Application of Highway Safety Manual Draft Chapter

Predicting highway safety performance in quantitative terms on the basis of a set of highway geometric design and traffic control features is the core of the ongoing Highway Safety Manual (HSM) development. While great efforts have been made to develop safety performance models for different types of highways, evaluating the applicability of the models with crash data from states that were not used in the original model development is equally important. This step is critical in future HSM applications because of many noticeable differences in crash data recording practice and driving behaviors across the various states. Potential application concerns need to be evaluated fully and addressed before the manual is officially published. This paper presents the application experience of the safety prediction models and methodology for segments of two-lane rural roads as proposed for inclusion in Part III of the HSM. The study used data for state routes in Louisiana. The results indicate that the prediction models performed reasonably well: there were small differences between the observed and predicted crash frequencies, with calibration parameters applied, for all segments on rural two-lane highways in Louisiana. The results at different analysis levels (from individual segments to a network of segments) are summarized, and several application issues such as the level of effort versus degree of accuracy, simplification of the calibration process, and the need for a reliability measurement are discussed.

A coordinated effort is under way to develop a Highway Safety Manual (HSM) for use in making quantitative estimates of the safety performance of specific highway types and quantitative estimates of proposed improvements to specific highway types. The highway types being addressed in the first edition of HSM are rural two-lane highways, rural multilane highways, and urban and suburban arterials. Explicit consideration of pedestrian safety on urban and suburban arterials is considered critical to implementation of the first edition of HSM. The objective of the present research was to develop a methodology for quantifying the effects of existing site characteristics and proposed improvements on urban and suburban arterials on pedestrian safety. The pedestrian safety prediction methodology has been developed to function as a component of the overall safety prediction methodology for urban and suburban arterials proposed for the first edition of HSM. In the present research, a methodology for prediction of vehicle–pedestrian collisions at signalized intersections was developed. This methodology includes base models for three- and four-leg signalized intersections and several accident modification factors.

The Highway Safety Manual (HSM) estimates roadway safety performance based on predictive models that were calibrated using national data. Calibration factors are then used to adjust these predictive models to local conditions for local applications. The HSM recommends that local calibration factors be estimated using 30 to 50 randomly selected sites that experienced at least a total of 100 crashes per year. It also recommends that the factors be updated every two to three years, preferably on an annual basis. However, these recommendations are primarily based on expert opinions rather than data-driven research findings. Furthermore, most agencies do not have data for many of the input variables recommended in the HSM. This dissertation is aimed at determining the best way to meet three major data needs affecting the estimation of calibration factors: (1) the required minimum sample sizes for different roadway facilities, (2) the required frequency for calibration factor updates, and (3) the influential variables affecting calibration factors. In this dissertation, statewide segment and intersection data were first collected for most of the HSM recommended calibration variables using a Google Maps application. In addition, eight years (2005-2012) of traffic and crash data were retrieved from existing databases from the Florida Department of Transportation. With these data, the effect of sample size criterion on calibration factor estimates was first studied using a sensitivity analysis. The results showed that the minimum sample sizes not only vary across different roadway facilities, but they are also significantly higher than those recommended in the HSM. In addition, results from paired sample t-tests showed that calibration factors in Florida need to be updated annually. To identify influential variables affecting the calibration factors for roadway segments, the variables were prioritized by combining the results from three different methods: negative binomial regression, random forests, and boosted regression trees. Only a few variables were found to explain most of the variation in the crash data. Traffic volume was consistently found to be the most influential. In addition, roadside object density, major and minor commercial driveway densities, and minor residential driveway density were also identified as influential variables.

Egypt is a middle-income country, belonging to the Eastern Mediterranean Region (EMR). Egypt used to be the worst performing EMR country, with over 45 fatalities per 100,000 population. It is considered an example case of many EMR countries in the region where there is a need for quantitative assessment of safety on its roads. The Highway Safety Manual (HSM) offers several functions to calculate safety performance (SPFs), which can estimate crash frequency on a highway network according to its geometric and traffic characteristics. The HSM was developed in the United States using road and crash data specific to its environment. Jurisdictions and other non-U.S. regions were encouraged to develop locally derived models suitable for the local characteristics of roads and crashes. The objective of this paper is to assess the suitability of using the HSM models on rural multi-lane divided highways in Egypt. The calibrated SPFs considering Egyptian road factors were compared with the actual crash events. The results showed that there was a need to develop locally derived SFPs using negative binomial models. The modeling approach and a validation procedure were presented and are applicable when developing local SPFs to compare how well the model performs to the actual data, and to HSM calibrated models. The models were created using 90% of the data and tested on the remaining 10% of the data. The study found that the results relating to driver behavior would help designers in regions other than the United States to benefit from the HSM.

This study tries to develop a Korea highway safety evaluation proto type model for quantified safety evaluation. We develop a base model and crash modification factors (CMFs) through reviewing the procedures and methodologies in Highway Safety Manual. At the same time, we try to carefully consider collectable data and roadway environment of Korea that are different from the USA. CMFs are developed through models and expert's judgement. Developed CMFs are for driveway density, pedestrian crosswalk, existence of median, terrain types, and land-use types. Also, verification of the developed base model and CMFs were performed using two routes of Korea's rural two-lane highways while comparing with roadway safety design synthesis and Crash Prediction Module (CPM) of Interactive Highway Safety Design Model (IHSDM). Applicability analysis was then performed in two aspects of predictability and adaptability of the proto type model. As a result, CMFs and predicted accident estimates of CPM are similar with the developed Korea proto type model. However, the base model and CMFs of the developed proto type for Korea are more precise than the others when evaluating sections where the number of actual accidents is higher. It is found that considering CMF for land-use type is justifiable when developing a Korea highway safety evaluation proto type model. This study validates the possibility of developing a full scale of Korea highway safety evaluation model.

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.

Calibration and development of safety performance functions for two-way stop-control intersections on rural two-lane highways in Louisiana

The datasets and tool presented in this article are related to the research article entitled “Improving crash predictability of the Highway Safety Manual through optimizing local calibration process” (Dadvar et al., 2020) [1], in which these datasets were used to investigate alternative local calibration methods for the Highway Safety Manual (HSM) [2] local application. The datasets are integrated Highway Safety Information System (HSIS) [3] state-wide rural two-lane, two-way roads (R2U) data from states of Illinois and Washington.The HSIS is a database that maintains motor vehicle crash data, roadway inventory, and traffic volume data for several US states. It is an excellent source of data to highway safety research and can be used to investigate many research questions. However, to prepare an analysis-ready roadway safety dataset based on the HSIS or any databases that store the relevant data in multiple different datasets, the researchers should integrate multiple datasets, merge or unmerge and remove certain inconsistent records, and finally clean the integrated dataset. The HSIS staff is usually accommodating and eager to help, but sometimes the nature of data needs is complicated and laborious.A tool named Roadway Safety Data Integrator (RSDI) was developed for combining, segmenting, and selecting homogeneous (unchanged during the study period for certain variables of interest) HSIS roadway segments and also crash assignment by desired crash attributes (e.g., crash severity or type). The RSDI tool can be helpful for integrating different safety-related datasets such as roadway inventory (including grade, curve, and other subsets), traffic volume, and motor vehicle crash data; also, it can do required segmentation and identify the homogeneous roadway segments over the desired years of study that are the basis for development and calibration of the HSM predictive models.The shared datasets contain homogeneous roadway segments, geometric details, and crash data for six years from Illinois (2005–10) and Washington (2010–15). The datasets and RSDI tool would be important sources generally for investigating highway safety research questions and in particular, for HSM-related analyses. The RSDI tool can be used for similar purposes and it is not limited to the HSIS data. It can be used for segmentation and finding homogeneous segments of any datasets that follow linear referencing.The datasets and RSDI tool are hosted in the Mendeley Data repository [4].

Safety has always been an important component in the planning, design, and operation of highways. A framework for highway safety mitigation is outlined in this paper that provides a logical and comprehensive context within which efforts to improve highway safety can be made. This framework incorporates predictive methods of highway safety analysis to allow transportation officials to proactively improve the safety of the transportation system. This framework has been developed around the Roadway Safety Management Process contained in the Highway Safety Manual (HSM) and has six primary steps: 1) Network Screening, 2) Diagnosis, 3) Countermeasure Selection, 4) Economic Appraisal, 5) Project Prioritization, and 6) Effectiveness Evaluation. In addition to methods used in the HSM, alternative methods of predictive highway safety analysis are discussed in context of their application. This framework for safety mitigation can be applicable to transportation agencies worldwide to maximize benefits resulting from highway safety investment.

This report documents an analysis of traffic crash data in Kentucky for the years of 2009 through 2013. A primary objective of this study was to determine average crash statistics for Kentucky highways. Rates were calculated for various types of highways and for counties and cities. Difference criteria were used for exposure. Average and critical numbers and rates of crashes were calculated for various types of highways in rural and urban areas. These rates used crashes identified on highways where traffic volumes were available. Improved methods of identifying crash locations have resulted in higher rates for the last couple of years. The crash rate data can be used in Kentucky’s procedure to identify locations that have abnormal rates or numbers of crashes. The other primary objective of this study was to provide data that can be used in the preparation of the problem identification portion of Kentucky’s Annual Highway Safety Plan. County and city crash statistics were analyzed. A summary of results and recommendations in several problem identification areas is presented. These general areas include; alcohol involvement, occupant protection, speed, teenage drivers, pedestrians, bicycles, motorcycles, trucks, and vehicle defects. Other areas included in the analysis for which specific recommendations were not made include, school bus crashes and train crashes. The crash data are contained in the Collision Report Analysis for Safer Highways (CRASH) data base. This data base is updated daily so the number of crashes in a given calendar year will continue to change for a substantial time after the end of that year.

Objective The study used data for crashes that occurred on two-lane rural highways in Granada, Spain, over a four-year period (2016–2019). The obtained dataset consisted of 1,291 crashes. The data was used to identify the variables that influence crash injury severity. Methods In this study, a hybrid meta-learning methodology based on Bayesian Networks (BNs) and Random Forest (RF) was used. To the authors’ knowledge, these methods have never been combined in a single analysis. BNs identified the main factors influencing crash injury severity and the categories of these factors that are most important for each injury severity. These factors were ranked in importance from highest to lowest using RF. Results It was found that the following variables significantly increase the risk of a fatality in a traffic crash: driver age, crash type, time of day, main crash cause according to police assessment, and lane width. In addition, it was found that the following categories of these factors were associated with a higher risk of fatality: driver age over 45, rollover crash type, nighttime lighting conditions, police reported driver distractions, and lane width of less than 3.25 meters. Conclusion It became evident that using a hybrid meta-learning methodology based on BNs and RF was helpful in identifying the most significant factors, which influence the negative outcome of crashes. Local criteria that may evaluate the traffic safety of present and future highways should be developed and put into use in order to improve the level of traffic safety in two-lane rural highways, reducing the severity of injuries caused by traffic crashes and improving traffic safety.

The Highway Safety Manual (HSM) provides guidance and tools to conduct quantitative safety analysis. Crash prediction models are used to estimate the expected number of crashes per year, by facility type, severity, and crash type. There are two approaches for applying the HSM crash prediction methodology to local conditions: (1) calibration of models provided in the HSM; or (2) development of jurisdiction-specific models. There are some instances in which model calibration may not be appropriate. To illustrate this case, 601 urban signalized four-leg intersections (U4SG) in Missouri were used to obtain the calibration factor, assess the quality of the calibration factor, and develop jurisdiction-specific models. For U4SG total crashes, the calibration factor for Missouri conditions was 3.98 (standard deviation, 0.13). The assessment of the calibration factor showed a disproportional difference between the observed data in Missouri and the HSM model. Thus, the calibration was deemed inappropriate and the development of Missouri-specific models was supported. The models were developed for severities Fatal and Injury (FI) and Property Damage Only (PDO) crashes. The predictor variables considered were intersection AADT, posted speed limit, signal control type, exclusive left turn lanes, exclusive right turn lanes, right turn on red prohibited, and facilities of interest within 1,000 ft from the intersection (bus stops, schools, and alcohol sale establishments). Functional forms for all predictor variables were optimized. The log-likelihood, inverse overdispersion, and Cumulative Residuals (CURE) plots showed satisfactory measures of model accuracy.

<p>The Highway Safety Manual uses safety performance functions (SPFs) and crash modification factors (CMFs) to quantify the safety effects of a roadway design and operational changes. A number of substantial methodological issues in SPF and CMF estimation exist that have not yet been completely addressed. Notably, although applicability of SPFs and CMFs may significantly vary by crash severity, only limited consideration is given to severity distribution. Moreover, when developing crash modification functions (CMFunctions), most of the current state-of-the-art studies only rely on the functional forms that may not address the non-linear effects of roadway attributes on safety. In addition, while many studies advocate using traffic conflicts as surrogate measures of safety to address some issues in CMF estimation, most of the current measures do not sufficiently take into account the severity of conflicts.</p> <p>The thesis is based on an amalgamation of four research papers that separately delve into these topics in Chapters 3 to 6. Chapter 3 develops and compares alternative approaches for predicting number of crashes for each severity level. The applicability of a two-stage SDF (severity distribution function) modeling approach is the main focus of this chapter. Chapter 4 applies Bayesian Markov Chain Monte Carlo (MCMC) simulation to investigate the functional forms used to derive CMFunctions in cross- sectional regression models. This chapter mainly focuses on the methodology and uses freeway median width as a case study. Chapter 5 aims to demonstrate the versatility of the approach by applying that to passing lanes on two-lane roads. The results of these two applications highlight the importance of using the functional forms that can capture non-linear effects of road attributes for CMF estimation in cross- sectional models, while providing robust CMFs for practical applications.</p> <p>Chapter 6 investigates a novel traffic technique using the data obtained from video observations at signalized intersections. The measures of post encroachment times and corresponding conflicting vehicle speeds are integrated to define a risk score which classifies conflicts into three severity levels. The results show strong relationships between the classified conflicts and both total and fatal/injury crashes and conform the promise of the approach for estimating CMFs where there are insufficient crash data for directly estimating them.</p>

Using multivariate adaptive regression splines (MARS) to develop crash modification factors for urban freeway interchange influence areas

Mobility, safety performance and environmental sustainability are priorities in the geometric design of roundabouts. This thesis presents a multi-objective optimization methodology for the geometric design of single-lane roundabouts. Mobility is defined in terms of roundabout delay and modeled using the (UK) empirical model. The collision frequency represents the safety objective, and modeled using the methodology outlined in the Highway Safety Manual. Environmental sustainability is represented by NOX, HC, CO2, and CO vehicle emissions and is modeled using the vehicle specific power (VSP) methodology. The presented model directly identifies the optimal geometric parameters of roundabouts. Traffic data, site conditions, and guidelines limitations were used as input data while the output decision values that minimize delay, collisions, and vehicle emissions are the optimal geometric parameters. The practical application of the proposed model is illustrated using an application example. The model was validated using an actual location, and a sensitivity analysis was conducted.