Developing a Clustering-Based Empirical Bayes Analysis Method for Hotspot Identification

This study explored potential use of a traffic conflict-based surrogate safety assessment as an alternative way of evaluating the safety performance of roads and identifying potential sites for safety improvement. The study compared two major safety assessment streams (a statistical modeling method and a traffic conflict-based method) in their evaluation of crash risk to investigate the performance of the traffic conflict-based method as an alternative to the crash-based method in identification of hot spots. The empirical Bayes (EB) method coupled with the safety performance function (SPF), called the EB-SPF method, was used as a benchmark, and the conventional crash frequency method was used as a comparison supplement. These two methods were viewed as the crash-based methods because they relied on crash data. Traffic conflicts were estimated through the microscopic traffic simulation model VISSIM. The safety evaluation was performed separately for 24 signalized intersections and 86 segments in the Tyson...

Identification of crash hot spots is the critical component of the highway safety management process. Errors in hot spot identification (HSID) may result in the inefficient use of resources for safety improvements. One HSID method that is based on the empirical Bayesian (EB) method has been widely used as an effective approach for identifying crash-prone sites. For the EB method, the negative binomial (NB) model is usually needed to obtain the EB estimates. Recently, some studies have shown that the Sichel (SI) model can be easily used in the EB modeling framework and potentially yield better EB estimates. The objective of this study was to compare the performance of the two crash prediction (SI and NB) models in identifying hot spots with the EB method. To accomplish the objective of this study, empirical crash data collected at highway segments in Texas were used to generate simulated crash counts. Three commonly used HSID methods (simple ranking, confidence interval, and EB) were applied with the use of simulated data. False positives, false negatives, and false identifications were calculated and compared across the methods. The simulation results in this study suggested that the SI-based EB method could consistently provide a better HSID result than the NB-based EB method. Moreover, EB methods yielded the lowest error percentage of the three HSID methods. This study confirmed that the EB technique was an effective method for identifying hazardous sites. On the basis of the findings in this study, it is recommended that transportation safety researchers consider the SI model as an alternative crash prediction model when the EB approach is used.

Traffic Modeling and Safety Analysis on Motorway Ramps

A fully Bayesian multivariate approach to before–after safety evaluation

Effect of increasing the freeway posted speed limit on entry ramp speed-change lane crash frequency

Investigating the effects of the fixed and varying dispersion parameters of Poisson-gamma models on empirical Bayes estimates

Identification of hot spots, also known as the sites with promise, black spots, accident-prone locations, or priority investigation locations, is an important and routine activity for improving the overall safety of roadway networks. Extensive literature focuses on methods for hot spot identification (HSID). A subset of this considerable literature is dedicated to conducting performance assessments of various HSID methods. A central issue in comparing HSID methods is the development and selection of quantitative and qualitative performance measures or criteria. The authors contend that currently employed HSID assessment criteria–namely false positives and false negatives–are necessary but not sufficient, and additional criteria are needed to exploit the ordinal nature of site ranking data. With the intent to equip road safety professionals and researchers with more useful tools to compare the performances of various HSID methods and to improve the level of HSID assessments, this paper proposes four quantitative HSID evaluation tests that are, to the authors’ knowledge, new and unique. These tests evaluate different aspects of HSID method performance, including reliability of results, ranking consistency, and false identification consistency and reliability. It is intended that road safety professionals apply these different evaluation tests in addition to existing tests to compare the performances of various HSID methods, and then select the most appropriate HSID method to screen road networks to identify sites that require further analysis. This work demonstrates four new criteria using 3 years of Arizona road section accident data and four commonly applied HSID methods [accident frequency ranking, accident rate ranking, accident reduction potential, and empirical Bayes (EB)]. The EB HSID method reveals itself as the superior method in most of the evaluation tests. In contrast, identifying hot spots using accident rate rankings performs the least well among the tests. The accident frequency and accident reduction potential methods perform similarly, with slight differences explained. The authors believe that the four new evaluation tests offer insight into HSID performance heretofore unavailable to analysts and researchers.

Pedestrian hybrid beacon signals: Identifying characteristics associated with negative consequences to reduce danger at mid-block locations

Applying quantile regression for modeling equivalent property damage only crashes to identify accident blackspots

The empirical Bayes (EB) method addresses two problems of safety estimation: it increases the precision of estimates beyond what is possible when one is limited to the use of a 2- to 3-year accident history, and it corrects for the regression-to-mean bias. The increase in precision is important when the usual estimate is too imprecise to be useful. The elimination of the regression-to-mean bias is important whenever the accident history of the entity is in some way connected with the reason why its safety is estimated. The theory of the EB method is well developed. It is now used in the Interactive Highway Safety Design Model and will be used in the Comprehensive Highway Safety Improvement Model. The time has come for the EB method to be the standard and staple of professional practice. The study’s goal is to facilitate the transition from theory into practice.

Hotspots identification (HSID), a reactive crash prediction based on the historical accident counts, is crucial to transport authorities for evaluating the risk level of the object road sites. The objective of the research is to identify unidentified hotspots that should have been treated. Numerous conventional HSID approaches have been developed and applied for decades, none of which takes daily variability of traffic flow and crash record into account. In this regard, we categorize the time of day into four groups: (1) morning peak hours, (2) afternoon peak hours, (3) daytime, and (4) night off-peak hours. The authors further apply this proposed methodology to Pacific Motorway Southeast Queensland section linking Brisbane to Gold Coast based on an Empirical Bayesian (EB) approach. Finally, the applications of these proposed EB-based methods and the conventional EB method are discussed through an aggregated view.

Data quality, including record inaccuracy and missingness (incompletely recorded crashes and crash underreporting), has always been of concern in crash data analysis. Limited efforts have been made to handle some specific aspects of crash data quality problems, such as using weights in estimation to take care of unreported crash data and applying multiple imputation (MI) to fill in missing information of drivers’ status of attention before crashes. Yet, there lacks a general investigation of the performance of different statistical methods to handle missing crash data. This paper is intended to explore and evaluate the performance of three missing data treatments, which are complete-case analysis (CC), inverse probability weighting (IPW) and MI, in crash severity modeling using the ordered probit model. CC discards those crash records with missing information on any of the variables; IPW includes weights in estimation to adjust for bias, using complete records’ probability of being a complete case; and MI imputes the missing values based on the conditional distribution of the variable with missing information on the observed data. Those missing data treatments provide varying performance in model estimations. Based on analysis of both simulated and real crash data, this paper suggests that the choice of an appropriate missing data treatment should be based on sample size and data missing rate. Meanwhile, it is recommended that MI is used for incompletely recorded crash data and IPW for unreported crashes, before applying crash severity models on crash data.

Assessments of the potential health impacts of contaminants and other environmental risk factors are often based on comparisons of disease rates among collections of spatially aligned areas. These comparisons are valid only if the observed rates adequately reflect the true underlying area‐specific risk. In areas with small populations, observed incidence values can be highly unstable and true risk differences among areas can be masked by spurious fluctuations in the observed rates. We examine the use of Bayes and empirical Bayes methods for stabilizing incidence rates observed in geographically aligned areas. While these methods improve stability, both the Bayes and empirical Bayes approaches produce a histogram of the estimates that is too narrow when compared to the true distribution of risk. Constrained empirical Bayes estimators have been developed that provide improved estimation of the variance of the true rates. We use simulations to compare the performance of Bayes, empirical Bayes, and constrained empirical Bayes approaches for estimating incidence rates in a variety of multivariate Gaussian scenarios with differing levels of spatial dependence. The mean squared error of estimation associated with the simulated observed rates was, on average, five times greater than that of the Bayes and empirical Bayes estimates. The sample variance of the standard Bayes and empirical Bayes estimates was consistently smaller than the variance of the simulated rates. The constrained estimators produced collections of rate estimates that dramatically improved estimation of the true dispersion of risk. In addition, the mean square error of the constrained empirical Bayes estimates was only slightly greater than that of the unconstrained rate estimates. We illustrate the use of empirical and constrained empirical Bayes estimators in an analysis of lung cancer mortality rates in Ohio.

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.

This paper presents a detailed analysis of the temporal transferability of heterogeneous overdispersion parameter negative binomial models for crash severity types in California. The Furnival–Wilson leaps and bounds algorithm was used to identify optimal safety performance function specifications. The overdispersion parameter was allowed to vary across roadway segments as a function of roadway geometrics. Sixty models were developed for five major severity outcomes for homogeneous roadway segments for each of the periods 2005 to 2010, 2011 to 2012, and 2005 to 2012. Model transferability tests were conducted with likelihood ratio tests, and it was determined that temporal transferability rates (from the 2005–2010 period to the 2011–2012 period) were poor. The findings indicate the potential time instability of safety performance function parameters. The analysis found a higher rate of transferability for rural safety performance functions compared with urban safety performance functions. The rate of transferability was highest for severe injury and fatal injury. This result shows that the role of the overdispersion parameter in the transferability of safety performance functions must be considered. The absence of information on changes in economic, environmental, vehicle fleet, and driver population data limits the specification of the overdispersion parameter to roadway geometrics. Although it is an improvement over the current practice of the use of a constant overdispersion parameter, economic, environmental, and vehicle–driver population data might be useful for improving model transferability over time. The implications are that safety analysis methods such as crash modification factors, network screening, and excess crash estimations must take into account parameter variability over time.