Active modes take up an increasingly important place on the global policy-making agenda. In the Netherlands, a country that is well-known for its high shares of walking and cycling, the government aims at achieving a modal shift among 200,000 commuting car drivers towards using the bicycle. To this end, policy measures need to be introduced. When the aim is to achieve a modal switch over an enduring period of time, it is more relevant to know the likelihood of including or excluding a mode in the mode choice set, compared to choosing a mode for a single trip. Therefore, we investigate the formation of the experienced choice set (set of modes used over a long period of time), where the aim is to identify determinants that influence the inclusion or exclusion of a mode in this set. We estimate discrete choice models, based on survey data from the Netherlands Mobility Panel (MPN) and a complementary survey, where individuals were asked to report the frequency of using certain modes of transport for commuting trips over the course of half a year. This study shows that the experienced choice set for commuting is unimodal for the majority of the individuals, and remains constant over time for most individuals. Reimbursement by the employer for using a certain mode is the most important determinant influencing the experienced choice set, followed by ownership characteristics and urban density. We show that the mode choice set formation depends on more determinants than previously assumed.

Head-on collisions on two-lane rural highways might result from failed passing maneuvers. This article investigates the hypothesis that drivers do not always estimate the required passing gap correctly, and their decision to overtake is made under a particular, even if small, probability that a crash will occur; this is the associated risk. The research investigates drivers' irrationality in evaluating the risks of different passing gaps and develops a risk-taking measure. This measure provides a tool for classifying drivers into different groups: risky, partially cautious, and cautious drivers. A comparison of the sociodemographic averages, driving style, and crash history parameters for these groups showed significant differences. The measure developed can be used for risk evaluation and as a measure of safety. It further provides a tool for classifying drivers into groups based on their risk-taking characteristics. This is particularly useful for safety education programs.

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.

Passing maneuver on rural two-lane highways is a complex task, which has a significant effect on capacity, level of service and safety. The maneuver is conditioned on the gap between two successive vehicles on the opposing lane. The minimum time to collision, defined as the remaining gap between the passing vehicle and the oncoming vehicle at the end of the passing process, expresses a measure of the risk involved in the passing maneuver. This paper develops a model that explains the minimum time to collision. The model formulation is based on the analysis of drivers' passing decisions on two-lane rural highways using an interactive driving simulator. The simulator enables the collection of vehicle speeds and positions for different road and traffic scenarios. In addition to the driver simulator, participants responded to a questionnaire which collected information about their socio-demographic characteristics. The composed dataset was analyzed and processed to develop a model that predicts the risk associated with the passing behavior. Tobit regression models were found to be more suitable, in comparison to ordinary least square models and Hazard-based Duration models. The explanatory variables tested represent road geometry, traffic conditions and drivers' characteristics. It was found that while the traffic related variables had the most important effect on the measure of risk chosen, factors related to the geometric design and the driver characteristics also had a significant contribution.

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.