Injury severity prediction of cyclist crashes using random forests and random parameters logit models.

Cycling provides numerous benefits to individuals and to society but the burden of road traffic injuries and fatalities is disproportionately sustained by cyclists. Without awareness of the contributory factors of cyclist death and injury, the capability to implement context-specific and appropriate measures is severely limited. In this paper, we investigated the effects of the characteristics related to the road, the environment, the vehicle involved, the driver, and the cyclist on severity of crashes involving cyclists analysing 72,363 crashes that occurred in Great Britain in the period 2016-2018. Both a machine learning method, as the Random Forest (RF), and an econometric model, as the Random Parameters Logit Model (RPLM), were implemented. Three different RF algorithms were performed, namely the traditional RF, the Weighted Subspace RF, and the Random Survival Forest. The latter demonstrated superior predictive performances both in terms of F-measure and G-mean. The main result of the Random Survival Forest is the variable importance that provides a ranked list of the predictors associated with the fatal and severe cyclist crashes. For fatal classification, 19 variables showed a normalized importance higher than 5% with the second involved vehicle manoeuvring and the gender of the driver of the second vehicle having the greatest predictive ability. For serious injury classification, 13 variables showed a normalized importance higher than 5% with the bike leaving the carriageway having the greatest normalized importance. Furthermore, each path from the root node to the leaf nodes has been retraced the way back generating 361 if-then rules with fatal crash as consequent and 349 if-then rules with serious injury crash as consequent. The RPLM showed significant unobserved heterogeneity in the data finding four normal distributed indicator variables with random parameters: cyclist age ≥ 75 (fatal prediction), cyclist gender male (fatal and serious prediction), and driver aged 55-64 (serious prediction). The model's McFadden Pseudo R2 is equal to 0.21, indicating a very good fit. Furthermore, to understand the magnitude of the effects and the contribution of each variable to injury severity probabilities the pseudo-elasticity was assessed, gaining valuable insights into the relative importance and influence of the variables. The RF and the RPLM resulted complementary in identifying several roadways, environmental, vehicle, driver, and cyclist-related factors associated with higher crash severity. Based on the identified contributory factors, safety countermeasures useful to develop strategies for making bike a safer and more friendly form of transport were recommended.

Systematic literature review of 10 years of cyclist safety research.

Cyclist safety is a research field that is gaining increasing interest and attention, but still offers questions and challenges open to the scientific community. The aim of this study was to provide an exhaustive review of scientific publications in the cyclist safety field. For this purpose, Bibliometrix-R tool was used to analyse 1066 documents retrieved from Web of Science (WoS) between 2012 and 2021. The study examined published sources and productive scholars by exposing their most influential contributions, presented institutions and countries most contributing to cyclist safety and explored countries open towards international collaborations. A keywords analysis provided the most frequent author keywords in cyclist safety shown in a word cloud with E-bike, behaviour, and crash severity representing the primary keywords. Furthermore, a thematic map of cyclist safety field drafted from the author's keywords was identified. The strategic diagram is divided in four quadrants and, according to both density and centrality, the themes can be classified as follows: 1) motor themes, characterized by high value of both centrality and density; 2) niche themes, defined by high density and low centrality; 3) emerging or declining themes, featured by low value of both centrality and density; and 4) basic themes, distinguished by high centrality and low density. The motor themes (i.e., the main topics in cyclist safety field) crash severity and bike network were further explored. The research findings will be useful to develop strategies for making bike a safer and more confident form of transport as well as to guide researchers towards the future scientific knowledge.

Analysis of contributory factors of fatal pedestrian crashes by mixed logit model and association rules.

Pedestrians are the most vulnerable road users and pedestrian crashes are a major concern both for their number and their severity. In Italy, pedestrians account for 34% of the road fatalities in urban area. To improve pedestrian safety, this study is aimed at analysing the roadway, environmental, vehicle, driver and pedestrian-related factors that are associated with fatal pedestrian crashes in Italy and providing insights for the development of effective countermeasures. This study used an econometric model, the mixed logit model, and a machine learning algorithm, the association rules, to analyse 101,032 pedestrian crashes that occurred in Italy. Study results identified several factors associated with fatal pedestrian crashes. The mixed logit identified 46 significant indicator variables (1 with random parameter), and the association rules provided 119 valid rules. F-measure and G-mean showed higher prediction performance of the mixed logit over the association rules. Study results recommend using both models as complementary approaches since their combination is effective in providing meaningful insights about pedestrian crash contributory factors and their interdependencies. To address the contributory factors identified by the study, behavioural/engineering pedestrian safety countermeasures are recommended. The findings provided new insights for transportation agencies to develop effective countermeasures for pedestrian safety improvement.

Safety Index for evaluation of urban roundabouts.

Recently, there is a growing interest in road safety assessments based on the examination of the characteristics of the road aimed at identifying the presence of risk factors. This approach, named road assessment program or network wide road safety assessment, is required by the EU Directive 2019/1936 on road infrastructure safety management. Reliable procedures for assessing the inherent safety of all the elements of the road network are required to conduct roadway safety assessments. To provide a contribution toward the development of procedures for network wide road safety assessment, this paper develops and validates a Safety Index (SI) for evaluating urban roundabouts. The SI is assessed both at the roundabout level as well as at the roundabout approach level. This procedure detects the safety issues that are the largest contributors to crash risk in order to identify the safety measures that provide the greatest crash reduction at roundabouts. The SI is formulated by combining two components: the exposure of road users to road hazards (Exposure) and the risk factors which increase the probability of involvement in crashes (Risk Index). The procedure considers 33 detailed safety issues and 5 general safety issues to compute the Risk Index. Criteria for identifying and ranking the safety issues are defined. The SI procedure was validated in a sample of 50 urban roundabouts located in Rome, Italy. The sample consisted of 12 single-lane roundabouts and 38 two-lane roundabouts, with a total number of approaches equal to 179. In these roundabouts, the SI scores and the EB crash estimates were compared with reference both to the whole roundabouts as well as to the single roundabout approaches. The correlation between the SI scores and EB estimates was highly significant both at the roundabout level (R2 = 0.85, t = 16.49, p-value < 0.001) as well as at the approach level (R2 = 0.56, t = 14.88, p-value < 0.001). The results from Spearman's rank-correlation analysis provided further validation for the SI indicating that rankings from the SI and the EB estimates agree at the 99.9 % confidence level both at the roundabout level (ρs = 0.80) as well as at the approach level (ρs = 0.70).

Studying the effects of an advanced driver-assistance system to improve safety of cyclists overtaking.

Among all crashes involving cyclists, a motorist approaching from behind a cyclist on a shared lane is particularly dangerous and likely to result in serious injuries and fatalities. Previous research has highlighted that inadequate lateral distance and high vehicle speed are among the main contributing factors of crashes involving cars overtaking cyclists. A new advanced driver assistance system (ADAS) which supports drivers as they overtake cyclists was designed to avoid or, at least, mitigate crashes. In human-machine interface (HMI) design, the information was presented via multiple modalities with a multistage warning system. A combination of lateral clearance (LC) and time-to-danger (TTD) parameters was used as ADAS activation criterion. Experimentation was carried out using the medium-fidelity driving simulator at the Transportation Research Institute (IMOB) of Hasselt University in Belgium. Forty-eight drivers drove the two-lane rural experimental route two times, in baseline condition and with the ADAS activated, testing three overtaking events. Statistical tests showed that the proposed in-vehicle driving assistance system had a significant effect in increasing 1) the length of the passing phase, 2) the LC in the overtaking passing phase, and 3) the TTD along the overtaking maneuver. No effect of the ADAS system on vehicle speed was observed. Overall, the designed system is effective in improving car-cyclist overtaking behaviour in terms of both safety and cyclists' mobility.

Rule discovery to identify patterns contributing to overrepresentation and severity of run-off-the-road crashes.

The main objective of this paper was to analyse the roadway, environmental, and driver-related factors associated with an overrepresentation of frequency and severity of run-off-the-road (ROR) crashes. The data used in this study refer to the 6167 crashes occurred in the section Naples-Candela of A16 motorway, Italy in the period from 2001 to 2011. The analysis was carried out using the rule discovery technique due to its ability of extracting knowledge from large amounts of data previously unknown and indistinguishable by investigating patterns that occur together in a given event. The rules were filtered by support, confidence, lift, and validated by the lift increase criterion. A two-step analysis was carried out. In the first step, rules discovering factors contributing to ROR crashes were identified. In the second step, studying only ROR crashes, rules discovering factors contributing to severe and fatal injury (KSI) crashes were identified. As a result, 94 significant rules for ROR crashes and 129 significant rules for KSI crashes were identified. These rules represent several combinations of geometric design, roadside, barrier performance, crash dynamic, vehicle, environmental and drivers' characteristics associated with an overrepresentation of frequency and severity of ROR crashes. From the methodological point of view, study results show that the a priori algorithm was effective in providing new information which was previously hidden in the data. Finally, several countermeasures to solve or mitigate the safety issues identified in this study were discussed. It is worthwhile to observe that the study showed a combination of factors contributing to the overrepresentation of frequency and severity of ROR crashes. Consequently, the implementation of a combination of countermeasures is recommended.

Crash modification functions for pavement surface condition and geometric design indicators.

The combined contribution to highway safety of pavement surface and geometric design indicators is not well investigated due to the complexity of data collection and high time variability of pavement surface conditions. Introduction of high efficiency equipment for comprehensive road surveys is mitigating this issue, expanding possibilities of data integration. In this framework, the present study developed crash modification functions (CMFs) of pavement surface and geometric design indicators for different crash types (total, run-off-the-road, and others), pavement conditions (dry and wet), and lighting conditions (daytime and nighttime) based on data from two-lane rural highways in Italy. Geometric and pavement data were surveyed with the Automatic Road Analyzer and the Grip Tester. Pavement surface condition data were corrected to the crash time by pavement performance deterioration models based on traffic load to account for the variation in pavement conditions over time. Crash, traffic and weather data were retrieved from national and local databases. This study used safety performance functions (SPFs), fitted with generalized linear modelling techniques and a negative binomial distribution error structure, for developing CMFs. The SPFs were used to quantify the effect of a specific variable on crash occurrence and CMFs were then derived from the model coefficients. CMFs were developed for the following parameters: Grip Number, International Roughness Index, curvature change ratio, coefficient of variation of the curvature, maximum superelevation deficiency, and minimum lane width. According to the study results, an increase in friction, as measured by the Grip Number, is associated with a reduction in crash frequency while an increase in roughness, as measured by the International Roughness Index, is associated with an increase in crash frequency. Thus, both pavement maintenance treatments aimed at increasing friction as well as treatments aimed at reducing irregularities have a positive safety effect, especially when wet, run-off-the-road or nighttime crashes are overrepresented. Study results allow to effectively integrate pavement management systems and safety management systems. When developing paving schedules, transportation agencies often base their decisions on asset management condition targets but do not explicitly account for the role of pavement conditions in highway safety. Availability of CMFs for both pavement surface parameters as well as geometric design parameters is crucial to improve pavement and geometric characteristics considering their safety effects.

Systemic approach to improve safety of urban unsignalized intersections: Development and validation of a Safety Index.

Methods based on crash data analysis are effective in identifying intersections with a potential for safety improvement. However, it is well recognized that crash data suffer from several shortcomings and that there are clues to safety other than crash occurrence. The systemic approach is an alternative method to address safety issues. In this approach, a transportation agency is able to identify priority locations based on the presence of risk factors rather than actual crashes. To promote wider use of the systemic safety approach, this paper aims at developing and validating a procedure to rank unsignalised urban intersections for safety improvement based on the evaluation of risk factors by road safety inspections. The procedure assesses a Safety Index (SI) that measures the safety performance of unsignalised urban intersections. The SI is formulated by combining two components of risk: the exposure of road users to road hazards (Exposure) and the risk factors, which increase the probability of involvement in crashes (Risk Index). The SI is made of two elements, one related to vehicles and one to pedestrians. Twenty-three detailed safety issues and ten general safety issues are assessed to compute the vehicle Risk Index and the pedestrian Risk Index. Safety issues were selected considering that they are common issues and that effective remedial measures exist and have already proven their effectiveness. Finally, criteria for identifying and ranking safety issues were defined. The SI has two main practical applications. High risk intersections, where safety measures that can reduce crash frequency exist, can be identified and ranked by the SI. Specific safety issues, that give more contribution to unsafety, are pointed out in order to give indication about more appropriate safety measures according to the systemic safety approach. The procedure was validated with a sample of eighty-nine urban intersections located in Orange County (Florida, U.S.). For these intersections, the SI scores, the Empirical Bayes (EB) safety estimates, and the potential for improvement (PFI) were compared. The correlation between the SI scores and the EB estimates was highly significant both for vehicles (R2 = 0.66) and pedestrians (R2 = 0.58) as well as for the total crashes (R2 = 0.68). The results from the Spearman's rank-correlation analysis provided further validation for the SI indicating that ranking from the SI and the EB estimates agree at the 99.9% confidence level for vehicles (ρs = 0.78), pedestrians (ρs = 0.93), and total (ρs = 0.93).

A data mining approach to investigate patterns of powered two-wheeler crashes in Spain.

Powered two-wheelers (PTWs) are growing globally each year as they are considered an attractive alternative to cars (flexible, small, affordable, fast and easy to park), especially on congested traffic situations. However, PTWs represent an important challenge for road safety. In fact, in 2016, Spain ranked fifth in terms of PTW fatalities among EU 28. For this reason, this paper aims to investigate which are the patterns among crash characteristics contributing to PTW crashes in Spain. Data from 78,611 crashes involving PTWs occurred in Spain in the period 2011-2013 were analyzed. The analysis was performed by using classification trees and rules discovery which are suitable models aimed at extracting knowledge and identifying valid and understandable patterns from large amounts of data previously unknown and indistinguishable. The response variables assessed in this study were severity and crash type. As a result, several combinations of road, environmental and drivers' characteristics associated with severity and typology of PTW crashes in Spain were identified. Based on the analysis results, several countermeasures to solve or mitigate the safety issues identified in the study were proposed. From the methodological point of view, study results show that both the classification trees and the a priori algorithm were effective in providing non-trivial and unsuspected relations in the data. Classification trees structure allowed a simpler understanding of the phenomenon under study while association discovery provided new information which was previously hidden in the data. Given that the results of the two different techniques were never contradictory, we recommend using classification trees and association discovery as complementary approaches since their combination is effective in exploring data providing meaningful insights about PTW crash characteristics and their interdependencies.

Development and evaluation of a web-based software for crash data collection, processing and analysis.

First step of the development of an effective safety management system is to create reliable crash databases since the quality of decision making in road safety depends on the quality of the data on which decisions are based. Improving crash data is a worldwide priority, as highlighted in the Global Plan for the Decade of Action for Road Safety adopted by the United Nations, which recognizes that the overall goal of the plan will be attained improving the quality of data collection at the national, regional and global levels. Crash databases provide the basic information for effective highway safety efforts at any level of government, but lack of uniformity among countries and among the different jurisdictions in the same country is observed. Several existing databases show significant drawbacks which hinder their effective use for safety analysis and improvement. Furthermore, modern technologies offer great potential for significant improvements of existing methods and procedures for crash data collection, processing and analysis. To address these issues, in this paper we present the development and evaluation of a web-based platform-independent software for crash data collection, processing and analysis. The software is designed for mobile and desktop electronic devices and enables a guided and automated drafting of the crash report, assisting police officers both on-site and in the office. The software development was based both on the detailed critical review of existing Australasian, EU, and U.S. crash databases and software as well as on the continuous consultation with the stakeholders. The evaluation was carried out comparing the completeness, timeliness, and accuracy of crash data before and after the use of the software in the city of Vico Equense, in south of Italy showing significant advantages. The amount of collected information increased from 82 variables to 268 variables, i.e., a 227% increase. The time saving was more than one hour per crash, i.e., a 36% reduction. The on-site data collection did not produce time saving, however this is a temporary weakness that will be annihilated very soon in the future after officers are more acquainted with the software. The phase of evaluation, processing and analysis carried out in the office was dramatically shortened, i.e., a 69% reduction. Another benefit was the standardization which allowed fast and consistent data analysis and evaluation. Even if all these benefits are remarkable, the most valuable benefit of the new procedure was the reduction of the police officers mistakes during the manual operations of survey and data evaluation. Because of these benefits, the satisfaction questionnaires administrated to the police officers after the testing phase showed very good acceptance of the procedure.

Decision Tree Ensemble Method for Analyzing Traffic Accidents of Novice Drivers in Urban Areas.

Presently, there is a critical need to analyze traffic accidents in order to mitigate their terrible economic and human impact. Most accidents occur in urban areas. Furthermore, driving experience has an important effect on accident analysis, since inexperienced drivers are more likely to suffer fatal injuries. This work studies the injury severity produced by accidents that involve inexperienced drivers in urban areas. The analysis was based on data provided by the Spanish General Traffic Directorate. The information root node variation (IRNV) method (based on decision trees) was used to get a rule set that provides useful information about the most probable causes of fatalities in accidents involving inexperienced drivers in urban areas. This may prove useful knowledge in preventing this kind of accidents and/or mitigating their consequences.

Longitudinal control behaviour: Analysis and modelling based on experimental surveys in Italy and the UK.

This paper analyses driving behaviour in car-following conditions, based on extensive individual vehicle data collected during experimental field surveys carried out in Italy and the UK. The aim is to contribute to identify simple evidence to be exploited in the ongoing process of driving assistance and automation which, in turn, would reduce rear-end crashes. In particular, identification of differences and similarities in observed car-following behaviours for different samples of drivers could justify common tuning, at a European or worldwide level, of a technological solution aimed at active safety, or, in the event of differences, could suggest the most critical aspects to be taken into account for localisation or customisation of driving assistance solutions. Without intending to be exhaustive, this paper moves one step in this direction. Indeed, driving behaviour and human errors are considered to be among the main crash contributory factors, and a promising approach for safety improvement is the progressive introduction of increasing levels of driving automation in next-generation vehicles, according to the active/preventive safety approach. However, the more advanced the system, the more complex will be the integration in the vehicle, and the interaction with the driver may sometimes become unproductive, or risky, should the driver be removed from the driving control loop. Thus, implementation of these systems will require the interaction of human driving logics with automation logics and then an enhanced ability in modelling drivers' behaviour. This will allow both higher active-safety levels and higher user acceptance to be achieved, thus ensuring that the driver is always in the control loop, even if his/her role is limited to supervising the automatic logic. Currently, the driving mode most targeted by driving assistance systems is longitudinal driving. This is required in various driving conditions, among which car-following assumes key importance because of the huge number of rear-end crashes. The increased availability of lower-cost information and communication technologies (ICTs) has enhanced the possibility of collecting copious and reliable car-following individual vehicle data. In this work, data collected from three different experiments, two carried out in Italy and one in the UK, are analysed and compared. The experiments involved 146 drivers (105 Italian drivers and 41 UK drivers). Data were collected by two instrumented vehicles. Our analysis focused on inter-vehicular spacing in equilibrium car-following conditions. We observed that (i) the adopted equilibrium spacing can be fitted using lognormal distributions, (ii) the adopted equilibrium spacing increases with speed, and (iii) the dispersion between drivers increases with speed. In addition, according to different headway thresholds (up to 1 second) a significant number of potentially dangerous behaviours is observed. Three different car-following paradigms are also applied to each of the experiments, and modelling parameters are calibrated and compared to obtain indirect confirmation about the observed similarities and differences in driving behaviour.

Safety performance functions incorporating design consistency variables.

Highway design which ensures that successive elements are coordinated in such a way as to produce harmonious and homogeneous driver performances along the road is considered consistent and safe. On the other hand, an alignment which requires drivers to handle high speed gradients and does not meet drivers' expectancy is considered inconsistent and produces higher crash frequency. To increase the usefulness and the reliability of existing safety performance functions and contribute to solve inconsistencies of existing highways as well as inconsistencies arising in the design phase, we developed safety performance functions for rural motorways that incorporate design consistency measures. Since the design consistency variables were used only for curves, two different sets of models were fitted for tangents and curves. Models for the following crash characteristics were fitted: total, single-vehicle run-off-the-road, other single vehicle, multi vehicle, daytime, nighttime, non-rainy weather, rainy weather, dry pavement, wet pavement, property damage only, slight injury, and severe injury (including fatal). The design consistency parameters in this study are based on operating speed models developed through an instrumented vehicle equipped with a GPS continuous speed tracking from a field experiment conducted on the same motorway where the safety performance functions were fitted (motorway A16 in Italy). Study results show that geometric design consistency has a significant effect on safety of rural motorways. Previous studies on the relationship between geometric design consistency and crash frequency focused on two-lane rural highways since these highways have the higher crash rates and are generally characterized by considerable inconsistencies. Our study clearly highlights that the achievement of proper geometric design consistency is a key design element also on motorways because of the safety consequences of design inconsistencies. The design consistency measures which are significant explanatory variables of the safety performance functions developed in this study are: (1) consistency in driving dynamics, i.e., difference between side friction assumed with respect to the design speed and side friction demanded at the 85th percentile speed; (2) operating speed consistency, i.e., absolute value of the 85th percentile speed reduction through successive elements of the road; (3) inertial speed consistency, i.e., difference between the operating speed in the curve and the average operating speed along the 5 km preceding the beginning of the curve; and (4) length of tangent preceding the curve (only for run-off-the-road crashes).

Effects on speed and safety of point-to-point speed enforcement systems: evaluation on the urban motorway A56 Tangenziale di Napoli.

In this paper, we evaluated the effects on speed and safety of the point-to-point (P2P) speed enforcement system activated on the urban motorway A56 in Italy. The P2P speed enforcement is a relatively new approach to traffic law enforcement that involves the calculation of the average speed over a section. To evaluate the speed effects, we performed a before-after analysis of speed data investigating also effects on non-compliance to speed limits. To evaluate the safety effects, we carried out an empirical Bayes observational before-and-after study. The P2P system led to very positive effects on both speed and safety. As far as the effects on the section average travel speeds, the system yielded to a reduction in the mean speed, the 85th percentile speed, the standard deviation of speed, and the proportion of drivers exceeding the speed limits, exceeding the speed limits more than 10km/h, and exceeding the speed limits more than 20km/h. The best results were the decrease of the speed variability and the reduction of the excessive speeding behaviour. The decrease in the standard deviation of speed was 26% while the proportion of light and heavy vehicles exceeding the speed limits more than 20km/h was reduced respectively by 84 and 77%. As far as the safety effects, the P2P system yielded to a 32% reduction in the total crashes, with a lower 95% confidence limit of the estimate equal to 22%. The greatest crash reductions were in rainy weather (57%), on wet pavement (51%), on curves (49%), for single vehicle crashes (44%), and for injury crashes (37%). It is noteworthy that the system produced a statistically significant reduction of 21% in total crashes also in the part of the motorway where it was not activated, thus generating a significant spillover effect. The investigation of the effects of the P2P system on speed and safety over time allowed to develop crash modification functions where the relationship between crash modification factors and speed parameters (mean speed, 85th percentile speed, and standard deviation of speed) was expressed by a power function. Crash modification functions show that the effect of speed on safety is greater on curves and for injury crashes. Even though the study results show excellent outcomes, we must point out that the crash reduction effects decreased over time and speed, speed variability, and non-compliance to speed limits significantly increased over time. To maintain its effectiveness over time, P2P speed enforcement must be actively managed, i.e. constantly monitored and supported by appropriate sanctions.

Emerging research methods and their application to road safety.

The study of road safety has seen great strides over the past few decades with advances in analytical methods and research tools that allow researchers to provide insights into the complex interactions of the driver, vehicle, and roadway. Data collection methods range from traditional traffic and roadway sensors to instrumented vehicles and driving simulators, capable of providing detailed data on both the normal driving conditions and the circumstances surrounding a safety critical event. In September 2011, the Third International Conference on Road Safety and Simulation was held in Indianapolis, Indiana, USA, which was hosted by the Purdue University Center for Road Safety and sponsored by the Transportation Research Board and its three committees: ANB20 Safety Data, Analysis and Evaluation, AND30 Simulation and Measurement of Vehicle and Operator Performance, and ABJ95 Visualization in Transportation. The conference brought together two hundred researchers from all over the world demonstrating some of the latest research methods to quantify crash causality and associations, and model road safety. This special issue is a collection of 14 papers that were presented at the conference and then peer-reviewed through this journal. These papers showcase the types of analytical tools needed to examine various crash types, the use of naturalistic and on-road data to validate the use of surrogate measures of safety, and the value of driving simulators to examine high-risk situations.

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.

Simulator evaluation of drivers' speed, deceleration and lateral position at rural intersections in relation to different perceptual cues.

Aim of the study was to investigate, by means of a driving simulator experiment, drivers' behaviour in terms of speed, deceleration, and lateral position on major approaches of rural intersections in relation to different perceptual cues. In the experiment, ten different design conditions with and without speed-reducing treatments along the approach to the intersection were tested. Twenty-three drivers drove a test route two times and data from the second drive were used for comparison. The order of the ten design conditions was counterbalanced for all the drivers to minimize the presentation order effect. Three different data analysis techniques were used: (a) cluster analysis of speed and lateral position data, (b) statistical tests of speed and lateral position data, and (c) categorical analysis of deceleration behaviour patterns. The most effective treatments were the dragon teeth markings (based on the principle of optical road narrowing), the colored intersection area (based on the principle of intersection highlighting), and the raised median island (based on the principle of physical road narrowing). These measures, in comparison to the base intersection, produced: (1) a significant speed reduction starting from 250 m before the intersection in the range between 13 and 23 km/h, (2) a significant change in the deceleration behaviour with a reduction in the proportion of drivers which did not decelerate, and (3) a shift away from the intersection of the deceleration beginning. Given the significant effects on drivers' behaviour, the dragon teeth markings, the colored intersection area, and the raised median island are strongly recommended for real world implementation.

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.

Traffic calming along rural highways crossing small urban communities: driving simulator experiment.

The paper investigated drivers' speed behaviour in a section of a rural highway crossing a small urban community in the existing scenario without any traffic calming device and in two different design scenarios with traffic calming in the urban community. Two gateways and four integrative traffic calming devices along the route within the urban area were tested. The gateways were aimed at slowing down the vehicles entering in the built-up area, while the traffic calming devices were aimed at complementing the gateway effect inside the built-up area. Two design options were tested: first option (alt1) is a combination of low cost measures, whereas the second option (alt2) is more expensive as includes a chicane and requires land acquisition. Drivers' behaviour was investigated by means of a driving simulator experiment. The VERA dynamic-driving simulator operating at the TEST Road Safety Laboratory located in Naples (Italy) was used. Simulation results were validated by the comparison of speed behaviour in the real world and in the driving simulator, in the scenario without traffic calming. Analysis of the driving simulator experiment results was performed using two different approaches: (a) explorative description of data by cluster analysis; (b) inferential procedures about population using statistical tests. Cluster analysis was carried out in order to test if the drivers' speed behaviour in the different design alternatives was substantially different. Statistical tests were performed in order to verify if speeds in specific sections were significantly different. Cluster analysis looked at speed profiles, whereas statistical tests looked at speed data in specific points. The obtained results showed a different behaviour of drivers approaching the urban community in the existing scenario and in the design scenarios. In the south direction, mean speed reduction ranging between 16 and 17 km/h, with 5% level of significance, was observed. In the north direction, mean speed reduction equal to 11 km/h, with 10% level of significance, was observed. Differences between the two design alternatives were not statistically significant. Along the urban community, a statistically significant mean speed reduction ranging between 9 and 15 km/h was observed in the south direction. In the north direction, speed reduction was not statistically significant. Overall, combined results of cluster analysis and statistical tests showed that the treatments were more effective in the direction with higher speeds in the base scenario.

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.