An analysis of motorcyclist injury severity by various crash configurations at T-junctions in the United Kingdom

Motorcyclists that have no protective structures while motorcycling as other occupants of automobiles do can be particularly vulnerable to accident injuries (i.e., motorcycles are not as crashworthy as automobiles). Motorcyclists' susceptibility to accident injuries in nature may act synergistically with the complexity of conflicting manoeuvres between motorcycles and other motor vehicles to increase their injury severities in accidents that take place at junctions (e.g., T-junction or crossroad). Previous studies have applied crash prediction models to investigate influential factors on the occurrences of different crash configurations among automobiles but statistical models of motorcyclist injury severity resulting from different motorcycle-car crash configurations have rarely been developed. This current research attempts to develop the appropriate statistical models of motorcyclist injury severity by various crash configurations conditioned on crash occurrence at T-junctions in the UK. T-junctions are selected in this study because such junctions represent the single greatest danger to motorcyclists - for junction-type accidents, the statistics from the UK Stats19 accident injury database over the years 1991 and 2004 suggested that T-junctions were ranked the highest in terms of injury severity (Le., accidents at T-junctions resulted in approximately 65% of all casualties that sustained fatal or serious injuries) and accident occurrence (i.e., accidents at Tjunctions accounted for 62% of all motorcyclist casualties). This may be in part because there is a comparatively large number ofT-junctions in the UK. Although the author was unable to take into account the exposure factor due to the lack of such data (Le., the total number of T-junctions, and the number of motorcycles travelling on these locations), it remains true that more severe accidents happen at T-junctions than any other type of junction. In this present study, motorcycle-car accidents at Tjunctions were classified into several crash configurations based on two methods that have been widely used in literature. The first method is based on the conflicts that arise from the pre-crash manoeuvres of the motorcycle and car. The second method is on the basis of first points of impact of the motorcycle and car. The crash configurations that are classified in this current study based on the mixture of these two methods include (a) accidents involving gap acceptance (i.e., approach-turn crash and angle crash), (b) head-on crash, and (c) same-direction crash (i.e., sideswipe crash and rear-end crash). Since injury severity levels in traffic accidents are typically progressive (ranging from no injury to fatal/death), the ordered response models have come into fairly wide use as a framework for analysing such responses. Using the accident data extracted from the Stats19 accident injury database over 14-year period (1991~2004), the ordered probit (OP) model of motorcyclist injury severity were estimated because the dependent variable (i.e., no injury, slight injury, KSI: killed or seriously injured) is intrinsically discrete and ordinal. A set of the independent variables were included as the predictor variables, including rider/motorist attributes, vehicle factors, weather/temporal factors, roadway/geometric characteristics, and crash factors. The current research firstly estimated the aggregate OP model of motorcyclist injury severity by motorcycle-car accidents in whole. Additional disaggregate models of motorcyclist injury severity by various crash configurations were subsequently conducted. It appears in this current research that while the aggregate model by motorcycle-car accidents in whole is useful to uncover a general overview of the factors that were associated with the increased motorcyclist injury severity, the dis aggregate models by various crash configurations provide valuable insights (that may not be uncovered by an aggregate crash model) that motorcyclist injury severity in different crash configurations are associated with different pre-crash conditions. For example, the preliminary analysis by conducting descriptive analysis reveals that the deadliest crash manner in approach-turn crashes and angle crashes was a collision in which a right-turn car collided with an approaching motorcycle. Such crash patterns that occurred at stop-/give-way controlled junctions appear to exacerbate motorcyclist injury severity. The disaggregate models by the deadliest crash manners in approachturn crashes and angle crashes suggest that injuries tended to be more severe in crashes where a right-turn motorist was identified to fail to yield to an approaching motorcyclist. Other disaggregate crash models also identified important determinants of motorcyclist injury severity. For instance, the estimation results of the head-on crash model reveal that motorcyclists were more injurious in collisions where curves were present for cars than where the bend was absent. Another noteworthy result is that a traversing motorcycle colliding with a travelling-straight car predisposed motorcyclists to a greater risk of KSIs. These findings were clearly obscured by the estimation of the aggregate model by accidents in whole. In the course of the investigation of the factors that affect motorcyclist injury severity, it became clear that another problem, that of a right-turn motorist's failure to yield to motorcyclists (for the deadliest crash patterns in both approach-turn crash and angle crash), needs to be further examined. The logistic models are estimated to evaluate the likelihood of motorist's right-of-way violation over non right-of-way violation as a function of human attributes, weather/temporal factors, roadway/geometric factors, vehicle characteristics, and crash factors. The logistic models uncover the factors determining the likelihood of motorists' failure to yield. Noteworthy findings include, for instance, teenaged motorists, elderly motorists, male motorists, and professional motorists (Le., those driving heavy goods vehicles and buses/coaches) were more likely to infringe upon motorcycle's right-of-way. In addition, violation cases appeared to be more likely to occur on non built-up roadways, and during evening/midnight/early morning hours This present research has attempted to fill the research gaps that crash prediction models focused on analysing motorcyclist injury severity in different crash configurations have rarely been developed. The results obtained in this current research, by exploring a broad range of variables including attributes of riders and motorists, roadway/geometric characteristics, weather/temporal factors, and vehicle characteristics, provide valuable insights into the underlying relationship between risk factors and motorcycle injury severity both at an aggregate level and at a disaggregate level. This research finally discusses the implications of the findings and offers a guideline for future research.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

An analysis of motorcyclist injury severity by various crash configurations at T-junctions in the United Kingdom

Motorcyclists that have no protective structures while motorcycling as other occupants of automobiles do can be particularly vulnerable to accident injuries (i.e., motorcycles are not as crashworthy as automobiles). Motorcyclists' susceptibility to accident injuries in nature may act synergistically with the complexity of conflicting manoeuvres between motorcycles and other motor vehicles to increase their injury severities in accidents that take place at junctions (e.g., T-junction or crossroad).Previous studies have applied crash prediction models to investigate influential factors on the occurrences of different crash configurations among automobiles but statistical models of motorcyclist injury severity resulting from different motorcycle-car crash configurations have rarely been developed.This current research attempts to develop the appropriate statistical models of motorcyclist injury severity by various crash configurations conditioned on crash occurrence at T-junctions in the UK. T-junctions are selected in this study because such junctions represent the single greatest danger to motorcyclists - for junction-type accidents, the statistics from the UK Stats19 accident injury database over the years 1991 and 2004 suggested that T-junctions were ranked the highest in terms of injury severity (Le., accidents at T-junctions resulted in approximately 65% of all casualties that sustained fatal or serious injuries) and accident occurrence (i.e., accidents at Tjunctions accounted for 62% of all motorcyclist casualties). This may be in part because there is a comparatively large number ofT-junctions in the UK. Although the author was unable to take into account the exposure factor due to the lack of such data (Le., the total number of T-junctions, and the number of motorcycles travelling on these locations), it remains true that more severe accidents happen at T-junctions than any other type of junction. In this present study, motorcycle-car accidents at Tjunctions were classified into several crash configurations based on two methods that have been widely used in literature. The first method is based on the conflicts that arise from the pre-crash manoeuvres of the motorcycle and car. The second method is on the basis of first points of impact of the motorcycle and car. The crash configurations that are classified in this current study based on the mixture of these two methods include (a) accidents involving gap acceptance (i.e., approach-turn crash and angle crash), (b) head-on crash, and (c) same-direction crash (i.e., sideswipe crash and rear-end crash).Since injury severity levels in traffic accidents are typically progressive (ranging from no injury to fatal/death), the ordered response models have come into fairly wide use as a framework for analysing such responses. Using the accident data extracted from the Stats19 accident injury database over 14-year period (1991~2004), the ordered probit (OP) model of motorcyclist injury severity were estimated because the dependent variable (i.e., no injury, slight injury, KSI: killed or seriously injured) is intrinsically discrete and ordinal. A set of the independent variables were included as the predictor variables, including rider/motorist attributes, vehicle factors, weather/temporal factors, roadway/geometric characteristics, and crash factors. The current research firstly estimated the aggregate OP model of motorcyclist injury severity by motorcycle-car accidents in whole. Additional disaggregate models of motorcyclist injury severity by various crash configurations were subsequently conducted. It appears in this current research that while the aggregate model by motorcycle-car accidents in whole is useful to uncover a general overview of the factors that were associated with the increased motorcyclist injury severity, the disaggregate models by various crash configurations provide valuable insights (that may not be uncovered by an aggregate crash model) that motorcyclist injury severity in different crash configurations are associated with different pre-crash conditions. For example, the preliminary analysis by conducting descriptive analysis reveals that the deadliest crash manner in approach-turn crashes and angle crashes was a collision in which a right-turn car collided with an approaching motorcycle. Such crash patterns that occurred at stop-/give-way controlled junctions appear to exacerbate motorcyclist injury severity. The disaggregate models by the deadliest crash manners in approach turn crashes and angle crashes suggest that injuries tended to be more severe in crashes where a right-turn motorist was identified to fail to yield to an approaching motorcyclist. Other disaggregate crash models also identified important determinants of motorcyclist injury severity. For instance, the estimation results of the head-on crash model reveal that motorcyclists were more injurious in collisions where curves were present for cars than where the bend was absent. Another noteworthy result is that a traversing motorcycle colliding with a travelling-straight car predisposed motorcyclists to a greater risk of KSIs. These findings were clearly obscured by the estimation of the aggregate model by accidents in whole.In the course of the investigation of the factors that affect motorcyclist injury severity, it became clear that another problem, that of a right-turn motorist's failure to yield to motorcyclists (for the deadliest crash patterns in both approach-turn crash and angle crash), needs to be further examined. The logistic models are estimated to evaluate the likelihood of motorist's right-of-way violation over non right-of-way violation as a function of human attributes, weather/temporal factors, roadway/geometric factors, vehicle characteristics, and crash factors. The logistic models uncover the factors determining the likelihood of motorists' failure to yield. Noteworthy findings include, for instance, teenaged motorists, elderly motorists, male motorists, and professional motorists (Le., those driving heavy goods vehicles and buses/coaches) were more likely to infringe upon motorcycle's right-of-way. In addition, violation cases appeared to be more likely to occur on non built-up roadways, and during evening/midnight/early morning hours This present research has attempted to fill the research gaps that crash prediction models focused on analysing motorcyclist injury severity in different crash configurations have rarely been developed. The results obtained in this current research, by exploring a broad range of variables including attributes of riders and motorists, roadway/geometric characteristics, weather/temporal factors, and vehicle characteristics, provide valuable insights into the underlying relationship between risk factors and motorcycle injury severity both at an aggregate level and at a disaggregate level. This research finally discusses the implications of the findings and offers a guideline for future research

Pokemon gaming causes pedestrians to run a red light: An observational study of crossing behaviours at a signalised intersection in Taipei City

Since the launch of the smartphone game “Pokemon Go”, the worldwide craze has led to numerous traffic crashes and injuries resulting from falling or tripping. This paper investigates the effects of several smartphone distracting activities (gaming, talking, texting, Web surfing, and listening to music) on the street-crossing behaviours of pedestrians in Taipei City, Taiwan. A field study using video cameras was conducted to observe pedestrian crossing behaviours (e.g., crossing time, sudden movements, running a red light, and walking outside the crosswalk) at a selected signalised intersection. Data such as phone features, distracting activities, and personal attributes of the pedestrians were obtained in interviews conducted after pedestrians had completed crossing the street. In total, 1995 pedestrians engaging in various smartphone activities were observed. Results indicate that unsafe crossing behaviours were more prevalent among those playing “Pokemon Go”. Texting via instant-message apps appeared to be the second-most risk distracting activity. Results of the logistic models reveal that contributing factors to unsafe behaviours include being a student, phone screen of 5 in. or larger, and having an unrestricted 4G Internet data allowance. Two interaction terms (gaming × students, and gaming × unlimited 4G data allowance) in the models appear to be important determinants of unsafe crossing behaviours. The current research suggests that to prevent potential crashes and injuries, smartphone gaming while crossing the street should be prohibited

Human Factors in the Cybersecurity of Autonomous Vehicles: Trends in Current Research

The cybersecurity of autonomous vehicles (AVs) is an important emerging area of research in traffic safety. Because human failure is the most common reason for a successful cyberattack, human-factor researchers and psychologists might improve AV cybersecurity by researching how to decrease the probability of a successful attack. We review some areas of research connected to the human factor in cybersecurity and find many potential issues. Psychologists might research the characteristics of people prone to cybersecurity failure, the types of scenarios they fail in and the factors that influence this failure or over-trust of AV. Human behavior during a cyberattack might be researched, as well as how to educate people about cybersecurity. Multitasking has an effect on the ability to defend against a cyberattack and research is needed to set the appropriate policy. Human-resource researchers might investigate the skills required for personnel working in AV cybersecurity and how to detect potential defectors early. The psychological profile of cyber attackers should be investigated to be able to set policies to decrease their motivation. Finally, the decrease of driver’s driving skills as a result of using AV and its connection to cybersecurity skills is also worth of research

State diagram for packed granular particles under shear: two types of /quaking/ and "shear unjamming"

Understanding intermittency, an ubiquitous behavior in flows of packed grains, is pivotal for establishing the rheology of granular material. A straightforward explanation has been missing despite the long development of theories at different levels of abstraction. In this work, we propose the use of a Stribeck-Hertz model that starts with the classic Coulomb friction but also takes into account the tribology between particles, i.e. the reduction of friction coefficient with speed as is commonly observed. Our numerical studies reveal a state diagram covering a wide range of packing fractions, and produce the quaking intermittency in the mid-range of a dimensionless shear rate defined accordingly, in consistence with our recent experimental observation [Phys. Rev. Lett. 126.128001 (2021)]. Monitoring the change of mean contact number allows us to distinguish two types of quaking. Above the random-close-packing density, the quakes are exclusively of the first type, occurred with a sudden increase of the contact number. At lower packing fractions, the dominant quaking depends in part on the dimensionless shear rate. The second type of quaking is identified as the prelude for a granular packing to "unjam" upon increase of the dimensionless shear rate -- a phenomenon that occurs only when the essential tribology is taken into accoun

Walking against traffic and pedestrian injuries in the United Kingdom: new insights

Background: Studies from Finland and Taiwan have shown that walking against traffic was beneficial for reducing pedestrian crashes and fatalities. This study examined whether such beneficial effects are consistent across various circumstances. Methods: This study aimed to investigate pedestrian fatalities in walking-against or with-traffic crashes by analysing the UK STATS19 crash data for the period between 1991 and 2020. We firstly employed Chi-square tests to examine risk factors for pedestrian injury severity. These variables were then incorporated into stepwise logistic regression models with multiple variables. We subsequently conducted joint effect analysis to investigate whether the beneficial effects of walking against traffic on injury severity vary across different situations. Results: Our data contained 44,488 pedestrian crashes, of which 16,889 and 27,599 involved pedestrians walking against and with traffic, respectively. Pedestrians involved in with-traffic crashes were more likely to sustain fatalities (adjusted odds ratio [AOR] = 1.542; confidence interval [CI] = 1.139–1.927) compared with those in walking against-traffic crashes. The detrimental effect of walking with traffic on fatalities appeared to be more pronounced in darkness-unlit conditions (AOR = 1.48; CI = 1.29–1.70), during midnight hours (00:00–06:59 am) (AOR = 1.60; CI = 1.37–1.87), in rural areas (AOR = 2.20; CI = 1.92–2.51), when pedestrians were elderly (≥ 65 years old) (AOR = 2.65, CI = 2.16–3.26), and when heavy goods vehicles were crash partners (AOR = 1.51, CI = 1.28–1.78). Conclusions: Walking against traffic was beneficial in reducing pedestrian fatalities compared with walking with traffic. Furthermore, such a beneficial effect was more pronounced in darkness-unlit conditions, at midnights (00:00–06:59 am), in rural areas, when pedestrians were elderly, and when heavy goods vehicles struck pedestrians

Helmet non-use by users of bikeshare programs, electric bicycles, racing bicycles, and personal bicycles: An observational study in Taipei, Taiwan.

The bikeshare program in Taipei City and New Taipei City, called U-bike, was launched in August 2012 and has more than 7500 bicycles operating out of 769 stations. Research has suggested that bicycle helmet use is a means of reducing morbidity and mortality among bike users. Helmets, however, are not available for rent when a U-bike is rented. The current research conducted an observational study to examine the prevalence of helmet non-use by users of the bikeshare program, electric bicycles, racing bicycles, and personal bicycles in Taipei City and New Taipei City. Trained observers using compact video cameras collected helmet non-use data during various times of the day and on different days of the week. Observers collected data on cyclist attributes, bicycle types, and helmet use at several selected locations within Taipei City and New Taipei City. U-bike users were found to be the least likely to wear helmets. Other noteworthy findings include that violations such as phone use, red-light violations, and travelling at ≥25 km/h were associated with riding without a helmet. Male users of racing bikes tended not to wear helmets, while female users of other bicycle types were less likely to use a helmet. Carrying passengers by users of electric bikes and personal bikes was a determinant of helmet non-use. This paper concludes with a discussion and recommendations for future research