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.

Highway Regional Classification Method Based on Traffic Flow Characteristics for Highway Safety Assessment

Accurate regional classification of highways is a critical prerequisite to implement a tailored safety assessment. However, there has been inadequate research on objective classification considering traffic flow characteristics for highway safety assessment purposes. We propose an objective and easily applicable classification method that considers the administrative divisions of South Korea. We evaluated the feasibility of this method through various theoretical analysis techniques using the data collected from 536 permanent traffic volume counting stations for the national highways in South Korea in 2019. The ratio of the annual average hourly traffic volume to the annual average daily traffic was used as the explanatory variable. The corresponding results of factor and cluster analyses with this ratio showed a 61% concordance with the urban, suburban, and rural areas classified by the administrative divisions. The results of two-sample goodness-of-fit tests also confirmed that the difference in the three distributions of hourly volume ratios was statistically significant. The results of this study can help enhance highway safety and facilitate the development and application of more appropriate highway safety assessment tools, such as Road Assessment Programs or crash prediction models, for specific regions using the proposed method.

Analysis of the Effect of the Speed Factor on Highway Safety Using the Machine Learning Method

Speed is one of the most important factors that can significantly change the severity of accidents. Providing a model with predictive factors leads to designing traffic plans to promote safety. This study aims to create statistical models for accidents occurred on Firuzkuh highway, Iran. Moreover, the probability of each type of accident was determined using the logit model. Various modeling methods, such as backward, forward, and entering methods, were evaluated to find the best method. Finally, since the backward method had the best performance in terms of R2 and goodness of fit, the logit model of accidents was created. According to the model, the independent variables of the 12-24 hours, rainy weather, a speed of 81-95 and 96-110 km/h, the lack of attention ahead and the Pride brand of vehicle increased the severity of accidents, while the variables with negative coefficients of Tuesdays, the summer and spring seasons, sunny weather, a male driver, and daylight, reduced the severity of accidents.

New Intersection Crash Prediction Models for the Second Edition of the Highway Safety Manual

The objective of this research was to develop new intersection crash prediction models for consideration in the second edition of the Highway Safety Manual (HSM), consistent with existing methods in HSM Part C and comprehensive in their ability to address a wide range of intersection configurations and traffic control types in rural and urban areas. The focus of the research was on developing safety performance functions (SPFs) for intersection configurations and traffic control types not currently addressed in HSM Part C. SPFs were developed for the following general intersection configurations and traffic control types: rural and urban all-way stop-controlled intersections; rural three-leg intersections with signal control; intersections on high-speed urban and suburban arterials (i.e., arterials with speed limits greater than or equal to 50 mph); urban five-leg intersections with signal control; three-leg intersections where the through movements make turning maneuvers at the intersections; crossroad ramp terminals at single-point diamond interchanges; and crossroad ramp terminals at tight diamond interchanges. Development of severity distribution functions (SDFs) for use in combination with SPFs to estimate crash severity as a function of geometric design elements and traffic control features was explored; but owing to challenges and inconsistencies in developing and interpreting the SDFs, it was recommended for the second edition of the HSM that crash severity for the new intersection configurations and traffic control types be addressed in a manner consistent with existing methods in Chapters 10, 11, and 12 of the first edition, without use of SDFs.

Understanding the Influence of Climate Elements on Traffic Behavior: The Wind Impact Approach

Due to intense highway congestion in Europe, increased percentage of highway accidents, as well as mortality rate, safety is an imperative in highway planning and design. Highway design safety standards have been researched extensively, but not enough attention has been paid to the surrounding environmental impacts, foremost climate elements. Therefore, this research attempts to understand the least researched climate element — the wind, and its impact on highway safety. The highway landscape falls under the category of the wind impacts that can cause significant problems for the drivers throughout the year. The values for wind direction, frequency and intensity were taken from the CARPATCLIM database. The evaluation of homogenized and harmonized set of data on a daily basis for a twenty-year period documented a variety of wind impacts on highway safety. It was found that the wind is constantly present throughout the year, with specific monthly oscillations. By using the ArcGis and the interpolation method, it has been clearly observed at which points the effect of intense winds was present the most. In order to understand the overall image of highway safety, fieldwork was conducted in various meteorological conditions. The checklists photo-documented and qualitatively described the observed extreme wind events (alone or combined with one more climate element). Based on everything described above, the image of the current situation was provided, and the proposal for control of the impact of wind using an adequate vegetation assembly (windbreaks) has been offered.

Quantification Metrics and Analysis of Human Factor, Workload, and Road Infrastructure

This paper describes how human factors (HF) and user workload (WL) can be used by highway designers and traffic engineers to quantify the potential safety of sections of highway. Users’ WL is a quantitative measure of HF. Both HF and WL are used successfully in other fields, such as aviation when pilots have difficulty in using instruments and in touch-down before the start or end of the runway. The traditional highway approach of gauging success is by counting crashes. But with fatalities exceeding 30,000 a year for more than 20 years, the time is right for a new method of analysis. The author has integrated specific WL metrics into a simplified example to aid designers, traffic engineers, and safety analysts in assessing user problems before building new projects or road upgrades. The example uses static and dynamic WL and alternating renewal (AR) metrics (not used by others) to quantify user WL in highway segments for the purpose of illustrating the variation of design and operational safety conditions. The example can be easily modified when new metrics are created, and it illustrates the use of WL and its associated highway safety implications. In short, the approach is based on common sense with trained engineering experience and logic integrated into data-driven safety analyses. The example is a continuation of an earlier FHWA research study illustrating the application of road safety audits and the Interactive Highway Safety Design Model (IHSDM). The IHSDM, Excel, and Google Earth were used because no funding was available for on-road data collection.