Equivalent Property Damage Only Research Articles

Spatial Statistics and Severity of Highway Accidents in Nakhon Pathom, Thailand

The aim of this study was to use spatial statistics and geographic information systems to identify high-risk areas for highway accidents in Nakhon Pathom, Thailand. Secondary data from the Ministry of Transport on the locations of accidents in the road network between 2021-2022 was analyzed using Equivalent Property Damage Only (EPDO), Spatial Autocorrelation, Kernel Density Estimation, and hotspot analysis. The study focused on Nakhon Pathom, a province in Central Thailand, and found that high-risk areas were concentrated along major routes with heavy traffic and high population density, including both urban and community areas. The study also identified specific risk spots, with Kamphaeng Saen District and Highways NO. 321(Kamphaeng Saen-Thung Khok Road), NO. 3231(Den Makham-Bang Len Road), and NO. 3232(Nong Phong Nok - Pai Chedi Road) being particularly affected, as well as Sam Phran District and Highway NO. 375(Ban Bo-Phra Prathon Road). These findings provide important insights into the clustering of accidents and their risk spots, which can be used to improve traffic safety in Nakhon Pathom.

Frequency Analysis of Equivalent Property-Damage-Only (EPDO) Crashes at Intersections

Traffic crashes are burdening societies with severe costs in terms of public health and economic loss. Intersection-related crashes are responsible for a large proportion of these losses due to their higher frequency and severity. Understanding the factors contributing to intersection crash frequency and severity is essential to mitigate their grave consequences. This study covered the analysis of roadway risk factors, influencing the frequency of equivalent property-damage-only (EPDO) crashes at intersections. The study included developing a negative binomial modeling framework to examine nine years of intersection crash records in the state of Wyoming. The modeling results revealed the key role of pavement friction in intersection safety and EPDO frequency. The findings also demonstrated that intersection location, grade, road functional classification, road surface type, the presence of guardrails, right shoulder type, and horizontal curvature all influence the EPDO crash frequency at intersections.

Application of Bayesian Semi-Parametric and Hierarchical Models for Analyzing Dispersed Traffic Barriers Crash Data

Introduction: Despite the traffic barriers effectiveness in reduction of the severity of run-off road crashes, the severity of barrier crashes still accounts for a significant fraction of road fatalities. Although extensive research has already been conducted in studying traffic barrier crashes, those studies mostly either consider the severity or frequency of crashes. Here, the equivalent property damage only (EPDO) was used to account for both aspects of crashes. While modeling EPDO crashes, there are challenges associated with that type of dataset including its sparse distribution, and the presence of heterogeneity in the dataset due to aggregation of various crash types. Methods: Ignoring the sparse nature of the data might result in biased or even erroneous results. Thus, in this study we identify factors to barriers EPDO crashes while considering the discussed challenges. Those consideration are especially important as in the next step we will employ the modeling results for conducting the cost-benefit analysis. Two main methods were considered in this study to address the discussed challenges including parametric and non-parametric Bayesian hierarchical models. A semiparametric Bayesian approach was used to relax the normality assumption by using a mixture of multivariate Dirichlet prior, defining a flexible nonparametric model for the random effects’ distribution, and using grouping to account for the heterogeneity due to the structure of the dataset. On the other hand, Bayesian hierarchical models with two distributions of Poisson and negative binomial with similar levels of hierarchy were considered. These models were chosen as closest models to the Bayesian semiparametric model. The incorporated models were compared in terms of deviance information criterion (DIC). Results and Discussion: The results highlighted that although the semi-parametric method outperforms the Bayesian hierarchical model with Poisson distribution, the Bayesian hierarchical model with negative binomial (NB) distribution outperform the semi-parametric model. The findings might be related to the severe sparse nature of the EPDO, which cannot optimally be accounted by semiparametric approach, and the model needs more flexibility. Conclusion: It was found that being unrestrained, driving in interstate system, driving in clear weather, light conditions, and driving in a higher traffic all increase the likelihood of EPDO crashes. Also, while some predictors were significant in less accommodative models of semi-parametric or Poisson models, they were not for Negative binomial model.

Complex Impacts of Traffic Citations on Road Safety

Wyoming has one of the highest fatality rates and lowest enforcement rates in the U.S. Thus, this study was conducted to see if there is any link between various citation types on the equivalent property damage only (EPDO) crashes, while taking into account interaction terms between various crashes and citation characteristics. To achieve the objective, we disaggregated the citations data into their related types, while aggregating the crash dataset across each segment through the Eastern direction of the interstate 80 in Wyoming. The results highlighted that there are important relations between types of crashes and related citations. For instance, it was found that there are (1) significant interaction terms between driving-too-fast-for-condition citations and underride crashes, (2) total number of commercial citations and commercial motor vehicle (CMV) crashes, and (3) between seatbelt citations and seatbelt-related crashes, highlighting, possibly, that the performance of the highway patrol was adjusted based on the specific roadway concerns, or there is indeed a significant link between related citation and crash types. While conducting the model with no interaction term, the results of comparison highlighted that the model erroneously highlight positive associations between citation types and the EPDO crashes. This is one of the earliest studies that considered disaggregated citation data and aggregated them with the crash data to highlight the important relationship between the citations and crashes.

Predicting pedestrian crash locations in urban India: An integrated GIS-based spatiotemporal HSID technique

Pedestrians are one of the most vulnerable road users globally. Recent years have witnessed an increasing interest among the scientific community to analyze and enhance pedestrians' safety in an environment dominated by motor vehicles. This study proposes a three-step methodology to identify current and future critical pedestrian crash hotspots. Firstly, available multi-year crash data from two cities in India is digitized, and the spatial autocorrelation tool is used to determine the pedestrian crash hotspots. Secondly, space-time cube and emerging hotspot analysis are carried out to predict crash hotspots along urban streets. Finally, Hotspot Identification (HSID) methods, i.e., Equivalent Property Damage Only (EPDO) and Upper-tail Critical Tests are used to rank the road links based on spatio-temporal crash severity leading to the identification of links needing urgent interventions. The proposed three-step integrated methodology is novel and has never been used to simultaneously identify and prioritize the critical pedestrian crash locations as it has been done in the present study. The developed methodology identifies sections of arterial roads—Strand Road and AJC Bose Road in Kolkata and Gota Road in Ahmedabad, as the critical hotspot links that require urgent intervention.

Crash proximity and equivalent property damage calculation techniques: An investigation using a novel horizontal curve dataset

Despite the numerous breakthroughs in crash analytics, there remains a lack of consensus among safety practitioners as to the optimal method for locating high crash locations. Two critical components in the traffic safety analysis process not agreed upon are 1) how the crash distance to a target location is included in the analysis and 2) how crashes are weighted based on crash-related characteristics. For example, the commonly used buffering technique to determine which crashes are associated with a specific target road segment does not associate crashes that are closer to a target road segment with any additional weight, even though it is likely to be more greatly associated with the characteristics of the target location. Additionally, the commonly used equivalent property damage only (EPDO) crash weight method has been found to weigh fatal crashes significantly more than serious injury crashes, even if the difference between the two outcomes was a single factor. This study proposes more robust crash weighting techniques for use in high-risk location identification using an application of a novel horizontal curve dataset. Specifically, a heteroscedastic censored regression approach was used to investigate the impact of different crash proximity weighting techniques and crash severity weighting methods on model outcomes. The results demonstrate that the use of a linear distance weighting factor used in conjunction with the buffering technique as well as a less precise EPDO weighting factor method results in more robust safety analysis outcomes. The improved results have the potential to improve hot spot identification and resource allocation at both the federal and regional levels by employing models that more accurately link specific crash segments with contributing crash characteristics.

Applying Machine Learning to Develop Lane Control Principles for Mixed Traffic

The mixed traffic environment often has high accident rates. Therefore, many motorcycle-related traffic improvements or control methods are employed in countries with mixed traffic, including slow-traffic lanes, motorcycle two-stage left turn areas, and motorcycle waiting zones. In Taiwan, motorcycles can ride in only the two outermost lanes, including the curb lane and a mixed traffic lane. This study analyzed the new motorcycle-riding space control policy on 27 major arterial roads containing 248 road segments in Taipei by analyzing before-and-after accident data from the years 2012–2018. In this study, the equivalent-property-damage-only (EPDO) method was used to evaluate the severity of crashes before and after the cancelation of the third lane prohibition of motorcycles (TLPM) policy. After EPDO analysis, the random forest analysis method was used to screen the crucial factors in accidents for specific road segments. Finally, a classification and regression tree (CART) was created to predict the accident improvement effects of the road segments with discontinued TLPM in different situations. Furthermore, to provide practical applications, this study integrated the CART results and the needs of traffic authorities to determine four rules for canceling TLPM. In the future, on the accident-prone road segment with TLPM, the inspection of the four rules can provide the authority to decide whether to cancel TLPM to improve the accident or not.

Applying different analytic methods to determine black spots in two-lane highways

Various analytic methods have been proposed to determine sections with the highest crash risk. Each method has unique specifications and tries to model the crash risk from a different viewpoint. The main objective of this article is to benefit the strengths of three methods that rely on accident data, road safety inspection, and traffic conflict technique to determine black spots for two-lane highways simultaneously. Fuzzy inference system (FIS) is considered as the method to combine the results of these methods and report one number (R MI) as the crash risk for each section. For comparative evaluations, a case study with 20 sections for two consecutive periods was considered in the roads of southeast of Iran. We have tried to select sections with various conditions from crash data, road condition, and surrogate safety measures viewpoint. First, the black spots are determined with the help of previous criteria such as crash frequency (CF), crash rate (CR), empirical Bayes (EB), and equivalent property damage only (EPDO). Then the black spots are specified by the new proposed criteria (R MI). Three tests are applied to compare the efficiency of these five methods. The results indicate that the proposed method is a powerful tool to identify black spots. R MI considers the frequency and severity of observed crashes and at the same time frequency and severity of predicted crashes based on road deficiencies and near crashes. Therefore, it has a more realistic attitude in black spot identification.

Cost–benefit analysis of traffic barrier geometric optimization, a hurdle machine learning‐based technique

AbstractAlthough it is well documented that optimized barrier dimensions would yield a benefit by enhancement of traffic safety, not many studies have been conducted quantifying how much money could be saved by enhancing barriers to their optimum heights, especially with the help of machine learning technique. Equivalent property damage only (EPDO) was considered as a response in the modeling to account for both aspects of crash frequency and severity. A hurdle model was developed to explore the impact of enhancing the geometric characteristics of barriers while accounting for zero inflation. The two components of the model, logistic regression and truncated negative binomial, could account for both abundance of zeroes and the skewed nature of non‐zero EPDO. The methodological approach provides a new understanding of the economic/monetary evaluation of an asset enhancement in the state with the help of machine learning technique. Our methodological approach provides the policy makers about the monetary benefit that they could expect by optimization of barriers heights. The analysis summarized in this paper will not only help Wyoming, but it provides other states with the justification of safety enhancement to their road barriers or assets.

Application of Bayesian Hierarchical Negative Binomial Finite Mixture Model for Cost-Benefit Analysis of Barriers Optimization, Accounting for Severe Heterogeneity

The Wyoming Department of Transportation (WYDOT) initiated a project to optimize the heights of barriers that are not satisfying the barrier design criteria, while prioritizing them based on an ability to achieve higher monetary benefits. The equivalent property damage only (EPDO) was used in this study to account for both aspects of crash frequency and severity. Data of this type are known to have overdispersion, that is having a variance greater than the mean. Thus, a negative binomial model was implemented to address the over-dispersion issue of the dataset. Another challenge of the dataset used in this study was the heterogeneity of the dataset. The data heterogeneity resulted from various factors such as data being aggregated across two highway systems, and the presence of two barrier types in the whole state. Thus, it is not practical to assign a subjective hierarchy such as a highway system or barrier types to address the issue of severe heterogeneity in the dataset. Under these conditions, a finite mixture model (FMM) was implemented to find a best distribution parameter to characterize the observations. With this technique, after the optimum number of mixtures was identified, those clusters were assigned to various observations. However, previous studies mostly employed just the finite mixture model (FMM), with various distributions, to account for unobserved heterogeneity. The problem with the FMM approach is that it results in a loss of information: for instance, it would come up with N number of equations, where each result would use only part of the whole dataset. On the other hand, some studies used a subjective hierarchy to account for the heterogeneity in the dataset, such as the effect of seasonality or highway system; however, those subjective hierarchies might not account for the optimum heterogeneity in the dataset. Thus, we implement a new methodology, the Bayesian Hierarchical Finite Mixture (BHFMM) to employ the FMM without losing information, while also accounting for the heterogeneity in the dataset, by considering objective and unbiased hierarchies. As the Bayesian technique has the shortcoming of labeling the observations due to label switching; the FMM parameters were estimated by maximum likelihood technique. Results of the identified model were converted to an equation for implementation of machine learning techniques. The heights were optimized to an optimal value and the EPDO was predicted based on the changes. The results of the cost–benefit analysis indicated that after spending about 4 million dollars, the WYDOT would not only recover the expenses, but could also expect to save more than $4 million additional dollars through traffic barrier crash reduction.

Comprehensive Evaluation of a Sparse Dataset, Assessment and Selection of Competing Models

With tremendous associated economic and social costs of crashes, researchers have been trying not only to identify the factors affecting crashes, but also to estimate those coefficients in the most accurate ways. Estimating model coefficients without accounting for a correct distribution would result in biased and erroneous results. This risk especially holds true when modeling skewed equivalent property damage only (EPDO) crashes with a preponderance of zeroes. The distribution of EPDO is known for not being modeled with known distributions such as Poisson or negative binomial distributions. This issue is highlighted in particular for a mountainous state like Wyoming that has very low traffic levels and a severely high crash rate. In addition, we included barriers in the model that did not experience any crashes but did suffer from being under-designed by geometric architects, thereby adding to the number of zero count observations. Various models with different distributional characteristics were considered and compared in this study. Comparisons were not just made across models in terms of their goodness of fit, but the estimated coefficients were also compared to see the impact of considering the wrong distributional assumptions on model parameter estimates. As the objectives of this study are to implement the identified results for optimization purposes and locate hazardous locations that could host future crashes, the results highlight accurate model estimations and the consequences of a failure to account for the right distributions. After conducting different goodness-of-fit measures, a hurdle model was proposed in this study to accommodate observations with zero crashes, and to account for a sparse distribution of EPDO crashes in the state of Wyoming. For the hurdle model, binary logistic regression was used to account for observations with zero crashes, while the negative binomial method was considered for non-zero observations. The findings of this study have direct implications on the allocation of limited funds for policymakers in Wyoming, as optimization could be conducted on the geometric characteristics of traffic barriers in future studies.

Two-Component Bayesian Hierarchical Models for Cost-Benefit Analysis of Traffic Barrier Crash Count

Road departure crashes tend to be hazardous, especially in rural areas like Wyoming. Traffic barriers could be installed to mitigate the severity of those crashes. However, the severity of traffic barriers crashes still persists. Besides various drivers and environmental characteristics, the roadways and barrier geometric characteristics play a critical role in the severity of barrier crashes. The Wyoming department of transportation (WYDOT) has initiated a project to identify and optimize the heights of those barriers that are below the design standard, while prioritizing them based on the monetary benefit. This is to optimize first barriers that need an immediate attention, considering the limited budget, and then all other barriers being under design. In order to account for both aspects of frequency and severity of crashes, equivalent property damage only (EPDO) was considered. The data of this type besides having an over-dispersion, exhibits excess amounts of zeroes. Thus, a two-component model was employed to provide a flexible way of addressing this problem. Beside this technique, one-component hierarchical modeling approach was considered for a comparison purpose. This paper presents an empirical cost-benefit analysis based on Bayesian hierarchical machine learning techniques. After identifying the best model in terms of the performance, deviance information criterion (DIC), the results were converted into an equation, and the equation was used for a purpose of machine learning technique. An automated method generated cost based on barriers’ current conditions, and then based on optimized barrier heights. The empirical analysis showed that cost-sensitive modeling and machine learning technique deployment could be used as an effective way for cost-benefit analysis. That could be achieved through measuring the associated costs of barriers’ enhancements, added benefits over years and consequently, barrier prioritization due to lack of available budget. A comprehensive discussion across the two-component models, zero-inflated and hurdle, is included in the manuscript.

Roadway Safety Management in Small Municipalities

Roadway safety management consists of network screening, diagnosis, countermeasure selection, economic appraisal, prioritization, and safety effectiveness. Application of the safety management process is limited in small municipalities because of data, statistical expertise, and resources required. This paper addresses the challenges faced by small jurisdictions and illustrates the implementation of the safety management process for local agencies in the Madison metropolitan area in Wisconsin. Jurisdiction-specific crash prediction models were developed by intersection type using data from over 4,000 intersections. Performance measures included the Equivalent Property Damage Only (EPDO) average crash frequency with Empirical Bayes adjustments and the Level of Service of Safety (LOSS). Wisconsin Crash Outcome Data Evaluation System (CODES) data was used to estimate local crash costs by severity and type. Sites were provisionally ranked in network screening, and diagnosis was automated based on intersection-observed crash types and distributions. Potential treatments were selected for each intersection and costs of treatments were obtained from local estimates and available literature. Crash cost benefit and treatment cost were used to estimate benefit–cost ratio (B/C) by site. A combination of sites that had the greatest overall cost-effective safety benefit on the network were selected through an incremental optimization process. This paper contributes to existing practice by illustrating the development of jurisdiction-specific crash prediction models, integration of pedestrian and cyclist crashes, application of EPDO and LOSS performance measures, and selection of sites with promise through an incremental optimization process for a given budget and resources available at the local level.

Modeling the equivalent property damage only crash rate for road segments using the hurdle regression framework

The understanding of the distributional characteristics of the equivalent property damage only (EPDO) crash rate is limited in the existing literature. Models without a proper distribution of EPDO rate could result in biased estimations and misinterpretations of factors. The importance of prediction accuracy and modeling performance for the EPDO rate should be acknowledged since they directly affect the allocation of limited public funds to safety management for road networks. The general objective of this study is to investigate the distributional characteristics of the EPDO rate and accordingly develop proper econometric models for connecting the EPDO rate to explanatory variables. A hurdle framework was proposed in order to accommodate the zero-positive mixed domain of the EPDO rate. For the positive part of the EPDO rate, three representative distributions (lognormal, gamma and normal) were tested and then the three hurdle models were compared against the Tobit model and the random-parameters Tobit model. The empirical results illustrate the lognormal hurdle model's superior modeling performance in comparison to the other four models, and more importantly that conclusion also holds for several different definitions of the EPDO rate under different combinations of property damage only (PDO) equivalency factors.

An evaluation of performance measures for hotspot identification

ABSTRACTAn important step in highway safety analysis is the identification of high-crash locations or “hotspots.” Various performance measures are currently used for hotspot identification (HSID). Current measures are mostly based on crash frequency. This article proposes a performance measure based on crash severity, namely, the crash factor measure (CFM). In this article, the performance measures are classified broadly by crash frequency and crash severity. The crash severity measures, the CFM, the equivalent property damage only (EPDO), and the Empirical Bayes supplemented with crash severity (EBCS), and the crash frequency measures, the crash rate (CR) and the Empirical Bayes (EB) are evaluated using eight tests based on empirical and simulated data. To ensure detailed assessment, the evaluation was carried out for three entire highway routes (an Interstate, a US, and a State highway), that is, beyond the top 1% to 10% of hotspots commonly examined. The results from the evaluation demonstrate that the EB and CFM measures performed consistently and are recommended for use. Based on inconsistent results, the CR measure is not recommended for HSID. Based on the overall results, it is also recommended that, for any proposed performance measure, the evaluation should be carried beyond 1% to 10% of the hotspots.

Fuzzy Cluster–Based Method of Hotspot Detection with Limited Information

In the absence of geometric design and traffic data, hot-spot identification (HSID) is done primarily with crash data only, based on techniques such as crash frequency (CF), fatal crash frequency (FCF), or equivalent property damage only (EPDO), despite the known limitations of these techniques. In this article, the authors propose an improved HSID technique that may be used with crash data only. Using disaggregate crash history information, this method estimates probabilities of crash severities by the major contributing factors using severity models. These probabilities are used to compute expected numbers of severe and fatal crashes at various locations which are then used to classify the locations into two fuzzy cluster, namely hotspot and non-hotspot using Fuzzy C-Means (FCM) algorithm. The identified hotspots are ranked based on their mean departure from the core of the hotspot cluster. These rankings are compared with rankings done using existing techniques namely CF, FCF, EPDO, and Empirical Bayes’ (EB). The proposed method is found to be a robust method for hotspot detection with performance better than existing methods that use crash data only and comparable to the EB method.

Geometric Design Consistency of Multiple Horizontal Curves on Two-lane Rural Highways

Safety on rural highways is of great concern as nearly two third of road fatalities are found to take place on such roads. High speed of vehicles is the characteristic of rural highways. Geometry is one of the factors that control the speed of vehicles and consequently, the crash occurrence. Alignment is one of the components of geometry and an abrupt change in the alignment is a leading cause for crashes in rural highways. Presence of a curve after a long tangent or a sharp curve after a flat curve is an example for inconsistency in the alignment. Such an alignment brings about unnecessary and unhealthy speed changes that may lead to crashes. Thus, a highway design can be evaluated based on consistency in geometry. Operating speed is the measure adopted for consistency evaluation in this study.The objectives of this study are to develop operating speed models for different classes of vehicles at salient locations of multiple horizontal curves and to develop criteria for evaluating the geometric design consistency. Data of 30 sites were made use for this purpose. Multiple linear regression technique was adopted for modelling. Models were developed for the tangent section and middle of curved sections. Length of preceding tangent is the variable influencing operating speed at tangent. Radius and length of curve significantly influence operating speed at middle of first curve and radius of first and second curve influences operating speed at middle of second curve. Operating speed Deviation from Design speed (ODD) and Speed Reduction (SR) are the consistency measures and Equivalent Property Damage Only (EPDO) crashes is the safety measure used in the study for developing criteria. The criteria developed can be used to evaluate the curve as a single element, and as successive elements (combinations of tangent/curve or curve/curve). Alignment can then be classified as Good, Fair or Poor using these criteria, and it helps a designer to make suitable modifications in the design of a multiple horizontal curve from safety point of view.

간선도로 기능별 교통사고모형 개발 - 청주시를 사례로 -

This study deals with the traffic accident of arterial link sections in the case of Cheongju. The purpose of the study is to develop the traffic accident model by the function of arterial links. In pursuing the above, this study gives particular attentions to developing the appropriate models using the accident data of main and minor arterial roads divided by 472 small link sections. The main results analyzed are as follows. First, as the t test on the accident characteristics of main and minor arterial roads shows that there are differences in the number of accident and EPDO(equivalent property damage only) between two roads, the development of models by function is analyzed to be appropriate. Second, it is analyzed that ZINB models are all statistically suitable to the number of accident and EPDO of main arterial roads. Third, the analysis shows that EPDOs of minor arterial roads fit to ZINB, and the number of the accident fit to ZIP model. Finally, the common variables of main arterial roads are evaluated to be the traffic volume and the number of inflection point, and those of minor be the average grade.

A comparative analysis of hotspot identification methods

The identification of crash hotspots is the first step of the highway safety management process. Errors in hotspot identification may result in the inefficient use of resources for safety improvements and may reduce the global effectiveness of the safety management process. Despite the importance of using effective hotspot identification (HSID) methods, only a few researchers have compared the performance of various methods. In this research, seven commonly applied HSID methods were compared against four robust and informative quantitative evaluation criteria. The following HSID methods were compared: crash frequency (CF), equivalent property damage only (EPDO) crash frequency, crash rate (CR), proportion method (P), empirical Bayes estimate of total-crash frequency (EB), empirical Bayes estimate of severe-crash frequency (EBs), and potential for improvement (PFI). The HSID methods were compared using the site consistency test, the method consistency test, the total rank differences test, and the total score test. These tests evaluate each HSID method's performance in a variety of areas, such as efficiency in identifying sites that show consistently poor safety performance, reliability in identifying the same hotspots in subsequent time periods, and ranking consistency. To evaluate the HSID methods, five years of crash data from the Italian motorway A16 were used. The quantitative evaluation tests showed that the EB method performs better than the other HSID methods. Test results highlight that the EB method is the most consistent and reliable method for identifying priority investigation locations. The EB expected frequency of total-crashes (EB) performed better than the EB expected frequency of severe-crashes (EBs), although the results differed only slightly when the number of identified hotspots increased. The CF method performed better than other HSID methods with more appealing theoretical arguments. In particular, the CF method performed better than the CR method. This result is quite alarming, since many agencies use the CR method. The PFI and EPDO methods were largely inconsistent. The proportion method performed worst in all of the tests. Overall, these results are consistent with the results of previous studies. The identification of engineering countermeasures that may reduce crashes was successful in all of the hotspots identified with the EB method; this finding shows that the identified hotspots can also be corrected. The advantages associated with the EB method were based on crash data from one Italian motorway, and the relative performances of HSID methods may change when using other crash data. However, the study results are very significant and are consistent with earlier findings. To further clarify the benefits of the EB method, this study should be replicated in other countries. Nevertheless, the study results, combined with previous research results, strongly suggest that the EB method should be the standard in the identification of hotspots.