Driver Car-Following Behavior Research Articles

Effects of Driver Gender, Driving Experience, and Visibility on Car-Following Behavior

Weather visibility interference has a significant impact on driver car-following behavior. To investigate drivers’ car-following behavior and emergency avoidance behaviors under different visibility disturbances, scenarios are constructed under different foggy concentration environments based on driving simulation, and the drivers’ response behaviors are collected in the stable car-following state and emergency rear-end scenarios. Exploring the differential effects of gender and driving experience on driving behavior for fog concentrations based on multifactorial analysis of variance. A quantitative model of car-following risk is constructed based on factor analysis, and a linear mixed model is used to explore the comprehensive effects of fog concentration, speed, and the following distance at the braking time on drivers’ braking reaction time by fully considering the differences in individual behaviors. The results show that driving behavior is significantly affected by visual visibility, driver’s gender, and driving experience. With the decrease of visibility, following driving speed decreases, the following distance is shortened, the headway decreases, and the standard deviation of lane lateral offset distance increases. The rear-end collision risk of an experienced driver is higher than that of a novice driver, and the rear-end collision risk of the female is higher than that of male. The risk of collision is higher when traveling in light fog. In emergency rear-end collision scenarios, as visibility decreases, braking reaction time increases, and the risk of collision conflict increases at the moment of driver braking. The braking reaction time of the driver decreases with the increase of the speed and increases with the increase of the distance when the front vehicle is braking. The results of this study provide theoretical support and technical reference for effectively improving driving safety in a bad-visibility environment.

An improved car-following model considering the influence of multiple preceding vehicles in the same and two adjacent lanes

To investigate the driver's car-following behavior in multiple lanes, this study develops an improved multi-lane full velocity difference (FVD) model. This model considers the positions and widths of multiple preceding vehicles in the same lane in the longitudinal direction, as well as the lateral offset distances of the immediately preceding vehicles in the same and two adjacent lanes in the lateral direction. Based on the linear stability analysis, it is found that an increase in the lateral offset distances between a vehicle and its predecessors in the same lane is negative to the traffic flow stability, whereas an increase in the widths of front vehicles in the same lane within a certain range is beneficial to the traffic flow stability. These theoretical findings are verified by numerical simulations. With the use of a publicly available dataset of vehicle trajectories from Southeast University, this study extracts the microscopic traffic flow parameters of the following vehicles and their associated vehicles on urban roads in China. Through parameter calibration and model comparison, it is found that the developed improved multi-lane FVD model has greater adaptability than the conventional FVD model and existing improved FVD models, and its mean absolute error and root mean square error are to some extent optimized. With the rapid development of connected vehicle technology, the developed multi-lane FVD model can be used to assist drivers in making rational behavioral decisions and improve traffic flow stability.

Impact of Autonomous Vehicles on the Car-Following Behavior of Human Drivers

The past few years have been witness to an increase in autonomous vehicle (AV) development and testing. However, even with a fully developed and comprehensively tested AV technology, AVs are anticipated to share the roadway network with human drivers for the unforeseeable future. In such a mixed environment, we use naturalistic driving data from the next generation simulation (NGSIM) and Lyft Level 5 (Lyft L5) prediction data sets to investigate whether the existence of AVs influences the car-following behavior of human drivers. We use time headway time series as a proxy to capture the car-following behavior of human drivers. We then develop a nested fixed model to find possible changes in behavior when human drivers are following different types of vehicles (i.e., human-driven vehicles or AVs). The factors included in this model are the platoon structure (a legacy vehicle following a legacy vehicle, and a legacy vehicle following an autonomous vehicle), road type (freeway and urban), time period (morning and afternoon), lane (right, middle, and left), and the source of the data (NGSIM and Lyft L5). Results indicate a statistically significant difference between the car-following behavior of drivers when they follow a human-driven vehicle compared to an AV. This change in the car-following behavior of drivers is manifested in the form of a reduction in the mean and variance of time headways when human drivers follow an AV. These findings can bridge the gap between anticipated and real-world impacts of AVs on traffic streams and roadway capacity, providing meaningful insights on the influence of AVs on the driving behavior of humans in a naturalistic driving environment.

A Trajectory Compensation Method Considering the Car-Following Behavior for Data Missing of Millimeter-Wave Radar in Roadside Detection Applications.

Concerning roadside traffic detection applications, and to address the millimeter-wave radar's missing data problem caused by target occlusion or the absence of features in low-speed conditions, this paper proposes a trajectory compensation method regarding car-following behavior. Referring to the installation scheme of the detector, a coordinate transformation method is presented to unify the radar spatial coordinates with the road coordinates. Considering the driver's car-following behavior, the optimal velocity model (OV), full velocity difference model (FVD), and the full velocity difference and acceleration (FVDA) model are applied for tracking the vehicle's trajectory related to the movement of the vehicle ahead. Finally, a data compensation scheme is presented. Taking actual trajectory data as samples, the proposed methods are verifiably useful for compensating for missing data and reconstructing target trajectories. Statistical results of different missing data trajectories demonstrate the rationality of the application of car-following models for the missing data compensation, and the FVDA model performs well compared with the OV and FVD models.

Influence of Autonomous Vehicles on Car-Following Behavior of Human Drivers

Despite numerous studies on general human–robot interactions, in the context of transportation, automated vehicle (AV)–human driver interaction is not a well-studied subject. These vehicles have fundamentally different decision-making logic compared with human drivers and the driving interactions between AVs and humans can potentially change traffic flow dynamics. Accordingly, through an experimental study, this paper investigates whether there is a difference between human–human and human–AV interactions on the road. This study focuses on car-following behavior and conducted several car-following experiments utilizing Texas A&M University’s automated Chevy Bolt. Utilizing NGSIM US-101 dataset, two scenarios for a platoon of three vehicles were considered. For both scenarios, the leader of the platoon follows a series of speed profiles extracted from the NGSIM dataset. The second vehicle in the platoon can be either another human-driven vehicle (scenario A) or an AV (scenario B). Data is collected from the third vehicle in the platoon to characterize the changes in driving behavior when following an AV. A data-driven and a model-based approach were used to identify possible changes in driving behavior from scenario A to scenario B. The findings suggested there is a statistically significant difference between human drivers’ behavior in these two scenarios and human drivers felt more comfortable following the AV. Simulation results also revealed the importance of capturing these changes in human behavior in microscopic simulation models of mixed driving environments.

Validation of the Rakha-Pasumarthy-Adjerid car-following model for vehicle fuel consumption and emission estimation applications

The Rakha-Pasumarthy-Adjerid (RPA) car-following model has been demonstrated to successfully replicate empirical driver car-following behavior. However, the validity of this model for fuel consumption and emission (FC/EM) estimation has yet to be studied. This paper attempts to address this research need by analyzing the applicability of the model for FC/EM estimation and comparing its performance to other state-of-practice car-following models; namely, the Gipps, Fritzsche and Wiedemann models. Naturalistic empirical data are employed to generate ground truth car-following events. The model-generated second-by-second Vehicle Specific Power (VSP) distributions for each car-following event are then compared to the empirical distributions. The study demonstrates that the generation of realistic VSP distributions is critical in producing accurate FC/EM estimates and that the RPA model outperforms the other three models in producing realistic vehicle trajectory VSP distributions and robust FC/EM estimates. This study also reveals that the acceleration behavior within a car-following model is one of the major contributors to producing realistic VSP distributions. The study further demonstrates that the use of trip-aggregated results may produce erroneous conclusions given that second-by-second errors may cancel each other out, and that lower VSP distribution errors occasionally result in greater bias in FC/EM estimates given the large deviation of the distribution at high VSP levels. Finally, the results of the study demonstrate the validity of the INTEGRATION micro-simulator, given that it employs the RPA car-following model, in generating realistic VSP distributions, and thus in estimating fuel consumption and emission levels.

Evaluation of Driver Car-Following Behavior Models for Cooperative Adaptive Cruise Control Systems

A cooperative adaptive cruise control (CACC) system targeted to obtain a high level of user acceptance must replicate the driving experience in each CACC vehicle without compromising the occupant’s comfort. “User acceptance” can be defined as the safety and comfort of the occupant in the CACC vehicle in terms of acceptable vehicle dynamics (i.e., the maximum acceleration or deceleration) and string stability (i.e., the fluctuations in the vehicle’s position, speed, and acceleration). The primary objective of this study was to develop an evaluation framework for the application of a driver car-following behavior model in CACC system design to ensure user acceptance in terms of vehicle dynamics and string stability. The authors adopted two widely used driver car-following behavior models, ( a) the optimum velocity model (OVM) and ( b) the intelligent driver model (IDM), to prove the efficacy of the evaluation framework developed in this research for CACC system design. A platoon of six vehicles was simulated for three traffic flow states (uniform speed, speed with constant acceleration, and speed with constant deceleration) with different acceleration and deceleration rates. The maximum acceleration or deceleration and the sum of the squares of the errors of the follower vehicle speed were measured to evaluate user acceptance in terms of vehicle dynamics and string stability. Analysis of the simulation results revealed that the OVM performed better at modeling a CACC system than did the IDM in terms of acceptable vehicle dynamics and string stability.

Calibration of Nonlinear Car-Following Laws for Traffic Oscillation Prediction

Frequency-domain analysis has been successfully used to (i) predict the amplification of traffic oscillations along a platoon of vehicles with nonlinear car-following laws and (ii) measure traffic oscillation properties (e.g., periodicity, magnitude) from field data. This paper proposes a new method to calibrate nonlinear car-following laws based on real-world vehicle trajectories, such that oscillation prediction (based on the calibrated car-following laws) and measurement from the same data can be compared and validated. This calibration method, for the first time, takes into account not only the driver's car-following behavior but also the vehicle trajectory's time-domain (e.g., location, speed) and frequency-domain properties (e.g., peak oscillation amplitude). We use Newell's car-following model (1961) as an example and calibrate its parameters based on a penalty-based maximum likelihood estimation procedure. A series of experiments using Next Generation Simulation (NGSIM) data are conducted to illustrate the applicability and performance of the proposed approach. Results show that the calibrated car-following models are able to simultaneously reproduce observed driver behavior, time-domain trajectories, and oscillation propagation along the platoon with reasonable accuracy.

New time-based surrogate safety measure to assess crash risk in car-following scenarios

Surrogate safety measures have been employed as road safety indicators for decades; numerous measures have been developed to replicate the volume and relative severity of adverse incidents in various regions. “Time-to-collision” (TTC) is one of the most frequently used time-based surrogate safety measures. In the current study, a new application of “TTC” has been developed for the enhancement of road safety assessment. Values for both the rate of “TTC” variation and the level of hazard associated with “TTC” have been amalgamated as an approximate safety indicator. A micro-simulation model is used to validate the newly developed measure with respect to the assessment of the level of safety associated with a freeway (Interstate-80). Results suggest that the developed measure more accurately considers driver car-following behavior; consequently, detection of potentially hazardous collisions is significantly improved. The proposed technique for collision prediction is shown to provide a more accurate level of risk assessment with respect to car-following scenarios; thus, it may present an opportunity for increasing the precision associated with in-vehicle collision avoidance warning systems (IVCAWS).

Do cooperative systems make drivers’ car-following behavior safer?

The main goal of in-vehicle technologies and co-operative services is to reduce congestion and increase traffic safety. This is achieved by alerting drivers on risky traffic conditions ahead of them and by exchanging traffic and safety related information for the particular road segment with nearby vehicles. Road capacity, level of service, safety, and air pollution are impacted to a large extent by car-following behavior of drivers. Car-following behavior is an essential component of micro-simulation models. This paper investigates the impact of an infrastructure-to-vehicle (I2V) co-operative system on drivers’ car-following behavior. Test drivers in this experiment drove an instrumented vehicle with and without the system. Collected trajectory data of the subject vehicle and the vehicle in front, as well as socio-demographic characteristics of the test drivers were used to estimate car-following models capturing their driving behavior with and without the I2V system. The results show that the co-operative system harmonized the behavior of drivers and reduced the range of acceleration and deceleration differences among them. The observed impact of the system was largest on the older group of drivers.

Exploring a Local Linear Model Tree Approach to Car‐Following

AbstractBecause car‐following (CF) models are fundamental to replicating traffic flow they have received considerable attention over the last 50 years. They are in a continuous state of improvement due to their significant role in traffic microsimulations, intelligent transportation systems, and safety engineering models. This article uses the local linear model tree (LOLIMOT) approach to model driver's CF behavior to incorporate human perceptual imperfections into a CF model. This model defines some localities in the input space. These localities are fuzzy and have overlaps with each other. Specific models for each of the localities are then defined and combined in a fuzzy manner to predict the final output. The model was developed using real world dynamic data sets. Three different data sets were used for training, testing, and validating the model. The performance of the model was compared to a number of existing CF models. The results showed very close agreement between the real data and the LOLIMOT outputs.

New Car-Following Model considering Impacts of Multiple Lead Vehicle Types

In the past decade, the development and the application of traffic micro-simulation to replicate real-world traffic behavior have become pervasive among traffic and transport researchers. The modeling of a driver's car-following behavior, which forms the fundamental component of traffic microsimulation, has meanwhile been an important research direction leading to the sophistication of traffic microsimulation. However, recent studies have pointed out that a driver's following behavior varies when the lead vehicle is a passenger car as opposed to a heavy vehicle. Nevertheless, existing models do not precisely address those differences. This oversight could diversely affect the accuracy of traffic microsimulations, particularly with the current trend of an increasing number of heavy vehicles in the traffic stream. A novel car-following model that considered the heterogeneity of lead vehicles was developed. Two types of lead vehicles were considered in this study: passenger cars and heavy vehicles. The model was developed on the basis of the local linear model tree approach. This approach is able to incorporate human perceptual imperfections into a car-following model. The input space is partitioned incrementally, and a linear model is developed for each locality (partition). The final output is calculated by the fuzzy combination of local models according to the validity function of each model. For training and testing purposes, two real-world data sets were obtained from a U.S. freeway under congested traffic conditions. The results showed very close agreement between the real data and the outputs of the proposed model.

Application of Naturalistic Driving Data to Modeling of Driver Car-Following Behavior

The driver-specific data available from naturalistic driving studies provide a unique perspective from which to test and calibrate car-following models. As equipment and data storage costs continue to decline, the collection of data through in situ probe-type vehicles is likely to become more popular, and thus there is a need to assess the feasibility of these data for the modeling of driver car-following behavior. This study focused on the costs and benefits of naturalistic data for use in mobility applications. Any project seeking to use naturalistic data should plan for a complex and potentially costly data reduction process to extract mobility data. A case study was based on data from the database of the 100-Car Study conducted by the Virginia Tech Transportation Institute. One thousand minutes' worth of data comprising more than 2,000 car-following events recorded across eight drivers from a section of multilane highway located near Washington, D.C., was compiled. The collected event data were used to calibrate four different car-following models, and a comparative analysis of model performance was conducted. The results of model calibration are given in tabular format, displayed on the fundamental diagram, and shown with sample event charts of speed versus time and headway versus time. When compared with the Gipps, intelligent driver, and Gaxis–Herman–Rothery models, the Rakha–Pasumarthy–Adjerid model was found to perform best in matching individual drivers and in matching aggregate results.

Driver Anticipation in Car Following

In normal driving conditions, drivers can usually anticipate with a good degree of accuracy the movements of vehicles ahead of them. Such anticipation can reduce the gaps between vehicles and produce high flows on freeway lanes. This anticipation effect is introduced in a car-following model in continuous space and discrete time, along with a set of driving rules that capture the car-following behavior of drivers. Detailed analysis shows that the model can simulate the full range of traffic conditions—free flow, congestion, and traffic jams under fixed and moving bottlenecks—and produce realistic flow capacities and fundamental diagrams with different levels of anticipation. Moreover, analysis of jam formation and decay reveals the underlying relationship between the parameters of the model and the triangular fundamental diagram. The platoon effect can be captured in the model by gap anticipation level (a) dependent on the gap.

Empirical Study of Impact of Icy Roadway Surface Condition on Driver Car-Following Behavior

Research was done to quantify the impact of icy roadway conditions on driver car-following behavior. The data used in the study were gathered by a group of researchers at Japan's Hokkaido University in a controlled environment under dry and icy roadway conditions. The data were used to calibrate the Van Aerde steady-state car-following model, along with vehicle acceleration and deceleration constraints. The impact of icy roadway conditions on five driver-specific car-following parameters [driver perception–reaction time (PRT), free-flow or desired speed, speed-at-capacity, capacity, and jam density] was conducted with one-way analysis of variance and Kruskal–Wallis tests. The results demonstrate that icy roadway conditions produce statistically significant differences at a 5% significance level. Specifically, icy roadway conditions reduce the mean free-flow speed, speed at capacity, and capacity by 28%, 13%, and 46%, respectively, compared with driving on dry roadways. The mean PRT for icy conditions is found to take 13% longer than driving under dry conditions. The longer PRTs can be attributed to drivers driving at lower speeds and larger spacing on icy surfaces compared with dry conditions.

Bayesian Calibration of Car-Following Models

Abstract Recent research has revealed that there exist large inter-driver differences in car-following behavior such that different car-following models may apply to different drivers. This study applies Bayesian techniques to the calibration of car-following models, where prior distributions on each model parameter are converted to posterior distributions. The priors and posteriors are then used to calculate the so-called ‘evidence’, which can be used to quantitatively assess how well different models explain one driver's car-following behavior. When considered over multiple drivers, the evidence represents probabilities for different models as a whole. These model probabilities can be used in a micro simulation, where for each driver first a model is drawn according to these probabilities, after which parameters are drawn from the posterior distribution for each parameter of that model that were obtained when calibrating the model. In a test case on actual data the Bayesian evidence indeed reveals inter-driver differences and it is shown how these differences can quantitatively be assessed.

Factor analysis for choosing input variables of a car-following model

The driver car-following is a very complex phenomenon and it is very hard to realistically simulate the driver is under the influence of many factors that cannot be identified. Most of the existing car-following models were developed on the basis of a sensible understanding of traffic phenomena, and some have made statistical analysis of the field data, but the question which factors affect driver car-following behavior is not well answered. Namely, the input variables of car-following models are usually chosen empirically and no further theoretical studies are done. In this paper, the nonlinear statistical method of factor analysis is used to extract useful information from representative field data and those endogenous variables with higher information are selected as input variables to establish a car-following model. Finally, the model is verified used data collected through the Five-Wheel systems experiment. For the covering abstract see ITRD E129315.

Driver's Car-Following Behavior

Driver's Car-Following Behavior

Car-Following Measurements, Simulations, and a Proposed Procedure for Evaluating Safety

Video data of highway traffic were used to study the effect of illumination, weather, and traffic density on the driver car-following behavior. A driving simulator was used to evaluate the effect of driver intention, gender, and experience on braking reaction time and following distance. The results show that the environmental factors influenence driver behavior mainly in congested traffic but not in free-flow traffic. Driver experience, gender and intention to pass all show some effects on car-following behavior. The distribution of driver reaction time fits a log-normal model. A safety evaluation procedure is proposed based on the obtained statistical distributions.