Reliability-based geometric design analysis is more suitable for explicitly addressing the level of variability and randomness associated with design inputs than is a more deterministic design approach. Reliability analysis was used to estimate the probability distribution of operational performance that might result from basic decisions made to achieve a design level of service (LOS) for number of lanes on a freeway. The concept was demonstrated with data from I-15 and I-80 in Utah. To account for uncertainty in the design inputs, statistical distributions were developed, and reliability analysis was carried out with Monte Carlo simulation. The outcome of this probabilistic analysis was a distribution of vehicle density for a given number of lanes during the design hour. The main benefit of reliability analysis is that it enables designers to explicitly consider uncertainties in their decision making and to illustrate specific values of the distributions that correspond with the target LOS (e.g., the 65th through 85th percentile density levels correspond to those in the design LOS). The results demonstrate how uncertainty in estimates of the percentage of daily traffic in the design hour, the directional distribution, the percentage of heavy vehicles, and the free-flow speed significantly contribute to variation in vehicle density on a freeway.
Highway and street designers deal with the challenge of designing for a broad range of driver, vehicle, and roadway characteristics and conditions. There is significant variability in design inputs and design controls that influence design criteria and design decisions. This variability has traditionally been considered implicitly through selected values for geometric design parameters and criteria. Average values are used when the variability in the input design parameters is small. Conservative values are used if the variability is larger, often the case within the highway geometric design context. Previous research has demonstrated that addressing this variability and uncertainty more explicitly as part of design decisions can lead to better-informed and more cost-effective design decisions. Probabilistic design approaches that quantify both risk and reliability have been successfully incorporated into other design disciplines for those reasons. These approaches have also been explored in the highway geometric design literature and have shown promise. They are likely to be central to future performance-based design initiatives, as outlined in a recently published framework on conducting performance-based geometric design analysis. Given the emerging importance of performance-based design and the need to address challenges regarding the current method of handling variability and uncertainty in the input design parameters, this paper presents a collective review and assessment of methodological alternatives for quantifying risk and reliability associated with geometric design criteria and decisions.
Road safety modelers frequently use average annual daily traffic (AADT) as a measure of exposure in regression models of expected crash frequency for road segments and intersections. Recorded AADT values at most locations are estimated by state and local transportation agencies with significant uncertainty, often by extrapolating short-term traffic counts over time and space. This uncertainty in the traffic volume estimates, often termed in a modeling context as measurement error in right-hand-side variables, can have serious effects on model estimation, including: 1) biased regression coefficient estimates; and 2) increases in dispersion. The structure and magnitude of measurement error in AADT estimates are not clearly understood by researchers or practitioners, leading to difficulties in explicitly accounting for this error in statistical road safety models, and ultimately in finding solutions for its correction. This study explores the impacts of measurement error in traffic volume estimates on statistical road safety models by employing measurement error correction approaches, including regression calibration and simulation extrapolation. The concept is demonstrated using crash, traffic, and roadway data from rural, two-lane horizontal curves in the State of Washington. The overall results show that the regression coefficient estimates with a positive coefficient were larger and those with a negative coefficient were smaller (i.e., more negative) when the measurement error correction methods were applied to the regression models of expected crash frequency. Future directions in applications of measurement error correction approaches to road safety research are provided.
Statistical road safety modelers have commonly used some combination of segment length and traffic volume as measures of exposure. Traffic volume is usually represented in statistical road safety models with annual average daily traffic (AADT), which turns out to be a highly influential right-hand-side variable for regression models of expected crash frequency. Models that use AADT alone do not explicitly capture differences in traffic volume patterns throughout the 24-h day; this factor can have significant effects on safety performance. This study adds to the existing literature by developing more disaggregated estimates of traffic volumes for day and night conditions in rural areas and modeling road safety using those estimates. The proposed approach is demonstrated with the data from all automatic traffic recorder stations in Utah, with subsequent safety analysis focused on rural two-lane horizontal curve segments. Universal kriging, along with multiple covariates, proved to be an effective spatial technique for predicting day and night traffic volumes at unmeasured locations using data from permanent traffic-recording stations. Predicted day and night traffic volume estimates were incorporated into statistical road safety models of the expected number of crashes on rural two-lane horizontal curves to determine how this new information affected safety model estimation results. The parameter estimate for the predicted ratio of night-to-day traffic volume was positive and statistically significant and verified the hypothesis that horizontal curves with higher proportions of traffic at night were expected to experience more crashes than similar curves with higher proportions of traffic during the day.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.