Analysis of safety impact of paved shoulder width on Czech secondary roads

Traffic safety is influenced, among other factors, by characteristics of the roads, which include the width of the shoulder. Shoulder width was noted to have a large effect on crash frequency, as well as on traffic speed. In this paper, we focused on paved shoulders. Previous studies confirmed that increasing the width of the paved shoulder is associated with a decrease in crash frequency. However, wider shoulders may encourage higher driving speed, which is related to an increase of impact speed and crash severity – this issue was hypothesized, but not statistically investigated. Thus, conclusions based on crashes and speeds contradict each other, and there is no simple answer to the question of the safety impact of wide shoulders. To address this gap, we analyzed a sample of two most typical categories of Czech secondary roads, which differ only in the paved shoulder width (S9.5 roads with 0.75m-wide shoulder, and S11.5 roads with 1.75m-wide shoulder) and thus present a suitable example for studying the safety impact of paved shoulder width. We used generalized linear models of crash frequency, and multinomial logistic models of crash severity (separately for single-vehicle and multi-vehicle crashes), as well as a statistical test of differences in speed for the two road categories. The results showed that: Firstly, there were fewer crashes on S11.5 roads compared to S9.5 roads; this was true for both single-vehicle and multi-vehicle crashes. Secondly, single-vehicle crashes on S11.5 roads were more severe compared to S9.5 roads; the change of severity in multi-vehicle crashes was not statistically significant. Thirdly, driving speeds on S11.5 roads were approx. by 7 km/h higher compared to S9.5 roads. These findings support the hypothesis of an association between wider shoulders, higher speeds, and increased crash severity, especially in the case of single-vehicle crashes. As a practical solution, various speed management measures, including widening to a 2+1 road, may be recommended.

Highway Safety Manual Calibration: Estimating the Minimum Required Sample Size

Crash frequency has been identified by many experts as one of the most important safety measures, and the Highway Safety Manual (HSM) encompasses the most commonly accepted predictive models for predicting the crash frequency on specific road segments and intersections. The HSM recommends that the models be calibrated using data from a jurisdiction where the models will be applied. One of the most common start-up issues with the calibration process is how to estimate the required sample size to achieve a specific level of precision, which can be a function of the variance of the calibration factor. The published research has indicated great variance in sample size requirements, and some of the sample size requirements are so large that they may deter state departments of transportation (DOT) from conducting calibration studies. In this study, an equation is derived to estimate the sample size based on the coefficient of variation of the calibration factor and the coefficient of variation of the observed crashes. Using this equation, a framework is proposed for state and local agencies to estimate the required sample size for calibration based on their desired level of precision. Using two recent calibration studies, South Carolina and North Carolina, it is shown that the proposed framework leads to more accurate estimates of sample size compared with current HSM recommendations. Whereas the minimum sample size requirement published in the HSM is based on the summation of the observed crashes, this paper demonstrates that the summation of the observed crashes may result in calibration factors that are less likely to be equally precise and the coefficient of the variation of the observed crashes can be considered instead.

Assessing Safety Performance on Urban and Suburban Roadways of Lower Functional Classification: A Comparison of Minor Arterial and Collector Roadway Segments

Previous research of urban roadway safety performance has generally focused on roadways of high functional classifications, such as principal arterials. However,roadways with lower functional classifications, including minor arterials and collectors, typically possess characteristics that differ from those of higher roadway classes. Therefore, assumptions made on the general effect of the predictor variables from typical safety performance functions may not apply to lower roadway classes. Toaddress these knowledge gaps, a safety performance evaluation of urban/suburban minor arterial and collector roadway segments was performed using traffic androadway data along with eight years of crash data from 189 miles of two-lane urban and suburban roadways in Washtenaw County, Michigan. Mixed-effect negativebinomial models with segment-specific random intercept were developed for minor arterial and collector road segments, considering total, fatal+injury, and propertydamage only crashes. In general, minor arterial roadways showed greater crash occurrence compared to collector roads. Posted speed limit had a significant positiveassociation with crash frequency, and this effect increased when the speed limit exceeded 40 mph. The effect of speed limit was stronger on minor arterial segmentsand for fatal+injury crashes. Additionally, driveway density was found to have a significant effect on safety performance, which was stronger for commercial/industrialdriveways compared to residential driveways and for collector roads compared to minor arterials, particularly when considering residential driveways. On-street parkingwas associated with lower crash occurrence, with a stronger effect on collector roadways, likely due to greater parking turnover when compared to minor arterials.

Safety Analysis of Signal-Coordinated Arterials with Multiple Heterogeneities

In the corresponding research available, the safety impact remains controversial in implementing signal coordination on arterials, which calls for an in-depth exploration with the appropriate statistical methods. Based on the traffic data from Ann Arbor City (Michigan, USA), the paper proposes a safety evaluation model considering the multiple heterogeneities. In terms of arterials with the coordinated signalization, modeling results imply that (1) the multivariate heterogeneity shows the strongest interaction on crash frequency, followed by the spatiotemporal and structural heterogeneities, and (2) the spatial variation is unrelated to the temporal change among crashes in the denoted traffic analysis zones (TAZs). In an attempt to alleviate the coupled crash risks along the coordinated arterials, the study emphasizes the necessity of dividing the subcontrol traffic areas in real time according to the correlative degree of crash distribution. Meanwhile, the modeling framework with multiple heterogeneities can be applied for the safety analysis of other urban roads.

Analysis of Crash Frequency and Crash Severity in Thailand: Hierarchical Structure Models Approach

Currently, research on the development of crash models in terms of crash frequency on road segments and crash severity applies the principles of spatial analysis and heterogeneity due to the methods’ suitability compared with traditional models. This study focuses on crash severity and frequency in Thailand. Moreover, this study aims to understand crash frequency and fatality. The result of the intra-class correlation coefficient found that the spatial approach should analyze the data. The crash frequency model’s best fit is a spatial zero-inflated negative binomial model (SZINB). The results of the random parameters of SZINB are insignificant, except for the intercept. The crash frequency model’s significant variables include the length of the segment and average annual traffic volume for the fixed parameters. Conversely, the study finds that the best fit model of crash severity is a logistic regression with spatial correlations. The variances of random effect are significant such as the intersection, sideswipe crash, and head-on crash. Meanwhile, the fixed-effect variables significant to fatality risk include motorcycles, gender, non-use of safety equipment, and nighttime collision. The paper proposes a policy applicable to agencies responsible for driver training, law enforcement, and those involved in crash-reduction campaigns.

Effect of Various Speed Management Strategies on Bicycle Crashes for Urban Roads in Central Florida

In recent years, cycling has become an increasingly popular transportation mode around the world. In comparison with other popular modes of transportation, cycling is economical and energy efficient. While many studies have been conducted for the analysis of bicycle safety, most were limited in bicycle exposure data and on-street data. This study tries to improve the current safety performance functions for bicycle crashes at urban corridors by utilizing crowdsource data from STRAVA and on-street speed management strategies data. Speed management strategies are any roadway alterations that cause a change in motorists’ driving behavior. In Florida, these speed management strategies are defined by the Florida Department of Transportation design manual. Considering the disproportionate representation of cyclists from the STRAVA data, adjustments were made to represent more accurately the cyclists based on the video detection data by developing a Tobit model. The adjusted STRAVA data was used for bicyclist exposure to analyze bicycle crashes on urban arterials. A Bayesian joint model was developed to identify the relations between the bicycle crash frequency and factors relating to speed management strategies. Other factors, such as vehicle traffic data, roadway information, socio-demographic characteristics, and land use data, were also considered in the model. The results suggest that the adjusted STRAVA data could be used as the exposure for bicycle crash analysis. The results also highlight the significant effects of speed management strategies, such as parking lots and surface pavement. It is expected that these findings could help engineers develop effective strategies to enhance safety for bicyclists.

Crash Prediction Models for Horizontal Curve Segments on Two-Lane Rural Roads in Thailand

The number of road crashes continues to rise significantly in Thailand. Curve segments on two-lane rural roads are among the most hazardous locations which lead to road crashes and tremendous economic losses; therefore, a detailed examination of its risk is required. This study aims to develop crash prediction models using Safety Performance Functions (SPFs) as a tool to identify the relationship among road alignment, road geometric and traffic conditions, and crash frequency for two-lane rural horizontal curve segments. Relevant data associated with 86,599 curve segments on two-lane rural road networks in Thailand were collected including road alignment data from a GPS vehicle tracking technology, road attribute data from rural road asset databases, and historical crash data from crash reports. Safety Performance Functions (SPFs) for horizontal curve segments were developed, using Poisson regression, negative binomial regression, and calibrated Highway Safety Manual models. The results showed that the most significant parameter affecting crash frequency is lane width, followed by curve length, traffic volume, curve radius, and types of curves (i.e., circular curves, compound curves, reverse curves, and broken-back curves). Comparing among crash prediction models developed, the calibrated Highway Safety Manual SPF outperforms the others in prediction accuracy.