Pedelecs (e-bikes), which facilitate higher speeds with less effort in comparison to traditional bicycles (t-bikes), have grown considerably in popularity in recent years. Despite the large expansion of this new transportation mode, little is known about the behavior of e-cyclists, or whether cycling an e-bike increases crash risk and the likelihood of conflicts with other road users, compared to cycling on t-bikes. In order to support the design of safety measures and to maximize the benefits of e-bike use, it is critical to investigate the real-world behavior of riders as a result of switching from t-bikes to e-bikes. Naturalistic studies provide an unequaled method for investigating rider cycling behavior and bicycle kinematics in the real world in which the cyclist regularly experiences traffic conflicts and may need to perform avoidance maneuvers, such as hard braking, to avoid crashing. In this paper we investigate cycling kinematics and braking events from naturalistic data to determine the extent to which cyclist behavior changes as a result of transferring from t-bikes to e-bikes, and whether such change influences cycling safety. Data from the BikeSAFE and E-bikeSAFE naturalistic studies were used in this investigation to evaluate possible changes in the behavior of six cyclists riding t-bikes in the first study and e-bikes in the second one. Individual cyclists' kinematics were compared between bicycle types. In addition, a total of 5092 braking events were automatically extracted after identification of dynamic triggers. The 286 harshest braking events (136 cases for t-bike and 150 for e-bike) were then validated and coded via video inspection. Results revealed that each of the cyclists rode faster on the e-bike than on the t-bike, increasing his/her average speed by 2.9-5.0 km/h. Riding an e-bike also increased the probability to unexpectedly have to brake hard (odds ratio = 1.72). In addition, the risk of confronting abrupt braking and sharp deceleration were higher when riding an e-bike than when riding a t-bike. Our findings provide evidence that cyclists' behavior and the way cyclists interact with other road users change when cyclists switch from t-bikes to e-bikes. Because of the higher velocity, when on e-bikes, cyclists appear to have harder time predicting movements within the traffic environment and, as a result, they need to brake abruptly more often to avoid collisions, compared with cycling on t-bikes. This study provides new insights into the potential impact on safety that a cycling society moving to e-bikes may have, indicating that e-cycling requires more reactive maneuvers than does cycling traditional bicycles and suggesting that any distractive activity may be more critical when riding e-bikes compared to traditional bikes.
Collisions with other vehicles represent the biggest threat to riders of powered-two-wheeler (PTW), and while emergency braking is the evasive manoeuvre most frequently required in PTW riding, many riders fail to perform it adequately due to constraints on response time precipitated by failures of perception, cognition and control actions. Effective rider training methods are necessary for the development of braking proficiency in response to emergency situations.This study proposes a testing and training paradigm that exploits a closer similitude with the real-world scenario by maintaining the natural coupling of action (vehicle manoeuvring) and perception (higher order skill) that underlies any coordinated response to an emergency event. The aim of this study was to understand the behaviour of the riders in the execution of emergency braking coupled with visual perception of vehicle motion as a response to an imminent collision and determine parameters that can be used to identify differences in skill level.Participants performed emergency braking trials in a realistic and controlled scenario using a mock-up of an intersection conflict with a real car initiating a left turn manoeuvre across the path of a PTW approaching from the opposite direction (Left Turn Across Path/Opposite Directions). Analysis of the deceleration patterns recorded during 12 trials per participant revealed that performance of braking in response to an unpredicted moving hazard differs from that in a planned self-timed hard braking. In addition, our results indicate that PTW rider performance may be assessed in a reliable and objective way using the combination of vehicle kinematics and human performance measures. The study identified four categories of riders classified by their level skills. Finally, an important finding was the lack of correlation of both years of riding experience and self-assessed overall riding skill with an objective measure of emergency braking performance such as effective deceleration. The results of this study will support a new training approach and provide insights for future design of active safety systems.
The most common evasive maneuver among motorcycle riders and one of the most complicated to perform in emergency situations is braking. Because of the inherent instability of motorcycles, motorcycle crashes are frequently caused by loss of control performing braking as an evasive maneuver. Understanding the motion conditions that lead riders to start losing control is essential for defining countermeasures capable of minimizing the risk of this type of crashes. This paper provides predictive models to classify unsafe loss of control braking maneuvers on a straight line before becoming irreversibly unstable. We performed braking maneuver experiments in the field with motorcycle riders facing a simulated emergency scenario. The latter involved a mock-up intersection in which we generated conflict events between the motorcycle ridden by the participants and an oncoming car driven by trained research staff. The data collected comprises 165 braking trials (including 11 trials identified as loss of control) with 13 riders representing four categories of braking skill, ranging from beginner to expert. Three predictive models of loss of control events during braking trials, going from a basic model to a more advanced one, were defined using logistic regressions as supervised learning methods and using the area under the receiver operating characteristic (ROC) curve as a performance indicator. The predictor variables of the models were identified among the parameters of the vehicle kinematics. The best model predicted 100% of the loss of control and 100% of the full control cases. The basic and the more advanced supervised models were adapted for loss of control identification with time series data, and the results detecting in real-time the loss of control events showed excellent performance as well as with the supervised models. The study showed that expert riders may maintain stability under dynamic conditions that normally lead less skilled riders to a loss of control or falling events. The best decision thresholds of the most relevant kinematic parameters to predict loss of control have been defined. The thresholds of parameters that typically characterize the loss of control such as the yaw rate and front-wheel lock duration were dependent on the rider skill levels. The peak-to-root-mean-square ratio of roll acceleration was the most robust parameter for identifying loss of control among all skill levels.
Objective: The number of e-bike users has increased significantly over the past few years and with it the associated safety concerns. Because e-bikes are faster than conventional bicycles and more prone to be in conflict with road users, e-bikers may need to perform avoidance manoeuvres more frequently. Braking is the most common avoidance manoeuvre, but is also a complex and critical task in emergency situations, since cyclists must reduce speed quickly without losing balance. The aim of this study is to understand the braking strategies of e-bikers in real-world traffic environments and to assess their road safety implications. This paper investigates 1) how cyclists on e-bikes use front and rear brakes during routine cycling, and 2) whether this behaviour changes during unexpected conflicts with other road users. Methods: Naturalistic data were collected from six regular bicycle riders who each rode e-bikes during a period of two weeks, for a total of 32.5 hours of data. Braking events were identified and characterized through a combined analysis of brake pressure at each wheel, velocity, and longitudinal acceleration. Furthermore, the braking patterns obtained during unexpected events were compared with braking patterns during routine cycling. Results: In the majority of braking events during routine cycling, cyclists used only one brake at a time, favouring one of the two brakes according to a personal pre-established pattern. However, the favoured brake varied among cyclists: 66% favoured the rear brake and 16% the front brake. Only 16% of the cyclists showed no clear preference, variously using rear brake, front brake, or combined braking (both brakes at the same time), suggesting that the selection of which brake to use depended on the characteristics of the specific scenario experienced by the cyclist rather than on a personal preference. In unexpected conflicts, generally requiring a larger deceleration, combined braking became more prevalent for most of the cyclists; still, when combined braking was not applied, cyclists continued to use the favoured brake of routine cycling. Kinematic analysis revealed that, when larger decelerations were required, cyclists more frequently used combined braking instead of single braking. Conclusions: The results provide new insights into the behaviour of cyclists on e-bikes and may provide support in the development of safety measures including guidelines and best practices for the optimal brake use. The results may also inform the design of braking systems intended to reduce the complexity of the braking operation.
Objective: Human error is considered the primary factor contributing to crashes involving powered-two-wheelers (PTW), however not human-factors-based crash analysis methodology has been developed to support effectiveness of rider training interventions. Our aim is to define a methodology that uses in-depth data to identify the skills needed by riders in the highest risk crash configurations to reduce casualty rates. Methods:The methodology is illustrated through a case study using in-depth data of a total of 803 powered-two-wheeler crashes cases. Seven types of high-risk crash configuration based on the pre-crash trajectories of the road-users involved were considered to investigate the human errors as crash contributors. Primary crash contributing factor, evasive manoeuvres performed, horizontal roadway alignment and speed-related factors were identified, along with the most frequent crash configurations and those with the greatest risk of severe injury.Results: Straight Crossing Path/Lateral Direction was the most frequent crash configuration and Turn Across Path/ Opposing Direction that with the greatest risk of serious injury were identified. Multi-vehicle crashes cannot be considered as a homogenous category of crashes to which the same human failure is attributed, as different interactions between motorcyclists and other road users are associated with both different types of human error and different rider reactions. Human error in multiple-vehicle crashes related to crossing paths configurations were different from errors related to rearend or head-on crashes. Multi-vehicle head-on crashes and single-vehicle collisions frequently occur along curves. The involved collision avoidance manoeuvres of the riders differed significantly among the highest risk crash configurations. The most relevant lack of skills are identified and linked to their most representative context. In most cases a combination of different skills was required simultaneously to avoid the crash. Conclusions:The results contribute to understand the motorcyclists' responses in high-risk crash scenarios. The findings underline the need to group accident cases, beyond the usual single-vehicle versus multi-vehicle collision approach. The different interactions with other road users should be considered in order to identify the competencies of the motorcyclists needed to reduce the risk of an accident. Our methodology can be applied to increase the motorcyclists' safety by supporting preventive actions based on riders' training and eventually ADAS design.
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