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.

Personal factors influencing the visual reaction time of pedestrians to detect turn indicators in the presence of Daytime Running Lamps.

Daytime running lamps (DRL) on vehicles have proven to be an effective measure to prevent accidents during the daytime, particularly when pedestrians and cyclists are involved. However, there are negative interactions of DRL with other functions in automotive lighting, such as delays in pedestrians' visual reaction time (VRT) when turn indicators are activated in the presence of DRL. These negative interactions need to be reduced. This work analyses the influence of variables inherent to pedestrians, such as height, gender and visual defects, on the VRT using a classification and regression tree as an exploratory analysis and a generalized linear model to validate the results. Some pedestrian characteristics, such as gender, alone or combined with the DRL colour, and visual defects, were found to have a statistically significant influence on VRT and, hence, on traffic safety. These results and conclusions concerning the interaction between pedestrians and vehicles are presented and discussed. Practitioner Summary: Visual interactions of vehicle daytime running lamps (DRL) with other functions in automotive lighting, such as turn indicators, have an important impact on a vehicle's conspicuity for pedestrians. Depending on several factors inherent to pedestrians, the visual reaction time (VRT) can be remarkably delayed, which has implications in traffic safety.

How to identify the key factors that affect driver perception of accident risk. A comparison between Italian and Spanish driver behavior.

Road crashes can be caused by different factors, including infrastructure, vehicles, and human variables. Many research studies have focused solely on identifying the key factors that cause road crashes. From these studies, it emerged that human factors have the most relevant impact on accident severity. More specifically, accident severity depends on several factors related directly to the driver, i.e., driving experience, driver's socio-economic characteristics, and driving behavior and attitudes. In this paper, we investigate driver behaviors and attitudes while driving and specifically focus on different methods for identifying the factors that most affect the driver's perception of accident risk. To this end, we designed and conducted a survey in two different European contexts: the city of Cosenza, which is located in the south of Italy, and the city of Granada, which is located in the south of Spain. Samples of drivers were contacted for their opinions on certain aspects of driving rules and attitudes while driving, and different types of questions were addressed to the drivers to assess their judgments of these aspects. Consequently, different methods of data analysis were applied to determine the aspects that heavily influence driver perception of accident risk. An experiment based on the stated preferences (SP) was carried out with the drivers, and the SP data were analyzed using an ordered probit (OP) model. Interesting findings emerged from different analyses of the data and from the comparisons among the data collected in the two different territorial contexts. We found that both Italian and Spanish drivers consider driving in an altered psychophysical state and violating the overtaking rules to be the most risky behaviors.