Long-term predictability of driver behavior

Multiple regression was used to predict current accidents and violations for 2283 subjects from Iowa and California who had completed the Driver Attitude Survey [Schuster and Guilford, 1964] 10–18 yr previously and had had driver records recorded at that time. It was found that, on the whole, current violations could be successfully predicted with these data, while current accidents could not be. The results lead to the conclusion that violation behavior is more stable over time than had previously been acknowledged, previous research having looked at driver behavior in consecutive time periods only.     

Unintentional shootings, highway crashes and acts of violence—A behavior paradigm

Characteristics of Vermont residents involved as shooters in unintentional shootings of all degrees of severity were compared with those of residents whom they shot and residents of same sex and similar age, most of whom owned guns but who were not involved in shooting incidents. In comparison with the other two groups, the shooters more often were previously known to the police, had arrests for violence, had arrests involving alcohol, and crashes and traffic citations during the previous 3 years and during their lifetimes. A behavior model is suggested, commonly involving alcohol, in which poor control of aggressive tendencies results in intentional acts of violence, and unintentional injury both on the highway and elsewhere.     

Perceived risk as a determinant of driver behavior

Decision skill appears to be the most error-prone component of the driving task. It may therefore be the prime source of road accidents. Decisions which drivers make about vehicle maneuvres appear to be largely a function of the risk they perceive in the situation. Three studies were therefore carried out to investigate drivers' risk-taking behavior.     

The effects of vehicle model and driver behavior on risk

We study the dependence of risk on vehicle type and especially on vehicle model. Here, risk is measured by the number of driver fatalities per year per million vehicles registered. We analyze both the risk to the drivers of each vehicle model and the risk the vehicle model imposes on drivers of other vehicles with which it crashes. The "combined risk" associated with each vehicle model is simply the sum of the risk-to-drivers in all kinds of crashes and the risk-to-drivers-of-other-vehicles in two-vehicle crashes. We find that most car models are as safe to their drivers as most sport utility vehicles (SUVs); the increased risk of a rollover in a SUV roughly balances the higher risk for cars that collide with SUVs and pickup trucks. We find that SUVs and to a greater extent pickup trucks, impose much greater risks than cars on drivers of other vehicles; and these risks increase with increasing pickup size. The higher aggressivity of SUVs and pickups makes their combined risk higher than that of almost all cars. Effects of light truck design on their risk are revealed by the analysis of specific models: new unibody (or "crossover") SUVs appear, in preliminary analysis, to have much lower risks than the most popular truck-based SUVs. Much has been made in the past about the high risk of low-mass cars in certain kinds of collisions. We find there are other plausible explanations for this pattern of risk, which suggests that mass may not be fundamental to safety. While not conclusive, this is potentially important because improvement in fuel economy is a major goal for designers of new vehicles. We find that accounting for the most risky drivers, young males and the elderly, does not change our general results. Similarly, we find with California data that the high risk of rural driving and the high level of rural driving by pickups does not increase the risk-to-drivers of pickups relative to that for cars. However, other more subtle differences in drivers and the driving environment by vehicle type may affect our results.     

Evidence that implementation intentions reduce drivers’ speeding behavior: Testing a new intervention to change driver behavior

Implementation intentions have the potential to break unwanted habits and help individuals behave in line with their goal intentions. We tested the effects of implementation intentions in the context of drivers’ speeding behavior. A randomized controlled design was used. Speeding behavior, goal intentions and theoretically derived motivational pre-cursors of goal intentions were measured at both baseline and follow-up (one month later) using self-report questionnaires. Immediately following the baseline questionnaire, the experimental (intervention) group (N = 117) specified implementation intentions using a volitional help sheet, which required the participants to link critical situations in which they were tempted to speed with goal-directed responses to resist the temptation. The control group (N = 126) instead received general information about the risks of speeding. In support of the hypotheses, the experimental group reported exceeding the speed limit significantly less often at follow-up than did the control group. This effect was specific to ‘inclined abstainers’ (i.e., participants who reported speeding more than they intended to at baseline and were therefore motivated to reduce their speeding) and could not be attributed to any changes in goal intentions to speed or any other measured motivational construct. Also in line with the hypotheses, implementation intentions attenuated the past-subsequent speeding behavior relationship and augmented the goal intention – subsequent speeding behavior relationship. The findings imply that implementation intentions are effective at reducing speeding and that they do so by weakening the effect of habit, thereby helping drivers to behave in accordance with their existing goal intentions. The volitional help sheet used in this study is an effective tool for promoting implementation intentions to reduce speeding.     

A utility maximization model of driver traffic safety behavior

A simple utility maximization model is presented to illustrate that risk compensation is a natural part of human behavior when individuals pursue multiple goals with limited resources. In this positive economic model driver safety effort is determined by a balance between reduced risk and increased disutility cost. Changes which affect the balance induce drivers to change their own safety efforts. Under plausible conditions a change in exogenous safety, which is beyond driver control, causes a compensatory change in driver effort in the opposite direction. A sample of special seat belt use studies illustratively indicates the usefulness of the model.     

Modeling driver stop/run behavior at the onset of a yellow indication considering driver run tendency and roadway surface conditions

The ability to model driver stop/run behavior at signalized intersections considering the roadway surface condition is critical in the design of advanced driver assistance systems. Such systems can reduce intersection crashes and fatalities by predicting driver stop/run behavior. The research presented in this paper uses data collected from two controlled field experiments on the Smart Road at the Virginia Tech Transportation Institute (VTTI) to model driver stop/run behavior at the onset of a yellow indication for different roadway surface conditions. The paper offers two contributions. First, it introduces a new predictor related to driver aggressiveness and demonstrates that this measure enhances the modeling of driver stop/run behavior. Second, it applies well-known artificial intelligence techniques including: adaptive boosting (AdaBoost), random forest, and support vector machine (SVM) algorithms as well as traditional logistic regression techniques on the data in order to develop a model that can be used by traffic signal controllers to predict driver stop/run decisions in a connected vehicle environment. The research demonstrates that by adding the proposed driver aggressiveness predictor to the model, there is a statistically significant increase in the model accuracy. Moreover the false alarm rate is significantly reduced but this reduction is not statistically significant. The study demonstrates that, for the subject data, the SVM machine learning algorithm performs the best in terms of optimum classification accuracy and false positive rates. However, the SVM model produces the best performance in terms of the classification accuracy only.     

A comparison of the factor loading matrices of two driver behavior investigations

The article reports on two analyses of driver behavior conducted separately, one carried out in Vienna (Austria) and the other in Tübingen (Germany). The data on which the analysis was based were in both cases subjective judgements of 40 behavior characteristics of drivers. In each case two observers accompanying the subject in his vehicle noted the degree in which these characteristics were evident on a nine-point rating scale. Differences between the Vienna investigation and the Tübingen study lay in the team of observers, the test route, the type of vehicle and the size of the samples. The programs for measuring similarities between factor matrices have only recently been developed. The comparison deals primarily with interpretation of the contents of the resulting factor loading matrix and additionally with the interpretation of the measures of similarity for each variable. The experiments are described only as far as is desirable for an understanding of the two basic matrices. The aim of the project was to determine components of driver behavior in terms of reliable, meaningful measures which allow generalization.     

How to identify the key factors that affect driver perception of accident risk. A comparison between Italian and Spanish driver behavior

Road crashes can be caused by different factors, including infrastructure, vehicles, and human variables. Many research studies have focused solely on identifying the key factors that cause road crashes. From these studies, it emerged that human factors have the most relevant impact on accident severity. More specifically, accident severity depends on several factors related directly to the driver, i.e., driving experience, driver's socio-economic characteristics, and driving behavior and attitudes. In this paper, we investigate driver behaviors and attitudes while driving and specifically focus on different methods for identifying the factors that most affect the driver's perception of accident risk. To this end, we designed and conducted a survey in two different European contexts: the city of Cosenza, which is located in the south of Italy, and the city of Granada, which is located in the south of Spain. Samples of drivers were contacted for their opinions on certain aspects of driving rules and attitudes while driving, and different types of questions were addressed to the drivers to assess their judgments of these aspects. Consequently, different methods of data analysis were applied to determine the aspects that heavily influence driver perception of accident risk. An experiment based on the stated preferences (SP) was carried out with the drivers, and the SP data were analyzed using an ordered probit (OP) model. Interesting findings emerged from different analyses of the data and from the comparisons among the data collected in the two different territorial contexts. We found that both Italian and Spanish drivers consider driving in an altered psychophysical state and violating the overtaking rules to be the most risky behaviors.     

Effect of shoulder width,guardrail and roadway geometry on driver perception and behavior

Roadway design is one of the most significant factors that affect driving behavior and perceived safety. The current study tests the combined effects of three roadway design elements – shoulders width, guardrail existence and roadway geometry (curvature) – on objective driving measures (speed and lane position), and subjective measures (perceived safe driving speed and estimated road safety).Twenty two drivers participated in an experiment with a driving simulation. In the first part objective driving data were collected, and in the second part subjective paper–pencil evaluations were requested of the perceived safety of 30 different scenarios that were previously experienced in the simulator. The scenarios consisted of the various combinations of the three roadway design elements.The results showed a significant effect of roadway geometry on both objective and subjective measures. The shoulders width had a significant effect on actual speed, on lane position, and on perceived safe driving speed, but only when a guardrail was present.These findings illustrate the perceptual role of a guardrail in defining the perceived safety margins that various shoulder widths provide. When a guardrail is absent, the width of the shoulder loses much of its benefits and effects on driving behavior. The results also demonstrate that roadway geometry can be used to reduce driving speeds, but at the same time it can have a negative effect on maintaining a stable lane position in sharp curves. Thus, controlling the width of road shoulders and the placement of guardrails seems to be a safer approach to speed and lane position control.     

Effect of shoulder width, guardrail and roadway geometry on driver perception and behavior

Roadway design is one of the most significant factors that affect driving behavior and perceived safety. The current study tests the combined effects of three roadway design elements – shoulders width, guardrail existence and roadway geometry (curvature) – on objective driving measures (speed and lane position), and subjective measures (perceived safe driving speed and estimated road safety).Twenty two drivers participated in an experiment with a driving simulation. In the first part objective driving data were collected, and in the second part subjective paper–pencil evaluations were requested of the perceived safety of 30 different scenarios that were previously experienced in the simulator. The scenarios consisted of the various combinations of the three roadway design elements.The results showed a significant effect of roadway geometry on both objective and subjective measures. The shoulders width had a significant effect on actual speed, on lane position, and on perceived safe driving speed, but only when a guardrail was present.These findings illustrate the perceptual role of a guardrail in defining the perceived safety margins that various shoulder widths provide. When a guardrail is absent, the width of the shoulder loses much of its benefits and effects on driving behavior. The results also demonstrate that roadway geometry can be used to reduce driving speeds, but at the same time it can have a negative effect on maintaining a stable lane position in sharp curves. Thus, controlling the width of road shoulders and the placement of guardrails seems to be a safer approach to speed and lane position control.     

Examining the influence of aggressive driving behavior on driver injury severity in traffic crashes

In this paper, we capture the moderating effect of aggressive driving behavior while assessing the influence of a comprehensive set of variables on injury severity. In doing so, we are able to account for the indirect effects of variables on injury severity through their influence on aggressive driving behavior, as well as the direct effect of variables on injury severity. The methodology used in the paper to accommodate the moderating effect of aggressive driving behavior takes the form of two models – one for aggressive driving and another for injury severity. These are appropriately linked to obtain the indirect and direct effects of variables. The data for estimation is obtained from the National Motor Vehicle Crash Causation Study (NMVCCS). From an empirical standpoint, we consider a fine age categorization until 20 years of age when examining age effects on aggressive driving behavior and injury severity.There are several important results from the empirical analysis undertaken in the current paper based on post-crash data collection on aggressive behavior participation just prior to the crash and injury severity sustained in a crash. Young drivers (especially novice drivers between 16 and 17 years of age), drivers who are not wearing seat belt, under the influence of alcohol, not having a valid license, and driving a pick-up are found to be most likely to behave aggressively. Situational, vehicle, and roadway factors such as young drivers traveling with young passengers, young drivers driving an SUV or a pick-up truck, driving during the morning rush hour, and driving on roads with high speed limits are also found to trigger aggressive driving behavior. In terms of vehicle occupants, the safest situation from a driver injury standpoint is when there are two or more passengers in the vehicle, at least one of whom is above the age of 20 years. These and many other results are discussed, along with implications of the result for graduated driving licensing (GDL) programs.     

An outcome evaluation of the ‘Skipper’ designated driver program

The general aim of designated driver programs is to reduce the level of drink driving by encouraging potential drink drivers to travel with a driver who has abstained from (or at least limited) consuming alcohol. Designated driver programs appear to be quite widespread around the world, however a limited number have been subject to rigorous evaluation. This paper reports results from an outcome evaluation of a designated driver program called ‘Skipper’, which was trialled in a provincial city in Queensland, Australia. The outcome evaluation included surveys three weeks prior to (baseline), four months following (1st follow-up), and 16 months following (2nd follow-up) the commencement of the trial in both the ‘intervention area’ (baseline, n = 202; 1st follow-up, n = 211; 2nd follow-up, n = 200) and a ‘comparison area’(baseline, n = 203; 1st follow-up, n = 199; 2nd follow-up, n = 201); and a comparison of random breath testing and crash data before and after the trial. The survey results indicate that awareness of the program in the intervention area was quite high four months following its introduction and that this was maintained at 16 months. The results also suggest that the ‘Skipper’ program and the related publicity had positive impacts on behaviour with an increase in the proportion of people participating in designated driver as a passenger. It is less clear, however, whether the ‘Skipper’ program impacted on other behaviours of interest, such as drink driving or involvement in alcohol-related crashes. Suggestions for further research and program improvement are discussed as well as limitations of the research.     

A survey of recent driver steering behavior models suited to accident studies

A literature survey for the years 1975–1980 has been carried out in the area of driver models describing steering behavior. The purpose of this search was to locate models which could be applied to potential roadway accident scenarios. A range of candidate driver models was found and their main features briefly outlined. Where possible, model parameters and sample response time histories have been included. It was found that no well-developed and validated model for the detailed study of accidents yet exists. The paper concludes with a brief assessment of the merits of the various models presented.     

Temporal perspective on individual driver behavior using electronic records of undesirable events

This paper explores In-Vehicle Data Recorders (IVDRs) information about the count of undesirable driving events (such as hard braking, lane changing, and sharp turning) of 148 individuals. The information was logged over three years and included time stamp information about the occurrence of undesirable driving events in each trip (N = 573,238). The objective was to gain deeper understanding about the heterogeneity among drivers with respect to behavior change over time, the effect of trip duration and the distribution of events count. Our findings show that in some respects drivers are similar: for all drivers, the variance of the events count was larger than the mean, indicating that the negative binomial distribution is suitable to model the distribution of events count per trip. Most drivers (95%) had lower events rate during longer trips, suggesting that a ‘simple’ events rate index is problematic when comparing between those driving longer trips and drivers driving short trips. In addition, most drivers (87%) improved their driving behavior throughout the measurement period. However, there are important differences among drivers in terms of the frequency of behavior change and the trends in behavior over time. These findings demonstrate the need for personalized examination of individual drivers. Several tools for such personalized examination were developed and discussed in this study.     

Framework design of highway traveller information system of Jiangsu province in China

Guidelines that were adopted for the implementation of the highway traveller information system (HTIS) in the Jiangsu province of China are presented. Framework design is discussed including HTIS architecture and functional requirements. The HTIS architecture levels, of which there are seven, are illustrated and the major functions of each logical level are discussed. Analysis is made of three topology patterns (centralised topology, distributed topology and hybrid topology) of the HTIS, and the topology framework based on hybrid topology is presented accordingly. Furthermore, the layout framework of the Jiangsu province HTIS with consideration of the freeway network and normal highway network. This layout framework establishes the foundation for further development of the Jiangsu province HTIS     

Quantifying the yellow signal driver behavior based on naturalistic data from digital enforcement cameras

The yellow signal driver behavior, reflecting the dilemma zone behavior, is analyzed using naturalistic data from digital enforcement cameras. The key variable in the analysis is the entrance time after the yellow onset, and its distribution. This distribution can assist in determining two critical outcomes: the safety outcome related to red-light-running angle accidents, and the efficiency outcome. The connection to other approaches for evaluating the yellow signal driver behavior is also discussed.The dataset was obtained from 37 digital enforcement cameras at non-urban signalized intersections in Israel, over a period of nearly two years. The data contain more than 200 million vehicle entrances, of which 2.3% (∼5 million vehicles) entered the intersection during the yellow phase. In all non-urban signalized intersections in Israel the green phase ends with 3 s of flashing green, followed by 3 s of yellow. In most non-urban signalized roads in Israel the posted speed limit is 90 km/h.Our analysis focuses on crossings during the yellow phase and the first 1.5 s of the red phase. The analysis method consists of two stages. In the first stage we tested whether the frequency of crossings is constant at the beginning of the yellow phase. We found that the pattern was stable (i.e., the frequencies were constant) at 18 intersections, nearly stable at 13 intersections and unstable at 6 intersections. In addition to the 6 intersections with unstable patterns, two other outlying intersections were excluded from subsequent analysis. Logistic regression models were fitted for each of the remaining 29 intersection. We examined both standard (exponential) logistic regression and four parameters logistic regression. The results show a clear advantage for the former. The estimated parameters show that the time when the frequency of crossing reduces to half ranges from1.7 to 2.3 s after yellow onset. The duration of the reduction of the relative frequency from 0.9 to 0.1 ranged from 1.9 to 2.9 s.     

Effects of fog,driver experience and gender on driving behavior on S-curved road segments

Driving on curved roads has been recognized as a significant safety issue for many years. However, driver behavior and the interactions among variables that affect driver performance on curves is complicated and not well understood. Previous studies have investigated various factors that influence driver performance on right- or left-turn curves, but have paid little attention to the effects of foggy weather, driver experience and gender on driver performance on complex curves. A driving simulator experiment was conducted in this study to evaluate the relationships between driving behavior on a continuous S-curve and foggy weather, driver experience and gender. The process of negotiating a curve was divided into three stages consisting of a straight segment, the transition from the straight segment to the S-curve and the S-curve. The experimental results indicated that drivers tended to drive more cautiously in heavy fog, but the driving risk was still increased, especially in the transition stage from the straight segment to the S-curve. The non-professional (NP) drivers were less sensitive to the impending change in the road geometry, and less skilled in both longitudinal and lateral vehicle control than the professional drivers. The NP female drivers in particular were found to be the most vulnerable group in S-curve driving.     

Driver anger on the information superhighway: A content analysis of online complaints of offensive driver behaviour

In recent years, several websites have been developed allowing drivers to post their complaints about other motorists online. These websites allow drivers to describe the nature of the offensive behaviour and to identify the offending motorist by vehicle type, colour, and license plate number. Some websites also ask drivers to list the location where the event took place and the exact date and time of the offence. The current study was a content analysis of complaints posted to RoadRagers.com between 1999 and 2007 (N = 5624). The purpose of the study was to: (1) assess the research value of this novel data source; (2) demonstrate the value of content analysis to the study of driver behaviour; (3) further validate an existing coding scheme; (4) determine whether this new data source would replicate previous research findings regarding the most frequent types of driver complaints and temporal distribution of these reports; (5) provide recommendations for improved driver training and public safety initiatives based on these data. A coding scheme that was originally developed for an assessment of complaints submitted to the Ontario Provincial Police (OPP) ( Wickens et al., 2005) was revised to accommodate the new dataset. The inter-rater reliability of the revised coding scheme as applied to the website complaints was very good (kappa = .85). The most frequently reported improper driver behaviours were cutting/weaving, speeding, perceived displays of hostility, and tailgating. Reports were most frequent on weekdays and during the morning and afternoon rush hour. The current study replicated several findings from the analysis of reports to the OPP, but possible differences in the sample and data collection method also produced some differences in findings. The value of content analysis to driver behaviour research and of driver complaint websites as a data source was demonstrated. Implications for driver safety initiatives and future research will be discussed.