Predicting cycling accident risk in Brussels: a spatial case-control approach

Cyclist safety around trams: A market survey

Tram systems present safety risks to cyclists, however only limited research has explored this topic, of which most has focused on crash and hospital data, and severe crash events. This paper presents the first known cyclist market survey focused specifically on the experience of cyclists related to safety around trams, including unreported incidents and those that did not result in hospital attendance. Findings suggest that track-skid incidents are more common than track-wedge incidents, in contrast to previous research that emphasizes track-wedging as a larger issue than skidding. This is may be explained by the differing outcomes, with track-wedging more likely to result in injury. This research is thus significant in identifying track skidding as a major risk concern, causing a majority of crashes, while also confirming that track wedging is the major severity concern. In the last five years, 21% of respondents were involved in at least one tram-track-related crash. This was less than the share of respondents involved in falls (50%), crashes relating to road defects (36%) or collisions with motor vehicles (29%). However, half of survey respondents (52%) reported cycling on roads with tram tracks for 0-20% of their cycling, which might suggest that tram track-related crash rates are high given that most inner-city cycling occurs on roads without tracks. Track-skidding was found to be associated with wet conditions. Those involved in at least one track-skid in the last five years where more likely to have been cycling more than 3 years, but involvement in track-wedging was more likely amongst those cycling > 10 years and aged < 45 years. Implications for research and practice are suggested.

Cycle fatalities in Delhi and their risk factors

There is a lack of research that investigates the risk factors of cycling in low- and middle-income countries. We present descriptive epidemiology of crashes that involved at least one fatal cyclist, over a three-year period (2016-2018) in Delhi, India. Next, we used an unmatched case-control approach to identify road design risk factors of cycle fatalities. Cases were road segments with at least one cycle fatality, and controls were those with none. We developed logistic regression models with cases and controls as binary outcomes to estimate the odds ratio of site characteristics. There were 167 crashes involving at least one cyclist fatality over the study period. Fatal cyclists were almost all males. They were less likely to be children or young adults and less likely to be residents of high-income localities, compared to the general population. One in ten crashes included more than one occupant on a cycle and 5% of fatal victims were pillion riders. Seventy percent crashes occurred at midblock, and majority were backend collisions. Regression shows that road width, traffic speed, and volume of heavy vehicles have strong positive effects on the fatality risk of cyclists. These results can inform strategies to design interventions for safety of cyclists.

Unraveling the relation between cycling accidents and built environment typologies: Capturing spatial heterogeneity through a latent class discrete outcome model

Today, cities seek to transition to more sustainable transportation modes. Cycling is critical in this shift, promoting a more beneficial lifestyle for most. However, cyclists are exposed to many hazardous circumstances or environments, resulting in accidents, injuries, and even death. Transport authorities must understand why accidents occur, to reduce the risk of those who cycle. This study applies a new modeling framework to analyze cycling accident severities. We employ a latent class discrete outcome model, where classes are derived from a Gaussian-Bernoulli mixture, applied to data from Berlin, and augmented with volunteered geographic information. We jointly estimate model components, combining machine learning and econometric approaches, allowing for more intricate and flexible representations while maintaining interpretability. Results show the potential of our approach. Risk factors are indexed depending on where accidents occurred and their contribution. We can discover complex relations between specific built environments and accident characteristics and uncover differences in the impact of certain accident factors on one environment typology but not others. Using multiple data sources also proves helpful as an additional layer of knowledge, providing unique value to understand and model cycling accidents. Another critical aspect of our approach is the potential for simulation, where locations can be examined through simulated accident features to understand the inherent risk of various locations. These findings highlight the ability to capture heterogeneity in accidents and their relation to the built environment. Capturing such relations allows for more direct countermeasures to risky situations or policies to be designed, simulated, and targeted.

Exploring temporal instability effects on bicyclist injury severities determinants for intersection and non-intersection-related crashes

Cycling is a sustainable and healthy mode of transportation with direct links to reducing traffic congestion, lowering greenhouse gas emissions, and improving air quality. However, from a safety perspective, bicyclists represent a risky road user group with a higher likelihood of sustaining severe injuries when involved in vehicle crashes. With various determinants known to affect bicyclist injury severity and vary across locations, this study investigates the factors affecting bicyclist injury severity and temporal instability, considering the location of crashes. More specifically, the objective of this study is to understand differences in injury severities of intersection and non-intersection-related single-bicycle-vehicle crashes using four year crash data from the state of Florida. Random parameters logit models with heterogeneity in the means and variances are developed to model bicyclist injury severity outcomes (no injury, minor injury, and severe injury) for intersection and non-intersection crashes. Several variables affecting injury severities are considered in model estimation, including weather, roadway, vehicle, driver, and bicyclist characteristics. The temporal stability of the model parameters is assessed for different locations and years using a series of likelihood ratio tests. Results indicate that the determinants of bicyclist injury severities change over time and location, resulting in different injury severities of bicyclists, with non-intersection crashes consistently resulting in more severe bicyclist injuries. Using a simulation-based out-of-sample approach, predictions are made to understand the benefits of replicating driving behaviour and facilities similar to intersections for non-intersection locations, which could benefit in reducing bicyclist injury severity probabilities.

Computer vision-based assessment of cyclist-tram track interactions for predictive modeling of crossing success

INTRODUCTION

Single Bicycle Brashes (SBCs) are common, and underreported in official statistics. In urban environments, light rail tram tracks are a frequent factor, however, they have not yet been the subject of engineering analysis.

METHOD

This study employs video-based analysis at nine Dublin city centre locations and introduces a predictive model for crossing success on tram tracks, utilising cyclist crossing angles within a Surrogate Measure of Safety (SMoS) framework. Additionally, Convolutional Neural Networks (CNNs) were explored for automatic estimation of crossing angles.

RESULTS

Modelling results indicate that cyclist crossing angle is a strong predictor of crossing success, and that cyclist velocity is not. Findings also highlight the prevalence of external factors which limit crossing angles for cyclists. In particular, kerbs are a common factor, along with passing/approaching vehicles or other cyclists. Furthermore, results indicate that further training on a relatively small sample of 100 domain-specific examples can achieve substantial accuracy improvements for cyclist detection (from 0.31AP0.5 to 0.98AP0.5) and crossing angle inference from traffic camera footage.

CONCLUSIONS

Ensuring safe crossing angles is important for cyclist safety around tram tracks. Infrastructural planners should aim for intuitive, self-explainable road layouts that allow for and encourage crossing angles of 60° or more - ideally 90°.

PRACTICAL APPLICATIONS

The SMoS framework and the open-source SafeCross1 application offer actionable insights and tools for enhancing cyclist safety around tram tracks.

Stuck in a (literal) tight spot: Cycling between tram rails, sharrows and parked cars

PROBLEM

Cyclists riding next to parked vehicles are at risk of crashes with opening vehicle doors. A central position, out of this dooring zone, decreases such a risk but comes with other problems like potentially smaller passing distances kept by overtaking motorists or having to cross tram rails.

METHOD

Factors influencing cyclists' choice of position were investigated by showing a total of 3,444 German cyclists different traffic situations in two online surveys. In the first study (N = 1,850), parked cars, the position of a cyclist riding ahead in the presented images (towards the curb/center of the lane), and presence and kind of sharrows were varied. As the variation in results for the different sharrow types was negligible, in Study 2 (N = 1,594), only the most common type was used. Whether cyclists prefer to accept the risk of falling while crossing tram rails or the risk of being too close to the curb or parked cars was investigated, varying the presence of tram rails, which has not been previously researched. In both studies, respondents indicated which position on the road they would choose in the depicted situations and answered questions about subjective safety, a factor closely related to cyclists' choice of position.

RESULTS

Cyclists chose positions farther towards the center of the road if there were parked cars and they chose an even more central position with tram rails. Respondents felt safer with sharrows on the road as well as in situations without parked cars and in situations without tram rails. Discussion and practical implications: The results indicate that, in addition to infrastructure characteristics, other cyclists' behavior (descriptive norm) influences cyclists' position on the road as well as their perceived safety. Implications for infrastructure design, especially regarding (the removal of) parked cars, are discussed.

Determinants of bicycle crashes at urban signalized intersections

PROBLEM

Bicycle volumes are increasing in many regions worldwide leading to higher relevance of an in-depth understanding of bicyclist safety mechanisms. Detailed studies on bicyclist safety that consider exposure and distinguish by intersection category and crash types are missing for urban signalized intersections, which are of particular relevance for bicyclist safety.

METHOD

Based on a comprehensive dataset of motorist and bicyclist volumes and infrastructure characteristics for a sample of 269 signalized intersections in two German cities, we utilize a top-down approach to analyze firstly, bicycle crashes of all types and secondly, bicycle crashes by type including turning, right-of-way and loss-of-control. A combination of descriptive statistics and Accident Prediction Models (APM) are applied as analysis methods.

RESULTS

Bicycle volumes are relevant for all types of intersections and crashes, whereas the effect of motor vehicle volumes differ between these different applications. The separation of bicyclists from motor vehicles in time and space increases their safety but also leads to behavioral adaption and risk compensation. The likelihood of right-of-way crashes even increases with more separation in the signaling scheme. The main predictor for loss-of-control crashes in terms of infrastructure are tram tracks.

SUMMARY

This study provides insights on relevant determinants of bicycle crashes at urban signalized intersections at several levels of detail. Exposure variables as well as the physical separation of bicyclists from motor vehicles show consistent effects on bicycle crash numbers whereas the effects of signaling differ between crash types.

PRACTICAL APPLICATIONS

The different types of intersections and crashes follow each specific mechanism of bicyclist safety. The separation of bicyclists and motorists in time and space are paramount at intersections with high bicycle volumes. Risk compensation such as red light running becomes more important as intersections get smaller and motor vehicle volumes decrease.

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.

Contraflows and cycling safety: Evidence from 22 years of data involving 508 one-way streets

Contraflow cycling on one-way streets is a low cost intervention that research shows can improve the cycling experience and increase participation. Evidence from several studies suggest that cyclists on contraflows have a lower crash risk. However, implementing contraflow cycling is often controversial, including in the United Kingdom (UK). In this paper we examine whether contraflow cycling on one-way streets alters crash or casualty rates for pedal cyclists. Focusing on inner London boroughs between 1998 and 2019, we identified 508 road segments where contraflow cycling was introduced on one-way streets. We identified road traffic crashes occurring within 10 m of these segments and labelled them as pre-contraflow, contraflow or contraflow removed crashes. We calculated rates using the number of crashes or casualties divided by the time exposed and generated 95 % confidence intervals using bootstrap resampling. We adjusted the rates for changes in cordon cycling volume and injury severity reporting. There were 1498 crashes involving pedal cyclists: 788 pre-contraflow, 703 contraflow and 7 following contraflow removal. There was no change in adjusted overall pedal cyclist crash or casualty rates when contraflow cycling was introduced. Proximity to a junction doubled the crash rate. The crash rate when pedal cyclists were travelling contraflow was the same as those travelling with flow. We have found no evidence that introducing contraflow cycling increases the crash or casualty rate for pedal cyclists. It is possible that such rates may indeed fall when contraflow cycling is introduced if more accurate spatio-temporal cycling volume data was available. We recommend all one-way streets are evaluated for contraflow cycling but encourage judicious junction design and recommend UK legislative change for mandatory-two-way cycling on one-way streets unless exceptional circumstances exist.

Applying OHSA to Detect Road Accident Blackspots

With increasing numbers of crashes and injuries, understanding traffic accident spatial patterns and identifying blackspots is critical to improve overall road safety. This study aims at detecting blackspots using optimized hot spot analysis (OHSA). Traffic accidents were classified by their participants and severity to explore the relationship between blackspots and different types of accidents. Based on the outputs of incremental spatial autocorrelation, OHSA was then implemented on different types of accidents. Finally, the performance of OHSA in evaluating the road safety level of the proposed RBT index are examined using a binary correlation analysis (i.e., R² = 0.89). The results show that: (1) The optimal scale distance varies from 0.6 km to 2.8 km and is influenced by the distance of the travel mode. (2) Central cities, with 54.6% of the total accidents, experiences more rigorous challenges regarding traffic safety than satellite cities. (3) There are many types of black spots in vulnerable communities, but in some specific areas, there are only black spots of non-motor vehicle accidents. Considering the practical significance of the above results, policy makers and traffic engineers are expected to give higher attention to central cities and vulnerable communities or prioritize the implementation of relevant optimization measures.

Factors impacting bike crash severity in urban areas

INTRODUCTION

Bicycling plays an important role as a major non-motorized travel mode in many urban areas. While increasingly serving as a key part of an integrated transportation demand management system and a sustainable mobility option, interest in biking as an active transportation mode has been unfortunately accompanied by an increase in the number of bike crashes, many with incapacitating injuries or fatal outcomes. Thus, to improve bicycling safety it is crucial to understand the critical factors that influence severe bicyclist crash outcomes, and to identify and prioritize policies and actions to mitigate these risks.

METHOD

The study reported herein was conducted with this objective in mind. Our approach involves the use of classification models (logistic regression, decision tree and random forest), as well as techniques for treating unbalanced data by under sampling, oversampling, and weighted cost sensitivity (CS) learning, applied to bike crash data from the State of Tennessee's two largest urban areas, Nashville and Memphis.

RESULTS

The results indicate that random forest with weighted CS offers the potential for greater explanatory accuracy, an important observation given the paucity of efforts to date in applying random forest to bike safety studies. Inadequate lighting conditions, crashes on roadways, speed limits, average annual daily traffic, number of lanes, and weekends are the critical features identified.

CONCLUSION

Based on these results, a series of specific, suggested policy changes are presented for implementation consideration.

PRACTICAL APPLICATIONS

There is existing guidance in FHWA Lighting Handbook and TDOT's Roadway Design Guidelines that spell out some engineering design solutions like lighting provisions, bicycle facility design, and traffic calming measures. These measures may alleviate the identified key features impacting fatal and incapacitating bicycle injuries. Further research should be conducted to gauge the efficacy of the solutions suggested.

Caught in the blind spot of a truck: A choice model on driver glance behavior towards cyclists at intersections

Vulnerable road users (VRUs) constitute an increasing proportion of the annual road fatalities across Europe. One of the crash types involved in these fatalities are blind spot crashes between trucks and bicyclists. Despite the presence of mandatory blind spot mirrors, truck drivers are often reported to have overlooked the presence of a bicyclist. This raises the question if and when truck drivers check their blind spot mirrors for the presence of bicyclists, and which factors contribute to such glance behavior. The current study presents the results of an analysis of naturalistic glance behavior by 39 truck drivers in 1,903 right-turning maneuvers at urban intersections, where in each maneuver there was a chance of crossing the path of a bicyclist. The descriptive analysis revealed that most often truck drivers did not cast a glance upon their blind spot mirrors as recommended by the examination guidelines. Furthermore, a choice model was developed with the main factors that have an impact on glance behavior. Drivers were more likely to glance with a priority regulation that allowed conflicts, with lower speed limits, with zebra crossings, without cyclist facilities, without a lead vehicle making the same maneuver, in presence of VRUs, without adverse sight conditions, in lower age groups, without certain non-driving related activities, when driving a truck with more direct vision on VRUs, and without a camera providing a view on the blind spot, and with less time between a standstill and starting the maneuver. Three factors did not significantly improve the choice model and were therefore left out, despite showing significant effects in bivariate tests: intersection layout (e.g., three vs. four legs), presence of advanced stopping lanes, and visual obstruction. Implications of the choice model are discussed for driver education (in terms of timely glances, reducing inattention, and hazard anticipation) and vehicle design (in terms of direct vision).

A comprehensive analysis of the relationships between the built environment and traffic safety in the Dutch urban areas

Built-environment factors potentially alleviate or aggravate traffic safety problems in urban areas. This paper aims to investigate the relationships of these factors with vehicle-bicycle and vehicle-vehicle property damage only (PDO) and killed and severe injury (KSI) crashes in urban areas. For this purpose, an area-level analysis using 100x100m² cells, along with a Spatial Hurdle Negative Binomial regression model were employed. The study area is composed of a selection of municipalities in the Netherlands-Randstad Area where major land-use developments have occurred since the 1970s. The study was conducted by developing a rich dataset composed of various national and local databases. The findings reveal that built-environment factors and land-use policies have substantial impacts on safety, which cannot be neglected. The factors explaining the land-use density and diversity in the area (e.g., urbanity and function mixing levels), as well as the land-use design characteristics (indicated by average age of the neighborhoods), traffic and road network characteristics, and proximity to different destinations influence the probability, frequency, and severity of crashes in urban areas. Furthermore, low socioeconomic levels are associated with a higher frequency of traffic crashes.

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.

Crash risk and subjective risk perception during urban cycling: Accounting for cycling volume

Most research concerned with cyclists' safety has been focused on the crash risk (i.e. their objective safety). However, there has been a growing interest in the perceived level of this risk (i.e. the subjective safety of cyclists). Crash risk and subjective risk perception in urban cycling appear to be mostly well aligned. For example, reduced speed limits have been found to reduce both objective and subjective risks (although there is also evidence for some incongruences). This absolute number of incidents could be misleading, as it does not account for potential differences in cycling volume (i.e. cyclists are likely to prefer streets with reduced speed limits). Thus, it may be important to adjust the absolute number of incidents relative number to the local cycling volume. In this research, we investigate the relation of cycling crashes and subjective risk perception (operationalized through reports from a crowd-sourcing project) for different types of cycling infrastructure and different speed limits, while accounting for the local cycling volume. We find that the absolute number of VGI reports and crashes can be misleading: whereas the absolute incident numbers, for example, suggest few benefits of cycling lanes and tracks, adjusting for the cycling volume reveals an increase of both objective and subjective safety as compared to streets without cycling infrastructure. We also identify situations where cyclists apparently underestimate the crash risk (i.e. on cycleways opposing the cars' traveling direction, and at streets with a speed limit of 30 km/h intersecting streets with higher speed limits). Additional research is required to understand the sources of these discrepancies, and how to make cyclists aware of them.

The Analysis of the Factors Influencing the Severity of Bicyclist Injury in Bicyclist-Vehicle Crashes

Transportation and technological development have for centuries strongly influenced the shaping of urbanized areas. On one hand, it undoubtedly brings many benefits to their residents. However, also has a negative impact on urban areas and their surroundings. Many transportation and technological solutions lead, for example, to increased levels of pollution, noise, excessive energy use, as well as to traffic accidents in cities. So, it is important to safe urban development and sustainability in all city aspects as well as in the area of road transport safety. Due to the long-term policy of sustainable transport development, cycling is promoted, which contributes to the increase in the number of this group of users of the transport network in road traffic for short-distance transport. On the one hand, cycling has a positive effect on bicyclists’ health and environmental conditions, however, a big problem is an increase in the number of serious injuries and fatalities among bicyclists involved in road incidents with motor vehicles. This study aims to identify factors that influence the occurrence and severity of bicyclist injury in bicyclist-vehicle crashes. It has been observed that the factors increasing the risk of serious injuries and deaths of bicyclists are: vehicle driver gender and age, driving under the influence of alcohol, exceeding the speed limit by the vehicle driver, bicyclist age, cycling under the influence of alcohol, speed of the bicyclist before the incident, vehicle type (truck), incident place (road), time of the day, incident type. The obtained results can be used for activities aimed at improving the bicyclists’ safety level in road traffic in the area of analysis.

Accounting for seasonal effects on cyclist-vehicle crashes

Crash data is usually aggregated over time where temporal correlation contributes to the unobserved heterogeneity. Since crashes that occur in temporal proximity share some unobserved characteristics, ignoring these temporal correlations in safety modeling may lead to biased estimates and a loss of model power. Seasonality has several effects on cyclists' travel behavior (e.g., the distribution of holidays, school schedules, weather variations) and consequently cyclist-vehicle crash risk. This study aims to account for the effect of seasonality on cyclist-vehicle crashes by employing two groups of models. The first group, seasonal cyclist-vehicle crash frequency, employs four vectors of the dependent variables for each season. The second group, rainfall involved cyclist-vehicle crash frequency, employs two vectors of the dependent variables for crashes that occurred on rainy days and non-rainy days. The two model groups were investigated using three modeling techniques: Full Bayes crash prediction model with spatial effects (base model), varying intercept and slope model, and First-Order Random Walk model with a spatial-temporal interaction term. Crash and volume data for 134 traffic analysis zones (TAZ's) in the City of Vancouver were used. The results showed that the First-Order Random Walk model with spatial-temporal interaction outperformed the other developed models. Some covariates have different associations with crashes depending on the season and rainfall conditions. For example, the seasonal estimates for the bus stop density are significantly higher for the summer and spring seasons than for the winter and autumn seasons. Also, the intersection density estimate for a rainy day is significantly higher than a non-rainy day. This indicates that on a rainy day each intersection to the network adds more risk to cyclists compared to a non-rainy day.

Association of Infrastructure and Route Environment Factors with Cycling Injury Risk at Intersection and Non-Intersection Locations: A Case-Crossover Study of Britain

Objective: This paper examines infrastructural and route environment correlates of cycling injury risk in Britain for commuters riding in the morning peak. Methods: The study uses a case-crossover design which controls for exposure. Control sites from modelled cyclist routes (matched on intersection status) were compared with sites where cyclists were injured. Conditional logistic regression for matched case–control groups was used to compare characteristics of control and injury sites. Results: High streets (defined by clustering of retail premises) raised injury odds by 32%. Main (Class A or primary) roads were riskier than other road types, with injury odds twice that for residential roads. Wider roads, and those with lower gradients increased injury odds. Guard railing raised injury odds by 18%, and petrol stations or car parks by 43%. Bus lanes raised injury odds by 84%. As in other studies, there was a ‘safety in numbers’ effect from more cyclists. Contrary to other analysis, including two recent studies in London, we did not find a protective effect from cycle infrastructure and the presence of painted cycle lanes raised injury odds by 54%. At intersections, both standard and mini roundabouts were associated with injury odds several times higher than other intersections. Presence of traffic signals, with or without an Advanced Stop Line (‘bike box’), had no impact on injury odds. For a cyclist on a main road, intersections with minor roads were riskier than intersections with other main roads. Conclusions: Typical cycling environments in Britain put cyclists at risk, and infrastructure must be improved, particularly on busy main roads, high streets, and bus routes.

An overview of tram tracks related cycling injuries in Ghent, Belgium

OBJECTIVE

There is a paucity of data on the incidence and severity of tram tracks related cycling injuries. The aim of this study is to get insight into the incidence, severity and characteristics of tram tracks related cycling injuries, potentially defining significant 'hotspots' in the Ghent city area.

METHODS

A one-year, multicenter, prospective, observational study was conducted. Patients presenting to the emergency departments of all 4 Ghent hospitals with tram tracks related cycling injury, were included. Data on patient demographics, circumstances of the accident and type of injury were collected.

RESULTS

149 patients were included, with a median age of 31 years. 42 patients had fractures, 39 patients required wound sutures, 79 and 49 patients suffered from bruising and abrasions respectively. Only 5 patients required admission. No patients died or suffered life-threatening injuries. Women (65.1%) presented more frequently than men (34.9%). Forty-tree percent of all accidents happened in autumn (p < 0.001). Accidents happened more frequently in rainy conditions (p < 0.001). Mean number of days off work was 2.7 days, significantly increasing to 6.56 days when sustaining a fracture or dislocation (p = 0.02).

CONCLUSION

Tram tracks are potentially dangerous and may lead to clinically important injuries and significant number of days off work.

Tram, rail, bicycle: An unhappy triad? Rising incidence and resource consumption of tramline-associated bicycling accidents in Bern, Switzerland

Bicycle traffic and the number of accidents have been increasing in Switzerland in recent years. However, little is known about specific types of bicycling accidents - such as tramline-associated bicycling accidents (TABA) - that are potentially avoidable. This retrospective single center study of emergency department (ED) consultations analyzed TABA in the city of Bern, Switzerland. We analyzed the medical records of adult patients who presented to the ED after any bicycle accident. Patient and consultation characteristics were extracted. Incidence, important characteristics and ED resource consumption of TABA were compared with bicycle accidents that did not involve tramlines (BA). Furthermore, injury patterns and predictors of resource consumption were determined in TABA. We included a total of 298 TABA and 2351 BA over the 5.6-year study period. TABA accounted for 11.2 % of all bicycling accidents. Descriptive analysis revealed a significant increase between 2013 (lowest) and 2016 (highest) of 33.0 % in the total number of BA and 132 % in the total number of TABA. Compared to BA, TABA patients were significantly older, more often female, had a less urgent triage, and less often needed resuscitation bay treatment, hospitalization or ICU-admission (all p < 0.05). In multivariable analysis, TABA were associated with greater needs for ED resources. Most TABA injuries were to limbs (70.5 %) or the head (53.0 %), including intracranial hemorrhage (2.3 %). The radiological work-up consumed most of the resources (37.5 %), followed by physicians' work (25.8 %). Statistically significant predictors of ED resource consumption in TABA were age, triage, resuscitation bay treatment, injuries to head/clavicle, and intoxication. One out of ten patients was intoxicated. Although TABA is associated with less severe trauma than BA, it bears the risk of significant morbidity and high ED resource needs. Intoxications contribute to this problem. Our findings underline the need for preventive measures to reduce TABA in the future.

Where to Ride? An Explorative Study to Investigate Potential Risk Factors of Personal Mobility Accidents

As a mobility of future, the popularity of personal mobility vehicles (PMs) is rapidly increasing worldwide. However, this boom in the use of PMs has resulted in a substantial number of accidents involving not only PM users but also other road users including pedestrians, bicyclists, and motor vehicle drivers. This study aims to explore the potential risk factors for the occurrence of PM-related accidents and the resulting injury severity using the Traffic Accident Analysis System (TAAS) of South Korea between 2017 and 2019. We found that PM-pedestrian accidents tend to occur on roads with wider sidewalks and bike lanes, possibly because the pedestrian-PM conflict increases in this road condition. There is still ongoing debate on whether it is appropriate for PMs to share the sidewalk with pedestrians. Some countries, including Korea, prohibit the use of PMs on sidewalks; however, in reality, this regulation is not well-observed because using PMs on roadways involves higher crash risk with motor vehicles. This study suggests one potential solution to ensure safety of PM users: expansion of bike lane infrastructure having physically separated bike lanes and sidewalks/motorways in addition to the formation and strict enforcement of appropriate safety rules for PM users.

Roles of infrastructure and land use in bicycle crash exposure and frequency: A case study using Greater London bike sharing data

Cycling is increasingly promoted as a sustainable transport mode. However, bicyclists are more vulnerable to fatality and severe injury in road crashes, compared to vehicle occupants. It is necessary to identify the contributory factors to crashes and injuries involving bicyclists. For the prediction of motor vehicle crashes, comprehensive traffic count data, i.e. AADT and vehicle kilometer traveled (VKT), are commonly available to proxy the exposure. However, extensive bicycle count data are usually not available. In this study, revealed bicycle trip data of a public bicycle rental system in the Greater London is used to proxy the bicycle crash exposure. Random parameter negative binomial models are developed to measure the relationship between possible risk factors and bicycle crash frequency at the zonal level, based on the crash data in the Greater London in 2012-2013. Results indicate that model taking the bicycle use time as the exposure measure is superior to the other counterparts with the lowest AIC (Akaike information criterion) and BIC (Bayesian information criterion). Bicycle crash frequency is positively correlated to road density, commercial area, proportion of elderly, male and white race, and median household income. Additionally, separate bicycle crash prediction models are developed for different seasons. Effects of the presence of Cycle Superhighway and proportion of green area on bicycle crash frequency can vary across seasons. Findings of this study are indicative to the development of bicycle infrastructures, traffic management and control, and education and enforcement strategies that can enhance the safety awareness of bicyclists and reduce their crash risk in the long run.

Safety effects of traffic lane and shoulder widths on two-lane undivided rural roads: A matched case-control study from Norway

This study estimates the effects of lane and shoulder widths on occurrence of head-on and single-vehicle accidents on rural two-lane undivided roads in Norway while considering the differences between winter and non-winter accidents and their severity levels. A matched case-control method was applied to calculate the odds ratios for lane and shoulder width categories, while controlling for the effects of AADT and adjusting for the effects of region, speed limit, segment length, share of long vehicles in AADT and horizontal alignment. The study used a sample of 71,999 roadway segments identified in GIS and 1886 related accidents recorded by the police in five-year period. The results suggest that it is relevant to consider winter and non-winter accidents as well as severe and slight accidents separately when studying the effects of lane and shoulder widths on the occurrence of head-on and single-vehicle accidents. When examining lane and shoulder widths for all related accidents, the lane widths 1.50-2.50 m and shoulder widths 0.50-0.75 m were relatively safer than other categories on Norwegian two-lane rural undivided roads.

Effects of built environment on bicycle wrong Way riding behavior: A data-driven approach

Bicycle wrong way riding (WWR) is a dangerous and often neglected behavior that engenders threats to traffic safety. Owing to the lack of exposure data, the detection of WWR and its relationship with the built environment (BE) factors remain unclear. Accordingly, this study fills the research gaps by proposing a WWR detection framework based on bike-sharing trajectories collected from Chengdu, China. Moreover, this study adopts Negative Binomial-based Additive Decision Tree to investigate the impacts of built environment on WWR frequencies. Results reveal that (1) WWR distribution is unaffected by different periods in a day; (2) road length is more influential than road level and road direction in WWR occurrence; (3) company, bus stop, subway station, residence, and catering facility are primary contributors affecting WWR behavior during peak hours, whereas education becomes an emerging influential variable during nonpeak hours; and most importantly, (4) these variables clearly present non-linear effects on the WWR frequencies. Therefore, geographically differentiated policies should be adopted for bicycle safety improvement.

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.

Not all protected bike lanes are the same: Infrastructure and risk of cyclist collisions and falls leading to emergency department visits in three U.S. cities

OBJECTIVE

Protected bike lanes separated from the roadway by physical barriers are relatively new in the United States. This study examined the risk of collisions or falls leading to emergency department visits associated with bicycle facilities (e.g., protected bike lanes, conventional bike lanes demarcated by painted lines, sharrows) and other roadway characteristics in three U.S. cities.

METHODS

We prospectively recruited 604 patients from emergency departments in Washington, DC; New York City; and Portland, Oregon during 2015-2017 who fell or crashed while cycling. We used a case-crossover design and conditional logistic regression to compare each fall or crash site with a randomly selected control location along the route leading to the incident. We validated the presence of site characteristics described by participants using Google Street View and city GIS inventories of bicycle facilities and other roadway features.

RESULTS

Compared with cycling on lanes of major roads without bicycle facilities, the risk of crashing or falling was lower on conventional bike lanes (adjusted OR = 0.53; 95 % CI = 0.33, 0.86) and local roads with (adjusted OR = 0.31; 95 % CI = 0.13, 0.75) or without bicycle facilities or traffic calming (adjusted OR = 0.39; 95 % CI = 0.23, 0.65). Protected bike lanes with heavy separation (tall, continuous barriers or grade and horizontal separation) were associated with lower risk (adjusted OR = 0.10; 95 % CI = 0.01, 0.95), but those with lighter separation (e.g., parked cars, posts, low curb) had similar risk to major roads when one way (adjusted OR = 1.19; 95 % CI = 0.46, 3.10) and higher risk when they were two way (adjusted OR = 11.38; 95 % CI = 1.40, 92.57); this risk increase was primarily driven by one lane in Washington. Risk increased in the presence of streetcar or train tracks relative to their absence (adjusted OR = 26.65; 95 % CI = 3.23, 220.17), on downhill relative to flat grades (adjusted OR = 1.92; 95 % CI = 1.38, 2.66), and when temporary features like construction or parked cars blocked the cyclist's path relative to when they did not (adjusted OR = 2.23; 95 % CI = 1.46, 3.39).

CONCLUSIONS

Certain bicycle facilities are safer for cyclists than riding on major roads. Protected bike lanes vary in how well they shield riders from crashes and falls. Heavier separation, less frequent intersections with roads and driveways, and less complexity appear to contribute to reduced risk in protected bike lanes. Future research should systematically examine the characteristics that reduce risk in protected lanes to guide design. Planners should minimize conflict points when choosing where to place protected bike lanes and should implement countermeasures to increase visibility at these locations when they are unavoidable.

A composite zonal index for biking attractiveness and safety

Zonal characteristics (e.g. built environment, network configuration, socio-demographics, and land use) have been shown to affect biking attractiveness and safety. However, previously developed bikeability indices do not account for cyclist-vehicle crash risk. This study aims to develop a comprehensive zone-based index to represent both biking attractiveness and cyclist crash risk. The developed Bike Composite Index (BCI) consists of two sub-indices representing bike attractiveness and bike safety, which are estimated using Bike Kilometers Travelled (BKT) and cyclist-vehicle crash data from 134 traffic analysis zones (TAZ) in the City of Vancouver, Canada. The Bike Attractiveness Index is calculated from five factors: bike network density, centrality, and weighted slope as well as land use mix and recreational density. The Bike Safety Index is calculated from bike network coverage, continuity, and complexity as well as signal density and recreational density. The correlation between the Bike Attractiveness Index and the Bike Safety Index in Vancouver is low (r = 0.11), supporting the need to account for both biking attractiveness and safety in the composite index.

Walking against or with traffic? Evaluating pedestrian fatalities and head injuries in Taiwan

BACKGROUND

Allowing contraflow cycling on one-way streets has been reported to reduce crash risks in Belgium and the United Kingdom. Similarly, walking against traffic on roadways without sidewalks substantially improves pedestrian safety. This study examined fatalities and head injuries sustained by pedestrians in against-traffic and with-traffic crashes.

METHODS

Using police-reported crash data in Taiwan between 2011 and 2016, fatalities and head injuries were compared for pedestrians involved in against-traffic and with-traffic crashes.

RESULTS

Of the 14,382 pedestrians involved in crashes, 10,749 and 3633 pedestrians in with-traffic and against-traffic crashes, respectively, were reported. Compared with pedestrians involved in against-traffic crashes, those in with-traffic crashes were more likely to sustain fatalities and head injuries. Results of logistic regression models revealed several influential factors on pedestrian fatalities and head injuries, including elderly pedestrians, male drivers, intoxicated drivers, rural roadways, unlit streets in darkness, limited sight distance, adverse weather conditions, midnight hours, and a heavy vehicle as the crash partner.

CONCLUSIONS

Pedestrians in with-traffic crashes were more likely to sustain fatalities and head injuries compared with those in against-traffic crashes. Furthermore, the negative effect of walking with traffic on injuries was more pronounced in reduced-visibility conditions.

Accounting for mediation in cyclist-vehicle crash models: A Bayesian mediation analysis approach

Cyclist safety is affected by many factors on the zonal level. Previous studies have found associations between cyclist-vehicle crashes and vehicle and bike exposures, network configuration, land use, road facility, and the built environment. In addition, the network configuration, land use, and road facility were found to affect bike exposure levels. The association of zonal characteristics with both exposure and crashes may bias the development of macro-level bike safety models. This paper aims to explain these associations simultaneously using a form of Structural Equation Modelling approach. The analysis assesses the mediated effects that some variables have on crashes through their effects on bike exposure (by setting bike exposure as a mediator). Data from 134 traffic analysis zones (TAZ's) in the City of Vancouver, Canada is used as a case study. The indirect effect of network configuration, land use, and road facility on cyclist-vehicle crashes was assessed through Bayesian mediation analysis. Mediation analysis is an approach used to estimate how one variable transmits its effects to another variable through a certain mediator. These effects could be direct only, indirect only (through a certain mediator), or both direct and indirect. The results showed that the bike kilometers travelled (BKT) was a mediator of the relationship between network configuration, land use, and road facility and cyclist-vehicle crashes. The mediation analysis showed that some variables have different direct and indirect effect on cyclist-vehicle crashes. This indicates that while some variables may have negative direct association with crashes, their total crash effect can be positive after accounting for their effect through exposure. For example, bike network coverage and recreational density have negative direct association with cyclist-vehicle crashes, and positive indirect association leading to positive total effect on cyclist-vehicle crashes.

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.

Effects of modal shares on crash frequencies at aggregate level

INTRODUCTION

Long-range transportation plans often involve proposals for improvements/ changes in different modes of travel. This means that modal share of trips generated at each traffic analysis zone (TAZ) by mode of travel needs to be predicted/ forecasted for safety evaluation purposes. The objective of this research study is to develop a series of aggregate crash prediction models (ACPMs) that relate with the modal split step of the conventional four-step demand models.

METHOD

The models are developed utilizing network and vehicular, socio-economical, trip production/attraction and trip frequencies by mode at TAZ-level as explanatory variables in a generalized linear regression with the assumption of a negative binomial error structure. Crash frequencies are split into total crashes (TC) and severe crashes (SC).

RESULTS

The models prove promising in estimating crash frequencies upon changes in modal shares, which is essential in safety assessment of alternate transportation demand management (TDM) scenarios. Trips made in car, bus, and bus Service mode became significant in the estimated TC and trips made in car, taxi, school service, bus service and moped mode became significant in the estimated SC ACPMs.

CONCLUSIONS

The ACPMs may be used from two different points of view. First and most appropriate use is to consider these as tools to forecast future crash frequencies and develop long-term plans to counteract. In the second point of view, ACPMs act as the primary planning tool to identify how any increase in a specific mode-ridership will contribute to crash frequencies. This is of great interest in developing plans that involve increased use of a specific mode.

PRACTICAL APPLICATION

As modal shares are forecasted in certain years into the future by the modal split step of demand modeling, crash frequencies could also be forecasted and safety implications of mobility improvement scenarios (e.g. increased number of trips by bus, car, etc.) would be evaluated.

Cycling injury risk in London: A case-control study exploring the impact of cycle volumes, motor vehicle volumes, and road characteristics including speed limits

Cycling injury risk is an important topic, but few studies explore cycling risk in relation to exposure. This is largely because of a lack of exposure data, in other words how much cycling is done at different locations. This paper helps to fill this gap. It reports a case-control study of cycling injuries in London in 2013-2014, using modelled cyclist flow data alongside datasets covering some characteristics of the London route network. A multilevel binary logistic regression model is used to investigate factors associated with injury risk, comparing injury sites with control sites selected using the modelled flow data. Findings provide support for 'safety in numbers': for each increase of a natural logarithmic unit (2.71828) in cycling flows, an 18% decrease in injury odds was found. Conversely, increased motor traffic volume is associated with higher odds of cycling injury, with one logarithmic unit increase associated with a 31% increase in injury odds. Twenty-mile per hour compared with 30mph speed limits were associated with 21% lower injury odds. Residential streets were associated with reduced injury odds, and junctions with substantially higher injury odds. Bus lanes do not affect injury odds once other factors are controlled for. These data suggest that speed limits of 20 mph may reduce cycling injury risk, as may motor traffic reduction. Further, building cycle routes that generate new cycle trips should generate 'safety in numbers' benefits.

Tram system related cycling injuries

INTRODUCTION

Understanding of tram-system related cycling injuries (TSRCI) is poor. The aim of this study was to report the spectrum of injuries, demographics and social deprivation status of patients. Secondary aims included assessment of accident circumstances, effects of TSRCI on patients' confidence cycling, together with time off work and cycling.

METHODS

A retrospective review of patients presenting to emergency services across all hospitals in Edinburgh and West Lothian with tram related injuries between May 2009 and April 2016 was undertaken. Medical records and imagining were analysed and patients were contacted by telephone.

RESULTS

191 cyclists (119 males, 72 females) were identified. 63 patients sustained one or more fractures or dislocations. Upper limb fractures/dislocations occurred in 55, lower limb fractures in 8 and facial fractures in 2. Most patients demonstrated low levels of socioeconomic deprivation. In 142 cases, the wheel was caught in tram-tracks, while in 32 it slid on tracks. The latter occurred more commonly in wet conditions (p = 0.028). 151 patients answered detailed questionnaires. Ninety-eight were commuting. 112 patients intended to cross tramlines and 65 accidents occurred at a junction. Eighty patients reported traffic pressures contributed to their accident. 120 stated that their confidence was affected and 24 did not resume cycling. Female gender (p < 0.001) and presence of a fracture/dislocation (p = 0.012) were independent predictors of negative effects on confidence. Patients sustaining a fracture/dislocation spent more time off work (median 5 days vs 1, p < 0.001) and cycling (median 57 days vs 21, p < 0.001).

CONCLUSIONS

TSRCI occur predominantly in young to middle-aged adults with low levels of socioeconomic deprivation, most commonly when bicycle wheels get caught in tram-tracks. They result in various injuries, frequently affecting the upper limb. Traffic pressures are commonly implicated. Most patients report negative effects on confidence and a sizeable minority do not resume cycling. TSRCI can result in significant loss of working and cycling days.

A cross-comparison of different techniques for modeling macro-level cyclist crashes

Despite the recognized benefits of cycling as a sustainable mode of transportation, cyclists are considered vulnerable road users and there are concerns about their safety. Therefore, it is essential to investigate the factors affecting cyclist safety. The goal of this study is to evaluate and compare different approaches of modeling macro-level cyclist safety as well as investigating factors that contribute to cyclist crashes using a comprehensive list of covariates. Data from 134 traffic analysis zones (TAZs) in the City of Vancouver were used to develop macro-level crash models (CM) incorporating variables related to actual traffic exposure, socio-economics, land use, built environment, and bike network. Four types of CMs were developed under a full Bayesian framework: Poisson lognormal model (PLN), random intercepts PLN model (RIPLN), random parameters PLN model (RPPLN), and spatial PLN model (SPLN). The SPLN model had the best goodness of fit, and the results highlighted the significant effects of spatial correlation. The models showed that the cyclist crashes were positively associated with bike and vehicle exposure measures, households, commercial area density, and signal density. On the other hand, negative associations were found between cyclist crashes and some bike network indicators such as average edge length, average zonal slope, and off-street bike links.

Where do bike lanes work best? A Bayesian spatial model of bicycle lanes and bicycle crashes

US municipalities are increasingly introducing bicycle lanes to promote bicycle use, increase roadway safety and improve public health. The aim of this study was to identify specific locations where bicycle lanes, if created, could most effectively reduce crash rates. Previous research has found that bike lanes reduce crash incidence, but a lack of comprehensive bicycle traffic flow data has limited researchers' ability to assess relationships at high spatial resolution. We used Bayesian conditional autoregressive logit models to relate the odds that a bicycle injury crash occurred on a street segment in Philadelphia, PA (n = 37,673) between 2011 and 2014 to characteristics of the street and adjacent intersections. Statistical models included interaction terms to address the problem of unknown bicycle traffic flows, and found bicycle lanes were associated with reduced crash odds of 48% in streets segments adjacent to 4-exit intersections, of 40% in streets with one- or two-way stop intersections, and of 43% in high traffic volume streets. Presence of bicycle lanes was not associated with change in crash odds at intersections with less or more than 4 exits, at 4-way stop and signalized intersections, on one-way streets and streets with trolley tracks, and on streets with low-moderate traffic volume. The effectiveness of bicycle lanes appears to depend most on the configuration of the adjacent intersections and on the volume of vehicular traffic. Our approach can be used to predict specific street segments on which the greatest absolute reduction in bicycle crash odds could occur by installing new bicycle lanes.

Identifying traffic accident black spots with Poisson-Tweedie models

This paper aims at the identification of black spots for traffic accidents, i.e. locations with accident counts beyond what is usual for similar locations, using spatially and temporally aggregated hospital records from Funen, Denmark. Specifically, we apply an autoregressive Poisson-Tweedie model, which covers a wide range of discrete distributions and handles zero-inflation as well as overdispersion. The estimated power parameter of the model was 1.6 (SE=0.06) suggesting a distribution close to the Pólya-Aeppli distribution. We identified nine black spots consistently standing out in all six considered calendar years and calculated by simulations a probability of p=0.03 for these to be chance findings. Altogether, our results recommend these sites for further investigation and suggest that our simple approach could play a role in future area based traffic accident prevention planning.

How bicycle level of traffic stress correlate with reported cyclist accidents injury severities: A geospatial and mixed logit analysis

Transportation agencies need efficient methods to determine how to reduce bicycle accidents while promoting cycling activities and prioritizing safety improvement investments. Many studies have used standalone methods, such as level of traffic stress (LTS) and bicycle level of service (BLOS), to better understand bicycle mode share and network connectivity for a region. However, in most cases, other studies rely on crash severity models to explain what variables contribute to the severity of bicycle related crashes. This research uniquely correlates bicycle LTS with reported bicycle crash locations for four cities in New Hampshire through geospatial mapping. LTS measurements and crash locations are compared visually using a GIS framework. Next, a bicycle injury severity model, that incorporates LTS measurements, is created through a mixed logit modeling framework. Results of the visual analysis show some geospatial correlation between higher LTS roads and "Injury" type bicycle crashes. It was determined, statistically, that LTS has an effect on the severity level of bicycle crashes and high LTS can have varying effects on severity outcome. However, it is recommended that further analyses be conducted to better understand the statistical significance and effect of LTS on injury severity. As such, this research will validate the use of LTS as a proxy for safety risk regardless of the recorded bicycle crash history. This research will help identify the clustering patterns of bicycle crashes on high-risk corridors and, therefore, assist with bicycle route planning and policy making. This paper also suggests low-cost countermeasures or treatments that can be implemented to address high-risk areas. Specifically, with the goal of providing safer routes for cyclists, such countermeasures or treatments have the potential to substantially reduce the number of fatalities and severe injuries.

[A spatially explicit analysis of traffic accidents involving pedestrians and cyclists in Berlin]

BACKGROUND

In many German cities and counties, sustainable mobility concepts that strengthen pedestrian and cyclist traffic are promoted. From the perspectives of urban development, traffic planning and public healthcare, a spatially differentiated analysis of traffic accident data is decisive.

OBJECTIVES

1) The identification of spatial and temporal patterns of the distribution of accidents involving cyclists and pedestrians, 2) the identification of hotspots and exploration of possible underlying causes and 3) the critical discussion of benefits and challenges of the results and the derivation of conclusions.

MATERIAL AND METHODS

Spatio-temporal distributions of data from accident statistics in Berlin involving pedestrians and cyclists from 2011 to 2015 were analysed with geographic information systems (GIS).

RESULTS

While the total number of accidents remains relatively stable for pedestrian and cyclist accidents, the spatial distribution analysis shows, however, that there are significant spatial clusters (hotspots) of traffic accidents with a strong concentration in the inner city area.

CONCLUSIONS

In a critical discussion, the benefits of geographic concepts are identified, such as spatially explicit health data (in this case traffic accident data), the importance of the integration of other data sources for the evaluation of the health impact of areas (traffic accident statistics of the police), and the possibilities and limitations of spatial-temporal data analysis (spatial point-density analyses) for the derivation of decision-supported recommendations and for the evaluation of policy measures of health prevention and of health-relevant urban development.

Pedestrian falls: A review of the literature and future research directions

INTRODUCTION

Pedestrian falls (PFs) - falls in public spaces without collisions with other road users - are a significant cause of serious transport-related injuries, amounting to three-quarters of all pedestrians admitted to hospital.

METHODS

This scoping review examined peer-reviewed research on PFs published between 1995 and 2015. Electronic databases (Scopus, SafetyLit, and PubMed) were used to find studies identifying PFs or outdoor falls (the latter also including falls in gardens).

RESULTS

We identified only 28 studies reporting relevant information on PFs (i.e., 15 prospective, 10 retrospective, and 3 intervention studies). The results show that more walking is related to a lower risk of PFs. Older people, especially older women, have a higher risk of (injurious) PFs. Outdoor fall victims have equally good or better health characteristics and scores on balance tests compared to those who have not experienced such falls. Road factors such as uneven surfaces, busy junctions, stairs, and slippery surfaces seem to play an important role in PFs, but much of the research on these factors is of a qualitative nature.

CONCLUSIONS

PF victims are generally in good health (apart from normal age-related problems) but at risk due to road factors.

PRACTICAL APPLICATIONS

We recommend to adopt a human factors approach. The road system should be adapted to human capabilities and limitations including those of pedestrians. Measures such as preventing uneven surfaces and good winter maintenance seem to be effective. However, we advise more quantitative research on road factors to inform design guidelines and standards for public space authorities given the qualitative nature of current research on road factors.

Can cycling safety be improved by opening all unidirectional cycle paths for cycle traffic in both directions? A theoretical examination of available literature and data

Many studies have found bicycle-motor vehicle crashes to be more likely on bidirectional cycle paths than on unidirectional cycle paths because drivers do not expect cyclists riding at the right side of the road. In this paper we discuss the hypothesis that opening all unidirectional cycle paths for cycle traffic in both directions prevent this lack of expectancy and accordingly improves cycling safety. A new national standard requires careful consideration because a reversal is difficult once cyclists are used to their new freedom of route choice. We therefore explored the hypothesis using available data, research, and theories. The results show that of the length of cycle paths along distributor roads in the Netherlands, 72% is bidirectional. If drivers would become used to cyclists riding at the left side of the road, this result raises the question of why bidirectional cycle paths in the Netherlands still have a poor safety record compared to unidirectional cycle paths. Moreover, our exploration suggested that bidirectional cycle paths have additional safety problems. It increases the complexity of unsignalized intersections because drivers have to scan more directions in a short period of time. Moreover, there are some indications that the likelihood of frontal crashes between cyclists increases. We reject the hypothesis that opening all unidirectional cycle paths for cycle traffic in both directions will improve cycle safety. We recommend more attention for mitigating measures given the widespread application of bidirectional cycle paths in the Netherlands.

Using data mining techniques to predict the severity of bicycle crashes

To investigate the factors predicting severity of bicycle crashes in Italy, we used an observational study of official statistics. We applied two of the most widely used data mining techniques, CHAID decision tree technique and Bayesian network analysis. We used data provided by the Italian National Institute of Statistics on road crashes that occurred on the Italian road network during the period ranging from 2011 to 2013. In the present study, the dataset contains information about road crashes occurred on the Italian road network during the period ranging from 2011 to 2013. We extracted 49,621 road accidents where at least one cyclist was injured or killed from the original database that comprised a total of 575,093 road accidents. CHAID decision tree technique was employed to establish the relationship between severity of bicycle crashes and factors related to crash characteristics (type of collision and opponent vehicle), infrastructure characteristics (type of carriageway, road type, road signage, pavement type, and type of road segment), cyclists (gender and age), and environmental factors (time of the day, day of the week, month, pavement condition, and weather). CHAID analysis revealed that the most important predictors were, in decreasing order of importance, road type (0.30), crash type (0.24), age of cyclist (0.19), road signage (0.08), gender of cyclist (0.07), type of opponent vehicle (0.05), month (0.04), and type of road segment (0.02). These eight most important predictors of the severity of bicycle crashes were included as predictors of the target (i.e., severity of bicycle crashes) in Bayesian network analysis. Bayesian network analysis identified crash type (0.31), road type (0.19), and type of opponent vehicle (0.18) as the most important predictors of severity of bicycle crashes.

Characteristics of cyclist crashes in Italy using latent class analysis and association rule mining

The factors associated with severity of the bicycle crashes may differ across different bicycle crash patterns. Therefore, it is important to identify distinct bicycle crash patterns with homogeneous attributes. The current study aimed at identifying subgroups of bicycle crashes in Italy and analyzing separately the different bicycle crash types. The present study focused on bicycle crashes that occurred in Italy during the period between 2011 and 2013. We analyzed categorical indicators corresponding to the characteristics of infrastructure (road type, road signage, and location type), road user (i.e., opponent vehicle and cyclist's maneuver, type of collision, age and gender of the cyclist), vehicle (type of opponent vehicle), and the environmental and time period variables (time of the day, day of the week, season, pavement condition, and weather). To identify homogenous subgroups of bicycle crashes, we used latent class analysis. Using latent class analysis, the bicycle crash data set was segmented into 19 classes, which represents 19 different bicycle crash types. Logistic regression analysis was used to identify the association between class membership and severity of the bicycle crashes. Finally, association rules were conducted for each of the latent classes to uncover the factors associated with an increased likelihood of severity. Association rules highlighted different crash characteristics associated with an increased likelihood of severity for each of the 19 bicycle crash types.

Safety effects of the London cycle superhighways on cycle collisions

This paper evaluates the effects of the London Cycle Superhighways (CS) on cycle collisions. A total of 45 CS segments and 375 control segments are observed for a period of 8 years in London. Variables such as road characteristics, crash history and socio-economic information are included in the data set. Traffic characteristics including traffic volume, cycle volume and traffic speed are obtained from Department for Transport. We first estimate the safety effects on the CS routes using Empirical Bayes methods. Then propensity score matching methods are also applied for comparison. The introduction of cycle superhighways caused cycling traffic volumes to increase dramatically along CS routes with no significant impacts on collision rates. Our models find that the increase in traffic was associated with a rise in annual total cycle collisions of around 2.6 per km (38% in percentage). However, when we re-estimate the effects based on cycle collision rates rather than levels, our results also show that the CS routes are not more dangerous or safer than the control roads. Among the four CS routes, CS3 performs the best in protecting cyclists with a large proportion of segregated lanes whilst the cyclists have to share the lanes with motorists on other routes. It is recommended that consistent safety designs should be applied on all CS routes for a safer cycling environment.

Exploring the impacts of factors contributing to tram-involved serious injury crashes on Melbourne tram routes

Previous research is limited regarding factors influencing tram-involved serious injury crashes. The aim of this study is to identify key vehicle, road, environment and driver related factors associated with tram-involved serious injury crashes. Using a binary logistic regression modelling approach, the following factors were identified to be significant in influencing tram-involved fatal crashes in Melbourne: tram floor height, tram age, season, traffic volume, tram lane priority and tram travel speed. Low floor trams, older trams, tram priority lanes and higher tram travelling speeds are more likely to increase tram-involved fatal crashes. Higher traffic volume decreases the likelihood of serious crashes. Fatal crashes are more likely to occur during spring and summer. Findings from this study may offer ideas for future research in the area of tram safety and help to develop countermeasures to prevent specific fatality types from occurring.

Bicycling crashes on streetcar (tram) or train tracks: mixed methods to identify prevention measures

BACKGROUND

Streetcar or train tracks in urban areas are difficult for bicyclists to negotiate and are a cause of crashes and injuries. This study used mixed methods to identify measures to prevent such crashes, by examining track-related crashes that resulted in injuries to cyclists, and obtaining information from the local transit agency and bike shops.

METHODS

We compared personal, trip, and route infrastructure characteristics of 87 crashes directly involving streetcar or train tracks to 189 crashes in other circumstances in Toronto, Canada. We complemented this with engineering information about the rail systems, interviews of personnel at seven bike shops about advice they provide to customers, and width measurements of tires on commonly sold bikes.

RESULTS

In our study, 32 % of injured cyclists had crashes that directly involved tracks. The vast majority resulted from the bike tire being caught in the rail flangeway (gap in the road surface alongside rails), often when cyclists made unplanned maneuvers to avoid a collision. Track crashes were more common on major city streets with parked cars and no bike infrastructure, with left turns at intersections, with hybrid, racing and city bikes, among less experienced and less frequent bicyclists, and among women. Commonly sold bikes typically had tire widths narrower than the smallest track flangeways. There were no track crashes in route sections where streetcars and trains had dedicated rights of way.

CONCLUSIONS

Given our results, prevention efforts might be directed at individual knowledge, bicycle tires, or route design, but their potential for success is likely to differ. Although it may be possible to reach a broader audience with continued advice about how to avoid track crashes, the persistence and frequency of these crashes and their unpredictable circumstances indicates that other solutions are needed. Using tires wider than streetcar or train flangeways could prevent some crashes, though there are other considerations that lead many cyclists to have narrower tires. To prevent the majority of track-involved injuries, route design measures including dedicated rail rights of way, cycle tracks (physically separated bike lanes), and protected intersections would be the best strategy.

Potential risk and its influencing factors for separated bicycle paths

In this paper, we propose two potential risk indicators to define and evaluate the safety of bicycle path at the microscopic level. Field bicycle data were collected from three survey sites under different traffic conditions. These two risk indicators based on speed dispersion were proposed and calculated during each 5-min interval. The risk influences of various widths of bicycle path and traffic conditions were analyzed by using one-way ANOVA. We further proposed a generalized linear model (GLM) for modeling and analyzing the relationships between bicycle risks and v/c ratio and percentages of electric bicycles, male cyclists, young cyclists, and loaded cyclists. The stepwise regression models were applied for determination of coefficients. The results show that the influences of gender and age of cyclists on potential risks are not significant. The risks increase with the width of bicycle path and percentage of electric bicycles, while only for wider bicycle path (4-lane case in this study), the risks are associated with whether or not cyclists are loaded. The findings could contribute for analysis and evaluation of the safety for bicycle path.

Hotspots and social background of urban traffic crashes: A case study in Cluj-Napoca (Romania)

Mobility practices have changed dramatically in Romanian towns over the last 25 years, following the collapse of socialist mobility restrictions. Urban areas like Cluj-Napoca are facing both increasing immigration and car mobility, and therefore increasing levels of road traffic crashes. The analysis of traffic crashes is one of the most important elements for improving the road safety policy. This paper is divided in two parts. In the first one, the authors focus on identifying the traffic crash hotspots along the street network, while in the second part they discuss the social background of road traffic crash occurrence. The first step in analyzing traffic crashes is to determine crash hotspots. A four-year record (2010-2013) provided by the Traffic Department of the General Inspectorate of Romanian Police (GIRPTD) was used. As a method of hotspot determination, the Kernel Density Estimation tool was employed, in the frame of the spatial analysis along network (SANET). The outcome was the hotspot map of traffic crashes in Cluj-Napoca. The results have revealed 4 categories of street segments: not-dangerous, low-dangerous, medium-dangerous and high-dangerous. Based on this classification, at least 4 dangerous areas were identified, located at the city entrances-exits (in the West, North-West and East) and the city center (the most dangerous zone). The second part of the paper focuses on social groups involved in car crashes. The following are considered: age, gender and blood alcohol concentration of the person (driver or pedestrian) found guilty for every individual crash.

Physical Activity through Sustainable Transport Approaches (PASTA): a study protocol for a multicentre project

INTRODUCTION

Only one-third of the European population meets the minimum recommended levels of physical activity (PA). Physical inactivity is a major risk factor for non-communicable diseases. Walking and cycling for transport (active mobility, AM) are well suited to provide regular PA. The European research project Physical Activity through Sustainable Transport Approaches (PASTA) pursues the following aims: (1) to investigate correlates and interrelations of AM, PA, air pollution and crash risk; (2) to evaluate the effectiveness of selected interventions to promote AM; (3) to improve health impact assessment (HIA) of AM; (4) to foster the exchange between the disciplines of public health and transport planning, and between research and practice.

METHODS AND ANALYSIS

PASTA pursues a mixed-method and multilevel approach that is consistently applied in seven case study cities. Determinants of AM and the evaluation of measures to increase AM are investigated through a large scale longitudinal survey, with overall 14,000 respondents participating in Antwerp, Barcelona, London, Örebro, Rome, Vienna and Zurich. Contextual factors are systematically gathered in each city. PASTA generates empirical findings to improve HIA for AM, for example, with estimates of crash risks, factors on AM-PA substitution and carbon emissions savings from mode shifts. Findings from PASTA will inform WHO's online Health Economic Assessment Tool on the health benefits from cycling and/or walking. The study's wide scope, the combination of qualitative and quantitative methods and health and transport methods, the innovative survey design, the general and city-specific analyses, and the transdisciplinary composition of the consortium and the wider network of partners promise highly relevant insights for research and practice.

ETHICS AND DISSEMINATION

Ethics approval has been obtained by the local ethics committees in the countries where the work is being conducted, and sent to the European Commission before the start of the survey. The PASTA website (http://www.pastaproject.eu) is at the core of all communication and dissemination activities.