Unsafe cycling behaviours and near crashes among Italian cyclists

This study investigates the direct and indirect effect of three types of unsafe behaviours (i.e. errors, generic violations and smartphone-specific violations) on the likelihood of near crashes and actual crashes among Italian cyclists. We considered smartphone-specific violations as a different unsafe behaviour subtype that enhances the probability of committing errors, thus increasing the likelihood of being involved in near crashes. Furthermore, we hypothesized that near crashes will predict actual crashes. Results revealed that errors predicted near crashes, whereas generic and smartphone-specific violations did not. Near crashes mediated the effect of errors on crashes. Moreover, smartphone-specific violations predicted crashes throughout its consecutive effects on errors and near crashes. These findings contribute to deepen our understanding of the relationship between cyclists\u2019 unsafe behaviours, near crashes and actual crashes. To our knowledge, the present study is the first that links errors to near crashes among cyclists

Characteristics of cyclist crashes in Italy using latent class analysis and association rule mining

The factors associated with severity of the bicycle crashes may differ across different bicycle crash patterns. Therefore, it is important to identify distinct bicycle crash patterns with homogeneous attributes. The current study aimed at identifying subgroups of bicycle crashes in Italy and analyzing separately the different bicycle crash types. The present study focused on bicycle crashes that occurred in Italy during the period between 2011 and 2013. We analyzed categorical indicators corresponding to the characteristics of infrastructure (road type, road signage, and location type), road user (i.e., opponent vehicle and cyclist\u2019s maneuver, type of collision, age and gender of the cyclist), vehicle (type of opponent vehicle), and the environmental and time period variables (time of the day, day of the week, season, pavement condition, and weather). To identify homogenous subgroups of bicycle crashes, we used latent class analysis. Using latent class analysis, the bicycle crash data set was segmented into 19 classes, which represents 19 different bicycle crash types. Logistic regression analysis was used to identify the association between class membership and severity of the bicycle crashes. Finally, association rules were conducted for each of the latent classes to uncover the factors associated with an increased likelihood of severity. Association rules highlighted different crash characteristics associated with an increased likelihood of severity for each of the 19 bicycle crash types

Evaluation of user behavior and acceptance of an on-bike system

In this study, users\u2019 acceptance of an on-bike system that warns about potential collisions with motorized vehicles as well as its influence on cyclists\u2019 behavior was evaluated. Twenty-five participants took part in a field study that consisted of three different experimental tasks. All participants also completed a follow-up questionnaire at the completion of the three-task series to elicit information about the acceptance of the on-bike system. In the experiment phase, participants were asked to ride the bicycle throughout a circuit and to interact with a car at an intersection. Participants completed three laps of the circuit. The first lap involved no interaction with the car and served the purpose of habituation. In the second and third laps participants experienced a conflict with an incoming car at an intersection. In the second lap, the on-bike device was not activated, while in the third lap, participants received a warning message signaling the imminent conflict with the car. We compared the difference in user's behavior between the second lap (conflict with a car without the warning of the on-bike system) and the third lap (conflict with a car with the warning of the on-bike system). Results showed that, when entering the crossroad, participants were more likely to decrease their speed in case of warning of the on-bike system. Further, the on-bike system was relatively well accepted by the participants. In particular, participants did not report negative emotions when using the system, while they trusted it and believed that using such technology would be free from effort. Participants were willing to spend on average 57.83 \u20ac for the system. This study highlights the potential of the on-bike system for promoting bicycle safety

Social Influence and Different Types of Red-Light Behaviors among Cyclists

Accident analysis and studies on traffic revealed that cyclists\u2019 violation of red-light regulation is one typical infringement committed by cyclists. Furthermore, an association between cyclists\u2019 crash involvement and red-light violations has been found across different countries. The literature on cyclists\u2019 psychosocial determinants of red-light violation is still scarce. The present study, based on the classification of cyclists\u2019 red-light behavior in risk-taking (ignoring the red-light and traveling through the junction without stopping), opportunistic (waiting at red-lights but being too impatient to wait for green signal and subsequently crossing the junction), and law-obeying (stopping to obey the red-light), adopted an eye-observational methodology to investigate differences in cyclists\u2019 crossing behavior at intersections, in relation to traffic light violations and the presence of other cyclists. Based on the social influence explanatory framework, which states that people tend to behave differently in a given situation taking into consideration similar people\u2019s behaviors, and that the effect of social influence is related to the group size, we hypothesized that the number of cyclists at the intersection will have an influence on the cyclists\u2019 behavior. Furthermore, cyclists will be more likely to violate in an opportunistic way when other cyclists are already committing a violation. Two researchers at a time registered unobtrusively at four different intersections during morning and late afternoon peak hour traffic, 1381 cyclists approaching the traffic light during the red phase. The 62.9% violated the traffic control. Results showed that a higher number of cyclists waiting at the intersection is associated with fewer risk-taking violations. Nevertheless, the percentage of opportunistic violation remained high. For the condition of no cyclist present, risk-taking behaviors were significantly higher, whereas, they were significantly lower for conditions of two to four and five or more cyclists present. The percentage of cyclists committing a red-light violation without following any other was higher for those committing a risk-taking violation, whereas those following tended to commit opportunistic violations more often

Factors contributing to bicycle\u2013motorised vehicle collisions: a systematic literature review

Bicycle\u2013motorised vehicle (BMV) collisions account for the majority of the recorded bicyclists\u2019 fatalities and serious injuries. This systematic review intends to examine the main factors contributing to BMV collisions. We performed a comprehensive literature review on Scopus, TRID, ProQuest, and Web of Science databases. Fifty-nine English-language peer-reviewed articles met the eligibility criteria and were included in the final analysis. The main factors contributing to BMV collisions identified were classified in accordance with a recently published conceptual framework for road safety. The majority of studies have identified factors related to road users\u2019 behaviour (59.3%) and infrastructure characteristics (57.6%). A minority of studies identified variables related to exposure (40.7%) and vehicles (15.3%) as contributor factors to BMV collisions. A small but significant proportion of studies (20.3%) provided evidence that environmental factors may also play a role, although to a lesser extent, in determining BMV collisions. In addition to the three factors comprised in the applied conceptual framework for road safety, we identified environmental conditions as a category of factors contributing to BMV collisions

Negative attitudes towards cyclists influence the acceptance of an in-vehicle cyclist detection system

The shift towards automation and safer vehicles will increasingly involve use of technological advancements such as Advanced Driver Assistance Systems (ADAS). Nevertheless, these technologies need to meet users\u2019 perceived needs to be effectively implemented and purchased. Based on an updated version of the Technology Acceptance Model (TAM), this study analyses the main determinants of drivers\u2019 intention to use an ADAS aimed at detecting cyclist and preventing potential collision with them through an auto-braking system. Even if the relevance of perceived usefulness, perceived ease of use and trust on the acceptance of a new system has been already discussed in literature, we considered the role of an external variable such as attitudes towards cyclists in the prediction of an ADAS aimed to improve the safety of cyclists. We administered a questionnaire measuring negative attitudes towards cyclists, trust, perceived usefulness, perceived ease of use and the behavioural intention to use the system to 480 Italian drivers. Path analysis using Bayesian estimation showed that perceived usefulness, trust in the system, and negative attitudes towards cyclists have a direct effect on the intention to use the ADAS. Considering the role of attitudes towards other road users in the intention to use new ADAS aimed to improve their safety could foster the user\u2019s acceptance, especially for those people who express a negative representation of cyclists and are even more unlikely to accept the technology