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Illegal lane-transgressing is a typical aberrant riding behavior of riders of two-wheelers, i.e., motorcycles, bicycles, and e-bikes, which is highly frequent in accident reports. However, there is insufficient attention to this behavior at present. This study aims to explore the socio-psychologic factors that influence the illegal lane-transgressing behavior of two-wheeler riders when overtaking. For this purpose, a questionnaire was first composed. The questionnaire included the behavioral intention of two-wheeler riders towards illegal overtaking behavior and five influencing factors: safety knowledge, descriptive norms, injunctive norms, perceived behavior control, and risk perception. Second, a survey was conducted on different two-wheeler riders in Xi'an. Third, various types of two-wheelers were analyzed jointly and separately by structural equation models and analyses of variance. Results show that e-bike riders were more similar to motorcycle riders in behavioral intentions, with their risk perception weaker than other riders. Descriptive norms and perceived behavior control played the most significant roles in the structural equation model. It was also found that two-wheeler riders with a car license had better traffic safety performance. Based on the above results, it is recommended that attention be paid to illegal lane-transgression in the process of law enforcement and education, and a higher level of safety training should be provided for two-wheeler riders.

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The study investigates the crash risk of powered two-wheelers (PTWs) involved in multiple conflict types, with different vehicle classes constituting a mixed traffic stream. This study uses the extreme value theory to estimate the crash risk after establishing the conflict thresholds for potential rear-end and side-swipe conflicts by accounting for interacting vehicle types. The study considers four vehicle pairs involving PTWs: PTW-PTW, PTW-MThW (motorized three-wheeler), PTW-Car, and PTW-Bus. The study found that the conflict thresholds corresponding to rear-end and side-swipe types increase with the interacting vehicle size. The crash risk is lowest for the PTW-PTW pair (0.315%) in rear-end conflicts, whereas the risk is the highest for the PTW-MThW pair (3.7%) in side-swipe conflicts. The crash risk corresponding to the PTW interacting with other vehicle types is higher than that of the PTW-PTW pair. Hence, the implementation of exclusive PTW lanes could be an effective risk mitigation strategy for PTW-dominant mixed traffic environments.

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Every year, several thousand powered two-wheeler (PTW) i.e., motorcycle, moped and scooter drivers and passengers die in traffic accidents in the EU. Despite the much higher risk of death and injuries for PTW vs. car users, there is a three-fold lack regarding collision warning technologies for PTWs: lack of research, lack of regulation and lack of availability in the market. Many injuries occur in rear-end collisions, when PTW is struck from the rear by another vehicle. In this paper we present a hybrid, multi-method simulation model that allows simulation of various situations in which a vehicle may collide with the rear end of a PTW. We have used this model to estimate the potential impact of market penetration of a novel PTW ESS + RECAS system, named MEBWS (Motorcycle Emergency Braking Warning System), within the EU on the number of traffic accidents and their consequences, which would contribute to the EU "Vision Zero" goal: "reduce road deaths to almost zero by 2050". MEBWS has been developed at the Faculty of Information Studies in Novo mesto and patented. Simulation results using EU traffic accident data show that with 100% market penetration of the MEBWS system in the EU, the total number of PTW rear-end collisions would decrease by 29.50%. This reduction would result in fewer injuries and a decrease in economic crash costs by €43,145,172, according to the standard EU methodology. With the MEBWS system enabled, the number of traffic accidents in the standard rear-end collision emergency braking scenarios Moto, normal drive, Moto, emergency stop and Moto, not moving decreased by 33.15%, 27.76% and 28.76%, respectively. In cases where the collision could not be prevented due to slow response of the following driver or very high relative speed of the vehicles, MEBWS reduced the relative speed at impact, resulting in a reduction of injury severity by up to 11.198%, as estimated by the amount of kinetic energy released at collision.

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The dynamic behavior of a Powered Two-Wheeler (PTW) is much more complicated than that of a car, which is due to the strong coupling between the longitudinal and lateral dynamics produced by the large roll angles. This makes the analysis of the dynamics, and therefore the design and synthesis of the controller, particularly complex and difficult. In relation to assistance in dangerous situations, several recent manuscripts have suggested devices with limitations of cornering velocity by proposing restrictive models. However, these models can lead to repulsion by the users of PTW vehicles, significantly limiting vehicle performance. In the present work, the authors developed an Advanced Rider-cornering Assistance System (ARAS) based on the skills learned by riders running across curvilinear trajectories using Artificial Intelligence (AI) and Neural Network (NN) techniques. New algorithms that allow the value of velocity to be estimated by prediction accuracy of up to 99.06% were developed using the K-Nearest Neighbor (KNN) Machine Learning (ML) technique.

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A reliable critical-scenario-based safety assessment of autonomous vehicles in China requires a thorough understanding of complex crash scenarios in Chinese background traffic. Based on actual crashes between a vehicle and a powered two-wheeler (PTW) in China, this study generated the autonomous driving testing scenarios from functional, logical and concrete levels. First, 239 video-recorded crash cases were selected from the China In-depth mobility Safety Study - Traffic Accident (CIMSS-TA) database. Using the k-medoids clustering method, six functional scenarios were generalized according to seven crash characteristics (time of day, road type, road surface, obstruction, motion of vehicle, motion of PTW, relative moving direction and position of PTW with respect to vehicle), which contained two straight road scenarios, two T-junction scenarios and two intersection scenarios. Then, using a trajectory analysis program written by Python, the dangerous time instant of each crash was extracted based on the relative trajectory. According to five dynamic parameters of dangerous time instant, namely vehicle velocity (Vehicle_V), PTW X'-coordinate velocity (PTW_VX'), PTW Y'-coordinate velocity (PTW_VY'), PTW X'-coordinate relative position (PTW_LocX') and PTW Y'-coordinate relative position (PTW_LocY'), a crash trigger scheme was built to remain a case challenging when the involved vehicle is replaced by an autonomous vehicle with completely different maneuvers. Using the kernel density estimation (KDE), the logical scenarios were evolved by calculating the distribution of these dynamic parameters in each cluster. The results showed that there were differences in the distribution of dynamic parameters between six functional scenarios. For instance, the Vehicle_V in the scenario where a vehicle turning right impacts with a right/right rear PTW traveling straight ahead was higher than that in the scenario where a vehicle changing to the left lane impacts with a left/left rear PTW traveling straight ahead, with ranges of (10 km/h, 30 km/h) and (5 km/h, 15 km/h), respectively. Finally, considering the correlation of dynamic parameters, a virtual crash generation approach based on the independent component analysis (ICA) representing the original crashes with independent parameters was proposed to obtain sufficient concrete testing scenarios. The results showed that the statistical characteristics of virtual crashes were consistent with those of original crashes. Therefore, the virtual crash generation approach was effective. And a concrete crossing testing scenario with the crash trigger conditions of Vehicle_V = 26.272 km/h, PTW_VX' = 15.567 km/h, PTW_VY' = -1.670 km/h, PTW_LocX' = -27.265 m and PTW_LocY' = 52. 149 m was especially demonstrated. This study provides a theoretical basis for generating autonomous driving testing scenarios and data support for establishing relevant testing schemes tailored to the traffic environment in China.

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Powered two-wheelers are a common means of transport all over the world. In several countries, primary motorcycles with high displacement involve another purpose, namely motorcycling is a leisure activity. Motorcycles are used as tools of transport pleasure as opposed to being purely used for individual commuting purposes. The aim of the current study involves investigating the relation between experienced riding pleasure and riding behavior in a field test. Specifically, N = 12 motorcyclists between 21 and 66 years of age were observed while riding for approximately 8 h on public roads. The measurement setup included a logger for vehicle dynamics and vehicle handling data, GNSS data, video data, and subjective measures recorded as audio comments at predefined points of interest along the round course. A comprehensive dataset with more than 6000 km of motorcycling was gathered. The results indicate that parameters of lateral vehicle behavior, such as the maximum lean angle, reflected riding pleasure. Interestingly, this is applicable for curvy sections as well as straight roads. High ratings of riding pleasure correlated with riding in snaky lines as a type of self-stimulation on straight sections. Longitudinal vehicle dynamics, such as the range of accelerations, tend to increase with the riding pleasure in curves. Hence, the effects are smaller than those for lateral vehicle behavior and not visible on straight sections. Generally, curvy sections on rural roads produce higher pleasure than straight roads. On a global level, riding pleasure increases during the first few hours of riding and subsequently decreases with respect to the time on task. The results are discussed in the context of studies on driving pleasure from the automotive sector and more fundamental psychological theories that explain pleasure as a physiological stimulation or flow. Several individuals ride motorcycles to experience pleasure. A better understanding of rider behavior in these situations can aid in deriving proper assistance and to provide individual support to a rider, thereby increasing riding pleasure as well as safety.

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The understanding of rider/vehicle interaction modalities remains an issue, specifically in the case of bend-taking. This difficulty results both from the lack of adequate instrumentation to conduct this type of study and from the variety of practices of this population of road users. Riders have numerous explanations of strategies for controlling their motorcycles when taking bends. The objective of this paper is to develop a data-driven methodology in order to identify typical riding behaviors in bends by using clustering methods. The real dataset used for the experiments is collected within the VIROLO++ collaborative project to improve the knowledge of actual PTW riding practices, especially during bend taking, by collecting real data on this riding situation, including data on PTW dynamics (velocity, normal acceleration, and jerk), position on the road (road curvature), and handlebar actions (handlebar steering angle). A detailed analysis of the results is provided for both the Anderson-Darling test and clustering steps. Moreover, the clustering results are compared with the subjective data of subjects to highlight and contextualize typical riding tendencies. Finally, we perform an in-depth analysis of the bend-taking practices of one subject to highlight the differences between different methods of controlling the motorcycle (steering handlebar vs. rider's lean) using the rider action measurements made by pressure sensors.

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Objective: Various definitions and uses of the term body region can be found in the literature. A definition of body regions using the Abbreviated Injury Scale (AIS) codes not strictly aligned with AIS chapters was developed for use in the European Commission-funded PIONEERS project (Protective Innovations of New Equipment for Enhanced Rider Safety). This work aims to examine the consequences of differently defined body regions on injury priority ranking using the percentage of patients showing at least moderate injury severity (AIS 2+) per regarded body region.Methods: Three different crash investigation data sets of injured riders and/or pillion riders of powered 2-wheelers (PTWs) were used for this analysis. The first contained data for 143 fatalities, the second contained data for 58 severely injured, and the last for contained data for 982 patients from a sample that was close to national representativeness. Frequency of injury was examined using body regions based on the AIS chapters (and first digit of the AIS Unique Identifier) and based on the PIONEERS definition.Results: Though different body region definitions did not result in different top-ranked body regions in terms of injury frequency, different definitions did provide different levels of information that impact priority within AIS chapter-defined regions. For PTW riders, cervical injuries are the highest priority spinal injuries. Thoracic and lumbar spinal injuries seem to occur together with other injuries in the thorax and abdominal region. Severe lower extremity injuries frequently involve the pelvis and the leg.Conclusions: Body regions need to be defined carefully to avoid misinterpretations. Publications that use body regions for their analysis to present injury frequencies should clearly define what they include in each region.

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OBJECTIVE: Motorcycle riders are involved in significantly more crashes per kilometer driven than passenger car drivers. Nonetheless, the development and implementation of motorcycle safety systems lags far behind that of passenger cars. This research addresses the identification of the most effective motorcycle safety solutions in the context of different countries. METHODS: A knowledge-based system of motorcycle safety (KBMS) was developed to assess the potential for various safety solutions to mitigate or avoid motorcycle crashes. First, a set of 26 common crash scenarios was identified from the analysis of multiple crash databases. Second, the relative effectiveness of 10 safety solutions was assessed for the 26 crash scenarios by a panel of experts. Third, relevant information about crashes was used to weigh the importance of each crash scenario in the region studied. The KBMS method was applied with an Italian database, with a total of more than 1 million motorcycle crashes in the period 2000-2012. RESULTS: When applied to the Italian context, the KBMS suggested that automatic systems designed to compensate for riders' or drivers' errors of commission or omission are the potentially most effective safety solution. The KBMS method showed an effective way to compare the potential of various safety solutions, through a scored list with the expected effectiveness of each safety solution for the region to which the crash data belong. A comparison of our results with a previous study that attempted a systematic prioritization of safety systems for motorcycles (PISa project) showed an encouraging agreement. CONCLUSIONS: Current results revealed that automatic systems have the greatest potential to improve motorcycle safety. Accumulating and encoding expertise in crash analysis from a range of disciplines into a scalable and reusable analytical tool, as proposed with the use of KBMS, has the potential to guide research and development of effective safety systems. As the expert assessment of the crash scenarios is decoupled from the regional crash database, the expert assessment may be reutilized, thereby allowing rapid reanalysis when new crash data become available. In addition, the KBMS methodology has potential application to injury forecasting, driver/rider training strategies, and redesign of existing road infrastructure.

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The risk of accident, injury and death is disproportionately higher for motorcycle riders than for motorists. In this paper, we investigate strategies of safety management associated with operation of powered two-wheel vehicles (motorcycles and scooters). Accident prevention is most often driven by an epidemiological approach that investigates the risk factors associated with accidents. By focusing on risk factors, these types of studies fail to examine the strengths of the system in any depth. In this paper we employ an ethnographic approach structured by reference to the framework of Cognitive Work Analysis, to identify how riders of powered two-wheel vehicles manage their own safety and the safety of others. We anticipate that this research will open up a rich, relatively untapped, area for exploration of safety interventions.

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Powered-two-wheelers (PTWs) constitute a very vulnerable type of road users. The notable increase in their share in traffic and the high risk of severe accident occurrence raise the need for further research. However, current research on PTW safety is not as extensive as for other road users (passenger cars, etc.). Consequently, the objective of this research is to provide a critical review of research on Power-Two-Wheeler behaviour and safety with regard to data collection, methods of analysis and contributory factors, and discuss the needs for further research. Both macroscopic analyses (accident frequency, accident rates and severity) and microscopic analyses (PTW rider behaviour, interaction with other motorised traffic) are examined and discussed in this paper. The research gaps and the needs for future research are identified, discussed and put in a broad framework. When the interactions between behaviour, accident frequency/rates and severity are co-considered and co-investigated with the various contributory factors (riders, other users, road and traffic environment, vehicles), the accident and injury causes as well as the related solutions are better identified.

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CONTEXT: Authorizing powered two-wheeler drivers to drive in lanes reserved to buses is a measure that is sometimes mentioned to improve mobility conditions for these users. But what effect would this measure have on the safety of these users and on the safety of the other users with whom they share the traffic space? OBJECTIVE: The objective of this study is to contribute elements to help answer this question. More precisely, the objective is to estimate the risk of having an accident per kilometer driven by powered two-wheeler drivers who drive in bus lanes and to compare this risk with that of powered two-wheeler drivers who drive in general traffic lanes. METHOD: Using the bodily injury accidents recorded by the police over two years on 13 roads in the city of Marseille and a campaign of periodical observations of powered two-wheeler traffic, we estimated the risk per kilometer driven by powered two-wheeler drivers who drive in bus lanes and compared it with that of drivers who do not drive in them. RESULTS: The results show that the risk for powered two-wheeler drivers who drive in bus lanes of being involved in a bodily injury accident is significantly higher than the risk run by drivers who drive in general traffic lanes. For the 13 roads studied, it is on average 3.25 times higher (95% CI: 2.03; 5.21). CONCLUSION: In the current situation, powered two-wheeler drivers who drive in bus lanes are less safe than those who drive in general traffic lanes. The analysis of police reports suggests that part of this increased risk comes from collisions between automobile drivers turning right and powered two-wheelers driving in the bus lane who continue straight ahead.

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