Young drivers display more risk-taking behavior than other age groups. Performing distracting tasks is one such risky behavior that is observed among young drivers. However, due to inexperience, young drivers may not be able to appropriately compensate for the effects of distractions. A driving simulator study with 53 young drivers (aged 18–21) was conducted to assess the level of engagement with an in-vehicle secondary task. A cluster analysis revealed three groups of drivers that differed based on eye glance behavior and driving performance: drivers with low-risk, moderate-risk, and high-risk behavior. A subset of these drivers was provided with feedback to help modulate their distracting activities with the riskiest group benefitting most from feedback as indicated by enhanced glance behavior and driving performance. The findings have implications for developing better crash countermeasures to mitigate the effects of distraction.

This study was conducted to examine the ability of two ramp metering strategies at reducing the real-time crash risk along a typical urban freeway. The strategies were tested using a simulated freeway network of Interstate-4 in Orlando, Fla. created in PARAMICS microsimulation. Measurements of the crash risk along the freeway were estimated using models created by the first writer which calculate rear-end and lane-change risks along the freeway using offline geometric characteristics and real-time loop detector data. The ramp metering strategies tested were the uncoordinated ALINEA algorithm and the coordinated Zone ramp metering algorithm. Additionally, two implementation methods of these algorithms were examined: the traffic-cycle realization which allows vehicles to enter the network in platoons and the one-car-per-cycle (OCPC) realization which allows a single vehicle to enter the freeway per traffic cycle. This study shows that the ALINEA and Zone algorithms both successfully reduce the real-time crash risk along the freeway. Comparing the two implementation methods, the traffic-cycle realization provides better safety and operational benefits than the OCPC realization. However, the OCPC realization provides better safety benefits when applied with the ALINEA algorithm. In general, the ALINEA algorithm performs better using shorter cycle lengths while the Zone algorithm performs best using longer cycle lengths. Comparing the two metering algorithms, the ALINEA algorithm proves to be superior to the Zone algorithm in providing the best overall safety benefits since it is more restrictive and generally allows fewer vehicles onto the network. However, at the 90% loading scenario, evidence of crash risk migration (the lowering of crash risk at one location combined with the increase at another) appears when the ALINEA algorithm is applied. This crash risk migration is a function of the specific geometry and demand patterns of the network used and is reduced by applying the Zone algorithm. This shows that while the ALINEA algorithm provides better overall results, there are cases where a less restrictive algorithm would perform better from a safety perspective.

There is an ongoing debate among transport planners and safety policy makers as to whether there is any association between the level of traffic congestion and road safety. One can expect that the increased level of traffic congestion aids road safety and this is because average traffic speed is relatively low in a congested condition relative to an uncongested condition, which may result in less severe crashes. The relationship between congestion and safety may not be so straightforward, however, as there are a number of other factors such as traffic flow, driver characteristics, road geometry, and vehicle design affecting crash severity. Previous studies have employed count data models (either Poisson or negative binomials and their extensions) while developing a relationship between the frequency of traffic crashes and traffic flow or density (as a proxy for traffic congestion). The use of aggregated crash counts at a road segment level or at an area level with the proxy for congestion may obscure the actual relationship. The objective of this study is to explore the relationship between the severity of road crashes and the level of traffic congestion using disaggregated crash records and a measure of traffic congestion while controlling for other contributory factors. Ordered response models such as ordered logit models, heterogeneous choice models, and generalized ordered logit (partially constrained) models suitable for both ordinal dependent variables and disaggregate crash data are used. Data on crashes, traffic characteristics (e.g., congestion, flow, and speed), and road geometry (e.g., curvature and gradient) were collected from the M25 London orbital motorway between 2003 and 2006. Our results suggest that the level of traffic congestion does not affect the severity of road crashes on the M25 motorway. The impact of traffic flow on the severity of crashes, however, shows an interesting result. All other factors included in the models also provide results consistent with existing studies.

Transportation agencies worldwide increasingly seek to abandon reactive approaches for safety enhancement in favor of approaches that incorporate safety in their transportation planning processes in a proactive, comprehensive, and systemwide context. To facilitate this task, this paper presents a framework which identifies candidate facilities in a transportation network for safety enhancement over a specified analysis period. Then for each candidate facility, the framework identifies safety enhancement projects on the basis of existing facility deficiencies and predominant accident patterns. Thus, a safety plan is developed to specify for each candidate facility, the year of the enhancement, the expected cost, and the resulting safety benefits. Also, where the safety budget is limited, the framework determines the subset of candidate facilities that should receive safety investment. At most transportation agencies, the typical transportation planning process is carried out only for engineering interventions. As such, the developed framework is engineering interventions only and thus excludes safety interventions related to road-user education, enforcement, and policy. With requisite data, the developed framework can be applied in safety planning and programming in the different modes of transportation–highway, airport, rail, and marine. Using an example in highway transportation, the paper applies the framework to develop a safety plan for a chosen network and thus demonstrates how safety could be included proactively in the network-level (systemwide) transportation planning process.

Dilemma zone (DZ) is an area where drivers can neither stop comfortably nor cross safely when they are presented with a yellow indication at an intersection. The DZ problem is a leading cause for crashes at high-speed signalized intersections. Researchers traditionally assume that vehicles in DZ are equally hazardous and therefore they use the number of vehicles in DZ as an indicator of safety. There is, however, a general understanding among researchers that a DZ-associated accident potential (dilemma hazard thereafter) will vary depending on the subject vehicle’s position and speed at the yellow onset. Based on the previous studies on drivers’ response to the yellow onset, vehicle kinematics, and a Monte Carlo simulation framework, we present a dilemma hazard model for vehicles in the DZ. In light of this model, each vehicle in DZ is assigned a hazard weight according to its position and speed when the yellow is presented. The proposed model is calibrated and validated using the vehicle trajectory data collected at a high-speed signalized intersection in Christiansburg, Va. Finally, the dilemma hazard model is used to obtain optimal clearance interval for the study site.

A detection scheme that uses classical and spatial statistics has been developed to identify roadways with the most severe safety needs. It is based on the null hypothesis that all roadways have the same crash risk, that is, all have the same nonfatal and fatal crash rates throughout the entire study region. Fatal and nonfatal crash rates, which are assumed to be randomly distributed as Poisson processes, are modeled with a marked homogeneous Poisson process model. Since the traffic exposures are typically unknown at the crash sites, they are predicted with a geostatistical model. Locations, where the null hypothesis is rejected, are safety treatment candidates. $P$ -value risk rankings are used to identify locations with the most severe safety needs. The results from alternative analyses—one using vehicle miles traveled and another using population as measures of traffic exposure—are conducted and compared. The state of New Hampshire is used in a case study. The effects of analyzing areas with small traffic exposures, the so-called “small-area estimation” problem, and the aim to develop a data-driven detection scheme that meets the requirements as an objective decision-making tool are discussed.

Emergency brake assist, adaptive cruise control, and alternative instantiations of intelligent vehicle control systems aspire to support the driver in controlling the vehicle and alleviate the incidents that would lead to collisions and injury. This paper resets to zero and based on data from the on-the-spot accident study challenges the capability of active safety systems to aim at the sources of longitudinal control failures. The road user interactions file from 3,024 road accidents in Thames Valley and Nottinghamshire in U.K. was analyzed. Interactions where “failure to stop” or “sudden braking” is the precipitating factor are analyzed and the main contributory factors are identified. Some of those factors are addressed by current and coming technologies—such as low road friction, excessive speed, and close following, but other common ones are significantly neglected—such as distraction, failure to judge other person’s path, failure to look, and “look, but did not see” instances.

A multitude of new technologies (ranging from guidance systems to speed-limit exceeding systems and to fatigue detection systems) are emerging, many of which are either explicitly targeted to older drivers or expected to benefit them the most. However, these same older drivers are more likely to find adapting to the use of such technologies challenging. Therefore, understanding older drivers’ perception of such devices will allow experts to take the necessary steps to ensure their smoother acceptance and complete success of their deployment. Using Greek drivers’ attitude data collected within the scope of an extensive recent survey in 23 European countries (the SARTRE-3 data set), a statistical analysis of the perception of usefulness and acceptance of new technologies by older drivers is presented. The results of the developed ordered logit models provide insight into the human-factors’ aspect of the introduction of advanced technologies with respect to these more sensitive segments of the driver population. For example, older respondents are, in general, more supportive of the considered in-vehicle technologies, while female respondents also show a higher willingness to adopt them.

Transportation agencies expend large amounts of money annually to maintain highway wearing surfaces. Wear depends mainly on axle weight, vehicle speed, temperature, surface type, and the type of tires mounted on vehicles. When studded tires are used, wear is increased significantly. Past studies on the use of studded tires have highlighted the need for a tool to better estimate the number of vehicles with studded tires that travel a road network. Currently, there is no such tool available. This paper presents a detection methodology using acoustic emission techniques to identify vehicles operating with studded tires. Data from an in-service test on a highway bridge were used for developing and evaluating two proposed detection schemes. It was found that using relatively simple detection algorithms, vehicles with studded tires could be discriminated reliably. Finally, a practicable integrated system is proposed that could be implemented for detection and monitoring of studded snow tire use on highway systems.