Alfonso Montella

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 methods 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.

Analysis of powered two-wheeler crashes in Italy by classification trees and rules discovery.

Aim of the study was the analysis of powered two-wheeler (PTW) crashes in Italy in order to detect interdependence as well as dissimilarities among crash characteristics and provide insights for the development of safety improvement strategies focused on PTWs. At this aim, data mining techniques were used to analyze the data relative to the 254,575 crashes involving PTWs occurred in Italy in the period 2006-2008. Classification trees analysis and rules discovery were performed. Tree-based methods are non-linear and non-parametric data mining tools for supervised classification and regression problems. They do not require a priori probabilistic knowledge about the phenomena under studying and consider conditional interactions among input data. Rules discovery is the identification of sets of items (i.e., crash patterns) that occur together in a given event (i.e., a crash in our study) more often than they would if they were independent of each other. Thus, the method can detect interdependence among crash characteristics. Due to the large number of patterns considered, both methods suffer from an extreme risk of finding patterns that appear due to chance alone. To overcome this problem, in our study we randomly split the sample data in two data sets and used well-established statistical practices to evaluate the statistical significance of the results. Both the classification trees and the rules discovery were effective in providing meaningful insights about PTW crash characteristics and their interdependencies. Even though in several cases different crash characteristics were highlighted, the results of the two the analysis methods were never contradictory. Furthermore, most of the findings of this study were consistent with the results of previous studies which used different analytical techniques, such as probabilistic models of crash injury severity. Basing on the analysis results, engineering countermeasures and policy initiatives to reduce PTW injuries and fatalities were singled out. The simultaneous use of classification trees and association discovery must not, however, be seen as an attempt to supplant other techniques, but as a complementary method which can be integrated into other safety analyses.

Identifying crash contributory factors at urban roundabouts and using association rules to explore their relationships to different crash types

The use of roundabouts improves intersection safety by eliminating or altering conflict types, reducing crash severity, and causing drivers to reduce speeds. However, roundabout performances can degrade if precautions are not taken during either the design or the operation phase. Therefore, additional information on the safety of the roundabouts is extremely helpful for planners and designers in identifying existing deficiencies and in refining the design criteria currently being used. The aim of the paper was to investigate the crash contributory factors in 15 urban roundabouts located in Italy and to study the interdependences between these factors. The crash data refer to the period 2003-2008. The identification of the crash contributory factors was based on site inspections and rigorous analyses performed by a team of specialists with a relevant road safety engineering background. Each roundabout was inspected once every year from 2004 to 2009, both in daytime and in nighttime. Overall, 62 different contributory factors and 2156 total contributory factors were identified. In 51 crashes, a single contributory factor was found, whereas in the other 223 crashes, a combination of contributory factors was identified. Given the large amount of data, the interdependences between the contributory factors and between the contributory factors and the different crash types were explored by an association discovery. Association discovery is the identification of sets of items (i.e., crash contributory factors and crash types in our study) that occur together in a given event (i.e., a crash in our study). The rules were filtered by support, confidence, and lift. As a result, 112 association rules were discovered. Overall, numerous contributory factors related to the road and environment deficiencies but not related to the road user or to the vehicle were identified. The most important factors related to geometric design were the radius of deflection and the deviation angle. In existing roundabouts, the improvement of these factors might be quite expensive, but the crucial role of a moderate radius of deflection and a large deviation angle in the design of new roundabouts was stressed. Many of the contributory factors were related to markings and signs, and these factors could be easily removed with low-cost safety measures. Furthermore, because of the association between the markings, signs, and geometric design contributory factors, the study results suggest that the improvement in markings and signs might also have a significant effect in the sites where geometric design deficiencies were identified as contributory factors.

Safety Reviews of Existing Roads: Quantitative Safety Assessment Methodology

Safety reviews of existing roads are becoming an accepted practice in many agencies around the world. These reviews can be highly cost-effective, but the subjective nature of the process can give rise to inconsistencies that limit their effectiveness. To address this issue, a technique to support safety reviews to quantify the safety gains that could be achieved by addressing the problems identified in the review process is presented. The approach is based on known accident relationships. A systematic process to determine which road features should be investigated and how each feature should be evaluated during the review is described. The procedure addresses rural two-lane highways at nonintersections. From the process, a potential for a safety improvement index (PFI) was calculated. Validation of the procedure was carried out by a comparison of the PFI values with the expected collision frequency. PFI was assessed in 406 km of rural two-lane rolling highways in Italy. Collision frequency was determined by application of a collision prediction model, calibrated in the study network, and was refined by application of the empirical Bayes (EB) technique. Correlation between EB safety estimates and PFI values is highly significant, with 93% of the variation in the estimated number of accidents explained by the PFI value. Because of the validation and quantitative nature of the PFI, the procedure can be used to support safety reviews and decision making.

Safety Index for Evaluation of Two-Lane Rural Highways

The methodological approach to safety evaluation of two-lane rural highway segments that is presented uses both analytical procedures referring to alignment design consistency models and safety inspection processes. A safety index (SI) that quantitatively measures the relative safety performance of a road segment is calculated from the procedure. The SI is formulated by combining three components of risk: the exposure of road users to road hazards, the probability of a vehicles being involved in an accident, and the resulting consequences should an accident occur. This systematic and replicable procedure integrates two different, complementary approaches–one based on design consistency evaluations and the other on safety inspections–and makes it possible to address a wide variety of safety issues effectively. A further advantage of the procedure is its applicability on highways where crash data are either not available or unreliable. Validation of the procedure was carried out on a sample of roads by a comparison of the risk rank obtained by using the SI and accident history. The SI was assessed in 30 segments chosen from a sample of two-lane rural highways in Italy, and the actual accident situation was obtained with the empirical Bayes (EB) procedure. Spearmans rank correlation was used to determine the level of agreement between the rankings obtained with the two techniques. The results from the Spearmans rank-correlation analysis validate the SI, indicating that the ranking from the SI scores and the EB estimates agrees at the 99.9% level of significance with a correlation coefficient of 0.87.

Crash Prediction Models for Rural Motorways

In this paper, crash prediction models for estimating the safety of rural motorways are presented. Separate models were developed for total crashes and severe (fatal plus all injury) crashes. Generalized linear modeling techniques were used to fit the models, and a negative binomial distribution error structure was assumed. The study used a sample of 2,245 crashes (728 severe crashes) that occurred from 2001 to 2005 on Motorway A16 between Naples and Canosa in Italy. Many characteristics of the motorway were substandard. The motorway allowed investigation of a wide spectrum of geometric configurations. The models were developed by the stepwise-forward procedure with explanatory variables related to traffic volume and composition, horizontal alignment, vertical alignment, design consistency, sight distance, roadside context, cross section, speed limits, and interchange ramps. The decision to keep a variable in the model was based on two criteria. The first was whether the t-ratio of the variables estimated coefficient was significant at the 5% level. The second criterion was related to the improvement of goodness-of-fit measures of the model that includes that variable. Goodness-of-fit measures were the parameter Rα2 and Akaikes information criterion. All the parameters have a logical and expected sign. The most important result was that design consistency measures significantly affected road safety, not only on two-lane rural highways, but also on motorways.

Safety Evaluation of Curve Delineation Improvements: Empirical Bayes Observational Before-and-After Study

The objective of this study was to evaluate the safety effectiveness of treatments aimed at improving horizontal curve delineation. Different target crash types were considered in the analysis: total, nighttime, daytime, rainy, nonrainy, run-off-road, non-run-off-road, injury, and property damage only. The treatment sites for this study were 15 curves of the motorway A16 Naples–Canosa in Italy. All curves were characterized by small radius, large deflection angle, available sight distance smaller than the stopping sight distance, and superelevation smaller than the demanded one at both the design and the operating speed. Treatment typologies included installation of chevron signs; installation of curve warning signs and chevron signs; and installation of curve warning signs, chevron signs, and sequential flashing beacons along the curve. An empirical Bayes observational before-and-after study was performed. Results indicate statistically significant crash reductions in total, nighttime, daytime, rainy, nonrainy, run-off-road, and property-damage-only crashes. Total crash reduction is estimated to be 39.4%. Treatment effectiveness is greater for curves with radius less than or equal to 300 m and for curves with deflection angle greater than 60 gon (100 gon = 90°). Total crash reduction is estimated to be 52.2% for curves with radius less than or equal to 300 m and 51.0% for curves with deflection angle greater than 60 gon. The most effective treatment was the installation of curve warning signs, chevron signs, and sequential flashing beacons along the curve. The statistically significant estimate of total crash reduction for this treatment is 47.6%.

Roundabout In-Service Safety Reviews: Safety Assessment Procedure

The number of roundabout intersections is quickly increasing around the world, mainly because of their good safety performance. However, roundabout performance can be degraded if precautions are not taken during either the design or the operation phase. Safety review methodologies are well suited for roundabout analysis, but their effectiveness can be improved substantially if the review recommendations are supported by a quantitative safety assessment. A roundabout safety assessment procedure is presented. Validation of the safety assessment procedure was carried out by a comparison of the results with empirical Bayes (EB) safety estimates. From the procedure, a safety index (SI) for roundabout approaches and for the whole number of roundabouts is assessed. The SI was assessed for 55 approaches of 15 urban roundabouts in Italy. For approaches, the correlation between the SI values and the EB safety estimates is highly significant (t = 15.62, p-value < 0.001), with 83% of the variation in the estimated number of crashes explained by the SI value. For all roundabouts, the correlation between SI values and EB safety estimates remains highly significant (t = 14.27, p-value < 0.001), with 94% of the variation in the estimated number of crashes explained by the SI value. The SI procedure makes it possible to support review recommendations by a quantitative safety evaluation. The SI can be used to rank both roundabouts and roundabout approaches by priority according to potential crash reduction from the safety measures. Furthermore, the SI procedure allows detection of safety issues that contribute the most to safety problems.

Perceptual Measures to Influence Operating Speeds and Reduce Crashes at Rural Intersections: Driving Simulator Experiment

The aim of this paper is to investigate, by means of a dynamic driving simulator experiment, the behavior of road users at rural intersections in relation to perceptual measures designed for increasing hazard detection. In the experiment 10 configurations of tangents were tested: Alt1, base tangent; Alt2, four-leg base intersection; Alt3, intersection with reduced sight distance; and Alt4 through Alt10, intersections with perceptual treatments. The Virtual Environment for Road Safety high-fidelity dynamic-driving simulator, operating at the Technology Environment Safety Transport Road Safety Laboratory located in Naples, Italy, was used. Analysis of the results used two approaches: (a) explorative description of data by cluster analysis and (b) inferential procedures about population using statistical tests. Results showed that the speed behavior in the tangents was significantly affected by the presence of the intersections and by the perceptual treatments. Intersections without perceptual treatments significantly affected driver speeds in the 250 m preceding the intersection. Perceptual treatments helped the driver to detect the intersection earlier and to slow down. Dragon teeth markings, colored intersection area, and raised median island performed better than the other perceptual treatments. They produced significant average speed reduction in the 150 m preceding the intersection ranging between 16 km/h and 23 km/h. Study results support real-world implementation of perceptual measures in rural intersections because they are low-cost, fast implementation measures with a high potential to be cost-effective.

Safety evaluation of automated section speed enforcement system

Because speeding is one of the most significant contributing factors to fatal crashes, most road agencies attempt to achieve the right operating speed by imposing speed limits. Speed limit violations are prevalent, even on motorways with speed cameras. A problem with speed camera enforcement is that some motorists brake before passing a camera location and then exceed the speed limit after passing. This sudden braking can cause dangerous situations, crashes, and traffic jams. Furthermore, safe operating speed is not achieved where there are no cameras, especially where enforcement is overt, as in Italy. A new technique to overcome these problems is an automated section speed enforcement system, Safety Tutor. Unlike conventional speed meters, which measure vehicle speed at one point, the new technique determines average speed over a long distance. This study evaluated the safety effectiveness of the Safety Tutor system installed on Italian Motorway A1 Milan–Naples in 2007. An empirical Bayes observational before-and-after study was performed. The estimate of the total crash reduction is 31.2%, with a lower 95% confidence limit of 24.3%. The greatest crash reductions were observed for severe crashes and crashes at curves. Reduction was 55.6% for severe crashes, 26.6% for nonsevere crashes, 43.4% at curves, and 28.4% at tangents. However, the systems effectiveness decreased over time. The crash reduction was 39.4% in the first semester after the systems activation and 18.7% in the fifth semester after activation. Results strongly support activation of the new automated system owing to highly significant and substantial safety effects. The decrease in system effectiveness over time may be correctible with higher levels of enforcement.