This research presents the data collection, specification and estimation of a route choice model for intercity truck trips, with a focus on toll road usage. The data was obtained from driver-validated and enhanced GPS records. A mixed logit model with a path-size factor is specified. It accounts for heterogeneity among drivers using distributed coefficients for travel time and its variability. The estimation results show wide heterogeneity among drivers based on employment type and availability of electronic toll collection tags. Toll value of time and toll value of reliability distributions are derived. The model application is demonstrated on several trip corridors.

Driving assistance systems such as Adaptive Cruise Control (ACC) and automated vehicles can contribute to mitigate traffic congestion, accidents, and levels of emissions. Automated vehicles may increase roadway capacity, improve traffic flow stability, and speed up the outflow from a queue (1). The functionalities of automated systems have been gradually introduced into the market, such as in the case of Adaptive Cruise Control (ACC). The ACC assists drivers in maintaining a desired speed and time headway, therefore influencing substantially the performance of the driving task. On-road studies have shown potential safety benefits of ACC systems that are inactive at low speeds when they are activated (2-5). In certain traffic situations, drivers may prefer to disengage ACC and resume manual control (6). These transitions between automation and manual driving are called control transitions (7) and may influence considerably traffic flow efficiency (8) and safety (9). Recently, full-range ACC systems that can operate in dense traffic have been introduced into the market. These ACC systems are more likely to be active in dense traffic conditions and have a positive impact on traffic flow efficiency

Adaptive Cruise Control (ACC) and automated vehicles can contribute to reduce traffic congestion and accidents. Recently, an on-road study has shown that drivers may prefer to deactivate full-range ACC when closing in on a slower leader and to overrule it by pressing the gas pedal a few seconds after the activation of the system. Notwithstanding the influence of these control transitions on driver behaviour, a theoretical framework explaining driver decisions to transfer control and to regulate the target speed in full-range ACC is currently missing.

This research develops a modelling framework describing the underlying decision-making process of drivers with full-range ACC at an operational level, grounded on Risk Allostasis Theory (RAT). Based on this theory, a driver will choose to resume manual control or to regulate the ACC target speed if its perceived level of risk feeling and task difficulty falls outside the range considered acceptable to maintain the system active. The feeling of risk and task difficulty evaluation is formulated as a generalized ordered probit model with random thresholds, which vary between drivers and within drivers over time. The ACC system state choices are formulated as logit models and the ACC target speed regulations as regression models, in which correlations between system state choices and target speed regulations are captured explicitly. This continuous-discrete choice model framework is able to address interdependencies across drivers’ decisions in terms of causality, unobserved driver characteristics, and state dependency, and to capture inconsistencies in drivers’ decision making that might be caused by human factors.

The model was estimated using a dataset collected in an on-road experiment with full-range ACC. The results reveal that driver decisions to resume manual control and to regulate the target speed in full-range ACC can be interpreted based on the RAT. The model can be used to forecast driver response to a driving assistance system that adapts its settings to prevent control transitions while guaranteeing safety and comfort. The model can also be implemented into a microscopic traffic flow simulation to evaluate the impact of ACC on traffic flow efficiency and safety accounting for control transitions and target speed regulations

The study examined the influence of affect induction on actual risk-taking behavior in a driving simulator, as well as the links between personal variables (relevance of driving to self-esteem, sensation seeking) and the level of risky driving. Eighty young drivers aged 18–21 (M = 19.24, SD = 0.75) were randomly divided into four induction groups: relaxing positive affect; arousing positive affect; negative affect; and neutral affect. The participants drove on a simulator, with various parameters of risky driving measured before and after emotion priming. As predicted, arousing positive affect and negative affect led to increased risky driving, whereas relaxing positive affect moderated risk-taking. In addition, the results confirm previous findings regarding the personal variables, revealing that higher levels of relevance of driving to self-esteem and sensation seeking are associated with higher levels of risk-taking in the simulated driving. The findings indicate that the driver's emotional state has a significant effect on risk-taking on the road. Moreover, they show that the conventional use of negative affect in safe driving campaigns is liable to heighten the tendency for risky driving rather than reduce it. In contrast, relaxing positive affect was found to lead to lower risk-taking. The study is unique in revealing a correlation between results previously obtained for the willingness to drive recklessly and actual risky driving behavior observed on a driving simulator. By expanding the understanding of the motivations for youngsters’ risky driving, the study may aid in designing effective, theoretically sound, interventions aimed at reducing the tendency for dangerous driving among young drivers.

Automated vehicles and driving assistance systems such as adaptive cruise control (ACC) are expected to reduce traffic congestion, accidents, and levels of emissions. Field operational tests have found that drivers may prefer to deactivate ACC in dense traffic flow conditions and before changing lanes. Despite the potential effects of these control transitions on traffic flow efficiency and safety, most mathematical models evaluating the impact of ACC do not adequately represent that process. This research aimed to identify the main factors influencing drivers' choice to resume manual control. A mixed logit model that predicted the choice to deactivate the system or overrule it by pressing the gas pedal was estimated. The data set was collected in an on-road experiment in which 23 participants drove a research vehicle equipped with full-range ACC on a 35.5-km freeway in Munich, Germany, during peak hours. The results reveal that drivers were more likely to deactivate the ACC and resume manual control when approaching a slower leader, when expecting vehicles cutting in, when driving above the ACC target speed, and before exiting the freeway. Drivers were more likely to overrule the ACC system by pressing the gas pedal a few seconds after the system had been activated and when the vehicle decelerated. Everything else being equal, some drivers had higher probabilities to resume manual control. This study concludes that a novel conceptual framework linking ACC system settings, driver behavior characteristics, driver characteristics, and environmental factors is needed to model driver behavior in control transitions between ACC and manual driving.

Automated vehicles and driving assistance systems such as Adaptive Cruise Control (ACC) are expected to reduce traffic congestion, accidents and levels of emissions. Field Operational Tests have found that drivers may prefer to deactivate ACC in dense traffic flow conditions and before changing lanes. Despite the potential effects of these control transitions on traffic flow efficiency and safety, most mathematical models evaluating the impact of ACC do not adequately represent this process. This research aims to identify the main factors influencing drivers’ choice to resume manual control. A mixed logit model that predicts the choice to deactivate the system or overrule it by pressing the gas pedal was estimated. The dataset was collected in an on-road experiment in which twenty-three participants drove a research vehicle equipped with full-range ACC on a 35.5-km freeway in Munich during peak hours. The results reveal that drivers are more likely to deactivate the ACC and resume manual control when approaching a slower leader, when expecting vehicles cutting in, when driving above the ACC target speed, and before exiting the freeway. Drivers are more likely to overrule the ACC system by pressing the gas pedal a few seconds after the system has been activated, and when the vehicle decelerates. Everything else being equal, some drivers have higher probabilities to resume manual control. We conclude that a novel 16 conceptual framework linking ACC system settings, driver behavior characteristics, driver characteristics and environmental factors is needed to model driver behavior in control transitions between ACC and 18 manual driving

The rapid increase in the availability of smartphones and other infotainment devices, and their widespread use while driving, contributes significantly to car crash rates. Understanding of the impact of distracting activities on drivers’ behavior is essential in order to support the development of effective technology and policy solutions to mitigate its potential risk. The main objective of this study is to investigate the impact of several distracting activities on driving performance. A driving simulator experiment was developed to collect data on several driver performance measures while undertaking different distracting activities. 101 volunteer drivers participated in the study (68 males and 33 females) with age range between 18 to 57 years old. Each driver drove four scenarios on a two-lane rural highway, while undertaking various activities: (1) using a hand-held cell-phone; (2) texting; (3) eating; and (4) a control scenario (no distracting activity). In all of these scenarios the distracting activity took place during the entire scenario. Each scenario took on average 4 minutes to complete. The speeds and accelerations of the other vehicles within the scenarios were chosen randomly from pre-defined uniform distributions. Data on the longitudinal and lateral movements of the vehicles were recorded in the experiment and various measures were calculated from this raw data. In addition, all participants completed a questionnaire on their personal characteristics, their frequency of involvement in distracting activities and level of distractibility, and the Cognitive Failures Questionnaire. Significant differences were found in the driving performance measures among drivers undertaking the various distracting activities. In particular, texting had the largest negative effect on driving performance. The results of this study show that distracting activities negatively impact driving performance for both genders and all age groups, regardless of their experience in performing a second task while driving.

Vigilant care aims at reducing adolescent risk behaviors while matching parental involvement to the level of alarm signs. This study examined the effect of parent training in vigilant care and technological feedback on driving risk of novice male drivers. A sample of 217 Israeli families was divided into four conditions: a) no-feedback, b) individual feedback, c) family feedback, and d) family feedback plus parent training in vigilant care. Feedback and risk assessment were conducted through in-vehicle data recorders. A significant difference was found in favor of the vigilant care group compared to the no feedback group. When only the drivers in the high risk percentiles were considered, the vigilant care group was found superior to the family feedback group. The findings suggest that parental training in vigilant care may help reduce driving risk.

This study focuses on investigating the driving behavior of young novice male drivers during the first year of driving (three months of accompanied driving and the following nine months of solo driving). The study's objective is to examine the potential of various feedback forms on driving to affect young drivers' behavior and to mitigate the transition from accompanied to solo driving. The study examines also the utility of providing parents with guidance on how to exercise vigilant care regarding their teens' driving. Driving behavior was evaluated using data collected by In-Vehicle Data Recorders (IVDR), which document events of extreme g-forces measured in the vehicles. IVDR systems were installed in 242 cars of the families of young male drivers, however, only 217 families of young drivers aged 17-22 (M = 17.5; SD = 0.8) completed the one year period. The families were randomly allocated into 4 groups: (1) Family feedback: In which all the members of the family were exposed to feedback on their own driving and on that of the other family members; (2) Parental training: in which in addition to the family feedback, parents received personal guidance on ways to enhance vigilant care regarding their sons' driving; (3) Individual feedback: In which family members received feedback only on their own driving behavior (and were not exposed to the data on other family members); (4) Control: Group that received no feedback at all. The feedback was provided to the different groups starting from the solo period, thus, the feedback was not provided during the supervised period. The data collected by the IVDRs was first analyzed using analysis of variance in order to compare the groups with respect to their monthly event rates. Events' rates are defined as the number of events in a trip divided by its duration. This was followed by the development and estimation of random effect negative binomial models that explain the monthly event rates of young drivers and their parents. The study showed that: (1) the Parental training group recorded significantly lower events rates (-29%) compared to the Control group during the solo period; (2) although directed mainly at the novice drivers, the intervention positively affected also the behavior of parents, with both fathers and mothers in the Parental training group improving their driving (by -23% for both fathers and mothers) and mothers improving it also in the Family feedback group (by -30%). Thus, the intervention has broader impact effect beside the targeted population. It can be concluded that providing feedback on driving behavior and parental training in vigilant care significantly improves the driving behavior of young novice male drivers. Future research directions could include applying the intervention to a broader population, with larger diversity with respect to their driving records, culture, and behaviors. The challenge is to reach wide dissemination of IVDR for young drivers accompanied by parents' involvement, and to find the suitable incentives for its sustainability.

Young drivers in Israel, as in other parts of the world, are over-represented in car crashes. In an effort to reduce their crash involvement, a graduated driver licensing (GDL) system has been introduced, which requires all new drivers to be accompanied by an experienced driver for the first three months after obtaining a driving license. This study aims to characterize the driving patterns of young drivers in the accompanied driving period and immediately thereafter. We use information gathered from an in-vehicle data recorder (IVDR), which was installed in the primary vehicles driven by 217 young male drivers. It monitors all trips made with the vehicle and identifies the driver. We study the amount of driving young drivers undertake and the characteristics of the temporal and spatial distributions of these trips. We find substantial differences between the driving patterns characteristics in the two periods. These changes suggest an increase in exposure to risk in the solo period: The young drivers almost double the amount of driving they undertake in the solo period compared to the accompanied period. The amount of driving is highest immediately after the transition to solo driving, and gradually decreases in the weeks that follow. The timing of their driving time also changes as they drive much more during riskier conditions in the late evening and night hours and in more complex driving environments in built areas and on arterial and collector roads during the solo period. These results may be useful in that they can be used to generate realistic guidance to novice drivers and their accompanying drivers on required or suggested amounts of accompanied driving overall and in various situations. They may also suggest ways to refine the constraints imposed on novice drivers within the GDL program, such as on nighttime driving.

This study examines the impact of the provision of feedback and guidance about parental monitoring on the safety performance of young male drivers during their first year of driving. The research used an in-vehicle data recorder (IVDR), which documented events of extreme gravitational forces measured in the vehicles that participated in the experiment. Two hundred forty-two families of young male drivers participated in the research. Participants were randomly allocated into four groups: (a) family feedback, no guidance, in which all members of a family were exposed to feedback on their own driving and on that of other family members; (b) family feedback, parental guidance, in which, in addition to the family feedback, parents received personal guidance on ways to enhance their involvement with and monitor their sons' driving; (c) individual feedback, no guidance, in which family members received feedback only on their own driving behavior and not that of other family members; and (d) a control group, which received no feedback at all. IVDRs were installed in family cars for 12 months, starting from the time that the young driver received his driver's license. This period included the initial 3 months of the accompanied driving phase and 9 months of independent driving. The driving exposure of young drivers increased significantly during the solo period compared with that during the accompanied period. The results indicate substantial differences in behavior between young drivers in the control group and the group that received both feedback and guidance on parental involvement.

A substantial proportion of the road network in most countries consists of two-lane highways. Available gaps for passing are a fundamental element in the operation of such highways. Providing passing opportunities is important for reducing the formation of vehicle platoons in the traffic flow, increasing the level of service, and improving safety. Passing opportunities also affect fuel consumption and emissions. Despite the importance of passing on two-lane highways, few studies have focused on exploring passing gap definitions when modeling passing behavior. Research was done to investigate various definitions of passing gaps, and these definitions were used to develop passing gap acceptance models. Data on passing maneuvers collected with a driving simulator were used to develop and calibrate three models. The generic structure of these models was composed of the drivers' desire to pass and their gap acceptance decisions. The impact of traffic characteristics, road geometry, and driver characteristics was included in these models. The results show that the passing gap definition has a significant impact on the models' ability to explain passing behavior. Moreover, the estimation results show that modeling a driver's desire to pass the vehicle ahead has a statistically significant contribution in explaining passing behavior. Variables that capture the impact of the traffic conditions, geometric characteristics of the road section, driver characteristics, and the unobserved heterogeneity in the driver population were found to have a significant impact on drivers' desire to pass and their gap acceptance decisions.

Two-lane highways make up a substantial proportion of the road network in most of the world. Passing is among the most significant driving behaviors on two-lane highways. It substantially impacts the highway performance. Despite the importance of the problem, few studies attempted to model passing behavior. In this research, a model that attempts to capture both drivers' desire to pass and their gap acceptance decisions to complete a desired passing maneuver is developed and estimated using data on passing maneuvers collected with a driving simulator. Sixteen different scenarios were used in the experiment in order to capture the impact of factors related to the various vehicles involved, the road geometry and the driver characteristics in the model. A passing behavior model is developed that includes choices in two levels: the desire to pass and the decision whether or not to accept an available passing gap. The probability to complete a passing maneuver is modeled as the product of the probabilities of a positive decision on both these choices. The estimation results show that modeling the drivers' desire to pass the vehicle in front has a statistically significant contribution in explaining their passing behavior. The two sub-models incorporate variables that capture the impact of the attributes of the specific passing gap that the driver evaluates and the relevant vehicles, the geometric characteristics of the road section and the driver characteristics and account for unobserved heterogeneity in the driver population.

Passing manoeuvres on rural two-lane highways significantly affect highway capacity, safety and level of service. This article presents an analysis of data on drivers' passing decisions on two-lane rural highways that were collected with an interactive driving simulator. Measurements of the speeds and positions of all vehicles in several different scenarios were collected and processed to generate observations of gap acceptance behaviour. In addition, participants responded to a questionnaire which collected information on their socio-demographic and driving styles characteristics. These data were utilised to develop a model that explains the decision whether to pass or not, using variables that capture the impact of the road geometry, traffic conditions and drivers' characteristics. It was found that while the traffic related variables had the most important effect on passing decision, factors related to the geometric design and the driver characteristics also had a significant effect on these decisions.

Overtaking maneuvers on two-lane rural roads are difficult maneuvers which involve relatively complicated decisions. The main hypothesis tested in this paper is that the frequency of overtaking maneuvers on a driving simulator is associated with a faulty decision making style in the Iowa Gambling Task (IGT), a popular decision task employed for assessing cognitive impulsivity. In a controlled study, 36 participants drove a scenario involving multiple overtaking decisions in an interactive driving simulator (STISIM) and also completed the IGT. The results show a significant negative correlation of about 0.3 between the IGT performance and the number of overtaking maneuvers, the average driving speed, and the acceleration noise. We also found a positive correlation of 0.5 between IGT performance and the percent of aborted overtaking maneuvers. A cognitive modeling analysis shows that the associations appear to be modulated by weighting of gains compared to losses obtained during repeated play. These results demonstrate that the IGT has a potential to predict risk prone behavior in overtaking maneuvers and in driving in general.

This paper presents research to evaluate drivers' passing maneuvers on two-lane rural roads using data collected with STISIM, an interactive driving simulator. In addition to the observations of driving behavior obtained in the simulator experiments, drivers' socioeconomic characteristics and indicators of their driving style were collected using self-reported questionnaires. The participants were asked to drive a 9.5 km twolane rural road section with no intersections. The positions and speeds of the subject vehicle and the other simulated vehicles were recorded at a resolution of 0.1 seconds. The data collected in the experiment was used to develop a model that explains drivers' passing decisions. The results indicate that the speed of the subject vehicle and its relations to the vehicle being passed are the most important factors affecting passing behavior. In addition, drivers' socio-demographic characteristics and driving styles also affect passing decisions.