Most operating speed studies have focused on modeling a specific percentile speed, most notably the 85th, as a function ofthe road geometrícs, This method has resulted in some drawbacks, such as the loss of information due to speed data aggregation, the inability to capture speed dispersion, and few references about the effects of the driving culture and vehicIe characteristics on the practiced speeds. Therefore, ao operating speed frontier model to improve speed prediction capabilities, is presented. The deterministic component of the model represents the maximum operating spot speed as a function ofthe local geometric features, whereas the disturbance term incIudes the nongeometric effects, such as driving behavior, type of vehicIe, and road environment. Data are collected in 88 curves and tangents of Portuguese two-lane highways located outside urban areas; approximately 18,000 free-fíow vehicIes were observed. Following ao innovative approach to operating speed modeling, the model is estimated with a stochastic frontier regression between the speeds of ali free-fíow vehicIes and the geometric features at the measurement sítes. lo addition to the maximum operating speed, the new model is capable of estimating aoy percentile speed through the cumulative function of the one-síded disturbance while avoiding speed data aggregation. Moreover, the road geometric features required to implement the model are easy to obtain either by consulting the design project or by performing on-síte measurements; this ability contributes to the model's applicability in different regions.Operating speed studies have gained relevance across the past decades since several countries started to consider the predicted dri ving speed as an input to the definition of roadway geometric standards in the guidelines for road designo In several studies the research community, public authorities, and road operators have developed the prediction of operating speed and evaluated the effects of different factors on the speed, such as road geometry and functional classification, roadside interference, traftic, speed lirnits, and weather conditions. These studies produced a large number of tools for speed modeling (1, 2) and design consistency evaluation (3, 4) that are used by practitioners worldwide.The AASHTO Green Book recognizes the 85th percentile of the speed distribution as the most commonly used operating speed measure (5). However, in Transportation Research Circular E-CI5I (1), it is pointed out that most regression models estimate only a specific percentile speed, which is one of the main deficiencies in speed modeling. Tarris et alo reported that the loss of information due to speed data aggregation reduces the total variability and the nature of the variability associated with the regression function; this loss may bias the influence of road geometrics (6). Tarris et alo propose that modeling the entire free-f1ow speed distribution may help to overcome the problem. Figueroa Medina and Tarko developed speed models for different percent...

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The differences in the effects of risk factors between the classifications highlight the importance of using a reliable classification of injury severity. Additionally, the relationship between LHS and MAIS levels is quite different among countries, supporting the previous conclusion that bias is expected in the assessment of risk factors if an injury severity classification other than MAIS is used.

Speed is a key performance measure in economic and environrnental analyses of two-lane highways. Speed, combined with the percentage of time spent following, is also used in the assessment of levei of service.Under free-ftow conditions, the circulation of a given vehicle is not con- Speed is a major factor in the assessment of road performance. Depending on the functional classification of a given road, a design speed is established, and engineers define the geometric features of the road to ensure that drivers can, in normal traffic conditions, achieve the expected average travei speed to reach their destinations on time. Furthermore, an operating speed may be estimated for performance evaluation during the road operation period. Real environment speed measurements are required, and the operating speed is typically associated with the 85th percentile of the observed speed distribution (1). Speed is usually recognized by road planners, designers, and users as an important measure for the evaluation ofthe levei of service, speed limit defínition, design consistency and safety analyses, and other essential studies.Recognizing the role of speed in road performance evaluation, the Highway Capacity Manual 2010 (HCM) (2) recommends speed as the most appropriate concept for use in the economic DC, 2013, pp. 12-18. DOI: 10.3141/2348 and environmental analyses of two-Iane highways, including the assessment of the impact on air quality and noise leveI. ln addition, the HCM methodology to assess the levei of service of this type of road also uses the average travei speed as an input, which in tum depends on the free-flow speed (FFS) and on the traffic volume. ln other words, the average travei speed adds the effects of the delays caused by the remaining traffic to the FFS. The FFS reflects the drivers' response to the road's geometric and environmental features because drivers are not affected by the presence of other vehicles. The definition of the FFS proposed by the HCM is similar to the definition of operating speed given by the AASHTO Green Book (1). However, operating speed may also be affected by drivers' perception of risk, speed limit and enforcement, general driving practices and culture, and vehicle technology. For this reason, speed prediction models have been developed in different regions worldwide and in different time periods.Reference manuals and national guides for road design usually define operating speed for a road section rather than for specific design elements. The HCM (2) establishes a base FFS as the speed observed for roads presenting no access points and lane and shoulder widths equal to or greater than 3.6 m and 1.8 m, respectively. The HCM also provides an FFS estimation model, taking into account speed reductions to the base FFS caused by smaller cross sections and higher densities of access points.The AASHTO Green Book (1) also provides recommendations for design and operating speeds. Different design speeds are suggested according to the functional classification intended for a planned ro...

Drowsiness and fatigue are major safety issues that cannot be measured directly. Their measurements are sustained on indirect parameters such as the effects on driving performance, changes in physiological states, and subjective measures. We divided this study into two distinct lines. First, we wanted to find if any driver’s physiological characteristic, habit, or recent event could interfere with the results. Second, we aimed to analyze the effects of subjective sleepiness on driving behavior. On driving simulator experiments, the driver information and driving performance were collected, and responses to the Karolinska Sleepiness Scale (KSS) were compared with these parameters. The results showed that drowsiness increases when the driver has suffered a recent stress situation, has taken medication, or has slept fewer hours. An increasing driving time is also a strong factor in drowsiness development. On the other hand, robustness, smoking habits, being older, and being a man were revealed to be factors that make the participant less prone to getting drowsy. From another point of view, the speed and lane departures increased with the sleepiness feeling. Subjective drowsiness has a great correlation to drivers’ personal aspects and the driving behavior. In addition, the KSS shows a great potential to be used as a predictor of drowsiness.

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