Marco De Angelis

Factors contributing to bicycle–motorised vehicle collisions: a systematic literature review

ABSTRACT Bicycle–motorised vehicle (BMV) collisions account for the majority of the recorded bicyclists’ 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’ 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.

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

Accident analysis and studies on traffic revealed that cyclists’ violation of red-light regulation is one typical infringement committed by cyclists. Furthermore, an association between cyclists’ crash involvement and red-light violations has been found across different countries. The literature on cyclists’ psychosocial determinants of red-light violation is still scarce. The present study, based on the classification of cyclists’ 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’ 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’s 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’ 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.

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’s 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.

High-Accuracy Tracking Using Ultrawideband Signals for Enhanced Safety of Cyclists

In this paper, an ultrawideband localization system to improve the cyclists’ safety is presented. The architectural solutions proposed consist of tags placed on bikes, whose positions have to be estimated, and anchors, acting as reference nodes, located at intersections and/or on vehicles. The peculiarities of the localization system in terms of accuracy and cost enable its adoption with enhanced risk assessment units situated on the infrastructure/vehicle, depending on the architecture chosen, as well as real-time warning to the road users. Experimental results reveal that the localization error, in both static and dynamic conditions, is below 50 cm in most of the cases.

Unsafe cycling behaviours and near crashes among Italian cyclists

ABSTRACT 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’ unsafe behaviours, near crashes and actual crashes. To our knowledge, the present study is the first that links errors to near crashes among cyclists.

Cyclists’ Anger As Determinant of Near Misses Involving Different Road Users

Road anger constitutes one of the determinant factors related to safety outcomes (e.g., accidents, near misses). Although cyclists are considered vulnerable road users due to their relatively high rate of fatalities in traffic, previous research has solely focused on car drivers, and no study has yet investigated the effect of anger on cyclists’ safety outcomes. The present research aims to investigate, for the first time, the effects of cycling anger toward different types of road users on near misses involving such road users and near misses in general. Using a daily diary web-based questionnaire, we collected data about daily trips, bicycle use, near misses experienced, cyclist’s anger and demographic information from 254 Spanish cyclists. Poisson regression was used to assess the association of cycling anger with near misses, which is a count variable. No relationship was found between general cycling anger and near misses occurrence. Anger toward specific road users had different effects on the probability of near misses with different road users. Anger toward the interaction with car drivers increased the probability of near misses involving cyclists and pedestrians. Anger toward interaction with pedestrians was associated with higher probability of near misses with pedestrians. Anger toward cyclists exerted no effect on the probability of near misses with any road user (i.e., car drivers, cyclists or pedestrians), whereas anger toward the interactions with the police had a diminishing effect on the occurrence of near misses’ involving all types of road users. The present study demonstrated that the effect of road anger on safety outcomes among cyclists is different from that of motorists. Moreover, the target of anger played an important role on safety both for the cyclist and the specific road users. Possible explanations for these differences are based on the difference in status and power with motorists, as well as on the potential displaced aggression produced by the fear of retaliation by motorized vehicle users.

Human-Automation Interaction in Automated Vehicles: An Innovative HMI Design Approach. The Case of Elderly and Cyclists

In recent years, significant progresses have been achieved in automated driving technologies and highly Automated Vehicles (AVs) are expected to become available to end-users within a decade. At the same time, many countries around the world are facing a demographic shift toward an aging society. Level 3 to 4 AVs will allow users to be released from the driving task for extended periods; however, they will be requested to take back control of the vehicle in specific situations. In the specific case of older road users, take over request stations could lead to significant complications in safety as well as mobility. Furthermore, communication needs to grant safe interactions between AVs and vulnerable road users, such as cyclists, should be deeply investigated and solutions should be proposed. This highlights the importance of designing AVs interfaces that are user friendly, safe, adaptable, and accessible for elderly drivers and for vulnerable road users.