Unsafe Bicycling Behavior in Changsha, China: A Video-Based Observational Study

Validity across four common street-crossing distraction indicators to predict pedestrian safety

BACKGROUND

Multiple distraction indicators have been applied to measure street-crossing distraction but their validities in predicting pedestrian safety are poorly understood.

METHODS

Based on a video-based observational study, we compared the validity of four commonly used distraction indicators (total duration of distraction while crossing a street, proportion of distracted time over total street-crossing time, duration of the longest distraction time, and total number of distractions) in predicting three pedestrian safety outcomes (near-crash incidence, frequency of looking left and right, and speed crossing the street) across three types of distraction (mobile phone use, talking to other pedestrians, eating/drinking/smoking). Change in Harrell's C statistic was calculated to assess the validity of each distraction indicator based on multivariable regression models including only covariates and including both covariates and the distraction indicator.

RESULTS

Heterogeneous capacities in predicting the three safety outcomes across the four distraction indicators were observed: 1) duration of the longest distraction time was most predictive for the occurrence of near-crashes and looks left and right among pedestrians with all three types of distraction combined and talking with other pedestrians (Harrell's C statistic changes ranged from 0.0310 to 0.0335, P < 0.05), and for the occurrence of near-crashes for pedestrians involving mobile phone use (Harrell's C statistic change: 0.0053); 2) total duration of distraction was most predictive for speed crossing the street among pedestrians with the combination and each of the three types of distraction (Harrell's C statistic changes ranged from 0.0037 to 0.0111, P < 0.05), frequency of looking left and right among pedestrians distracted by mobile phone use (Harrell's C statistic change: 0.0115), and the occurrence of near-crash among pedestrians eating, drinking, or smoking (Harrell's C statistic change: 0.0119); and 3) the total number of distractions was the most predictive indicator of frequency of looking left and right among pedestrians eating, drinking, or smoking (Harrell's C statistic change: 0.0013). Sensitivity analyses showed the results were robust to change in grouping criteria of the four distraction indicators.

CONCLUSIONS

Future research should consider the pedestrian safety outcomes and type of distractions to select the best distraction indicator.

Characteristics and Outcomes of Shared Bicycle-Related Injuries from a Large Emergency Medical Centre in China, 2017–2021

Objective

The aim of this study is to investigate the characteristics and outcomes of shared bicycle-related injuries from a large emergency medical centre in China in five years from January 2017 to December 2021.

Methods

This study was conducted by reviewing the electronic medical record database of a large hospital in China for cases of shared bicycle-related injuries in five years from January 2017 to December 2021. The collected information included demographic data, injury characteristics, and outcomes. Multivariate logistic regression analysis was used to identify risk factors for fatal injury among victims.

Results

Most shared bicycle-related injuries occurred in male (60.50%), aged 18–35 (38.81%). Company employees (29.28%) were the majority of victims of shared bicycle-related injuries. Riding in a motor vehicle lane was the most common unsafe riding behaviour (26.52%). The lower limb was the most frequently injured body region (25.28%). Bruising (28.73%) was the most commonly diagnosed injury type. The fatality rate was 9.53%, 72.24% of victims recovered well without permanent disability, and 18.23% of victims had permanent disabilities. The length of hospital stay of most of the victims (67.54%) was less than 7 days, and the hospitalization cost of most of the victims (51.93%) was less than 20,000 Yuan. Riding in a motor vehicle lane, running red lights, and cycling against traffic flow are risk factors for fatal injury.

Conclusions

This study indicated that shared bicycle-related injuries make up a sizeable proportion of injuries presenting to the emergency department and with diverse injury characteristics and outcomes. The findings reflect that shared bicycle-related injury has become a public health problem. Therefore, it is necessary to establish injury prevention strategies for the safety of shared bicycle users.

Effectiveness of a helmet promotion campaign, China

Objective

To evaluate the effectiveness of a 2020 nationwide helmet promotion campaign, in terms of helmet wearing and correct helmet wearing, aimed at electric bike riders and motorcyclists in China.

Methods

We obtained 192 hours of film of traffic before (2019) and after (2021) implementation of the campaign at eight road intersections in Changsha, recording cyclist (traditional and electric) and motorcyclist helmet-wearing behaviour during both weekdays and the weekend, and peak and off-peak traffic. We extracted data on rider characteristics and helmet-wearing behaviour. We applied a logistic regression to obtain estimates of helmet wearing and correct helmet wearing, and calculated odds ratios adjusted for rider variables.

Findings

We filmed 11 525 cyclists and motorcyclists, 5256 (45.6%) before and 6269 (54.4%) after the campaign. We estimated a substantial increase in the overall percentage of helmet wearing from 8.8% (95% confidence interval, CI: 8.0–9.6) to 62.0% (95% CI: 60.8–63.2). After controlling for covariates, we noted that helmet wearing increased in all groups. However, we observed a decrease in the overall percentage of correct helmet wearing from 91.9% (95% CI: 89.4–94.3) to 83.5% (95% CI: 82.3–84.7). Post-campaign, we estimated the highest percentage of helmet wearing for delivery riders (88.8%) and lowest for traditional cyclists (3.8%); we estimated the lowest percentage of correct helmet wearing for three-wheeled motorcyclists (58.8%).

Conclusion

To increase helmet wearing and correct helmet wearing, we recommend amending the campaign to include traditional cyclists as well as education and legislation on the correct fastening of helmet chinstraps.

Risks for bicycle-related injuries in Al Ain city, United Arab Emirates: An observational study

Traffic-related injuries are a serious health problem. Traffic safety is a priority reflected in the United Nations Sustainable Development Goals. Data on current hazards for bicycle-related injuries from the United Arab Emirates are lacking. The aim of our observational study was to assess the behavior of bicyclists on the roads in Al Ain City, United Arab Emirates and compare our current results with a previous study from 2004.We adapted and tested a structured data collection form. Different sectors of Al Ain were randomly selected to cover the whole city during different times. Bicyclists were observed without direct contact.Out of 1129 bicyclists, 97.6% were males and 13.2% children. 39.4% were cycling on main roads with high-density traffic, 33.1% were cycling against the traffic, 39.3% were cycling at night, and 96.8% of them were not using lights. Only 2.1% of the bicyclists used helmets. A higher proportion of female than male cyclists used helmets (25.9% vs 1.5%; P < .001, Fisher exact test). There was an increase in cycling with the traffic (P < .001) and in use of helmets (P < .025) compared with the previous study.Unsafe practices of bicyclists and low use of helmets despite legislation persist in Al Ain. There is a need to raise bicycle safety awareness and improve enforcement of bicycle helmet legislation. This should be directed toward expatriate workers, children, parents, and maids. Environmental changes, namely building separate bicycle lanes, can increase safety for cycling.

Exploring Factors Impacting on the Lane Choice of Riders of Non-Motorized Vehicles at Exit Legs of Signalized At-Grade Intersections

For most signalized at-grade intersections, exclusive lanes for non-motorized vehicles have been applied to improve the level of service, capacity and safety of both motorized vehicles and non-motorized vehicles. However, because of various factors, riders of non-motorized vehicles have been observed using lanes for motorized vehicles instead of lanes for non-motorized vehicles, which usually negatively influences the performance of signalized intersections and sometimes may cause serious problems such as traffic congestion and accidents. The objective of this paper is to explore factors influencing the lane choice of riders of non-motorized vehicles at exit legs of signalized at-grade intersections and develop a prediction model for riders’ lane choice. Data concerning the lane choice of riders of non-motorized vehicles and other impacting factors were collected at exit legs of four typical signalized at-grade intersections. Applying binary logistic regression, a probability prediction model was developed to explain how various factors influence the lane choice of riders of non-motorized vehicles. The prediction model indicates that female riders of non-motorized vehicles have a higher probability of choosing the lane for non-motorized vehicles than male riders. Compared with riders of non-motorized vehicles powered by electricity, riders of traditional man-powered bicycles are more likely to choose the lane for non-motorized vehicles. Right-turning riders of non-motorized vehicles are more likely to choose the lane for non-motorized vehicles than straight-going riders, who in turn, are more likely to choose the lane for non-motorized vehicles than left-turning riders. Decreasing the volume of non-motorized vehicles, increasing the volume of motorized vehicles, and widening the lane for non-motorized vehicles will increase the probability of the correct choice of lane for non-motorized vehicles. The predictions of the model are in good agreement with the observed facts. The model is meaningful for guidance on the design and management of signalized at-grade intersections.

Cyclists at Roundabouts: Risk Analysis and Rational Criteria for Choosing Safer Layouts

Cycling for transportation is an important resource to reduce urban traffic congestion, enhance personal health, reduce energy consumption, and improve air quality, and the safety of cyclists in the cities is becoming a topic of growing interest. As shown in the literature, an important number of cyclist fatalities is due to road crashes occurring at urban intersections. This study combines a probabilistic and a damage model to perform a risk analysis for the collisions between motor vehicles and bicycles in the merging and diverging conflict points of a single-lane conventional roundabout with four arms, characterized by a permanent traffic flow. The probabilistic model is based on Poisson’s law and is aimed to measure the probability of a collision between bikes and motor vehicles within the elementary unit of exposure in each conflict point of the roundabout. The damage model exploits the reaction time of a road user to avoid a collision and has been built to develop a danger classification for the conflict points. The goal of this study is then to estimate the so-called risk of collision at the roundabout, to compare different possible layouts for various traffic volumes with increasing bike flows and geometric configurations, and to identify the most effective solutions to improve safety for cyclists. The results demonstrate the risk reduction given by a roundabike compared to a standard layout where cyclists and motor vehicles share the circulatory roadway. Therefore, the study here presented could help road managers to implement mitigation strategies taking into consideration both geometric and functional constraints.

Impacts of COVID-19 pandemic on user behaviors and environmental benefits of bike sharing: A big-data analysis

The COVID-19 pandemic spreads rapidly around the world, and has given rise to huge impacts on all aspects of human society. This study utilizes big data techniques to analyze the impacts of COVID-19 on the user behaviors and environmental benefits of bike sharing. In this study, a novel method is proposed to calculate the trip distances and trajectories via a python package OSMnx so as to accurately estimate the environmental benefits of bike sharing. In addition, we employ the topological indices arising from complex network theory to quantitatively analyze the transformation of user behavior pattern of bike sharing during the COVID-19 pandemic. The results show that this pandemic has impacted the user behaviors and environmental benefits of bike sharing in Beijing significantly. During the pandemic, the estimated reductions of energy consumption and emissions on 6th Feb decreased to approximately 1 in 17 of those on a normal day, and the environmental benefits at most recovered to 70% of those in normal days. The impacts of COVID-19 on the environmental benefits in different districts are different. Furthermore, the decline of average strength and strength distribution obeying exponential distribution but with different slope rates suggests that people are less likely to take bike sharing to the places where were popular before. The pandemic has also increased the average trip time of bike sharing. Our research may facilitate the understanding of the impacts of COVID-19 pandemic on our society and environment, and also provide clues to adapt to this unprecedented pandemic so as to respond to similar events in the future.