Advancing Cycling Safety: On-Bike Alert System Utilizing Multi-Layer Radar Point Cloud Clustering for Coarse Object Classification.

Cyclists are considered to be vulnerable road users (VRUs) and need protection from potential collisions with cars and other vehicles induced by unsafe driving, dangerous road conditions, or weak cycling infrastructure. Integrating mmWave radars into cycling safety measures presents an efficient solution to this problem given their compact size, low power consumption, and low cost compared to other sensors. This paper introduces an mmWave radar-based bike safety system designed to offer real-time alerts to cyclists. The system consists of a low-power radar sensor affixed to the bicycle, connected to a micro-controller, and delivering a preliminary classification of detected obstacles. An efficient two-level clustering based on the accumulation of radar point clouds from multiple frames with a temporal projection from previous frames into the current frame is proposed. The clustering is followed by a coarse classification algorithm in which we use relevant features extracted from the resulting clusters. An annotated RadBike dataset composed of radar point cloud data synchronized with RGB camera images is developed to evaluate our system. The two-level clustering outperforms the DBSCAN algorithm, achieving a v-measure score of 0.91, compared to 0.88 with classical DBSCAN. Different classifiers, including decision trees, random forests, support vector machines (SVMs), and AdaBoost, have been assessed, with an overall accuracy of 87% for the three main object classes: four-wheeled, two-wheeled, and others. The system has the ability to improve rider safety on the road and substantially reduce the frequency of incidents involving cyclists.

One Metre Plus (1M+): A Multifunctional Open-Source Sensor for Bicycles Based on Raspberry Pi.

During the last decade, bicycles equipped with sensors became an essential tool for research, particularly for studies analyzing the lateral passing distance between motorized vehicles and bicycles. The objective of this article is to describe a low-cost open-source sensor called one metre plus (1m+) capable of measuring lateral passing distance, registering the geographical position of the cyclist, and video-recording the trip. The plans, codes, and schematic design are open and therefore easily accessible for the scientific community. This study describes in detail the conceptualization process, the characteristics of the device, and the materials from which they are made. The study also provides an evaluation of the product and describes the sensor's functionalities and its field of application. The objective of this project is to democratize research and develop a platform/participative project that offers tools to researchers worldwide, in order to standardize knowledge sharing and facilitate the comparability of results in various contexts.

Traveling by Bus Instead of Car on Urban Major Roads: Safety Benefits for Vehicle Occupants, Pedestrians, and Cyclists.

Some studies have estimated fatality and injury rates for bus occupants, but data was aggregated at the country level and made no distinction between bus types. Also, injured pedestrians and cyclists, as a result of bus travel, were overlooked. We compared injury rates for car and city bus occupants on specific urban major roads, as well as the cyclist and pedestrian injuries associated with car and bus travel. We selected ten bus routes along major urban arterials (in Montreal, Canada). Passenger-kilometers traveled were estimated from vehicle counts at intersections (2002-2010) and from bus passenger counts (2008). Police accident reports (2001-2010) provided injury data for all modes. Injury rates associated with car and bus travel were calculated for vehicle occupants, pedestrians, and cyclists. Injury rate ratios were also computed. The safety benefits of bus travel, defined as the number of vehicle occupant, cyclist, and pedestrian injuries saved, were estimated for each route. Overall, for all ten routes, the ratio between car and bus occupant injury rates is 3.7 (95% CI [3.4, 4.0]). The rates of pedestrian and cyclist injuries per hundred million passenger-kilometers are also significantly greater for car travel than that for bus travel: 4.1 (95% CI [3.5, 4.9]) times greater for pedestrian injuries; 5.3 (95% CI [3.8, 7.6]) times greater for cyclist injuries. Similar results were observed for fatally and severely injured vehicle occupants, cyclists, and pedestrians. At the route level, the safety benefits of bus travel increase with the difference in injury rate associated with car and bus travel but also with the amount of passenger-kilometers by bus. Results show that city bus is a safer mode than car, for vehicle occupants but also for cyclists and pedestrians traveling along these bus routes. The safety benefits of bus travel greatly vary across urban routes; this spatial variation is most likely linked to environmental factors. Understanding the safety benefits of public transit for specific transport routes is likely to provide valuable information for mobilizing city and transportation planners.

Conventional or parking-protected bike lanes? A Full-Bayesian before-and-after assessment.

OBJECTIVE: Biking infrastructure plays a crucial role in ensuring cyclists' safety and encouraging more people to bike. Recently, many North American municipalities started to adopt a new bike lane design, namely the parking-protected bike lane (PPBL), in which the bike lane is placed between the sidewalk and the parking lane. This study aims to assess the safety impacts associated with converting conventional bike lanes (CBLs) to PPBLs. METHODS: To that end, collision and traffic data were collected at 19 street sections from three corridors in Vancouver and Ottawa before and after the conversion. Poisson-Lognormal Linear Intervention model was developed to undertake a Full Bayesian before-and-after analysis to evaluate the change in the frequency of bike-vehicle collisions and other collaterally affected collisions (i.e., total and rear-end collisions) after implementing PPBLs. RESULTS: Reductions of 31.2%, 16.5%, and 4.4% were observed for total, rear-end, and bike collisions, respectively, after implementing the PPBLs, but the results varied significantly depending on the corridor characteristics. CONCLUSION: Overall, PPBLs demonstrated positive impacts on cyclist safety in some corridors, but their performance is highly sensitive to bike path opening density, intersection density, and intersection treatments.

Drivers passing cyclists: How does sight distance affect safety? Results from a naturalistic study.

INTRODUCTION: Cycling is popular for its ecological, economic, and health benefits. However, especially in rural areas, cyclists may need to share the road with motorized traffic, which is often perceived as a threat. Overtaking a cyclist is a particularly critical maneuver for drivers as they need to control their lateral clearance and speed when passing the cyclist, possibly in the presence of oncoming vehicles or view-obstructing curves. An overtaking vehicle can destabilize the cyclist when passing with low clearance and high speed. At the same time, the cyclist may get scared and eventually stop cycling. In this work, we investigated how visibility regarding available sight distance-an important factor for infrastructure design and regulation-affects drivers' behavior when overtaking cyclists. METHOD: Using four roadside-based traffic sensors, we collected naturalistic data that contained kinematics of drivers overtaking cyclists on a rural road in Sweden. We modeled lateral clearance and speed at the passing moment in response to variables such as sight distance and oncoming traffic with a Bayesian multivariate approach. RESULTS: Fitted on 81 maneuvers, the model revealed that drivers reduced lateral clearance under reduced sight distance. Speed was similarly reduced, however, not as clearly. When an oncoming vehicle was present, it had a similar-yet stronger-effect than sight distance. While we found an overall correlation between clearance and speed, some maneuvers were recorded at critically low clearance. CONCLUSIONS: Cyclists' safety is endangered when passed by drivers under reduced visibility or close to oncoming traffic. PRACTICAL APPLICATIONS: Decision-making for infrastructure and policymaking should aim at prohibiting overtaking in areas with reduced visibility or close oncoming traffic. The model developed in this study may serve as a reference to vehicle active-safety systems and automated driving. The collected and processed data may support evaluating driver models fitted on less ecologically valid data and simulated active-safety systems.

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.

Systematic literature review of 10 years of cyclist safety research.

Cyclist safety is a research field that is gaining increasing interest and attention, but still offers questions and challenges open to the scientific community. The aim of this study was to provide an exhaustive review of scientific publications in the cyclist safety field. For this purpose, Bibliometrix-R tool was used to analyse 1066 documents retrieved from Web of Science (WoS) between 2012 and 2021. The study examined published sources and productive scholars by exposing their most influential contributions, presented institutions and countries most contributing to cyclist safety and explored countries open towards international collaborations. A keywords analysis provided the most frequent author keywords in cyclist safety shown in a word cloud with E-bike, behaviour, and crash severity representing the primary keywords. Furthermore, a thematic map of cyclist safety field drafted from the author's keywords was identified. The strategic diagram is divided in four quadrants and, according to both density and centrality, the themes can be classified as follows: 1) motor themes, characterized by high value of both centrality and density; 2) niche themes, defined by high density and low centrality; 3) emerging or declining themes, featured by low value of both centrality and density; and 4) basic themes, distinguished by high centrality and low density. The motor themes (i.e., the main topics in cyclist safety field) crash severity and bike network were further explored. The research findings will be useful to develop strategies for making bike a safer and more confident form of transport as well as to guide researchers towards the future scientific knowledge.

The impacts of non-motorized traffic enforcement cameras on red light violations of cyclists at signalized intersections.

INTRODUCTION: With a significant increase in accidents involving cyclists, more attention has been paid to cycling safety. Previous studies on traffic accident revealed that red-light violations of non-motorized vehicles have become the leading cause of crashes at signalized intersections. The objective of this study is to investigate the impact of non-motorized traffic enforcement cameras (NTECs) on the red-light running behavior of cyclists, including ordinary e-bike riders, delivery e-bike riders, and bicyclists. METHOD: An observational study of 5,217 cyclists was conducted at six primary intersections in the downtown areas of Nanjing, China. A random parameter logit model was used to explore the safety effect of the NTECs and other factors related to red-light violation behavior. RESULTS: The results indicate higher reductions in red-light violations at intersections with the NTECs compared than at the non-adjacent intersections without the NTECs. Furthermore, the NTECs demonstrated a beneficial but smaller impact on the reduction of violations at adjacent intersections. Another primary finding was that the effects of the NTECs varied among three types of cyclists (ordinary e-bike riders, delivery e-bike riders, and bicyclists). CONCLUSIONS: The NTECs were found to be most effective in the case of delivery e-bike riders, followed by ordinary e-bike riders and bicyclists. In addition, the factors associated with the red-light violation behaviors of these three groups were also found to be different. In general, group size, maximum waiting time, waiting position, and visual search were significantly related to the probability of red-light violations in all three groups. PRACTICAL APPLICATIONS: Based on these findings, this study provides some feasible suggestions for improving the effect of the NTECs and for the future extension of the NTECs installation, such as the randomization of the enforcement and publicity campaigns.

Cycle Aware: Piloting a module for novice drivers.

INTRODUCTION: In low-cycling countries, motor-vehicle traffic and driver behavior are well known barriers to the uptake of bicycles, particularly for utility cycling. Lack of separation between cyclists and faster-moving traffic is one key issue, while attitudes of drivers toward and/or harassment of cyclists is another. Cyclist-related driver education has been recommended as a means to improve driver-cyclist interactions. METHODS: The driver licensing process provides an opportunity for such education. The Cycle Aware module was developed to test and enhance novice drivers' knowledge of interacting safely with cyclists. It was piloted across three Australian jurisdictions targeting both novice and experienced drivers. Participants were asked to complete the Cycle Aware module and an accompanying survey. A total of 134 novice and 97 experienced drivers completed the survey with 42 novice and 50 experienced drivers going on to complete the module. RESULTS: Both groups of drivers scored equally well in the module but the very youngest and very oldest participants were more likely to have some incorrect responses. We did not find any relationship between correct module scores and attitudes toward cyclists. Survey results showed both novice and experienced drivers had somewhat positive attitudes toward cyclists. The two cohorts differed on several attitude questions. Sixty percent (60%) of novices compared to 30% of experienced drivers reported feeling concerned when sharing the road with cyclists, and novices were less likely to agree that cyclists had a right to use the roads. Conclusions and practical applications: The analysis suggests novices need to be better equipped to share roads confidently with cyclists and to recognize cyclists as legitimate traffic participants.

Are bicycle lanes effective? The relationship between passing distance and road characteristics.

An on-road observational study of 162 cyclists was conducted in the Australian cities of Melbourne, Perth, Geelong, and Bendigo. Participants had a distance sensor and two video cameras fitted to their bicycle for two weeks while they cycled on their usual routes, producing 46,769 events where a motor vehicle passed a bicycle. This was the largest study to-date to record passing behavior on public roads, and a large number of road and traffic attributes that might affect passing distance were included in the analysis. When drivers pass cyclists on roads with painted bicycle lanes, they tend to give more space than on roads without bicycle lanes. This is true even when controlling for the space available on the roadway. Drivers also travel in a more predictable fashion, with less variability in passing distances when a bicycle lane is present. Protected bicycle lanes completely remove the risk of passing events less than 1 m. However, where it is not possible to build a protected bicycle lane it is preferable to have a painted bicycle lane than no bicycle lane at all. Other protective factors include: wider lanes, single lane roads, smaller vehicles, and the removal of on-street parking.

A study on the cyclist head kinematic responses in electric-bicycle-to-car accidents using decision-tree model.

Due to the high frequent traffic accidents involving electric bicycles (E-bike), it urgently needs improved protection of cyclists, especially their heads. In this study, by adjusting the initial impact velocities of E-bike and car, initial impact angle between E-bike and car, initial E-bike impact location, and body size of cyclist, 1512 different accident conditions were constructed and simulated using a verified E-bike-to-car impact multi-body model. The cyclist's head kinematic responses including the head relative impact velocity, WAD (Wrap around distance) of head impact location and HIC15 (15 ms Head Injury Criterion) were collected from simulation results to make up a dataset for data mining. The decision tree models of cyclist's head kinematic responses were then created from this dataset and verified accordingly. Based on simulated results obtained from decision tree models, it can be found as follows. 1. In the E-bike-to-car accidents, the average head impact relative velocity and WAD of head impact location are higher than those in the car-to-pedestrian accidents. 2. Increasing the initial impact velocity of car can increase the cyclist's head relative impact velocity, WAD of head impact location, and HIC15. 3. The WAD of cyclist's head impact location is also significantly affected by the initial impact angle between E-bike and car and body size of cyclist: the WAD of head impact location becomes higher with increasing initial impact angle between E-bike and car and body size of cyclist. 4. The effects of initial E-bike impact location on the WAD of cyclist's head impact location is not significant when initial E-bike impact location is concentrated in the region of 0.25 m around the centerline of the car.

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.

Exploring regional differences in cyclist safety at roundabouts: A comparative study between the UK (based on Northumbria data) and Belgium.

The level of safety for cyclists at roundabouts may vary according to national differences not only in the design itself but also sociodemographic, cyclist and driver behaviour as well as environmental factors. This paper investigates the national influence on cyclist casualty severity at roundabouts by comparing the United Kingdom (using Northumbria as a representative sample) and Belgium. The data included speed limits, socio-demographic characteristics, environmental conditions and driver/cyclist behaviour-related contributory factors. First, a logistic regression analysis for the UK data, including 864 cyclist casualties, was carried out. Increasing the speed limit by ten units (for example 30mph to 40mph) increased the probability of a cyclist being killed or seriously injured by 10%. A cyclist casualty was more than three times more likely to be killed or seriously injured (the odds ratio is 3.02) where sudden braking was recorded as a contributory factor. Second, a separate logistic regression analysis for Belgium was conducted. Cyclists ignoring the priority at roundabouts increased the probability of a fatal or seriously injured collisions (the odds ratio is 2.71). Comparing the individual analysis for both countries, the influence of cyclist age was consistent. Each one-year increase in cyclist age increases chance of being killed or seriously injured as opposed to not being killed or seriously injured by 2 % (odds ratio is 1.02) in both UK and Belgium. A final comparative analysis was applied considering proxy variables for both countries. Three-way chi-square tests of independence showed that all non-behavioural variables (i.e. sociodemographic characteristics, speed limit, and environmental conditions) were found to be statistically different between UK and Belgium for both slight and killed and seriously injured casualties. This suggests that driver/cyclist interaction and behaviour in the two countries is generally similar whilst speed limits, the sociodemographic characteristics of cyclists and environmental conditions are specific for each country. The third part of the logistic regression analysis suggested that the country residual was highly statistically significant. This indicates that there are some statistically significant differences with respect to the characteristics of the two regional datasets used in the analysis.

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.

The novel approaches to classify cyclist accident injury-severity: Hybrid fuzzy decision mechanisms.

In this study, two novel fuzzy decision approaches, where the fuzzy logic (FL) model was revised with the C4.5 decision tree (DT) algorithm, were applied to the classification of cyclist injury-severity in bicycle-vehicle accidents. The study aims to evaluate two main research topics. The first one is investigation of the effect of road infrastructure, road geometry, street, accident, atmospheric and cyclist related parameters on the classification of cyclist injury-severity similarly to other studies in the literature. The second one is examination of the performance of the new fuzzy decision approaches described in detail in this study for the classification of cyclist injury-severity. For this purpose, the data set containing bicycle-vehicle accidents in 2013-2017 was analyzed with the classic C4.5 algorithm and two different hybrid fuzzy decision mechanisms, namely DT-based converted FL (DT-CFL) and novel DT-based revised FL (DT-RFL). The model performances were compared according to their accuracy, precision, recall, and F-measure values. The results indicated that the parameters that have the greatest effect on the injury-severity in bicycle-vehicle accidents are gender, vehicle damage-extent, road-type as well as the highly effective parameters such as pavement type, accident type, and vehicle-movement. The most successful classification performance among the three models was achieved by the DT-RFL model with 72.0 % F-measure and 69.96 % Accuracy. With 59.22 % accuracy and %57.5 F-measure values, the DT-CFL model, rules of which were created according to the splitting criteria of C4.5 algorithm, gave worse results in the classification of the injury-severity in bicycle-vehicle accidents than the classical C4.5 algorithm. In light of these results, the use of fuzzy decision mechanism models presented in this study on more comprehensive datasets is recommended for further studies.

Cyclist-vehicle crash modeling with measurement error in traffic exposure.

Exposure measures are always among the explanatory variables of any crash model. Regardless of the technique used to model crash, the mean crash frequency will increase with an increase in exposure since more crashes are likely to occur at higher exposure. For cyclist-vehicle crash models, bike and vehicle exposure measures are essential for an accurate and reliable estimate of the cyclist crash risk. However, traffic exposure measures are an example of variables that are measured with error. Generally, measurement error in regression estimates has three effects: 1) produce bias in parameter estimation for statistical models, 2) lead to a loss of explanation power, 3) mask important features of the data. This study proposes a full Bayesian Poisson Lognormal crash models that account for measurement error in traffic exposure measures (i.e., Vehicle Kilometers Travelled and Bike Kilometers Travelled). The underlying approach is to adjust the traffic exposure measures for measurement error to improve the accuracy of the crash model and crash model estimates. The full Bayesian models are developed using data for 134 traffic analysis zones (TAZs) in the city of Vancouver, Canada. The results show that Poisson Lognormal models that account for measurement error have a better fit for the modeled cyclist-vehicle crash data compared to traditional Poisson Lognormal models. The estimates of the Poisson Lognormal model that accounts for measurement error are consistent, with traditional Poisson Lognormal models' estimates except for the BKT and VKT estimates. Estimates of the BKT and VKT increased after introducing measurement error, which indicates an underestimation (downward bias) to BKT and VKT estimates in case of overlooking measurement error.

How much space do drivers provide when passing cyclists? Understanding the impact of motor vehicle and infrastructure characteristics on passing distance.

BACKGROUND: Understanding factors that influence the distance that drivers provide when passing cyclists is critical to reducing subjective risk and improving cycling participation. This study aimed to quantify passing distance and assess the impact of motor vehicle and road infrastructure characteristics on passing distance. METHODS: An on-road observational study was conducted in Victoria, Australia. Participants had a custom device installed on their bicycle and rode as per their usual cycling for one to two weeks. A hierarchical linear model was used to investigate the relationship between motor vehicle and infrastructure characteristics (location, presence of on-road marked bicycle lane and the presence of parked cars on the kerbside) and passing distance (defined as the lateral distance between the end of the bicycle handlebars and the passing motor vehicle). RESULTS: Sixty cyclists recorded 18,527 passing events over 422 trips. The median passing distance was 173 cm (Q1: 137 cm, Q3: 224 cm) and 1085 (5.9%) passing events were less than 100 cm. Relative to sedans, 4WDs had a reduced mean passing distance of 15 cm (Q1: 12 cm, Q3: 17 cm) and buses had a reduced mean passing distance of 28 cm (Q1: 16 cm, Q3: 40 cm). Relative to passing events that occurred on roads without a marked bicycle lane and without parked cars, passing events on roads with a bike lane with no parked cars had a reduced mean passing distance of 27 cm (Q1: 25 cm, Q3: 29 cm), and passing events on roads with a bike lane and parked cars had a mean lower passing distance of 40 cm (Q1: 37 cm, Q3: 43 cm). CONCLUSIONS: One in every 17 passing events was a close (<100 cm) passing event. We identified that on-road bicycle lanes and parked cars reduced passing distance. These data can be used to inform the selection and design of cycling-related infrastructure and road use with the aim of improving safety for cyclists.

Assessing the safety criticality of driver behavior toward cyclists at intersections.

Objective: Detailed analyses of car-to-cyclist accidents show that drivers intending to turn right at T-junctions collide more often with cyclists crossing from the right side on the bicycle lane than drivers intending to turn left. This fact has led to numerous studies examining the behavior of drivers turning left and right. However, the most essential question still has not been sufficiently answered: is the behavior of drivers intending to turn right generally more safety critical than the behavior of those intending to turn left? The purpose of this article is to provide a method that allows to determine whether a driver's behavior toward cyclists can retrospectively be assessed as critical or non-critical.Methods: Several theoretical considerations enriched by findings of experimental studies were employed to devise a multi-measure method. This method was applied to a dataset containing real-world approaching behavior of 48 drivers turning right and left at four T-junctions with different sight obstructions. For each driver a behavior-specific criticality was defined based on both, their driving and gaze behavior. Moreover, based on the behavior-specific criticality of each driver, the required field of view to see a cyclist from the right was defined and was set into relation with the available field of view of the T-junction.Results: The results show that only a small portion of the drivers within the dataset would have posed an actual risk to cyclists crossing from the right side. Those situations with a higher safety criticality did not only arise when drivers intended to turn right, but also left.Conclusion: Therefore, the analysis can only provide an explanation for the higher proportion of accidents between drivers turning right and cyclists crossing from the right side in certain situations. Further research, for example analyses of exposure data regarding the frequency of turning manoeuvers at T-junctions, is needed in order to explain the higher proportion of accidents between drivers turning right and cyclists crossing from the right side.

A methodology to estimate the number of unsafe vehicle-cyclist passing events on urban arterials.

In this study interactions between motorized vehicles and bicycles were studied by analyzing the overtaking behavior of motorized vehicles when passing bicycles on urban arterials. A methodology is presented to estimate the number of 'unsafe' passing events on 4-lane urban arterials with no on-street bike lanes. A 'critical passing distance' is defined to classify expected passing maneuvers i.e. when a motorized vehicle overtakes a bicycle, into 'safe' and 'unsafe' passing events. The proposed method enables calculation of the expected number of 'unsafe passing' events based on the expected bicycle demand, road segment's length, AADT, speed limit, and traffic signal timing parameters. The 'critical passing distance' is an input parameter and can be set by the planner. Given the number of expected 'unsafe passing' events, and institutional safety objectives and standards in terms of acceptable risk levels for cyclists, transportation planning departments can use the proposed methodology to decide whether provision of a specific cycling facility is necessary for a given road segment.

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.