Estimating Percentile Speeds from Maximum Operating Speed Frontier

Abstract

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...

Flexible Stochastic Frontier Approach to Predict Spot Speed in Two-Lane Highways

“…Compared to the authors' previous research (Lobo et al 2013;Lobo et al 2014), the database was enriched in this study with data from two recently built IP/IC roads. These roads are representative of the two-lane IP/IC roads existing in Portugal featuring overall design speeds of 80 to 90 km/h.…”

“…Tarris et al (1996) referred 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, which may bias the effects of road geometrics, proposing that modeling the entire free-flow speed distribution may help to overcome the problem. The only existing models Despite also using the entire free-flow speed distribution, the authors' approach to percentile speed modeling (Lobo et al 2014) is based on stochastic frontier models usually applied in the field of econometrics (Aigner et al 1977;Meeusen and van der Broeck 1977). The OSFM is estimated using the maximum likelihood method to establish an upper speed frontier, which represents the speeds of the fastest free-flow drivers in good weather and pavement conditions as a function of road geometrics.…”

“…The data treatment included the transformation of the continuous variables in log terms to comply with the OSFM formulation (Lobo et al 2014), as well as the creation of dummy variables to represent specific effects and/or to improve the model specifications. The speed predictors are described as follows: Tables 1 and 2 for N roads and IP/IC roads, respectively.…”

“…This work builds on the authors' previous studies on operating speed modeling (Lobo et al 2013;Lobo et al 2014), bringing together the major accomplishments of those studies while enlarging their scope and applicability. The OSFM formulation was developed and successfully tested in Lobo et al (2014).…”

“…The OSFM formulation was developed and successfully tested in Lobo et al (2014). Compared to previous models, the new models combine the estimation of any percentile speed defined by the user with the consideration of the segment characteristics and allow speed predictions for roads with higher design speeds.…”