Infrastructural and Human Factors Affecting Safety Outcomes of Cyclists

Integrating visual factors in crash rate analysis at Intersections: An AutoML and SHAP approach towards cycling safety

Cycling crashes constitute a significant and rising share of traffic accidents. Consequently, exploring factors affecting cycling safety has become a priority for both governmental bodies and scholars. However, most existing studies have neglected the vision factors capable of quantitatively describing the city-level cycling environment. Moreover, they have relied on limited models that lack interpretability and fail to capture the spatial variations in the contribution of factors. To address these gaps, this research proposed a framework that used origin-destination-based cycling flow and vision factors generated from Google Street View images to identify the leading factors. It also employed the comparative Automatic Machine Learning and interpretable SHAP value-based geospatial analysis to explain each factor's contribution to the cycling crash risk, with a particular focus on the spatial variations in the influence of vision factors. The effectiveness of this framework was validated by a case study in Manhattan, which examined the leading risk factors of cycling crash rates at intersections. The results showed that the LightGBM model, with selected subsets of factors, outperformed other models. Through SHAP explanations of global feature importance, the study identified the proportion of road barriers, the proportion of open sky, and the number of visible trucks as the leading visual risk factors. Additionally, using SHAP-based geospatial analysis, the study revealed the local variations in the effects of these three factors and identified eight areas with higher cycling crash rates. Based on these findings, the study provided practical measures for a safer cycling environment in Manhattan.

Aberrant behaviors of drivers involved in crashes and related injury severity: Are there variations between the major cities in the same country?

INTRODUCTION

Crash data analyses based on accident datasets often do not include human-related variables because they can be hard to reconstruct from crash data. However, records of crash circumstances can help for this purpose since crashes can be classified considering aberrant behavior and misconduct of the drivers involved.

METHOD

In this case, urban crash data from the 10 largest Italian cities were used to develop four logistic regression models having the driver-related crash circumstance (aberrant behaviors: inattentive driving, illegal maneuvering, wrong interaction with pedestrian and speeding) as dependent variables and the other crash-related factors as predictors (information about the users and the vehicles involved and about road geometry and conditions). Two other models were built to study the influence of the same factors on the injury severity of the occupants of vehicles for which crash circumstances related to driver aberrant behaviors were observed and of the involved pedestrians. The variability between the 10 different cities was considered through a multilevel approach, which revealed a significant variability only for the inattention-related crash circumstance. In the other models, the variability between cities was not significant, indicating quite homogeneous results within the same country.

RESULTS

The results show several relationships between crash factors (driver, vehicle or road-related) and human-related crash circumstances and severity. Unsignalized intersections were particularly related to the illegal maneuvering crash circumstance, while the night period was clearly related to the speeding-related crash circumstance and to injuries/casualties of vehicle occupants. Cyclists and motorcyclists were shown to suffer more injuries/casualties than car occupants, while the latter were generally those exhibiting more aberrant behaviors. Pedestrian casualties were associated with arterial roads, heavy vehicles, and older pedestrians.

Anticipatory information makes the difference: Behavioral effects and user assessments of a cyclist warning system to enhance cyclists' situation awareness

PROBLEM

Increasing numbers of crashes involving pedelecs, and particularly older pedelec users, induce a need to enhance cycling safety. We evaluated a prototype cyclist warning system (CWS) that aims to increase situation awareness (SA) by alerting to safety critical events (SCE) with trimodal (auditory, visual, tactile).

METHOD

To investigate the effects of CWS usage, we conducted a 2x2 mixed design bicycle simulator study with factors (1) CWS usage (within: rides WITH vs. WITHOUT CWS) and (2) age group (between: younger vs. older cyclists) on braking reaction time, gaze behavior, mental workload, and perceived safety. In sum, N = 64 participants (n = 32 younger, 18-40 years; n = 32 older, ≥ 55 years) took part in the study and experienced two balanced blocks of short rides including SCE of particular relevance for cycling safety.

RESULTS

CWS usage resulted in earlier braking reactions to all investigated SCE and partly earlier fixation on the critical interaction partners (CIP) indicating increased cyclists' SA. Consistently to behavioral measures, participants' assessments regarding perceived safety further supported the safety improvements derived from CWS independently of age group. Moreover, CWS usage did not add to mental workload ratings. Age effects were selectively found for gaze data showing that across all SCE, older adults fixated longer and more frequently on street alignment, and less frequently on other road users.

DISCUSSION

Taken together, the CWS evaluation showed promising results indicating the potential of the tested CWS to increase SA and enhance cyclists' safety both on a behavioral level and regarding subjective assessments. Further research should address the systems' safety potential under real-world conditions and for situations of higher complexity.

PRACTICAL APPLICATIONS

Understanding the potential impact of road safety measures such as CWS is important to contribute effectively to reducing SCE.

Assessing bicycle crash risks controlling for detailed exposure: A Copenhagen case study

A better understanding of factors associated with bicycle crashes can inform future efforts to limit crash risks. Many previous studies have analysed crash risk based on crash databases. However, these can only provide conditional information on crash risks. A few recent studies have included aggregate flow measures in their crash risk analyses. This study incorporates detailed bicycle flow to investigate factors related to bicycle crashes. Specifically, the study assesses the relative crash risk given various conditions by applying Palm distributions to control for exposure. The study specifically investigates the relationship between weather and time conditions and the relative risk of bicycle crashes at a disaggregate level. The study uses bicycle crash data from police reports of bicycle crashes from 2017-2020 in the greater Copenhagen area (N = 4877). The relations between the bicycle crash risk and the air temperature and wind speeds are found to be highly non-linear. The relative risk of bicycle crashes is elevated at low and high temperatures (0 °C ¿ x, x ¿ 21 °C). The results also show how decreasing visibility relates to increasing bicycle crash risk. Meanwhile, cycling during the early morning peak (7-8) and afternoon peak hours (15-18) is related to an increased risk of bicycle crashes. While some of the effects are likely spurious, they highlight specific conditions associated with higher relative risk. Finally, the results illustrate the increased risk at weekend night times when cyclists are likely to bike under the influence of alcohol. In conclusion, the analysis confirms that visibility, slippery surfaces, and intoxication are all factors associated with a higher risk of bicycle crashes. Hence, it is relevant to consider how infrastructure planning and preventive measures can modify the bicycle environment to minimise these risks.

Risky business: Comparing the riding behaviours of food delivery and private bicycle riders

The growth in the gig economy and a preference for home delivery of meals due to COVID-19 have led to huge growth in the food delivery business internationally and consequent road safety concerns. There is increasing evidence that delivery riding is an occupation with significant road safety risks because work pressures encourage risky behaviours. However, there is little or no research that directly compares delivery and private riders. Thus, the aim of this study was to examine the impact of riding for work by comparing the observable riding behaviours of food delivery and private bicycle riders. Specifically, this investigation used decision trees to analyse the prevalence and patterns of risky riding behaviours of 2274 bicycle food delivery riders (BFDRs) and 1127 private bicycle riders observed in the inner suburbs of Brisbane, Australia. The results showed that helmet use was higher for BFDRs than private riders (99.8% versus 93.4%) but varied by company and for some companies, female BFDRs had lower wearing rates. Male BFDRs on electric bikes were more likely to wear helmets than those on standard bikes (99.7% versus 94.9%). Using a handheld mobile phone or having a mobile phone in a cradle was less common for one company (0.6%) than for the others (3.0%) or among private riders (1.8%). Among riders from the Other Companies, using a handheld mobile phone was more common on standard bikes and differed by time of day. Female BFDRs were more likely to be observed using handheld mobile phones. Overall, 24.0% of riders facing a red traffic or pedestrian signal ("red light") did not stop. This was more common among riders who rode on the footpath (Australian term for sidewalk), and particularly those who moved between the footpath and the road on electric bikes (49.5%) and among those who rode in the wrong direction in the traffic lane (55.0%). Whether the rider was a BFDR or private rider had little influence on red light running. The results suggest that BFDRs are not more likely to perform the risky behaviours examined, but that other factors such as bicycle type, gender, time of day and infrastructure appear to be more important determinants. However, the differences among companies suggest that organisational factors deserve further investigation.

Studying the effects of an advanced driver-assistance system to improve safety of cyclists overtaking

Among all crashes involving cyclists, a motorist approaching from behind a cyclist on a shared lane is particularly dangerous and likely to result in serious injuries and fatalities. Previous research has highlighted that inadequate lateral distance and high vehicle speed are among the main contributing factors of crashes involving cars overtaking cyclists. A new advanced driver assistance system (ADAS) which supports drivers as they overtake cyclists was designed to avoid or, at least, mitigate crashes. In human-machine interface (HMI) design, the information was presented via multiple modalities with a multistage warning system. A combination of lateral clearance (LC) and time-to-danger (TTD) parameters was used as ADAS activation criterion. Experimentation was carried out using the medium-fidelity driving simulator at the Transportation Research Institute (IMOB) of Hasselt University in Belgium. Forty-eight drivers drove the two-lane rural experimental route two times, in baseline condition and with the ADAS activated, testing three overtaking events. Statistical tests showed that the proposed in-vehicle driving assistance system had a significant effect in increasing 1) the length of the passing phase, 2) the LC in the overtaking passing phase, and 3) the TTD along the overtaking maneuver. No effect of the ADAS system on vehicle speed was observed. Overall, the designed system is effective in improving car-cyclist overtaking behaviour in terms of both safety and cyclists' mobility.

Secondary task engagement, risk-taking, and safety-related equipment use in German bicycle and e-scooter riders - An observation

The behavioral safety of e-bike and e-scooter riders is a significant concern in traffic safety. In an observational study in Braunschweig, Germany, 4,514 bicycle and e-scooter riders were observed concerning their used vehicles type, secondary task engagement, use of additional safety equipment, and traffic rule violation. Overall, 13.4% of all riders were engaged in any secondary task, wearing headphones or earphones being the most frequent behavior (6.7%), followed by conversations with other cyclists (3.7%). Banned mobile phone use was low (0.8%). Secondary task engagement was positively correlated with traffic rule violations and at-fault conflicts and negatively with the use of additional safety equipment. Cluster analysis on vehicle types and behaviors revealed five groups of riders, two with relatively high numbers of risky behaviors: young and middle-aged, predominantly male riders of conventional bicycles, and a group of demographically similar users of electric bikes and e-scooters. Campaigns targeted at these specific groups may help reduce risky behaviors.

How safe do you feel? - A large-scale survey concerning the subjective safety associated with different kinds of cycling lanes

There is ample evidence that adequate cycling infrastructure increases cyclists' safety. There is less research to what extent the specific design of cycling lanes affects subjective safety. We address this question by analysing data from a large-scale online survey, where participants rated images illustrating a wide range of cycling infrastructure designs for the anticipated level of subjective safety when imagining to cycle at the displayed location. Cycling tracks are perceived as safer than cycling lanes, which in turn are preferred over cycling on the street. Physical separations from the car lane, a greater lane width, and a coloured surface contribute most to a high subjective safety of cycling lanes. Additional buffers on the left- and right side of cycling lanes can have varying effects. On narrower cycling lanes, people experience extensive buffer designs as rather constraining and as impairing their safety. Combining several safety features (i.e. a sufficient demarcation of the left buffer and a coloured surface) is not necessarily beneficial for subjective safety. Our findings are mostly in line with findings on the factors benefitting or impairing objective safety. However, the relation of subjective and objective safety requires further attention.

Cyclists' Perception of Maintenance and Operation of Cycling Infrastructure-Results From a Norwegian Survey

The Norwegian authorities want to limit the extent of car use in city areas to existing levels. Such a limitation would help combat climate change, improve health of citizens, and alleviate congestion. This implies that any further increase in transport needs will have to be met by walking, cycling and use of public transport. Reaching this ambitious goal requires knowledge about cyclists' preferences concerning operation and maintenance (M&O) of roads and foot/cycle paths. Previous research suggests that M&O have great implications for travel mode choice, bicycle route/path choice, safety, security, and comfort. With the need to serve bicyclists of all ages and genders, this study additionally explores which M&O of roads and foot/cycle the different demographic groups perceive positively or negatively. This article reports results from a nationwide survey in the summer of 2019. Two thousand three hundred seventy-six cyclists across Norway (55% male; 29% <40; 17% >60) participated to determine the cyclists' perceptions about year-round M&O of roads and foot/cycle paths. Respondents, rather than being randomly selected, completed an internet-linked survey. The variables included maintenance of foot/cycle paths in terms of salt and snow plowing and operation and maintenance of roads in terms of glass, holes/bumps, and conditions. Our results suggest that female cyclists suffer more from adverse conditions than do males. We also find that males are more likely to cycle during winter, which is an additional indication that adverse conditions affect women and men differently. Surprisingly, older cyclists report to be less affected by poor conditions than younger cyclists. Self-selection to participate in the survey among older cyclists might be an important explanation for this result. Cycling conditions vary greatly between geographical areas, reflecting the large climatic variations across Norway. Most respondents have experienced a cycle accident where conditions contributed, and many sometimes forfeit cycling due to adverse conditions. Implications for future research and practice of M&O are discussed.

Pedestrians’ Injury Severity in Traffic Accidents in Spain: A Pedestrian Actions Approach

Road traffic accidents are currently between the seventh and tenth leading cause of death in the world, with approximately 1.35 million people killed per year. Despite extensive efforts by governments, according to the World Health Organization, road accidents still cause far too many deaths, especially among pedestrians, cyclists and two-wheel motor vehicle riders, who together account for almost 50% of road traffic fatalities. In particular, Spain had 410,974 traffic accidents between 2016 and 2019, involving 722,516 vehicles and 61,177 pedestrians with varying degrees of injury. This study uses the Bayesian network method to understand how the pedestrians’ responsibility and actions at the time of the traffic accident affect the injury suffered by said pedestrian, also considering the variables of the road infrastructure and vehicles at the accident site. The results confirm that the variables linked to the unsafe behavior of pedestrians, and their responsibility in traffic accidents, increase the risk of suffering serious or fatal injuries during an accident; for example, if a pedestrian is distracted this increases his/her probability of suffering a severe injury (27.86%) with respect to not being distracted (20.73%). Conditions related to traffic in high-speed areas, areas with no or poor lighting, and areas lacking sidewalks, also record increases in pedestrian injury, as is the case in the age group of pedestrians over 60 years of age.

An in-depth analysis of self-reported cycling injuries in single and multiparty bicycle crashes in Denmark

INTRODUCTION

Cycling is one of the main forms of transportation in Denmark. However, while the number of traffic crash fatalities in the country has decreased over the past decade, the frequency of cyclists killed or seriously injured has increased. The high rate of serious injuries and fatalities associated with cycling emphasizes the increasing need for mitigating the severity of such crashes.

METHOD

This study conducted an in-depth analysis of cyclist injury severity resulting from single and multiparty bicycle-involved crashes. Detailed information was collected using self-reporting data undertaken in Denmark for a 12-month period between 1 November 2012 and 31 October 2013. Separate multilevel logistic (MLL) regression models were applied to estimate cyclist injury severity for single and multiparty crashes. The goodness-of-fit measures favored the MLL models over the standard logistic models, capturing the intercorrelation among bicycle crashes that occurred in the same geographical area.

RESULTS

The results also showed that single bicycle-involved crashes resulted in more serious outcomes when compared to multiparty crashes. For both single and multiparty bicycle crash categories, non-urban areas were associated with more serious injury outcomes. For the single crashes, wet surface condition, autumn and summer seasons, evening and night periods, non-adverse weather conditions, cyclists aged between 45 and 64 years, male sex, riding for the purpose of work or educational activities, and bicycles with light turned-off were associated with severe injuries. For the multiparty crashes, intersections, bicycle paths, non-winter season, not being employed or retired, lower personal car ownership, and race bicycles were directly related to severe injury consequences. Practical Applications: The findings of this study demonstrated that the best way to promote cycling safety is the combination of improving the design and maintenance of cycling facilities, encouraging safe cycling behavior, and intensifying enforcement efforts.

Causes of pedelec (pedal electric cycle) single accidents and their influence on injury severity

Pedelecs (pedal electric cycles) have become increasingly popular in recent years. This increase may trigger many positive effects, but unfortunately it also results in an increase in accidents, especially single accidents, involving these vehicles. This investigation analyses 1738 free text descriptions of pedelec single accidents written by police officers on site. The accidents occurred in 2016 and 2017 in 14 federal German states. The aim is to evaluate whether pedelec single accidents have specific causes that have not yet been recorded in the official accident statistics. Results show that most cases can be assigned to categories addressing the user (60 %) or the road design (54 %, accidents can be assigned to several categories). With this dataset it is also possible to analyse the severity of injuries using ordered probit analysis. Of the nine categories where severe injuries are expected, most address the user. In addition, serious injuries are expected when problems are encountered on downhill slopes or involve technical faults. An additional ordered probit analysis with a second dataset, based on different sources but using the same categorisation, supports these results.

Unraveling Urban Form and Collision Risk: The Spatial Distribution of Traffic Accidents in Zanjan, Iran

Official statistics demonstrate the role of traffic accidents in the increasing number of fatalities, especially in emerging countries. In recent decades, the rate of deaths and injuries caused by traffic accidents in Iran, a rapidly growing economy in the Middle East, has risen significantly with respect to that of neighboring countries. The present study illustrates an exploratory spatial analysis' framework aimed at identifying and ranking hazardous locations for traffic accidents in Zanjan, one of the most populous and dense cities in Iran. This framework quantifies the spatiotemporal association among collisions, by comparing the results of different approaches (including Kernel Density Estimation (KDE), Natural Breaks Classification (NBC), and Knox test). Based on descriptive statistics, five distance classes (2-26, 27-57, 58-105, 106-192, and 193-364 meters) were tested when predicting location of the nearest collision within the same temporal unit. The empirical results of our work demonstrate that the largest roads and intersections in Zanjan had a significantly higher frequency of traffic accidents than the other locations. A comparative analysis of distance bandwidths indicates that the first (2-26 m) class concentrated the most intense level of spatiotemporal association among traffic accidents. Prevention (or reduction) of traffic accidents may benefit from automatic identification and classification of the most risky locations in urban areas. Thanks to the larger availability of open-access datasets reporting the location and characteristics of car accidents in both advanced countries and emerging economies, our study demonstrates the potential of an integrated analysis of the level of spatiotemporal association in traffic collisions over metropolitan regions.

Evaluation of a New Urban Cycling Infrastructure in Caceres (Spain)

The purpose of the research is the evaluation of the impact of a new bike lane network built in Cáceres (Spain) in October 2019, a city with a weak tradition in the use of bicycles. Prior to the new project, the percentage of displacement by bicycle was under 0.2% (by contrast, private cars was 56%). The project has introduced a real network in 33 new streets and avenues, and 250 new parking places for bicycles. The impact has been evaluated by two online surveys, one conducted in November–December 2019 just after the inauguration, and the other conducted in January–February 2020. For that, the impact evolution after the first 3 months has also been evaluated. The result allows to analyze the citizens’ response to the new infrastructure; the main conclusion is that the effect of the built infrastructure is clearly positive in terms of new users, new potential users, and bike general image for the citizens. Moreover, this positive effect has grown after the first 3 months.

The Munich Bikeability Index: A Practical Approach for Measuring Urban Bikeability

This research addresses the phenomenon of varying bicycle friendliness in urban areas and considers which elements are necessary to design a city in a bike-friendly manner. It aims to provide a deeper understanding of the term bikeability, in relation to the established term walkability, and methods to create models that measure the degree of bikeability in urban areas. We explain different established models and compare their computational bases. The focus of this paper is to define a computational methodology built within a Geographic Information System (GIS) and a subsequent evaluation based on an investigation area in Munich, Germany. We introduce a bikeability index for specific investigation areas and geovisualize four selected factors of this index. The resulting map views show the road segments of the traffic network where the conditions for biking are adequate, but also those segments which need to be improved.

An Investigation into Unsafe Behaviors and Traffic Accidents Involving Unlicensed Drivers: A Perspective for Alignment Measurement

Road traffic plays a vital role in countries’ economic growth and future development. However, traffic accidents are considered a major public health issue affecting humankind. Despite efforts by governments to improve traffic safety, the misalignment between the policy efforts and on-ground infringements, distractions and breaches reflect the regulatory failure. This paper uses the Bayesian network method to investigate unsafe behaviors and traffic accidents involving unlicensed drivers as a perspective for the regulatory alignment assessment. The findings suggest that: (1) unlicensed drivers are more likely to have unsafe driving behaviors; (2) the probability of being involved in a severe traffic accident increases when the drivers are unlicensed and decreases in the case of licensed drivers; (3) young drivers are noticeably more likely to engage in unsafe behaviors, usually leading to serious injuries and deaths, when their driving licenses are invalid; (4) women are more likely to engage in right-of-way violations and to have collisions with no serious injuries, contrary to unlicensed men drivers, who are involved in other types of traffic accidents resulting in serious injuries.

Cost Overrun Causative Factors in Road Infrastructure Projects: A Frequency and Importance Analysis

Road infrastructure projects are essential for a country’s economic and social development. Due to the magnitude, the projects are associated with considerable economic investments that in the case of failure can seriously affect regions’ economies. Despite the importance, roads from different countries are affected by cost overruns, hence, it is essential to identify and analyze the causative factors to focus the search for mitigation solutions. There are several studies focused on the cost factors identification, however, studies are lacking that synthesize and analyze the frequency and importance with which the factors have been reported to obtain a phenomenon overview. Therefore, this paper focuses on analyzing the frequency and importance with which cost overrun factors are reported in road projects. The research method consisted of a systematic review compound of five principal stages: (1) question formulation; (2) searching of relevant documents; (3) document selection; (4) evidence collection, analysis and synthesis; and (5) results’ report. Thirty-eight cost overrun factors were identified and classified into 14 categories. According to the Influence Index, the five most important and frequent cost overrun factors were: (1) failures in design, (2) price variation of materials, (3) inadequate project planning, (4) project scope changes, and (5) design changes.

Crash risk and subjective risk perception during urban cycling: Evidence for congruent and incongruent sources

BACKGROUND

Urban planners frequently neglect the role of subjective risk perception during urban cycling. Several findings suggest a complex relationship between the risk of being involved in a crash and the subjective anticipation of this risk.

DATA COLLECTION AND METHODS

We investigate the relation of objective risks (operationalized through crashes involving cyclists) and subjective risk perception (operationalized through citizens' reports in a crowdsourcing project) in a medium-sized German city. Using GIS methods, these datasets are linked to various infrastructure and traffic properties that have been found relevant for cycling safety.

RESULTS

Despite a generally high alignment of objective and subjective risk, our findings highlight that the subjective risk perception at a given location can deviate significantly from the actual crash risk. For example, the subjective perception of high risk on one-way streets with bikeways in opposing direction is not matched by a high level of objective risk. Vice versa, some rather dangerous situations (e.g., tram stops) are not perceived as particularly dangerous.

CONCLUSIONS

Understanding why and where cyclists over- or underestimate the actual crash risk may provide a foundation for the design of safer cycling infrastructures, as well as for promoting cycling as a comfortable mode of transportation.

Personality and Behavioral Predictors of Cyclist Involvement in Crash-Related Conditions

In recent years, the increasing rate of road crashes involving cyclists with a disproportionate overrepresentation in injury statistics has become a major concern in road safety and public health. However, much remains unknown about factors contributing to cyclists’ high crash rates, especially those related to personal characteristics. This study aims to explore the influence of cyclist personality traits and cycling behaviors on their road safety outcomes using a mediated model combining these constructs. A total of 628 cyclists completed an online questionnaire consisting of questions related to cycling anger, impulsiveness, normlessness, sensation seeking, risky cycling behaviors, and involvement in crash-related conditions in the past year. After the psychometric properties of the employed scales were examined, the relationships among the tested constructs were investigated using structural equation modeling. The results showed that cyclists’ crash risks were directly predicted by risky cycling behaviors and cycling anger, and the effects of cycling anger, impulsiveness, as well as normlessness on crash risks, were mediated by cycling behaviors. The current findings provide insight into the importance of personality traits in impacting cycling safety and could facilitate the development of evidence-based prevention and promotion strategies targeting cyclists in China.

Integrating Spatial and Temporal Approaches for Explaining Bicycle Crashes in High-Risk Areas in Antwerp (Belgium)

The majority of bicycle crash studies aim at determining risk factors and estimating crash risks by employing statistics. Accordingly, the goal of this paper is to evaluate bicycle–motor vehicle crashes by using spatial and temporal approaches to statistical data. The spatial approach (a weighted kernel density estimation approach) preliminarily estimates crash risks at the macro level, thereby avoiding the expensive work of collecting traffic counts; meanwhile, the temporal approach (negative binomial regression approach) focuses on crash data that occurred on urban arterials and includes traffic exposure at the micro level. The crash risk and risk factors of arterial roads associated with bicycle facilities and road environments were assessed using a database built from field surveys and five government agencies. This study analysed 4120 geocoded bicycle crashes in the city of Antwerp (CA, Belgium). The data sets covered five years (2014 to 2018), including all bicycle–motorized vehicle (BMV) crashes from police reports. Urban arterials were highlighted as high-risk areas through the spatial approach. This was as expected given that, due to heavy traffic and limited road space, bicycle facilities on arterial roads face many design problems. Through spatial and temporal approaches, the environmental characteristics of bicycle crashes on arterial roads were analysed at the micro level. Finally, this paper provides an insight that can be used by both the geography and transport fields to improve cycling safety on urban arterial roads.

A Rare Event Modelling Approach to Assess Injury Severity Risk of Vulnerable Road Users

Vulnerable road users (VRUs) represent a large portion of fatalities and injuries occurring on European Union roads. It is therefore important to address the safety of VRUs, particularly in urban areas, by identifying which factors may affect the injury severity level that can be used to develop countermeasures. This paper aims to identify the risk factors that affect the severity of a VRU injured when involved in a motor vehicle crash. For that purpose, a comparative evaluation of two machine learning classifiers—decision tree and logistic regression—considering three different resampling techniques (under-, over- and synthetic oversampling) is presented, comparing both imbalanced and balanced datasets. Crash data records were analyzed involving VRUs from three different cities in Portugal and six years (2012–2017). The main conclusion that can be drawn from this study is that oversampling techniques improve the ability of the classifiers to identify risk factors. On the one hand, this analysis revealed that road markings, road conditions and luminosity affect the injury severity of a pedestrian. On the other hand, age group and temporal variables (month, weekday and time period) showed to be relevant to predict the severity of a cyclist injury when involved in a crash.

Positioning Depression as a Critical Factor in Creating a Toxic Workplace Environment for Diminishing Worker Productivity

This study determined how a toxic workplace environment can influence worker productivity, directly and indirectly, using work depression as a mediating variable. A toxic workplace environment with multiple dimensions (harassment, bullying, ostracism, and incivility) was used in this study. We used a questionnaire survey approach to evaluate the data. A total of 53 items were used in the questionnaire with a five-point Likert scale. The data were collected from 23 branches of five Chinese banks in the vicinity of Shanghai. The authors distributed 250 questionnaires among targeted employees (senior managers, middle managers, and administrative staff) and received 186 filled questionnaires, among which six were incomplete. Thus, the completed sample size of the research was 180, and the overall response rate was 72%. To estimate the proposed relationships in the research model, we used partial least-squares structural equation modeling (PLS-SEM 3.2). The outcomes of this study indicate that for direct and indirect relationships, a toxic workplace environment negatively influences worker productivity. Moreover, the outcomes of this study also show that work depression negatively impacts worker productivity. The study concludes with a discussion, limitations, and future research directions.

Traffic Crash Evolution Characteristic Analysis and Spatiotemporal Hotspot Identification of Urban Road Intersections

Road traffic safety is a key concern of transport management as it has severely restricted Chinese economic and social development. With the objective to prevent and reduce road traffic crashes, this study proposes a comprehensive spatiotemporal analysis method that integrates the time-space cube analysis, spatial autocorrelation analysis, and emerging hot spot analysis for exploring the traffic crash evolution characteristics and identifying crash hot spots. These analyses are all conducted by the corresponding toolbox of ArcGIS 10.5. Then, a small sized-city of China (i.e., Wujiang) is selected as the case study, and the historical traffic crash data occurring at the road intersections of Wujiang for the year 2016 are analyzed by the proposed method. The analysis process identifies the high incidence locations of traffic crashes, then presents the spatial change trend and statistical significance of the crash locations. Finally, different types of crash hotspots, as well as their evolution patterns over time, are determined. The results illustrate that the traffic crash hotspots of road intersections are primarily distributed in the Northeast area of Wujiang’s major urban area, while the crash cold spots are concentrated in the Southwest of Wujiang, which points out the direction for crash prevention. In addition, the finding has a potential engineering application value, and it is of great significance to the sustainable development of Wujiang.

When age means safety: Data to assess trends and differences on rule knowledge, risk perception, aberrant and positive road behaviors, and traffic crashes of cyclists

This data article examines the association between age, knowledge of traffic rules, risk perception, risky and positive behaviors on the road and traffic safety outcomes of cyclists. The data was collected using a structured self-administrable and online-based questionnaire, applied to a full sample of 1064 cyclists. The data contains 4 parts: descriptive statistics; graphical trends for each study variable according to age; Post-Hoc (Tukey-HSD) comparisons between cyclists classified in the different age groups; and, finally, the dataset for further explorations in this regard. For further information, it is convenient to read the full article entitled “Explaining Self-Reported Traffic Crashes of Cyclists: An Empirical Study based on Age and Road Risky Behaviors” (Useche et al., 2019) [1].

Distraction of cyclists: how does it influence their risky behaviors and traffic crashes?

Background

Undisputedly, traffic crashes constitute a public health concern whose impact and importance have been increasing during the past few decades. Specifically, road safety data have systematically shown how cyclists are highly vulnerable to suffering traffic crashes and severe injuries derived from them. Furthermore, although the empirical evidence is still very limited in this regard, in addition to other human factors involved in cycling crashes, distractions while cycling appear to be a major contributor to the road risk of cyclists.

Objectives

The main objectives of this study were, first, to explore the prevalence and trends of cycling distractions within an international sample of bike users, and second, to determine the influence of such distractions on road crashes suffered by cyclists, simultaneously considering the explanatory role of risky behaviors (errors and traffic violations) as potentially mediating variables between cycling distractions and traffic crashes.

Methods

For this cross-sectional study, we analyzed the data obtained from 1,064 cyclists—61.2% male and 38.8% female—from 20 different countries, who answered an on-line questionnaire on cycling-related features, habits, behaviors and accidents.

Results

The prevalence of different cycling distractions oscillated between 34.7% and 83.6%. The most common distractions were those related to the behavior of other users, physical elements of the road, weather conditions and phone calls. Age trends and differences were also found, thus establishing a positive association between age and distractibility during cycling. Furthermore, the effect of distractions on traffic crashes of cyclists was significant when tested together with age, risk perception and risky behaviors on the road.

Conclusion

The results of this study support the hypotheses that distractions have a major prevalence among bike users, and that they play a significant role in the prediction of the traffic crash rates of cyclists, through the mediation of risky behaviors.

Does gender really matter? A structural equation model to explain risky and positive cycling behaviors

INTRODUCTION

While the use of bicycles as mean of transport is growing worldwide, the increasing rates of traffic crashes involving cyclists have turned into a relevant scientific, public health, and road safety concern. According to several studies, and despite the fact that some countries are taking part in preventive actions, the data indicate that the problem of cycling injuries implies high costs for the community welfare, for the economy, and for healthcare systems, thus proving a clear need for solutions. In this regard, and considering the available empirical evidence, risky and positive riding behaviors have gained significant weight in terms of explaining, intervening in, and preventing traffic crashes of cyclists, and some evidence suggests that gender may influence the road behavior of users.

OBJECTIVE

The objective of this study was to examine the effect of gender on cyclists' risky and positive riding behavior, considering a set of demographic, psychosocial and bike-use-related variables as potential predictors.

METHOD

For this cross-sectional study, data from 1064 cyclists (61.2% males and 38.8% females, aged between 17 and 80) from 20 countries, responding an electronic survey, were analyzed through a multi-group structural equation modeling approach.

RESULTS

Although hourly intensity, psychological distress and level of knowledge of traffic rules similarly predict the risky road behaviors of both genders, age and risk perception are significant behavioral predictors only in the case of male cyclists. On the other hand, positive behaviors of men are predicted by cycling intensity, knowledge of traffic rules and risk perception, while in the case of women psychological distress predicts -to a significant extent- positive behaviors. Age had no significant effect on the explanation of positive behaviors.

CONCLUSION

The findings of this study support the influence of gender in the statistical explanation of risky and protective behaviors, and they also reveal differentiating variables predicting the riding behavior of male and female cyclists.

Geographical Assessment of Low-Carbon Transportation Modes: A Case Study from a Commuter University

This case study examines the geographic variation in students’ low-carbon transportation (LCT) modes to a commuter university campus. Three major goals are accomplished from this research: (1) identifying commuting zones for the bicycling, walking, and transit mode choice for UNCG students; (2) understanding whether the real vs. perception of space can be predictive to mode choice; and (3) understanding the relative importance of demographic, psychological, and logistic factors on students’ mode choice, using a suite of variables developed in multiple fields. Our analyses support the assertion that various physical, demographic, and psychological dimensions influence LCT mode choice. While the presence of sidewalks is conducive to walking, the distance, either perceived or actual, within 1.61 km from UNCG is the most important factor for walking mode share. The bicycling commute is not associated with either the distance or presence of bicycle lanes, while transit ridership most likely increases if students live >8 km from the UNCG campus with the nearest bus stop within 1 km from home. Given the limited bicycle lanes in Greensboro, students who commute to campus by bicycle are resilient to unfavorable bicycle conditions by sharing the road with cars and adjusting their travel routes. Our findings also concur with previous studies showing that bicycle commuters are disproportionately represented by self-identified whites while bus riders are disproportionately comprised of self-identified non-whites. Our analyses support Greensboro’s current planning and policy emphasis on low-carbon travel behaviors via equitable and safe transit-oriented multi-modal infrastructures, and suggest that UNCG should utilize its influence to advocate and further facilitate these ongoing efforts.

An Empirical Study Analyzing Job Productivity in Toxic Workplace Environments

Purpose: This empirical study aims to determine the effects of a toxic workplace environment, which can negatively impact the job productivity of an employee. Methodology: Three hundred questionnaires were randomly distributed among the staff members of seven private universities in Pakistan with a final response rate of 89%. For analysis purposes, AMOS 22 was used to study the direct and indirect effects of the toxic workplace environment on job productivity. Confirmatory Factor Analysis (CFA) was conducted to ensure the convergent and discriminant validity of the factors, while the Hayes mediation approach was used to verify the mediating role of job burnout between the four dimensions of toxic workplace environment and job productivity. A toxic workplace with multiple dimensions, such as workplace ostracism, workplace incivility, workplace harassment, and workplace bullying, was used in this study. Findings: By using the multiple statistical tools and techniques, it has been proven that ostracism, incivility, harassment, and bullying have direct negative significant effects on job productivity, while job burnout was shown to be a statistical significant mediator between the dimensions of a toxic workplace environment and job productivity. Finally, we concluded that organizations need to eradicate the factors of toxic workplace environments to ensure their prosperity and success. Practical Implications: This study encourages managers, leaders, and top management to adopt appropriate policies for enhancing employees&rsquo; productivity. Limitations: This study was conducted by using a cross-sectional research design. Future research aims to expand the study by using a longitudinal research design.