How similar are two-unit bicycle and motorcycle crashes?

This paper explores the similarities and differences between bicycle and motorcycle crashes with other motor vehicles. If similar treatments can be effective for both bicycle and motorcycle crashes, then greater benefits in terms crash costs saved may be possible for the same investment in treatments. To reduce the biases associated with under-reporting of these crashes to police, property damage and minor injury crashes were excluded. The most common crash type for both bicycles (31.1%) and motorcycles (24.5%) was intersection from adjacent approaches. Drivers of other vehicles were coded most at fault in the majority of two-unit bicycle (57.0%) and motorcycle crashes (62.7%). The crash types, patterns of fault and factors affecting fault were generally similar for bicycle and motorcycle crashes. This confirms the need to combat the factors contributing to failure of other drivers to yield right of way to two-wheelers, and suggest that some of these actions should prove beneficial to the safety of both motorized and non-motorized two-wheelers. In contrast, child bicyclists were more often at fault, particularly in crashes involving a vehicle leaving the driveway or footpath. The greater reporting of violations by riders and drivers in motorcycle crashes also deserves further investigation

Learning from insurance data: Injuries to other road users in motorcyclist at-fault crashes

In multi-vehicle motorcycle crashes, the motorcycle rider is less likely to be at-fault but more commonly severely injured than the other road user. Therefore, not surprisingly, crashes in which motorcycle riders are at-fault and particularly the injuries to the other road users in these crashes have received little research attention. This paper aims to address this gap in the literature by investigating the factors influencing the severity of injury to other road users in motorcyclist-at-fault crashes. Five years of data from Queensland, Australia, were obtained from a database of claims against the compulsory third party (CTP) injury insurance of the at-fault motorcyclists. Analysis of the data using an ordered probit model shows higher injury severity for crashes involving young (under 25) and older (60+) at-fault motorcyclists. Among the not at-fault road users, the young, old, and males were found to be more severely injured than others. Injuries to vehicle occupants were less severe than those to pillions. Crashes that occurred between vehicles traveling in opposite directions resulted in more severe injuries than those involving vehicles traveling in the same direction. While most existing studies have analyzed police reported crash data, this study used CTP insurance data. Comparison of results indicates the potential of using CTP insurance data as an alternative to police reported crash data for gaining a better understanding of risk factors for motorcycle crashes and injury severity

Common hazards and their mitigating measures in work zones: A qualitative study of worker perceptions

Road construction and maintenance activities present challenges for ensuring the safety of workers and the traveling public alike. Hazards in work zones are typically studied using historical crash records but the current study took a qualitative approach by interviewing 66 workers from various work zones in Queensland, Australia. This supplemented and enhanced the limited available data regarding the frequency and nature of work zone crashes in Australia, provided worker insights into contributing factors, and assessed their opinions on the likely effectiveness of current or future approaches to hazard mitigation. Workers may not be aware of objective data regarding effectiveness, but their attitudes and consequent levels of compliance can influence both the likelihood of implementation and the outcomes of safety measures. Despite the potential importance of worker perceptions, they have not been studied comprehensively to date, and thus this study fills a significant gap in the literature. Excessive vehicle speeds, driver distraction and aggression towards roadworkers, working in wet weather, at night and close to traffic stream were among the most common hazards noted by workers. The safety measures perceived to be most effective included police presence, active enforcement, and improving driver awareness and education about work zones. Worker perceptions differed according to their level of exposure to hazards

A comparison of self-nominated and actual speeds in work zones

Despite significant research on drivers’ speeding behavior in work zones, little is known about how well drivers’ judgments of appropriate speeds match their actual speeds and what factors influence their judgments. This study aims to fill these two important gaps in the literature by comparing observed speeds in two work zones with drivers’ self-nominated speeds for the same work zones. In an online survey, drivers nominated speeds for the two work zones based on photographs in which the actual posted speed limits were not revealed. A simultaneous equation modelling approach was employed to examine the effects of driver characteristics on their self-nominated speeds. The results showed that survey participants nominated lower speeds (corresponding to higher compliance rates) than those which were observed. Higher speeds were nominated by males than females, young and middle aged drivers than older drivers, and drivers with truck driving experience than those who drive only cars. Larger differences between nominated and observed speeds were found among car drivers than truck drivers. These differences suggest that self-nominated speeds might not be valid indicators of the observed work zone speeds and therefore should not be used as an alternative to observed speed data

On the speed reduction potential of pilot vehicle use in work zones

Despite significant research efforts to understand the speed reduction potentials of work zone interventions, little is known about the reductions achievable by the use of pilot vehicles. This paper innovatively examines the speed reduction potential of pilot vehicle in a Queensland rural highway work zone. Analysis of five days’ speed data showed that pilot vehicle reduced mean speeds at the treatment location, but not downstream. The proportion of speeding vehicles was also reduced, particularly those travelling at 10 km/h or more above the posted limit. Motorists were more likely to speed during the day, under a 40 km/h limit and when traffic volumes were higher. While it is commonly believed that pilot vehicle controls the speeds of all following vehicles, results of this study showed that pilot car had greater effects on reducing speeds of vehicles following it closely than those which are far behind in a traffic stream. To maximize these benefits, it is necessary to ensure that the pilot vehicle itself is not speeding

Driver beliefs regarding the benefits of reduced speeds

Despite many studies of the benefits of reducing driving speeds for safety, vehicular emissions, and stress in driving, little is known regarding how drivers perceive these benefits and the factors influencing their beliefs. This paper examines the factors influencing driver perceptions of the benefits attainable by reducing travel speeds. Driver perceptions of the extent to which reducing speed would lead to improved safety, lower emissions, and reduced stress and road rage were collected in an online survey of 3538 drivers in Queensland, Australia. An analysis using seemingly unrelated regression showed that drivers of automatic cars and bicycle commuters more strongly agreed that lower speeds would provide these benefits than other drivers, while drivers who used premium fuel thought otherwise. Users of ethanol blended fuel believed more strongly that reductions in speeds would reduce emissions. Young drivers less strongly agreed regarding both emissions and stress than older. Females, drivers of small cars, and those who drive frequently with passengers agreed more strongly that speed reductions would improve safety and reduce stress and road rage. These findings indicate a need to develop targeted educational and training programs to help drivers better understand these benefits to improve their willingness to reduce speeds

CyclingNet: Detecting cycling near misses from video streams in complex urban scenes with deep learning

Cycling is a promising sustainable mode for commuting and leisure in cities. However, the perception of cycling as a risky activity reduces its wide expansion as a commuting mode. A novel method called CyclingNet has been introduced here for detecting cycling near misses from video streams generated by a mounted frontal camera on a bike regardless of the camera position, the conditions of the built environment, the visual conditions and without any restrictions on the riding behaviour. CyclingNet is a deep computer vision model based on a convolutional structure embedded with self-attention bidirectional long-short term memory (LSTM) blocks that aim to understand near misses from both sequential images of scenes and their optical flows. The model is trained on scenes of both safe rides and near misses. After 42 hours of training on a single GPU, the model shows high accuracy on the training, testing and validation sets. The model is intended to be used for generating information that can draw significant conclusions regarding cycling behaviour in cities and elsewhere, which could help planners and policy-makers to better understand the requirement of safety measures when designing infrastructure or drawing policies. As for future work, the model can be pipelined with other state-of-the-art classifiers and object detectors simultaneously to understand the causality of near misses based on factors related to interactions of road users, the built and the natural environments

Observational study of compliance with Queensland bicycle helmet laws

Mandatory bicycle helmet laws have been found to increase helmet wearing rates in Australia and internationally. However, much of the research on factors influencing compliance with the Australian helmet laws is dated or focuses on commuters and city areas only. To address this gap, video recordings of bicycle riders were undertaken at 17 sites across Queensland, Australia, representing a mixture of on- and off-road locations, speed limits and regions. Helmet status was able to be determined for 98% of riders observed. The level of compliance with the laws was very high, with 98.3% of the more than 27,000 riders observed wearing helmets. Riders riding on roads were less compliant than those riding on bicycle paths, but no significant differences were observed between the school-holiday and school-term periods. Among the on-road riders, boys were less compliant than girls and overall children were less compliant than adults. Higher compliance levels were found for group riders, road bike riders, lycra-clad riders, during morning hours, and on 50 km/h or lower speed limit roads. While the overall level of compliance was very high, certain subgroups were identified as a possible focus for interventions to further improve the compliance level, for example children (particularly boys) riding mountain bikes away from groups during the afternoon hours on 60 km/h roads

Variational-LSTM autoencoder to forecast the spread of coronavirus across the globe

Modelling the spread of coronavirus globally while learning trends at global and country levels remains crucial for tackling the pandemic. We introduce a novel variational-LSTM Autoencoder model to predict the spread of coronavirus for each country across the globe. This deep Spatio-temporal model does not only rely on historical data of the virus spread but also includes factors related to urban characteristics represented in locational and demographic data (such as population density, urban population, and fertility rate), an index that represents the governmental measures and response amid toward mitigating the outbreak (includes 13 measures such as: 1) school closing, 2) workplace closing, 3) cancelling public events, 4) close public transport, 5) public information campaigns, 6) restrictions on internal movements, 7) international travel controls, 8) fiscal measures, 9) monetary measures, 10) emergency investment in health care, 11) investment in vaccines, 12) virus testing framework, and 13) contact tracing). In addition, the introduced method learns to generate a graph to adjust the spatial dependences among different countries while forecasting the spread. We trained two models for short and long-term forecasts. The first one is trained to output one step in future with three previous timestamps of all features across the globe, whereas the second model is trained to output 10 steps in future. Overall, the trained models show high validation for forecasting the spread for each country for short and long-term forecasts, which makes the introduce method a useful tool to assist decision and policymaking for the different corners of the globe

Cycling near misses: A review of the current methods, challenges and the potential of an AI-embedded system

Whether for commuting or leisure, cycling is a growing transport mode in many countries. However, cycling is still perceived by many as a dangerous activity. Because the mode share of cycling tends to be low, serious incidents related to cycling are rare. Nevertheless, the fear of getting hit or falling while cycling hinders its expansion as a transport mode and it has been shown that focusing on killed and seriously injured casualties alone only touches the tip of the iceberg. Compared with reported incidents, there are many more incidents in which the person on the bike was destabilised or needed to take action to avoid a crash; so-called near misses. Because of their frequency, data related to near misses can provide much more information about the risk factors associated with cycling. The quality and coverage of this information depends on the method of data collection; from survey data to video data, and processing; from manual to automated. There remains a gap in our understanding of how best to identify and predict near misses and draw statistically significant conclusions, which may lead to better intervention measures and the creation of a safer environment for people on bikes. In this paper, we review the literature on cycling near misses, focusing on the data collection methods adopted, the scope and the risk factors identified. In doing so, we demonstrate that, while many near misses are a result of a combination of different factors that may or may not be transport-related, the current approach of tackling these factors may not be adequate for understanding the interconnections between all risk factors. To address this limitation, we highlight the potential of extracting data using a unified input (images/videos) relying on computer vision methods to automatically extract the wide spectrum of near miss risk factors, in addition to detecting the types of events associated with near misses