scholarly journals Application of the Empirical Bayes Method with the Finite Mixture Model for Identifying Accident-Prone Spots

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Yajie Zou ◽  
Kristian Henrickson ◽  
Lingtao Wu ◽  
Yinhai Wang ◽  
Zhaoru Zhang
Keyword(s):  
Empirical Bayes ◽  
Safety Management ◽  
Identification Accuracy ◽  
Expected Number ◽  
Performance Function ◽  
Crash Data ◽  
Safety Performance Function ◽  
Traffic Characteristic ◽  
Rural Highway ◽  
Better Than

Hotspot identification (HSID) is an important component of the highway safety management process. A number of methods have been proposed to identify hotspots. Among these methods, previous studies have indicated that the empirical Bayes (EB) method can outperform other methods for identifying hotspots, since the EB method combines the historical crash records of the site and expected number of crashes obtained from a safety performance function (SPF) for similar sites. However, the SPFs are usually developed based on a large number of sites, which may contain heterogeneity in traffic characteristic. As a result, the hotspot identification accuracy of EB methods can possibly be affected by SPFs, when heterogeneity is present in crash data. Thus, it is necessary to consider the heterogeneity and homogeneity of roadway segments when using EB methods. To address this problem, this paper proposed three different classification-based EB methods to identify hotspots. Rural highway crash data collected in Texas were analyzed and classified into different groups using the proposed methods. Based on the modeling results for Texas crash dataset, it is found that one proposed classification-based EB method performs better than the standard EB method as well as other HSID methods.

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

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Yajie Zou ◽  
Xinzhi Zhong ◽  
John Ash ◽  
Ziqiang Zeng ◽  
Yinhai Wang ◽  
...  
Keyword(s):  
Empirical Bayes ◽  
Reference Group ◽  
Analysis Method ◽  
Crash Frequency ◽  
Crash Data ◽  
The Road ◽  
Safety Performance Functions ◽  
Bayes Analysis ◽  
Rural Highway ◽  
Selection Of

Hotspot identification (HSID) is a critical part of network-wide safety evaluations. Typical methods for ranking sites are often rooted in using the Empirical Bayes (EB) method to estimate safety from both observed crash records and predicted crash frequency based on similar sites. The performance of the EB method is highly related to the selection of a reference group of sites (i.e., roadway segments or intersections) similar to the target site from which safety performance functions (SPF) used to predict crash frequency will be developed. As crash data often contain underlying heterogeneity that, in essence, can make them appear to be generated from distinct subpopulations, methods are needed to select similar sites in a principled manner. To overcome this possible heterogeneity problem, EB-based HSID methods that use common clustering methodologies (e.g., mixture models, K-means, and hierarchical clustering) to select “similar” sites for building SPFs are developed. Performance of the clustering-based EB methods is then compared using real crash data. Here, HSID results, when computed on Texas undivided rural highway cash data, suggest that all three clustering-based EB analysis methods are preferred over the conventional statistical methods. Thus, properly classifying the road segments for heterogeneous crash data can further improve HSID accuracy.

scholarly journals Rural two-lane two-way three-leg and four-leg stop-controlled intersections: predicting road safety effects

2017 ◽  
Vol 12 (2) ◽  
pp. 117-126 ◽  
Author(s):  
Salvatore Antonio Biancardo ◽  
Francesca Russo ◽  
Daiva Žilionienė ◽  
Weibin Zhang
Keyword(s):  
Estimating Equation ◽  
Safety Performance ◽  
Crash Frequency ◽  
Performance Function ◽  
Left Turn ◽  
Explanatory Variables ◽  
Crash Data ◽  
Safety Performance Function ◽  
Lane Width ◽  
Residuals Analysis

The study focused on grade-level rural two-lane two-way three-leg and two-lane two-way four-leg stop-controlled intersections located in the flat area with a vertical grade of less than 5%. The goal is to calibrate one Safety Performance Function at these intersections by implementing a Generalized Estimating Equation with a binomial distribution and compare to the results with yearly expected crash frequencies by using models mainly refered to the scientific literature. The crash data involved 77 two-lane two-way intersections, of which 25 two-lane two-way three-leg intersections are without a left-turn lane (47 with left-turn lane), 5 two-lane two-way four-leg intersections without a left-turn lane (6 with a left-turn lane). No a right-turn lane is present on the major roads. Explanatory variables used in the Safety Performance Function are the presence or absence of a left-turn lane, mean lane width including approach lane and a left-turn lane width on the major road per travel direction, the number of legs, and the Total Annual Average Daily Traffic entering the intersection. The reliability of the Safety Performance Function was assessed using residuals analysis. A graphic outcome of the Safety Performance Function application has been plotted to easily assess a yearly expected crash frequency by varying the Average Annual Daily Traffic, the number of legs, and the presence or absence of a left-turn lane. The presence of a left-turn lane significantly reduces the yearly expected crash frequency values at intersections.

Comparative Analysis of Empirical Bayes and Bayesian Hierarchical Models in Hotspot Identification

2019 ◽  
Vol 2673 (7) ◽  
pp. 111-121 ◽  
Author(s):  
Xiaoyu Guo ◽  
Lingtao Wu ◽  
Yajie Zou ◽  
Lee Fawcett
Keyword(s):  
Hierarchical Model ◽  
Empirical Bayes ◽  
Safety Management ◽  
Bayesian Hierarchical Model ◽  
Parameter Estimates ◽  
Bayesian Hierarchical ◽  
Safety Performance Function ◽  
Significance Levels ◽  
Mean Differences ◽  
Safety Investments

Hotspot identification is an important step in the highway safety management process. Errors in hotspot identification (HSID) may result in an inefficient use of limited resources for safety improvements. The empirical Bayesian (EB) HSID has been widely applied as an effective approach in identifying hotspots. However, there are some limitations with the EB approach. It assumes that the parameter estimates of the safety performance function (SPF) are correct without any uncertainty, and does not consider temporal instability in crashes, which has been reported in recent studies. The Bayesian hierarchical model is an emerging technique that addresses the limitations of the EB method. Thus, the objective of this study is to compare the performance of the standard EB method and the Bayesian hierarchical model in identifying hotspots. Three methods (crash rate, EB, and the Bayesian hierarchical model) were applied to identify risky intersections with different significance levels. Four evaluation tests (site consistency, method consistency, total rank differences, and Poisson mean differences tests) were conducted to assess the performance of these three methods. The testing results suggest that: (1) the Bayesian hierarchical model outperforms the crash rate and the EB methods in most cases, and the Bayesian hierarchical model improves the accuracy of HSID significantly; and (2) hotspots identified with crash rates are generally unreliable. This is significant for roadway agencies and practitioners trying to accurately rank sites in the roadway network to effectively manage safety investments. Roadway agencies and practitioners are encouraged to consider the Bayesian hierarchical model in identifying hotspots.

scholarly journals Collective Risk Ranking of Highway Segments on the Basis of Severity-Weighted Crash Rates

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Dawei Li ◽  
Mustafa F. M. Al-Mahamda
Keyword(s):  
Empirical Bayes ◽  
Safety Information ◽  
Crash Severity ◽  
Regression To The Mean ◽  
Factors Affecting ◽  
Crash Data ◽  
Crash Rates ◽  
Risk Ranking ◽  
Collective Risk ◽  
Major Factors

This study is intended to focus on the major factors affecting traffic crash rates and severity levels, in addition to identifying crash-prone locations (i.e., black spots) based on the two indicators. The available crash data for different road segments used for the analysis were obtained from the Washington state database provided by the Highway Safety Information System (HSIS) for the years 2006 to 2011. A Random Forest (RF) classifier was used to predict the outcome level of crash severity, while crash rates were predicted by applying RF regressor. Certain features were selected for each model besides the abstraction of new features to check if there are unobserved correlations affecting the independent variables, such as accounting for the number and weight of crashes within 1 km2 area by implementing the Getis-Ord Gi∗ index. Moreover, to calculate the collective risk (CR) score, crash rates were adjusted to incorporate crash severity weights (cost per severity type) and regression-to-the-mean (RTM) bias via Empirical Bayes (EB) method. Finally, segments were ranked according to their CR score.

Safety Evaluation of Alternative Audible Lane Departure Warning Treatments in Reducing Traffic Crashes: An Empirical Bayes Observational Before–After Study

2018 ◽  
Vol 2672 (21) ◽  
pp. 30-40 ◽  
Author(s):  
Lingtao Wu ◽  
Srinivas R. Geedipally ◽  
Adam M. Pike
Keyword(s):  
Empirical Bayes ◽  
Safety Evaluation ◽  
Highway Safety ◽  
Warning Systems ◽  
Lane Departure Warning ◽  
Crash Data ◽  
Pavement Markings ◽  
Lane Departure ◽  
Run Off ◽  
Single Vehicle

Roadway departure crashes are a major contributor to traffic fatalities and injury. Rumble strips have been shown to be an effective countermeasure in reducing roadway departure crashes. However, some roadway situations, for instance, inadequate shoulder width or roadway surface depth, have limited the application of conventional milled or rolled in rumble strips. Alternative audible lane departure warning systems, including profile (audible) pavement markings and preformed rumble bars, are increasingly used to overcome the limitations that exist with the milled rumble strips. So far, the safety effectiveness of these alternative audible lane departure warning systems has not been extensively assessed. The main purpose of this paper is to examine the safety effect of installing profile pavement markings and preformed rumble bars. Specifically, this study developed crash modification factors for these treatments that quantify the effectiveness in reducing single-vehicle-run-off-road (SVROR) and opposite-direction (OD) crashes. Traffic, roadway, and crash data at the treated sites on 189 miles of rural two-lane highways in Texas were analyzed using an empirical Bayes (EB) before–after analysis method. Safety performance functions from the Highway Safety Manual and Texas Highway Safety Design Workbook were used in the EB analysis. The results revealed a 21.3% reduction in all SVROR and OD crashes, and 32.5% to 39.9% reduction in fatal and injury SVROR and OD crashes after installing profile pavement marking and preformed rumble bars.

Two Problems of Averaging Arising in the Estimation of the Relationship Between Accidents and Traffic Flow

1998 ◽  
Vol 1635 (1) ◽  
pp. 37-43 ◽  
Author(s):  
Abraham Mensah ◽  
Ezra Hauer
Keyword(s):  
Traffic Flow ◽  
Annual Average ◽  
Performance Function ◽  
Safety Performance Function ◽  
Time Period ◽  
Annual Average Daily Traffic ◽  
Averaging Problem ◽  
Long Time ◽  
The Relationship ◽  
The Ideal

A function linking the expected accident frequency to traffic flow is called a safety performance function (SPF). SPFs are estimated from data for various facilities and accident types. Typically, accident counts over a period of a year or more, and estimates of average flow for such periods, serve as data. The ideal is for SPFs to represent cause-effect regularities. However, because accident counts are for a long time period and because average flows are used, two issues of averaging arise. First, the cause-effect relationship is between accidents and the flows prevailing near the time of accident occurrence. Therefore, ideally, these should be the argument of the SPF. In practice, however, either because of lack of detail or difficulties of estimation, average flows are used for estimation. The question is what problems arise when average flows, such as annual average daily traffic, instead of the flows at the time of the accident are used as the argument of the SPF. This is the argument averaging problem. Second, there are at least two (daytime and nighttime) and perhaps many more cause-effect SPFs that prevail in the course of a year. Ideally, each relationship should be estimated separately. The question is what problems arise if one joint SPF is estimated when two or more separate functions should have been used. This is the function averaging problem. After analysis, how to account and how to correct for the argument averaging problem are shown. At this time, avoiding the function averaging problem by estimating daytime and nighttime SPFs separately can be the only recommendation.

A Novel Technique to Identify Hot Zones for Active Commuters’ Crashes

2018 ◽  
Vol 2672 (38) ◽  
pp. 266-276 ◽  
Author(s):  
Ahmed Osama ◽  
Tarek Sayed ◽  
Emanuele Sacchi
Keyword(s):  
Mahalanobis Distance ◽  
Empirical Bayes ◽  
Negative Binomial ◽  
Active Transportation ◽  
Spatial Effects ◽  
Distance Method ◽  
Crash Data ◽  
Safety Improvement ◽  
Pedestrian Crashes ◽  
Psi Method

This paper presents an approach to identify and rank accident-prone (hot) zones for active transportation modes. The approach aims to extend the well-known empirical Bayes (EB) potential for safety improvement (PSI) method to cases where multiple crash modes are modeled jointly (multivariate modeling). In this study, crash modeling was pursued with a multivariate model, incorporating spatial effects, using the full Bayes (FB) technique. Cyclist and pedestrian crash data for the City of Vancouver (British Columbia, Canada) were analyzed for 134 traffic analysis zones (TAZs) to detect active transportation hot zones. The hot zones identification (HZID) process was based on the estimation of the Mahalanobis distance, which can be considered an extension to the PSI method in the context of multivariate analysis. In addition, a negative binomial model was developed for cyclist and pedestrian crashes, where the EB PSI for each mode crash was quantified. The cyclist and pedestrian PSIs were combined to detect active transportation hot zones. Overall, the Mahalanobis distance method is found to outperform the PSI method in terms of consistency of results; and discrepancy is observed between the hot zones identified using both approaches.

Effects of Rural Highway Median Treatments and Access

2005 ◽  
Vol 1931 (1) ◽  
pp. 99-107 ◽  
Author(s):  
J. L. Gattis ◽  
Ramasubramaniyan Balakumar ◽  
Lynette K. Duncan
Keyword(s):  
Land Use ◽  
Crash Severity ◽  
Crash Frequency ◽  
Crash Data ◽  
Crash Rates ◽  
Access Frequency ◽  
Stop Sign ◽  
Median Volume ◽  
Rural Highway ◽  
Median Width

The safety records of rural and suburban four-lane highways in Arkansas as a function of median treatment and access density were examined. The study excluded roadways with posted speeds lower than 64 km/h (40 mph) and excluded fully controlled access roadways. When entering an urban area, the segments were normally terminated when the first traffic signal or stop sign was encountered. By using 3 years of crash data, the analyses revealed a number of relationships relating crash frequency to median, volume, and access frequency attributes. Crash severity and crash type were also examined. As median width increased, there was a weak but statistically significant decline in the crash rate. There was a weak but statistically significant increase in the crash rate as access density increased. The roadways with shoulders and depressed medians had the lowest crash rates, and the roadways with no median (i.e., painted centerline) and curbs had the worst safety record. An inspection of these data suggests that there may be a correlation between median type and land use type: certain types of median are more likely to be present in certain land use environments. This raises the possibility that in this and in other studies of the safety effects of median treatments, the findings may be influenced or skewed by correlations between median type and land use or surroundings or by other factors.

How the choice of safety performance function affects the identification of important crash prediction variables

2016 ◽  
Vol 88 ◽  
pp. 1-8 ◽  
Author(s):  
Ketong Wang ◽  
Jenna K. Simandl ◽  
Michael D. Porter ◽  
Andrew J. Graettinger ◽  
Randy K. Smith
Keyword(s):  
Safety Performance ◽  
Performance Function ◽  
Crash Prediction ◽  
Safety Performance Function
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