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 @en

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.@en

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.@en

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.@en

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 @en

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.@en

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@en