Crash Types Research Articles

Injury Risk Assessment and Interpretation for Roadway Crashes Based on Pre-Crash Indicators and Machine Learning Methods

Pre-crash injury risk (IR) assessment is essential for guiding efforts toward active vehicle safety. This work aims to conduct crash severity assessment using pre-crash information and establish the intrinsic mechanism of IR with proper interpretation methods. The impulse–momentum theory is used to propose novel a priori formulations of several severity indicators, including velocity change (ΔV), energy equivalent speed (EES), crash momentum index (CMI), and crash severity index (CSI). Six IR models based on different machine learning methods were applied to a fusion dataset containing 24,082 vehicle-level samples. Prediction results indicate that the pre-crash indicators (PCIs) are more influential than the commonly used basic crash information because the average accuracy of six models can be improved by 14.35% after utilizing PCIs. Furthermore, the features’ importance and their marginal effects are interpreted based on parameter estimation, Shapley additive explanation value, and partial dependence. The ΔV, EES, and CMI are identified as the determinant indicators of the potential IR, and their partial distributions are significantly influenced by the crash type and impact position. Based on partial dependence probabilities, the study establishes decision thresholds for PCIs for each severity category for different impact positions, which can serve as a useful reference for developing targeted safety strategies. These results suggest that the proposed method can effectively improve pre-crash IR assessment, which can be readily transferred to safety-related modeling in an active traffic management system.

Investigation on Hazardous Material Truck Involved Fatal Crashes Using Cluster Correspondence Analysis

Although hazardous material (HAZMAT) truck-involved crashes are uncommon compared to other types of traffic crashes, these crashes pose considerable threats to the public, property, and environment due to the unique feature of low probability with high consequences. Using ten-year (2010–2019) crash data from the Fatality Analysis Reporting System (FARS) database, this study applies cluster correspondence analysis to identify the underlying patterns and the associations between the risk factors for HAZMAT-truck-involved fatal crashes. A low-dimensional space projects the categorical variables (including the crash, road, driver, vehicle, and environmental characteristics) into different clusters based on the optimal clustering validation criterion. This study reveals that fatal HAZMAT-truck-involved crashes are highly distinguishable concerning collision types (angle and front-to-front crashes, single-vehicle crashes, and front-to-end crashes) and roadway geometric variables, such as two-way undivided roadways, curve alignments, and high-speed (65 mph or more) urban interstate highways. Driver behavior (distraction, asleep or fatigue, and physical impairment), lighting conditions (dark–lighted and dark–not lighted), and adverse weather are also interrelated. The findings from this study will help HAZMAT carriers, transportation management authorities, and policymakers develop potential targeted countermeasures for HAZMAT-truck-involved crash reduction and safety improvement.

Enhanced Aggregate Framework to Model Crash Frequency by Accommodating Zero Crashes by Crash Type

In recent years, joint count and fractional split model structure based approaches have emerged as a credible alternative for multivariate crash frequency dependent variables. However, current approaches in the fractional split theme have a limitation. The fractional split component of these frameworks allocates a proportion to all crash configurations. It is possible that, across spatial units, several crash configurations might have a large share of zero crashes. In the traditional multivariate context, in the presence of a high share of zeros, researchers employ zero-inflated or hurdle variants such as the zero inflated negative binomial model. The current research effort improves the fractional split based multivariate model systems with an explicit consideration for the potential presence of zeros by crash configuration. The newly included binary component can be employed to identify safer (or riskier) zones by crash configuration. The framework also accommodates unobserved heterogeneity across the components of the model system. The proposed model structure is estimated using zonal data from the Central Florida region, U.S., for 2016. The model considered six crash types—rear-end, angle, sideswipe, single-vehicle, multi-vehicle (three or more), and non-motorized crashes. The model estimation is conducted using an exhaustive set of independent variables. The model results clearly highlight the importance of accommodating zero crashes by crash type in the analysis. The model exercise is further augmented with a validation analysis.

Left-turn crashes and motorcycle safety

Objective To provide updated statistics on crashes in which another vehicle turns left in front of an oncoming motorcycle and discuss the potential of left turn assist technology. Methods Motorcycle driver involvements in 2-vehicle fatal and police-reported crashes during 2017-2021 were tabulated by crash type, with a focus on crash types involving vehicles turning. Results Crashes in which another vehicle turned left in front of an oncoming motorcycle were, by far, the most frequent type of fatal 2-vehicle motorcycle crash, at 26%. Conclusion There is a large opportunity to reduce harm by specifically addressing crashes in which another vehicle turns left in front of an oncoming motorcycle – ideally using a variety of countermeasures simultaneously.

Self-Paced Ensemble-SHAP Approach for the Classification and Interpretation of Crash Severity in Work Zone Areas

The identification of causative factors and implementation of measures to mitigate work zone crashes can significantly improve overall road safety. This study introduces a Self-Paced Ensemble (SPE) framework, which is utilized in conjunction with the Shapley additive explanations (SHAP) interpretation system, to predict and interpret the severity of work-zone-related crashes. The proposed methodology is an ensemble learning approach that aims to mitigate the issue of imbalanced classification in datasets of significant magnitude. The proposed solution provides an intuitive way to tackle issues related to imbalanced classes, demonstrating remarkable computational efficacy, praiseworthy accuracy, and extensive adaptability to various machine learning models. The study employed work zone crash data from the state of New Jersey spanning a period of two years (2017 and 2018) to train and evaluate the model. The study compared the prediction outcomes of the SPE model with various tree-based machine learning models, such as Light Gradient Boosting Machine, adaptive boosting, and classification and regression tree, along with binary logistic regression. The performance of the SPE model was superior to that of tree-based machine learning models and binary logistic regression. According to the SHAP interpretation, the variables that exhibited the highest degree of influence were crash type, road system, and road median type. According to the model, on highways with barrier-type medians, it is expected that crashes that happen in the same direction and those that happen at a right angle will be the most severe crashes. Additionally, this study found that severe injuries were more likely to result from work zone crashes that happened at night on state highways with localized street lighting.

Factors associated with driver injury severity of motor vehicle crashes on sealed and unsealed pavements: Random parameter model with heterogeneity in means and variances

The effect of sealed or unsealed road pavements on motorist’s injury severities has not been extensively explored. This study collected a four-year crash dataset (2015–2018) from South Australia to explore this issue. The data shows 3,812 and 1,086 crashes at sealed and unsealed pavement surfaces, respectively, during those years. This study examines the consequence of sealed and unsealed pavements on driver injury severity outcomes of motor vehicle crashes. A mixed logit model was developed by accounting for heterogeneity in means and variances of the random parameters. The variables were distributed among several categories: driver, temporal, spatial, roadway characteristics, crash type, vehicle type, and vehicle movement. Four random parameters were observed in the sealed model, whereas five parameters were in the unsealed one. Moreover, the sealed pavements model showed substantial heterogeneity in means of four of the random parameters, while the unsealed pavements model has some heterogeneity in both means and variances of some of the random parameters. Marginal effect results indicate that two indicator variables have enlarged the likelihood of driver severe injury consequences in sealed, alcohol involvement and posted speed limit > 100 km/hr. Additionally, four other significant variables sustain the probability of severe injury outcomes at unsealed pavement like male drivers, middle-aged drivers, rollover crash types, and crashes at straight roads. Based on these variables, various countermeasures were recommended to enhance the safety of both types of pavements.

Spatial analysis of moving-vehicle crashes and fixed-object crashes based on multi-scale geographically weighted regression

Previous researches have demonstrated that traffic crashes in urban areas are geographical events and strongly linked to local characteristics such as road network and land attributes. However, with a significant emphasis on moving-vehicle crashes, the spatial pattern of fixed-object crashes is unclear so far. The difference between these two types of crashes, and whether existing spatial tools such as geographically weighted regression can interpretate the occurrence mode have not been investigated before. To fill this gap, this paper focuses on understanding the spatial features and occurrence of these two types of crash, i.e., moving-vehicle and fixed-object on the city level. Crash data from Dalian, China were aggregated into subdistricts and calibrated with multi-scale geographically weighted regression (MGWR) models. A noticeable but similar clustering pattern was revealed in both types, with spatial overlap of their accident-prone regions. The spatial influence of explanatory variables (road network, geographic, demographic, socio-economic, and land-use variables) was also found mostly similar in both types of crashes. However, fixed-object crash in downtown is more affected by node count, while POI entrance/exit count, especially those in areas with more industrial zones tend to significantly reduce crash risk. In both types of crashes, terrain slope rather than elevation is found to mitigate the crash risk, especially in the downtown area. Compared to traditional Geographically Weighted Regression (GWR) with a fixed bandwidth, the improvement in modeling performance using MGWR highlights the reasonability and benefits to consider the influence scale of each contributing factor in urban spatial analysis of traffic collisions. This study could help transportation authorities identify high-risk regions, understand their contributing factors and take precautions for improving the local traffic safety.

Effectiveness of bicycle helmets and injury prevention: a systematic review of meta-analyses

To mitigate the risk of injuries, many countries recommend bicycle helmets. The current paper seeks to examine the effectiveness of bicycle helmets by performing a systematic review focusing on meta-analyses. First, the current paper explores the findings of studies that employ meta-analyses using bicycle crash data. Second, the results are discussed considering the findings from research analyzing bicycle helmet effectiveness in a laboratory using simulation, and then are complemented with key methodological papers that address cycling and the overall factors contributing to the injury severity. The examined literature confirms that wearing a helmet while cycling is beneficial, regardless of age, crash severity, or crash type. The relative benefit is found to be higher in high-risk situations and when cycling on shared roads and particularly preventing severe head injuries. The results from the studies performed in laboratories also suggest that the shape and size of the head itself play a role in the protective effects of helmets. However, concerns regarding the equitability of the test conditions were found as all reviewed studies used a fifty-percentile male head and body forms. Lastly, the paper discusses the literature findings in a broader societal context.

DESIGN AND MODELLING OF GROUND PROXIMITY WARNING SYSTEM (GPWS) FOR TRAINING AID

Controlled flight into terrain (CFIT) is the most common type of aircraft crashes in the world of aviation today. Following the sheer volume of terrain collisions, there was an introduction of CFIT classification to enable the grouping into one category of all crashes involving aircrafts colliding with terrains. To avert CFIT accidents, the Ground Proximity Warning System (GPWS) was introduced onboard the aircraft for the flight crew to be aware of the moment the aircraft is approaching a ground terrain via automatic and very distinct acoustic warning. This paper presents a digital display ground proximity warning system as a teaching aid with audio prompt using ultrasonic sensor to produce forward distance from aircraft to obstacle ahead, height, inclination of aircraft in relation to its terrain beneath the aircraft and a digital computer interface for instrument visualization for better performance using graphic user interface (GUI) thereby enabling clearer understanding on GPWS. The Arduino IDE using Arduino sketch and C language were used on the Arduino Uno. The aircraft was integrated with different electronic modules which includes Arduino single board, Ultrasonic sensor, accelerometer and transceiver module, and all these uses the Arduino to read the signal from the sensor, processes and transmit to the base station wirelessly via radio frequency. Simulation results indicates that the responses of the models are a replica of the actual aircraft operations.

Multi-vehicle safety functions for freeway weaving segments using lane-level traffic data

This study develops Safety Performance Functions (SPFs) for freeway weaving segments. Due to the coexistence of three different movements including through, merging, and divering traffic, the probability of crashes in weaving segments is higher compared to other segment types. Further, the traffic flow in this section is the most unstable. Hence, to analyze detailed traffic conditions, this study utilized lane-level traffic data. The SPFs were developed using the Poisson Lognormal (PLN) regression model technique. The results showed that different traffic parameters were significant based on the types of crashes. For the rear-end crashes model, more general traffic conditions of the weaving segment were found to be significantly associated with the crash frequency such as the natural logarithm of average speed of through lanes. Nevertheless, for the sideswipe and angle crashes models, the traffic variables which are directly related to the weaving movements were selected as significant factors such as the off-ramp volume ratio, and standard deviation of speed of the rightmost lane. The results presented in this study can be meaningful in that they can serve as a basis for the weaving segments related safety evaluation studies. In addition, the developed models’ results can be a great source to establish operational strategies to improve traffic safety on freeway weaving segments.

The association between strengthened cellphone laws and police-reported rear-end crash rates

Introduction: Prior evaluations of the connection between cellphone bans and crashes show unclear results. California, Oregon, and Washington enacted legislation (effective in 2017) to update earlier bans specific to handheld conversation and texting. This study evaluated the relationship between the laws and rear-end rates, a crash type sensitive to visual-manual cellphone use, in California, Oregon, and Washington. Method: Negative binomial regression compared the change in monthly per capita rear-end crash rates in California, Oregon, and Washington before and after the law changes relative to two control states, Colorado and Idaho, during 2015–2019. Analyses examined (a) rear-end crashes with injuries in all three study states, including minor to fatal injuries; and (b) rear-end crashes of all severities in California and Washington, including property-damage-only crashes and crashes with injuries; Oregon was excluded from this analysis because of a 2018 change to its reporting criteria for property-damage-only crashes. Results: Washington’s strengthened law was associated with a significant 7.6% reduction in the rate of monthly rear-end crashes of all severities relative to the controls. Law changes in Oregon and Washington were associated with significant reductions of 8.8% and 10.9%, respectively, in the rates of monthly rear-end crashes with injury relative to the controls. California did not experience changes in rear-end crash rates of all severities or with injuries associated with the strengthened law. Conclusion: The results of this study are mixed, with law changes associated with significant reductions in rear-end crash rates in two of the three study states. Differences in the wording of the laws, levels of enforcement, and sanction severity may help explain the divergent results. Practical application: Crash reductions in Oregon and Washington suggest that enacting legislation that comprehensively bans practically all visual-manual cellphone activity may have made the laws easier to enforce and clarified to drivers that handheld cellphone use is unacceptable in these states.

Examination of factors associated with fault status and injury severity in intersection-related rear-end crashes: Application of binary and bivariate ordered probit models

Rear-end crashes are a relatively common crash type and often occur at or near intersections. Although rear-end crashes are generally less severe than some other crash types, there were still 2,346 fatal and 595,000 injury rear-end crashes in the US in 2019 alone. These crashes are generally caused by one at-fault driver who strikes a leading vehicle, and it may be useful to assess factors associated with a driver being at-fault. Additionally, it is important to analyze factors associated with injury severity outcomes in order to develop countermeasures aimed at preventing severe injuries. As such, this study investigates factors related to fault status and injury severity (and the interrelation between the two) in two-vehicle intersection-related rear-end crashes using data from a southwestern US state. A binary probit model was estimated to assess factors associated with fault status, while a bivariate ordered probit model was estimated to assess factors associated with driver injury severity by fault status. Importantly, by modelling the injury severity of both crash-involved drivers jointly, potential within-crash correlation can be accounted for. The results of the fault status model indicated numerous factors were associated with a fault status including vehicle type, driver age, and driver impairment or distraction. The results of the bivariate injury severity model indicated numerous factors were significantly associated with injury severity and importantly, differences were observed between at-fault and not-at-fault drivers. Ultimately, the results of this study may assist in development of targeted countermeasures aimed at reducing both crash occurrence and severe injury outcomes.

A Review of Surrogate Safety Measures Uses in Historical Crash Investigations

Historical road crash data are the main indicator for measuring road safety outcomes. Over the past few decades, significant efforts have been made in obtaining and exploiting Surrogate Safety Measures (SSMs). SSMs have the potential to provide excellent sustainable road safety indicators and proxy measurements which can complement traditional historical crash analyses or even substitute them. By using SSMs, crash data collection demands can be bypassed and areas can be investigated before crashes occur. Due to such advantages, the objective of the present research is to provide a review of the scientific literature regarding studies exploiting SSMs for historical crash record investigations. Specifically, 34 studies were examined, providing insights on the different types of SSMs collected under real road environment conditions, the way they are collected, their connection with specific road crash types, and the type of the developed statistical models are examined and discussed. Particular focus is also placed on the temporal dimension of the collection period of both SSMs and road crashes. Finally, the overall trends deriving from the reviewed studies are summarized and future research directions are provided.

Do driver monitoring technologies improve the driving behaviour of distracted drivers? A simulation study to assess the impact of an auditory driver distraction warning device on driving performance

Being distracted while driving is a major cause of road crashes. To help prevent this particular type of crash, new driver monitoring technologies track ocular and head movement and alert drivers when periods of prolonged distraction are detected, thus preventing hazardous situations on the road. In this mixed-factorial multi-level experiment, the behaviour of drivers was measured as they performed a secondary task while being monitored via an auditory Driver Distraction Warning (A-DDW) device which reminded them to look at the road ahead and cease the activity which was the source of distraction. Here, we evaluated the effectiveness of this type of DDW device by measuring longitudinal and lateral behaviour in situations where the driver is intentionally distracted for a significant period of time, and when the device repeatedly reminds the driver of his/her primary task.Forty-two participants were randomly stratified into three groups and subjected to the following distraction levels: (i) not distracted (baseline), (ii) distracted, and (iii) distracted but interacting with the A-DDW device. All participants drove in (i) free-flowing and (ii) stable traffic conditions along straight motorway segments. Speed and speed deviation values for distracted drivers were lower than for undistracted ones but they also experienced a significant deterioration in vehicle lateral control. Conversely, drivers assisted by the A-DDW device experienced a considerable improvement in their lateral control even though their speed and speed deviation values were higher. The reaction times of distracted drivers interacting with the A-DDW device deteriorated as revealed in cognitive tests conducted before and after the driving task.

Analyzing the Impacts of Intersection Treatments and Traffic Characteristics on Bicyclist Safety: Development of Data-Driven Guidance on the Application of Bike Boxes, Mixing Zones, and Bicycle Signals

Transportation modes converge at intersections resulting in conflicts between bicyclists and motor vehicles. A common conflict and crash type involving bicycles at intersections is the right-hook, where a right-turning vehicle collides with a through bicyclist. While various geometric and signal control treatments have been used in attempts to mitigate bicycle–vehicle crashes and conflicts, agencies often face questions about optimal treatment selection. To date, limited research has been conducted to analyze how certain treatments (bike boxes, mixing zones, and bicycle signals) along with traffic characteristics (e.g., vehicle, bicycle, and pedestrian volumes) affect the frequency of bicycle–vehicle conflicts, as well as the severity of such conflicts. To address this research need, this study provides an analysis of bicycle–vehicle conflicts extracted from field-collected videos at 12 intersections; three with bike boxes, three with mixing zones, three with bicycle signals, and three control sites with no specific bicycle treatment. From these videos, bicycle–vehicle conflicts were identified and their severity measured using post-encroachment time (PET), along with conflict-involved road user speeds and hourly road user volumes. From these data, a series of Poisson regression models were estimated to assess hourly predicted conflicts as a function of bicycle and vehicle volumes across different treatment types. Additionally, differences in conflict severity across treatments were analyzed, including use of a novel measure which incorporates both PET and vehicle speed. Ultimately, several differences in conflict frequency and severity were observed between the treatment types, providing new data-driven guidance to assist practitioners in treatment selection.

Using Bidirectional Encoder Representations from Transformers (BERT) to classify traffic crash severity types

Traffic crashes are a critical safety concern. Many studies have attempted to improve traffic safety by performing a wide range of studies on safety topics with the application of diverse statistical and machine learning models. The data elements contained in police-reported crash narrative information are not routinely analyzed with coded and structured crash data. In the recent years, unstructured textual contents in traffic crash narratives have been investigated by many researchers. However, most of these studies are basic text mining applications and often the dataset is limited in size. This study applied an advanced language model Bidirectional Encoder Representations from Transformers (BERT) to classify traffic injury types by using a dataset of over 750,000 unique crash narrative reports. The models have an 84.2% ±0.5 predictive accuracy and an Area Under the receiver operating Curve (AUC) of 0.93 ± 0.06 per class. Overall, the findings can assist safety engineers and analysts in determining the causes of a crash. The classification of crash injury types using a language model like BERT is a valuable tool for identifying additional factors that contribute to crashes, which can identify new areas for safety countermeasures and support the development of new safety strategies.

Bayesian Network for Motorcycle Crash Severity Analysis

Given the lack of protective structural barriers and advanced restraints, motorcyclists are vulnerable road users. In 2020 in the United States, motorcycle-involved fatalities occurred 28 times more frequently per vehicle mile traveled than passenger car occupant fatalities, causing 5,579 motorcycle-related fatalities—the highest number of motorcyclists killed since 1975. By identifying patterns and relationships between key contributing factors, strategies for reducing motorcycle crashes can be developed. In addition to current efforts, additional research must be conducted using innovative avenues, with increased funding. Bayesian networks can better discover the relationships between potential speed compliance variables. This study used six years (2014 to 2019) of motorcycle crash data in Louisiana to determine the conditional probabilities of the influential factors. In addition to the high contribution of alcohol involvement, two-way undivided roadways, 35 to 44 year-old drivers involved in improper driving behaviors, and crash types are the underlying factors associated with a considerable increase in motorcycle crash severity. The findings of this study can also be used for decision making and strategy development for motorcycle safety.

Effects on crash risk of automatic emergency braking systems for pedestrians and bicyclists

Objective The first automatic emergency braking (AEB) system was presented in 2003 and aimed to mitigate or reduce rear-end crashes. Since then, several AEB systems aimed to reduce other collision types have been introduced and studies have shown that they reduce crash risks. The aim with this study was to evaluate crash reductions of cars fitted with AEB systems with pedestrian detection and those with bicyclist detection. Methods The study is based on the Swedish Traffic Accident Data Acquisition that includes road traffic accidents reported by the police and by emergency hospitals. Crashes occurring between 2015 and 2020 and with cars from model years 2015 to 2020 were included. The statistical analysis used odds ratio calculations with an induced exposure approach where the outcomes of sensitive and nonsensitive crashes were studied. The sensitive crashes were hit pedestrians and bicyclists, respectively. The nonsensitive crash type in both comparisons was struck vehicles in rear-end crashes. Evaluations were also made for different light and weather conditions and for high and low speed roads. Results Seven hundred and twelve hit pedestrians and 1,105 hit bicyclists were included, and the nonsensitive crashes consisted of 1,978 vehicles. The overall reduction on crash risk for AEB with pedestrian detection was 8% (±15%; ns) and for AEB with bicyclist detection it was 21% (±17%). When separating for light conditions, no reduction in crash risk for AEB with pedestrian detection nor for AEB with bicyclist detection could be seen in darkness. However, in daylight and twilight conditions, AEB with pedestrian detection reduced pedestrian crash risk by 18% (±19%; ns) and AEB with bicyclist detection reduced bicyclist crash risk by 23% (±19%). No significant reductions could be seen when separating for weather conditions except for a 53% (±31%) reduction for bicyclists in rain, fog, and snowfall. A larger reduction on high-speed roads (50–120 km/h) compared with low-speed roads (10–40 km/h) was also found. Conclusions AEB systems with bicyclist detection were found to reduce the numbers of hit bicyclists, especially in daylight and twilight conditions. In darkness, no reduction for hit pedestrians or bicyclists was found.

Motorcycle rider error and engagement in distracting activities: A study using the Attention-Related Driving Errors Scale (ARDES-M)

Research on motorcycle rider distraction and inattention is limited, especially when compared to that on car drivers. This study examines motorcycle rider error and its relationship with engagement in distracting activities. Additionally, it provides an adaptation of the Attention-Related Driving Errors Scale for use with motorcycle riders (ARDES-M). A sample of 418 motorcyclists responded to a web questionnaire consisting of the ARDES-M, a self-report measure of Involvement in Distracting Activities (IDA), a measure of everyday inattention (ARCES, Attention Related Cognitive Errors Scale) and socio-demographic and riding-related questions. Riding errors were associated with a higher level of engagement in distracting activities and a greater propensity for inattention in daily life. ARDES-M scores were associated with various types of self-reported traffic crashes and near-crashes, while self-reported distractions (IDA) were only linked with near-crashes. The most widely self-reported distracting activities were using map navigation, listening to the radio or to music and adjusting vehicle devices. The various uses of cell phones were relatively minor. Young riders reported higher levels of engagement in distracting activities and committing more riding errors. The ARDES-M showed good psychometric properties for the sample in Spain.

Look right! The influence of bicycle crossing design on drivers’ approaching behavior

One of the most frequent crashes between cyclists and motor vehicles is the so-called “turning into” accident, where a motor vehicle turns into the main road and collides with a cyclist riding on the main road and crossing the vehicle’s course. Previous studies mainly examined this type of crash and its causes with accident analyses or observational approaches. This study uses a driving simulator to examine the effect of possible countermeasures like the drivers’ expectancy towards crossing cyclists, the view into the junction, and various bicycle crossing designs. N = 66 participants passed T-junctions that differ in the mentioned measures. Gaze and driving data were collected to assess the criticality of each approach. Results indicate that drivers approach marked bicycle crossings at a lower speed than unmarked crossings. Furthermore, crossings with pronounced designs showed more uncritical approaches. However, an alarming percentage of all approaches were critical because drivers showed no appropriate gaze behavior. This was even increased when the view into the junction was limited. The findings suggest that especially the view at junctions must not be obstructed to provide sufficient fields of view. Pronounced bicycle crossing, however, can enhance drivers’ approaching behavior and might help to reduce the frequency of “turning into” accidents.