parameters: maximal acceleration, minimal distance, and reaction time. It has been shown that the variability of those CF parameters (e.g., reaction time, minimal distance, and maximal acceleration) has a strong impact on pre-breakdown capacity variation and also on queue discharge flow. It has been concluded that this parameters variability does affect the drop (provided that the maximal acceleration has a relatively high mean value). Various distribution shapes (uniform, truncated Gaussian, and Gamma) have been explored. It has been realized that this does not have any significant impact on the capacity distribution. Concerning the amplitude of the capacity distribution, it is demonstrated that reaction time is the parameter with the highest impact followed by minimal distance. If all parameters vary with an amplitude of 30%, it is shown that the capacity standard deviation, in this scenario without lane changes, is about half the experimental values reported in the literature.

Learning-Based Adaptive Optimal Control for Connected Vehicles in Mixed Traffic: Robustness to Driver Reaction Time.

Through vehicle-to-vehicle (V2V) communication, both human-driven and autonomous vehicles can actively exchange data, such as velocities and bumper-to-bumper distances. Employing the shared data, control laws with improved performance can be designed for connected and autonomous vehicles (CAVs). In this article, taking into account human-vehicle interaction and heterogeneous driver behavior, an adaptive optimal control design method is proposed for a platoon mixed with multiple preceding human-driven vehicles and one CAV at the tail. It is shown that by using reinforcement learning and adaptive dynamic programming techniques, a near-optimal controller can be learned from real-time data for the CAV with V2V communications, but without the precise knowledge of the accurate car-following parameters of any driver in the platoon. The proposed method allows the CAV controller to adapt to different platoon dynamics caused by the unknown and heterogeneous driver-dependent parameters. To improve the safety performance during the learning process, our off-policy learning algorithm can leverage both the historical data and the data collected in real time, which leads to considerably reduced learning time duration. The effectiveness and efficiency of our proposed method is demonstrated by rigorous proofs and microscopic traffic simulations.

Empirical Verification of Car-Following Parameters Using Naturalistic Driving Data on Freeway Segments

AbstractMicroscopic traffic simulation is a well-established tool for the analysis of transportation systems, with a wide variety of applications in operations, safety, and planning. An essential c...

Modeling and simulation of vehicle group collaboration behaviors in an on-ramp area with a connected vehicle environment

As the merging section of an urban expressway main road and on-ramp traffic flow, on-ramp areas have serious merging conflicts. In the dynamic game between two streams of traffic flow, traffic congestion can occur and cause bottlenecks on expressways. With the development of vehicle intelligence and network technology, connected vehicles can form a platoon or a group and realize real-time dynamic communication. In this paper, the vehicle group collaboration behaviors of the main road and ramps in an on-ramp area are investigated with a connected vehicle environment, and the vehicle group model based on the theory of acceptable gaps is constructed. Using VISSIM simulation software, simulation scenarios are built based on time slice and virtual signal control strategies, and then, the proposed schemes are simulated and evaluated. By continuous optimization, the ideal time difference between the control ramp and the main road can be determined to form a better vehicle group length and acceptable gap, comprehensively utilize the limited road resources, improve the utilization rate of on-ramp areas and reduce delay. The experimental results show that the combined implementation of the mainline and ramp control strategies is more effective than that of only the mainline strategy. When the strategy cycle is large (more than 3 min), the time slice simulation strategy is more effective than the virtual signal strategy for improving the traffic efficiency of on-ramp areas. If the virtual signal strategy is adopted, a suitable signal cycle should be selected. The performance effect of vehicle generation schemes under different background traffic congestion is also discussed. Finally, the effect of micro car-following parameters (different levels of driving behaviors) on the platoon operation is evaluated in this paper.

Stability and safety evaluation of mixed traffic flow with connected automated vehicles on expressways

Introduction: Connected automated vehicles (CAVs) technology has deeply integrated advanced technologies in various fields, providing an effective way to improve traffic safety. However, it would take time for vehicles on the road to vehicles from human-driven vehicles (HDVs) progress to CAVs. Moreover, the Cooperative Adaptive Cruise Control (CACC) vehicle would degrade into the Adaptive Cruise Control (ACC) vehicle due to communication failure. Method: First, the different car-following models are used to capture characteristics of different types of vehicles (e.g., HDVs, CACC, and ACC). Second, the stability of mixed traffic flow is analyzed under different penetration rates of CAVs. Then, multiple safety measures, such as standard deviation of vehicle speed (SD), time exposed rear-end crash risk (TER), time exposed time-to-collision (TET), and time-integrated time-to-collision (TIT) are used to evaluate the safety of mixed traffic flow on expressways. Finally, the sensitivity of traffic demand, the threshold of time-to-collision (TTC), and the parameters of car-following models are analyzed based on a numerical simulation. Results: The results show that the ACC vehicle has no significant impact on the SD of mixed traffic flows, but it leads to the deterioration of TET and TIT, making the reduction proportion of TER slower. When the penetration rate exceeds 50%, the increase of CACC vehicles reduces traffic safety risks significantly. Furthermore, the increase in traffic demand and car-following parameters worsens traffic safety on expressways. Conclusions: This paper suggests that the CACC vehicles degenerate into ACC vehicles due to communication failure, and the safety risk of mixed traffic flow increases significantly. Practical Applications: The application of CAVs can improve the stability and safety of traffic flow.

A data-driven approach to calibrate microsimulation models based on the degree of saturation at signalized intersections

Microscopic traffic simulation is considered as a reliable tool in transportation planning and management. Rational solutions from such simulations are contingent upon how well the simulation software is calibrated and validated to replicate real-world road network scenarios. Most of the existing calibration and validation efforts are normally based on the comparative analysis between the built-in attributes of VISSIM and the real-world scenarios using measures of effectiveness (MOEs). VISSIM attributes such as the volume-to-capacity ratios, vehicle delay, and queue lengths, are normally used during the validation process of signalized intersections. However, validating VISSIM based on a non-inbuilt attribute is yet to be explored. This paper proposes a step-by-step procedure for calibrating signalized intersections in VISSIM based on a measurable variable, which is the degree of saturation. The approach was applied to a case study of four signalized intersections in Miami, Florida. The methodology utilized real-world vehicle trajectory data to determine the optimal values of VISSIM car-following parameters required for calibration. Statistical results revealed that both the saturation headways obtained from VISSIM and the saturation headways observed in the field follow the same distribution. The results signify that VISSIM could be calibrated using a non-inbuilt attribute, and moreover generates accurate data compared to the field measurements.

Statistical inference for two-regime stochastic car-following models

This paper presents the formulation of a family of two-regime car-following models where both free-flow and congestion regimes obey statistically independent random processes. This formulation generalizes previous efforts based on Brownian and geometric Brownian acceleration processes, each reproducing a different feature of traffic instabilities. The probability density of vehicle positions turns out to be analytical in our model, and therefore parameters can be estimated using maximum likelihood. This allows us to test a wide variety of hypotheses using statistical inference methods, such as the homogeneity of the driver/vehicle population and the statistical significance of the impacts of roadway geometry. Using data from two car-following experiments, we find that (i) model parameters are similar across repeated experiments within the same dataset but different across datasets, (ii) the acceleration error process is closer to a Brownian motion, and (iii) drivers press the gas pedal harder than usual when they come to an upgrade segment. Additionally, we explain the cause of traffic oscillations traveling downstream, which were observed both in the field data and in our simulations. The model is flexible so that newer vehicle technologies can be incorporated to test such hypotheses as differences in the car-following parameters of automated and regular vehicles, when data becomes available.

Car following and lane changing behavior using NGSIM and China data

Road safety is imperative theme because increasing road fatalities deaths in world. Besides road fatalities, traffic jam is increasing, human is frustrated for uncomfortable journey. The roads safety and passengers comfortable of the roadway system are vastly depended on the Car following (CF) and Lane Changing (LC) features of drivers. CF and LC theory describe the driver behavior by following paths in a traffic stream. In this research, researchers have compared to US-101 Next-Generation-Simulation (NGSIM) data with Beijing forth ring road, China freeways real trajectory data by CF and LC models. The CF data has been calibrated with Genetic Algorithm (GA). Reproducing Kernel Hilbert Space (RKHS) is generated the LC beginning and finishing points. Findings revealed that the CF parameters as maximum acceleration, minimum deceleration, free speed, minimum headway and stopping distance percentages of Chinese data are 74.71%, 79.95%, 66.57%, 0.018% and 65.65% respectively of NGSIM data. After completing the comparison, researchers have been found out optimization safety and comfortable acceleration-deceleration and LC beginning-finishing points of driver behavior. Here this analysis generates the driver behavior at real traffic network on the express highways of specific two roads US-101 (NGSIM) data and Chinese freeways data. Since NGSIM data is well simulated so road traffic is more safety and comfortable for journey.

Calibration of microscopic traffic simulation models using metaheuristic algorithms

Abstract This paper presents several metaheuristic algorithms to calibrate a microscopic traffic simulation model. The genetic algorithm (GA), Tabu Search (TS), and a combination of the GA and TS (i.e., warmed GA and warmed TS) are implemented and compared. A set of traffic data collected from the I-5 Freeway, Los Angles, California, is used. Objective functions are defined to minimize the difference between simulated and field traffic data which are built based on the flow and speed. Several car-following parameters in VISSIM, which can significantly affect the simulation outputs, are selected to calibrate. A better match to the field measurements is reached with the GA, TS, and warmed GA and TS when comparing with that only using the default parameters in VISSIM. Overall, TS performs very well and can be used to calibrate parameters. Combining metaheuristic algorithms clearly performs better and therefore is highly recommended for calibrating microscopic traffic simulation models.

Macroscopic and microscopic analyses of managed lanes on freeway facilities in South Florida

Abstract As congestion grows in metropolitan areas, agencies tend to utilize managed lanes on their freeway systems. Managed lanes have several forms and names, such as high-occupancy vehicle (HOV) lanes, high-occupancy toll (HOT) lanes, express lanes, and bus-only lanes. Although managed lanes have received significant attention as they increased the overall throughput and improved mobility without adding more lanes, little has been known about their operational capabilities. In addition, calibrating managed lane facilities can be challenging as they do not necessarily follow the same behavior with general purpose freeway lanes. This paper presents an operational analysis of two HOT lane segments located in South Florida. The sites are one-lane and two-lane segments separated by flexible pylons (FPs). The paper includes a macroscopic capacity analysis, and a microscopic calibration of the two sites using VISSIM microsimulation. The research findings assist in determining the capacity and speed-flow relationship of these segments, and also provide guidance for microsimulation model calibration for practitioners. The results of the study indicate that the percent drop in capacity for the one-lane FP site is 7.6% while the flow did not substantially change after the breakdown in the two-lane FP site. The research findings also include guidelines for simulating the breakdown events and calibrating one-lane and two-lane managed lane facilities in VISSIM microsimulation software. The Wiedemann car-following parameters (CC0 = 3.9 ft, CC1 = 1.9 s, CC2 = 26.25 ft, CC4 = −0.35, and CC5 = 0.35) provided the best fit for the one-lane FP site, while the combination (CC0 = 4.92 ft, CC1 = 1.9 s, CC2 = 39.37 ft, CC4 = −0.7, and CC5 = 0.7) parameters is recommended for the two-lane FP site.

Calibrating Vissim to Analyze Delay at Signalized Intersections

The level of service of an intersection is determined principally by control delay. Accordingly, control delay must be reproduced correctly when microscopic traffic simulation is used to evaluate intersections. This study demonstrated how Vissim could be calibrated for that purpose. Vissim models of four signalized intersections for which data had been collected were built. From these data, information that was extracted on headways, time to pass the intersection, and arrival distribution was used for calibration. Calibration of the headways resulted in car-following parameters for these intersections that differed substantially from the Vissim default values. An adjustment in the vehicle arrival distribution to the observed distribution was also necessary to reproduce the measured delay in the simulation.

A video-based approach to calibrating car-following parameters in VISSIM for urban traffic

Abstract Microscopic simulation models need to be calibrated to represent realistic local traffic conditions. Traditional calibration methods are conducted by searching for the model parameter set that minimizes the discrepancies of certain macroscopic metrics between simulation results and field observations. However, this process could easily lead to inappropriate selection of calibration parameters and thus erroneous simulation results. This paper proposes a video-based approach to incorporate direct measurements of car-following parameters into the process of VISSIM model calibration. The proposed method applies automated video processing techniques to extract vehicle trajectory data and utilizes the trajectory data to determine values of certain car-following parameters in VISSIM. This paper first describes the calibration procedure step by step, and then applies the method to a case study of simulating traffic at a signalized intersection in VISSIM. From the field-collected video footage, trajectories of 1229 through-movement vehicles were extracted and analyzed to calibrate three car-following parameters regarding desired speed, desired acceleration, and safe following distance, respectively. The case study demonstrates the advantages and feasibility of the proposed approach.

Micro-simulation of Desired Speed for Temporary Work Zone with a New Calibration Method

Nowadays, the studies of parameter calibration for long-term work zones are limited to driver behaviour and car-following parameters, and no research was found related to calibration of the desired speed distributions during temporary work zones. Obtaining realistic results from simulations of temporary work zones is difficult. Thus, it would be valuable for gaining more valid simulation data if a method of calibrating the desired speed distribution could be applied for traffic simulation model of highway temporary work zones. The calibration method was proposed in five steps: (1) collect and analyse data, (2) plot the travel speed cumulative frequency curves and calibrate the desired speed distribution, (3) conduct simulation runs, (4) validate the simulation results, and (5) propose a new calibration method, which was assessed by T-tests, and the results are very promising. Finally, a simplified calibration method called “Five-Point Method” is presented and the recommended values of five-point are given.

Simulation Guidance for Calibration of Freeway Lane Closure Capacity

This study provides a methodology for calibrating freeway work zone capacity in a microsimulation environment and guidance for replicating field-observed freeway work zone capacity through simulation. From 81 field observations at 12 U.S. work zone sites, 90 work zone sites from literature archival sources, and a macroscopic capacity model developed in NCHRP Project 03-107, the authors show how to replicate field-observed or forecast capacity in the Vissim simulation tool under various scenarios of freeway work zone lane closure. With guidance from an in-depth literature review, key car-following and lane-changing parameters are proposed as a result of the calibration effort and extensive sensitivity tests of numerous combinations of parameter values. Lane configuration specific guidance is provided for two key car-following parameters in Vissim, cc1 and cc2. During the process of developing the guidance, every tested capacity scenario was verified by using a lane use volume balance that was obtained upstream of the lane closure point in order to replicate realistic freeway work zone conditions. The calibration methodology as well as default parameter guidance developed through this research are useful to practitioners who wish to model freeway work zone impacts accurately through microsimulation.

Using a Living Laboratory to Support Transportation Research for a Freeway Work Zone

This paper presents the concept of a living laboratory (LL) and how it is applied to transportation operations research through a case study. This case study focuses on calibrating the Wiedemann car-following model parameters specific to freeway work zones. Applying the concept of an LL enables the experimental platform to be in a natural real-world environment. The design of this LL included the development of an instrumented research vehicle (IRV) to capture the natural car-following response of a driver when entering and passing through a freeway work zone. The development of a connected mobile traffic sensing (CMTS) system, which included state-of-the-art intelligent transportation systems (ITS) technologies, supports the LL environment by providing the connectivity, interoperability, and data processing of the natural, real-life setting. The IRV and CMTS system are tools designed based on the research objective to support the concept of an LL which facilitates the experimental environment to capture and calibrate natural driver behavior. This case study shows the application of an LL specific to operations research providing an experimental platform for evaluating the operational performance of a roadway in a real-time, connected, and collaborative natural environment.

Calibration, Estimation, and Sampling Issues of Car-Following Parameters

Drivers behave in different ways, and these different behaviors are a cause of traffic disturbances. A key objective for simulation tools is to correctly reproduce this variability, in particular for car-following models. From data collection to the sampling of realistic behaviors, a chain of key issues must be addressed. This paper discusses data filtering, robustness of calibration, correlation between parameters, and sampling techniques of acceleration-time continuous car-following models. The robustness of calibration is systematically investigated with an objective function that allows confidence regions around the minimum to be obtained. Then, the correlation between sets of calibrated parameters and the validity of the joint distributions sampling techniques are discussed. This paper confirms the need for adapted calibration and sampling techniques to obtain realistic sets of car-following parameters, which can be used later for simulation purposes.