Bicycle crash casualties in a highly motorized city

A correlated random parameters ordered probit approach to analyze the injury severity of bicycle-motor vehicle collisions at intersections

Bicycle-motor vehicle (BMV) accidents hold paramount importance due to their substantial impact on public safety. Specifically, road intersections, being critical conflict points, demand focused attention to reduce BMV crashes effectively and mitigate their severity. The existing research on the severity analysis of these crashes appears to have certain gaps that warrant further contribution. To address the mentioned limitations, this study first integrates multiple pre-collision features of the bicycles and vehicles to classify crash types based on the mechanism of the crashes. Then, the correlated random parameters ordered probit (CRPOP) model is employed to examine the factors influencing injury severity among bicyclists involved in intersection BMV crashes in Pennsylvania from 2013 to 2018. To gain deeper insights, this study conducts a separate analysis of crash data from 3-leg intersections, 4-leg intersections, and their combined scenarios, followed by a comparative examination of the results. The findings revealed that the presented crash typing approach yields new insights regarding injury severity outcomes. Moreover, in addition to exhibiting a comparable statistical performance contrasting to the more restricted models, the CRPOP model identified hidden correlations between three random parameters. Furthermore, the study demonstrated that analyzing combined crash data from the two intersection types obscured certain factors that were found significantly influential in the injury outcomes through analyzing sub-grouped data. Consequently, it is recommended to implement tailored countermeasures for each type of intersection.

Injury severity prediction of cyclist crashes using random forests and random parameters logit models

Cycling provides numerous benefits to individuals and to society but the burden of road traffic injuries and fatalities is disproportionately sustained by cyclists. Without awareness of the contributory factors of cyclist death and injury, the capability to implement context-specific and appropriate measures is severely limited. In this paper, we investigated the effects of the characteristics related to the road, the environment, the vehicle involved, the driver, and the cyclist on severity of crashes involving cyclists analysing 72,363 crashes that occurred in Great Britain in the period 2016-2018. Both a machine learning method, as the Random Forest (RF), and an econometric model, as the Random Parameters Logit Model (RPLM), were implemented. Three different RF algorithms were performed, namely the traditional RF, the Weighted Subspace RF, and the Random Survival Forest. The latter demonstrated superior predictive performances both in terms of F-measure and G-mean. The main result of the Random Survival Forest is the variable importance that provides a ranked list of the predictors associated with the fatal and severe cyclist crashes. For fatal classification, 19 variables showed a normalized importance higher than 5% with the second involved vehicle manoeuvring and the gender of the driver of the second vehicle having the greatest predictive ability. For serious injury classification, 13 variables showed a normalized importance higher than 5% with the bike leaving the carriageway having the greatest normalized importance. Furthermore, each path from the root node to the leaf nodes has been retraced the way back generating 361 if-then rules with fatal crash as consequent and 349 if-then rules with serious injury crash as consequent. The RPLM showed significant unobserved heterogeneity in the data finding four normal distributed indicator variables with random parameters: cyclist age ≥ 75 (fatal prediction), cyclist gender male (fatal and serious prediction), and driver aged 55-64 (serious prediction). The model's McFadden Pseudo R2 is equal to 0.21, indicating a very good fit. Furthermore, to understand the magnitude of the effects and the contribution of each variable to injury severity probabilities the pseudo-elasticity was assessed, gaining valuable insights into the relative importance and influence of the variables. The RF and the RPLM resulted complementary in identifying several roadways, environmental, vehicle, driver, and cyclist-related factors associated with higher crash severity. Based on the identified contributory factors, safety countermeasures useful to develop strategies for making bike a safer and more friendly form of transport were recommended.

Uncertainty matters: Bayesian modeling of bicycle crashes with incomplete exposure data

BACKGROUND

One major challenge faced by neighborhood-level bicycle safety analysis is the lack of complete and reliable exposure data for the entire area under investigation. Although the conventional travel-diary surveys, together with the emerging smartphone fitness applications and bike-sharing systems, provide straightforward and valuable opportunities to estimate territory-wide bicycle activities, the obtained ridership suffers inherently from underreporting.

METHODS

We introduced the Bayesian simultaneous-equation model as a sound methodological alternative here to address the uncertainty arising from incomplete exposure data when modeling bicycle crashes. The proposed method was successfully fitted to a crowdsourced dataset of 792 bicycle-motor vehicle (BMV) crashes aggregated from 209 neighborhoods over a 3-year period in Hong Kong.

RESULTS

Our analysis empirically demonstrated the bias due to omission of activity-based exposure measures or to the direct use of cycling distance extracted from the travel-diary survey without correcting for incompleteness. By modeling bicycle activities and the frequency of BMV crashes simultaneously, we also provided new evidence that an expansion of bicycle infrastructure was likely associated with a significant increase in cycling levels and a substantial reduction in the risk of BMV crashes, despite a slight increase in the absolute number of BMV crashes.

CONCLUSIONS

Our approach is promising in adjusting for the uncertainty in raw exposure data, extrapolating the missing exposure values, and untangling the linkage among built environment, bicycle activities, and the frequency of BMV crashes within a unified framework. To promote safer cycling, designated facilities should be provided to consecutively separate cyclists from motor vehicles.

Analysis of Bicycle-Motor Vehicle Crashes in San Antonio, Texas

Bicycling is inexpensive, environmentally friendly, and healthful; however, bicyclist safety is a rising concern. This study investigates bicycle crash-related key variables that might substantially differ in terms of the party at fault and bicycle facility presence. Employing 5 year (2014-2018) data from the Texas Crash Record and Information System database, the effect of these variables on bicyclist injury severity was assessed for San Antonio, Texas, using bivariate analysis and binary logistic regression. Severe injury risk based on the party at fault and bicycle facility presence varied significantly for different crash-related variables. The strongest predictors of severe bicycle injury include bicyclist age and ethnicity, lighting condition, road class, time of occurrence, and period of week. Driver inattention and disregard of stop sign/light were the primary contributing factors to bicycle-vehicle crashes. Crash density heatmap and hotspot analyses were used to identify high-risk locations. The downtown area experienced the highest crash density, while severity hotspots were located at intersections outside of the downtown area. This study recommends the introduction of more dedicated/protected bicycle lanes, separation of bicycle lanes from the roadway, mandatory helmet use ordinance, reduction in speed limit, prioritization of resources at high-risk locations, and implementation of bike-activated signal detection at signalized intersections.

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.

Modelling injury severity of bicyclists in bicycle-car crashes at intersections

Bicyclists are vulnerable road users as they are not protected during a road collision. Although numerous studies have been conducted to understand the parameters contributing to bicyclist's injury severity, most of these studies have focused on the relationship between crash severity and road, environmental, vehicle and human demographic parameters. No study has been found that investigated the relationship of bicyclist's injury severity with speed and mass of both vehicles, as well as other crash dynamics aspects. This study developed a modelling framework to investigate the effect of variables such as speed, mass and crash angle on bicyclist's injury severity in bicycle-car crashes at intersections. A combination of Newtonian Mechanics and statistical analysis was utilised to develop this theory. This modelling process followed a two-step approach. In the first step, Newtonian Mechanics was used to develop numerical models to estimate the impact force applied to the bicyclist. Variables affecting the associated impact forces were then identified. In the second step, a mixed binary logistic regression model was developed to estimate injury severity of a bicycle-vehicle crash as a function of mass of both vehicles, speed of both vehicles before and after the crash, restraint use and age of bicyclist. Transport Accident Commission (TAC) validated crash data was used to develop the model. The results of the numerical models showed that kinetic energy of the car before crash and kinetic energy of the bicycle after crash are important parameters affecting the injury severity of the cyclist in bicycle-vehicle crashes. The results of the mixed binary logistic regression model confirmed that the addition of kinetic energy of the car before crash and the kinetic energy of the bicycle post-crash had a statistically significant effect on injury severity of bicyclist. The results further showed that older bicyclists were involved in higher severity crashes and helmet-wearing reduced the injury severity of the bicyclist.

Fifty Years of Accident Analysis & Prevention: A Bibliometric and Scientometric Overview

Accident Analysis & Prevention (AA&P) is a leading academic journal established in 1969 that serves as an important scientific communication platform for road safety studies. To celebrate its 50^th anniversary of publishing outstanding and insightful studies, a multi-dimensional statistical and visualized analysis of the AA&P publications between 1969 and 2018 was performed using the Web of Science (WoS) Core Collection database, bibliometrics and mapping-knowledge-domain (MKD) analytical methods, and scientometric tools. It was shown that the annual number of AA&P's publications has grown exponentially and that over the course of its development, AA&P has been a leader in the field of road safety, both in terms of innovation and dissemination. By determining its key source countries and organizations, core authors, highly co-cited published documents, and high burst-strength publications, we showed that AA&P's areas of focus include the "effects of hazard and risk perception on driving behavior", "crash frequency modeling analysis", "intentional driving violations and aberrant driving behavior", "epidemiology, assessment and prevention of road traffic injuries", and "crash-injury severity modeling analysis". Furthermore, the key burst papers that have played an important role in advancing research and guiding AA&P in new directions - particularly those in the fields of crash frequency and crash-injury severity modeling analyses were identified. Finally, a modified Haddon matrix in the era of intelligent, connected and autonomous transportation systems is proposed to provide new insights into the emerging generation of road safety studies.

Characteristics of the road infrastructure and injurious cyclist crashes resulting in a hospitalisation

Faced with the current growth and change to Western Australia's road network as well as the promotion and increased uptake of cycling, further investigation into crash, injury and road infrastructure characteristics is necessary. An in-depth study was conducted of 100 cyclists who were injured due to involvement in a crash that occurred on-road and resulted in an admission to a hospital. Information collected included a researcher-administered questionnaire, crash details from the Integrated Road Information System (IRIS), injury information from the State Trauma Registry and a virtual on-line site inspection. Overall, 42 % of crashes involved a motor vehicle and 58 % did not involve a motor vehicle. Twenty-one percent of all crashes involved cyclist loss of control, 18% were crashes with another cyclist, 18% involved hitting an object and 1% involved a pedestrian. . Bicycle crashes were severely under-reported with only 40 percent reported to the Police. Approximately half of crashes occurred at intersections (51 %) and half at midblock (non-intersection) sites (49 %). Fifty-seven percent of crashes that occurred at intersections involved a motor vehicle, whereas only 27% of crashes that occurred at midblocks involved a motor vehicle. The majority of cyclists' injuries were classified as minor according to the Injury Severity Score with the mean number of body regions injured being 4.5 (SD = 2.2). The mean number of days in hospital care was 5.2 days (SD = 5.6, range: 1-33). These findings can be used to guide road infrastructure treatments that reduce the risk of bicycle crashes in Western Australia and insights may inform action in other jurisdictions where cycling is increasing in popularity.

Cyclists injured in traffic crashes in Hong Kong: A call for action

BACKGROUND

Perceived as a minor transportation mode mainly for recreation, cycling and its related safety issues have not been treated as a citywide concern in Hong Kong and have thus received inadequate research efforts. Our study aimed to illuminate the safety challenges faced by cyclists in Hong Kong.

METHODS

We examined the police crash records from 1998 to 2017 and developed a Bayesian Poisson state space model to evaluate the longitudinal change in traffic injuries to cyclists. We then used quasi-induced exposure to measure the annual relative risk of crash involvement for cycling. Based on an officially published travel characteristics survey, we further measured the risk of injury for cycling per minutes cycled.

RESULTS

Between 1998 and 2017, Hong Kong witnessed a more than twofold increase in the number of cyclist injuries, with an average annual increase rate of 5.18% (95% CI: 0.53%-12.77%). By 2017, cyclists were 2.21 (1.82-2.69) times more likely to be involved in traffic crashes than in 1998. Per 10 million minutes, the injury rates for cycling were 28.64 (27.43-29.70) and 42.54 (41.07-44.02) on weekdays during 2001-2003 and 2010-2012. After adjusting for sex and age groups, cyclists were 1.95 (1.43-2.61) times more likely to be injured in 2010-2012 than in 2001-2003. Per minutes traveled, cyclists also sustained significantly higher risks of fatality and injury than pedestrians, private car drivers and passengers, taxi passengers, public bus passengers, and minibus passengers. A comparison of Hong Kong with other regions suggests that Hong Kong is among the most dangerous areas for cycling in terms of fatality rate per minutes cycled.

CONCLUSIONS

Cyclist injuries have become a substantial public health burden in Hong Kong. A range of countermeasures with proven effectiveness should be promptly implemented to improve the safety of these vulnerable road users.

Investigating safety of vulnerable road users in selected EU countries

PROBLEM

Vulnerable road users comprise over half of all road accident victims in the EU and their safety situation is not improving as fast as for motorists. The paper examines factors affecting fatality risk of pedestrians, cyclists, motorcyclists, and moped riders in seven EU countries using data from CARE database.

METHOD

Comparing accident severity indicators between countries is problematic because of data quality issues, different degree of underreporting, and different exposure levels. To avoid bias arising from these issues, fatality risk is modeled with binary logistic regression. Risk factors considered include accident location by area type, junction type, and traffic control, as well as lighting condition. Results are presented as odds ratios of fatal accident outcome in different countries under specific circumstances compared to reference conditions. It is shown that the error in OR values due to underreporting is small.

RESULTS AND DISCUSSION

Wide confidence intervals of the odds ratios in some countries confirm problems with accident data quality. Fatality risk is always higher for non-urban versus urban area and for darkness versus daylight conditions, but the odds ratios are different for different countries. Inconsistent results are obtained for accident location with respect to junction and its control type. Possible reasons for these differences are suggested and discussed. Practical applications: The proposed method avoids the data quality bias of accident severity indicators, thus, it can be used in international comparisons of vulnerable road user accidents. The article findings also support the concept of changes in legislation, such as reducing the speed limit in urban areas in Poland at night. Generally, the experience of countries with low VRU fatality risk identified in the article can be transferred to those with a higher risk.

Evaluating the impact of Mobike on automobile-involved bicycle crashes at the road network level

As a booming system, free-floating bicycle-sharing (denoted as Mobike) attracts a large number of users due to the convenient utilization procedure. However, it brings about a rapid increase of bicycle volume on roadways, resulting in safety problems especially on road segments shared by automobiles and bikes. This study aimed to evaluate impacts of Mobike on automobile-involved bicycle crashes on shared roadways at a macro level, the network level. Relation between traffic volumes and crashes was first established. Then, the travel mode choice before and after supplying Mobike in the market was analyzed, based on which the multi-class multi-modal user equilibrium (MMUE) models were formulated and solved. Two attributes of Mobike, supply quantity and fare, were investigated via various scenarios. Results suggested the Mobike attracted more walkers than auto-users in travel mode choices, which caused the volume increase of bicycles but few volume decline of automobiles and resulted in more crashes. The supply quantity of Mobike had a negative impact on safety, while the fare had a positive effect. The total supply of Mobike in the market should be regulated by governments to avoid over-supply and reduce bicycle crashes. The fares should be also regulated by including taxes and insurances, which can be used to build up more separated bicycle facilities and cover the Mobike accidents, respectively. The findings of this study provide useful information for governments and urban transportation managers to improve bicycle safety and regulate the Mobike market.

Bicycle-Related Crashes in Hong Kong: Is it Possible to Reduce Mortality and Severe Injury in the Metropolitan Area?

Background

Cycling is not the primary mode of commuter transport in Hong Kong, yet cyclists are exposed to a high risk of injury and fatality in road crashes. It is essential to identify the significant factors contributing to severe injury among cyclists in Hong Kong.

Aim

To evaluate the effects of significant factors, including demographics, temporal distribution, cyclist behavior, road conditions, and weather, on the risk of severe and life-threatening injury among cyclists in road crashes in Hong Kong.

Method

The study was nested on a database known as Road Casualty Information System (RoCIS) which is a linked database between police traffic accident investigations reports and hospital injury records. A total of 682 victims were identified during the study period from 1 January 2004 to 31 December 2006. In particular, injured body part, demographics, helmet use, alcohol intoxication, weather conditions, road type and geometry, and collision characteristics of 682 trauma patients were the attributing variables of concern. The primary outcome measure was the injury severity of trauma patients which was classified into three levels: slight injury [Injury severity Scale (ISS) </=8], severe injury (ISS >/=9), and life-threatening injury (ISS >/=25). A multinomial logit regression model was established to evaluate the significance of factors contributing to severe and life-threatening injuries among cyclists in road crashes.

Results

The results indicated that middle-aged and elderly (35-54, RRR=2.48; and 55 or above, RRR=4.39) casualties and favourable weather conditions (2.56) significantly increased the risk of severe injury among cyclists. The presence of severe head injury (RRR=509.24), severe trunk injury (RRR=79.24), and the involvement of motor vehicles (RRR=27.18) substantially increased the risk of life-threatening injury to cyclists.

Conclusions

Middle-aged casualties, the presence of head injuries, and the involvement of motor vehicles all increase the risk of more severe injury in bicycle-related crashes. Safety education and countermeasures should target at middle-aged and elderly cyclists and discourage cycling on the motorway.

The role of intersection and street design on severity of bicycle-motor vehicle crashes

Background

Safety concerns are a major barrier to cycling. Intersection and street design variables such as intersection angles and street width might contribute to the severity of crashes and the safety concerns. In this study we examined whether these design variables were associated with bicycle-motor vehicle crashes (BMVC) severity.

Methods

Using the geographical information system and latitudes/longitudes recorded by the police using a global positioning device, we extracted intersection angles, street width, bicycle facilities, posted speed limits and annual average daily traffic from 3266 BMVC data from New York City police records. Additional variables about BMVC, including age and sex of the bicyclist, time of the day, road surface conditions, road character, vehicle type and injury severity, were obtained from police reports. Injury severity was classified as severe (incapacitating or killed) or non-severe (non-incapacitating, possible injury). The associations between injury severity and environment design variables were examined using multivariate log-binomial regression model.

Findings

Compared with crashes at orthogonal intersections, crashes at non-orthogonal intersections had 1.37 times (95% CI 1.05 to 1.80) and non-intersection street segments had 1.31 times (95% CI 1.01 to 1.70) higher risk of a severe injury. Crashes that involved a truck or a bus were twice as likely to result in a severe injury outcome; street width was not significantly associated with injury severity.

Conclusion

Crashes at non-orthogonal intersections and non-intersection segments are more likely to result in higher injury severity. The findings can be used to improve road design and develop effective safety interventions.

Latent segmentation based count models: Analysis of bicycle safety in Montreal and Toronto

The study contributes to literature on bicycle safety by building on the traditional count regression models to investigate factors affecting bicycle crashes at the Traffic Analysis Zone (TAZ) level. TAZ is a traffic related geographic entity which is most frequently used as spatial unit for macroscopic crash risk analysis. In conventional count models, the impact of exogenous factors is restricted to be the same across the entire region. However, it is possible that the influence of exogenous factors might vary across different TAZs. To accommodate for the potential variation in the impact of exogenous factors we formulate latent segmentation based count models. Specifically, we formulate and estimate latent segmentation based Poisson (LP) and latent segmentation based Negative Binomial (LNB) models to study bicycle crash counts. In our latent segmentation approach, we allow for more than two segments and also consider a large set of variables in segmentation and segment specific models. The formulated models are estimated using bicycle-motor vehicle crash data from the Island of Montreal and City of Toronto for the years 2006 through 2010. The TAZ level variables considered in our analysis include accessibility measures, exposure measures, sociodemographic characteristics, socioeconomic characteristics, road network characteristics and built environment. A policy analysis is also conducted to illustrate the applicability of the proposed model for planning purposes. This macro-level research would assist decision makers, transportation officials and community planners to make informed decisions to proactively improve bicycle safety - a prerequisite to promoting a culture of active transportation.

Safety in numbers for cyclists beyond national-level and city-level data: a study on the non-linearity of risk within the city of Hong Kong

OBJECTIVE

This paper examines the relationship between bicycle collisions and the amount of cycling at the local level. Most previous research has focused on national and city comparisons, little is known about differences within a city (the mesoscale).

METHODS

This study mainly used three types of data sets relating to bicycle collisions, use of bicycles and local neighbourhood characteristics in Hong Kong. In particular, bicycle usage, measured as bicycle-kilometres travelled, was estimated from travel surveys following the activity-based approach. Negative binomial regression models were established to model the relationship between the amount of cycling and the occurrence of bicycle collisions at the spatial scale of the Tertiary Planning Unit, which is the smallest planning unit of the city.

RESULTS

The numbers of bicycle collisions went up with the increasing use of bicycles, but the increase in the number of collisions in a given community was less than a linear proportion of the bicycle flow. When other local neighbourhood variables are controlled, the amount of cycling is a statistically significant variable in accounting for the number of collisions.

CONCLUSIONS

Even in a highly motorised city where bicycles are a minor transport mode, cyclists are less likely to be involved in road collisions in communities with higher cycling volume. Since cycling activities are likely to vary within a city, a more local-based approach in promoting cycling is needed. In particular, the higher safety risks in neighbourhoods of low bicycle usage, especially at an initial stage of promoting cycling, need to be addressed properly.

Database improvements for motor vehicle/bicycle crash analysis

Background

Bicycling is healthy but needs to be safer for more to bike. Police crash templates are designed for reporting crashes between motor vehicles, but not between vehicles/bicycles. If written/drawn bicycle-crash-scene details exist, these are not entered into spreadsheets.

Objective

To assess which bicycle-crash-scene data might be added to spreadsheets for analysis.

Methods

Police crash templates from 50 states were analysed. Reports for 3350 motor vehicle/bicycle crashes (2011) were obtained for the New York City area and 300 cases selected (with drawings and on roads with sharrows, bike lanes, cycle tracks and no bike provisions). Crashes were redrawn and new bicycle-crash-scene details were coded and entered into the existing spreadsheet. The association between severity of injuries and bicycle-crash-scene codes was evaluated using multiple logistic regression.

Results

Police templates only consistently include pedal-cyclist and helmet. Bicycle-crash-scene coded variables for templates could include: 4 bicycle environments, 18 vehicle impact-points (opened-doors and mirrors), 4 bicycle impact-points, motor vehicle/bicycle crash patterns, in/out of the bicycle environment and bike/relevant motor vehicle categories. A test of including these variables suggested that, with bicyclists who had minor injuries as the control group, bicyclists on roads with bike lanes riding outside the lane had lower likelihood of severe injuries (OR, 0.40, 95% CI 0.16 to 0.98) compared with bicyclists riding on roads without bicycle facilities.

Conclusions

Police templates should include additional bicycle-crash-scene codes for entry into spreadsheets. Crash analysis, including with big data, could then be conducted on bicycle environments, motor vehicle potential impact points/doors/mirrors, bicycle potential impact points, motor vehicle characteristics, location and injury.

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

This paper aims at predicting cycling accident risk for an entire network and identifying how road infrastructure influences cycling safety in the Brussels-Capital Region (Belgium). A spatial Bayesian modelling approach is proposed using a binary dependent variable (accident, no accident at location i) constructed from a case-control strategy. Control sites are sampled along the 'bikeable' road network in function of the potential bicycle traffic transiting in each ward. Risk factors are limited to infrastructure, traffic and environmental characteristics. Results suggest that a high risk is statistically associated with the presence of on-road tram tracks, bridges without cycling facility, complex intersections, proximity to shopping centres or garages, and busy van and truck traffic. Cycle facilities built at intersections and parked vehicles located next to separated cycle facilities are also associated with an increased risk, whereas contraflow cycling is associated with a reduced risk. The cycling accident risk is far from being negligible in points where there is actually no reported cycling accident but where they are yet expected to occur. Hence, mapping predicted accident risks provides planners and policy makers with a useful tool for accurately locating places with a high potential risk even before accidents actually happen. This also provides comprehensible information for orienting cyclists to the safest routes in Brussels.

Association of cyclists' age and sex with risk of involvement in a crash before and after adjustment for cycling exposure

This study aimed to estimate the association of cyclists' age and sex with the risk of being involved in a crash with and without adjustment for their amount of exposure. We used the distribution of the entire population and cyclists (total and non-responsible) involved in road crashes in Spain between 1993 and 2009 held by the Spanish National Institute of Statistics and the Spanish General Traffic Directorate to calculate rates of exposure and involvement in a crash. Males aged 45-49 years were used as the reference category to obtain exposure rate ratios (ERR) and unadjusted crash rate ratios (URR). We then used these values in decomposition analysis to calculate crash rate ratios adjusted for exposure (ARR). The pattern of ARR was substantially different from URR. In both sexes the highest values were observed in the youngest age groups, and the values decreased as age increased except for a slight increase in the oldest age groups. In males, a slight increase in the lowest and highest age categories was observed for crashes resulting in severe injury or death, and a decrease was observed for the youngest cyclists who were wearing a helmet. The large differences between age and sex groups in the risk of involvement in a cycling crash are strongly dependent on differences in their exposure rates. Taking exposure rates into account, cyclists younger than 30 years and older than 65 years of age had the highest risk of being involved in a crash.

A conceptual framework for road safety and mobility applied to cycling safety

Scientific literature lacks a model which combines exposure to risk, risk, and the relationship between them. This paper presents a conceptual road safety framework comprising mutually interacting factors for exposure to risk resulting from travel behaviour (volumes, modal split, and distribution of traffic over time and space) and for risk (crash and injury risk). The framework's three determinants for travel behaviour are locations of activities; resistances (generalized transport costs); needs, opportunities, and abilities. Crash and injury risks are modelled by the three 'safety pillars': infrastructure, road users and the vehicles they use. Creating a link in the framework between risk and exposure is important because of the 'non-linear relationship' between them, i.e. risk tends to decrease as exposure increases. Furthermore, 'perceived' risk (a type of travel resistance) plays a role in mode choice, i.e. the perception that a certain type of vehicle is unsafe can be a deterrent to its use. This paper uses theories to explain how the elements in the model interact. Cycling is an area where governments typically have goals for both mobility and safety. To exemplify application of the model, the paper uses the framework to link research on cycling (safety) to land use and infrastructure. The model's value lies in its ability to identify potential consequences of measures and policies for both exposure and risk. This is important from a scientific perspective and for policy makers who often have objectives for both mobility and safety.

Risk factors for causing road crashes involving cyclists: An application of a quasi-induced exposure method

A quasi-induced exposure approach was applied to the Spanish Register of Traffic Crashes to identify driver- and vehicle-related factors associated with the risk of causing a road crash involving a cyclist in Spain from 1993 to 2009. We analyzed 19,007 collisions between a bicycle and another vehicle in which only one of the drivers committed an infraction, and 13,540 records that included the group of non-infractor cyclists in the above collisions plus cyclists involved in single-bicycle crashes. Adjusted odds ratios were calculated for being responsible for each type of crash for each factor considered. Age from 10 to 19 years, male sex, alcohol or drug consumption and non-helmet use were cyclist-related variables associated with a higher risk of crash, whereas cycling more than 1h increased only the risk of single crashes. Bicycles with brake defects and ridden by two occupants were also at higher risk of involvement in a crash, whereas light defects were associated only with collisions with another vehicle. For drivers of the other vehicle, age more than 60 years, alcohol, not using safety devices and nonprofessional drivers were at higher risk. The risk of colliding with a bicycle was higher for mopeds than for passenger cars.

A prospective cohort study on minor accidents involving commuter cyclists in Belgium

The purpose of this study is to gain insight into bicycle accidents. Bicycle accident data and weekly exposure data were prospectively collected for one year to calculate the incidence rate (IR) of bicycle accidents. An accident was included if it occurred during utilitarian cycling, resulting in an acute injury with corporal damage. If an accident occurred, a detailed questionnaire was filled out to collect detailed information about its circumstances and consequences. A sample of 1087 regular (≥2 cycling trips to work a week) adult (40±10 years) cyclists was analyzed. Over the 1-year follow-up period, 20,107 weeks were covered, accumulating 1,474,978 cycled kilometers. Sixty-two participants were involved in 70 bicycle accidents, of which 68 were classified as 'minor'. The overall IR for the 70 accidents was 0.324 per 1000 trips (95% CI 0.248-0.400), 0.896 per 1000 h (95% CI 0.686-1.106) and 0.047 per 1000 km (95% CI 0.036-0.059) of exposure. Brussels-capital region is the region with the highest IR (0.086; 95% CI 0.054-0.118), with a significantly (P<0.05) higher IR compared to Flanders (0.037; 95% CI 0.025-0.050). Injuries were mainly caused by 'slipping' (35%) or 'collision with a car' (19%). The accidents caused abrasions (42%) and bruises (27%) to the lower (45%) and upper limbs (41%). Police, hospital emergency department or insurance companies were involved in only 7%, 10% and 30% of the cases, respectively. It is noteworthy that 37% of the participants indicated that they could have avoided the accident. In order to decrease the number of accidents, measures should be taken to keep cycling surfaces clean and decrease the number of obstacles on bicycle infrastructure. Roads and intersections need to be built so that the collisions between cars and bicycles are decreased to a minimum. Car drivers and cyclists should pay more attention towards each other. Underreporting of minor bicycle accidents in Belgium is confirmed, and is higher than expected. Reliable accident statistics, taking into account exposure, are needed to decide which road safety measures are the most effective. The 'safety in numbers' principle is also applicable for minor bicycle accidents.

Macroscopic spatial analysis of pedestrian and bicycle crashes

This study investigates the effect of spatial correlation using a Bayesian spatial framework to model pedestrian and bicycle crashes in Traffic Analysis Zones (TAZs). Aggregate models for pedestrian and bicycle crashes were estimated as a function of variables related to roadway characteristics, and various demographic and socio-economic factors. It was found that significant differences were present between the predictor sets for pedestrian and bicycle crashes. The Bayesian Poisson-lognormal model accounting for spatial correlation for pedestrian crashes in the TAZs of the study counties retained nine variables significantly different from zero at 95% Bayesian credible interval. These variables were - total roadway length with 35 mph posted speed limit, total number of intersections per TAZ, median household income, total number of dwelling units, log of population per square mile of a TAZ, percentage of households with non-retired workers but zero auto, percentage of households with non-retired workers and one auto, long term parking cost, and log of total number of employment in a TAZ. A separate distinct set of predictors were found for the bicycle crash model. In all cases the Bayesian models with spatial correlation performed better than the models that did not account for spatial correlation among TAZs. This finding implies that spatial correlation should be considered while modeling pedestrian and bicycle crashes at the aggregate or macro-level.