Different Types Of Road Users Research Articles

A novel framework for crash frequency prediction: Geographic support vector regression based on agent-based activity models in Greater Melbourne

The field of spatial analysis in traffic crash studies can often enhance predictive performance by addressing the inherent spatial dependence and heterogeneity in crash data. This research introduces the Geographical Support Vector Regression (GSVR) framework, which incorporates generated distance matrices, to assess spatial variations and evaluate the influence of a wide range of factors, including traffic, infrastructure, socio-demographic, travel demand, and land use, on the incidence of total and fatal-or-serious injury (FSI) crashes across Greater Melbourne’s zones. Utilizing data from the Melbourne Activity-Based Model (MABM), the study examines 50 indicators related to peak hour traffic and various commuting modes, offering a detailed analysis of the multifaceted factors affecting road safety. The study shows that active transportation modes such as walking and cycling emerge as significant indicators, reflecting a disparity in safety that heightens the vulnerability of these road users. In contrast, car commuting, while a consistent factor in crash risks, has a comparatively lower impact, pointing to an inherent imbalance in the road environment. This could be interpreted as an unequal distribution of risk and safety measures among different types of road users, where the infrastructure and policies may not adequately address the needs and vulnerabilities of pedestrians and cyclists compared to those of car drivers. Public transportation generally offers safer travel, yet associated risks near train stations and tram stops in city center areas cannot be overlooked. Tram stops profoundly affect total crashes in these areas, while intersection counts more significantly impact FSI crashes in the broader metropolitan area. The study also uncovers the contrasting roles of land use mix in influencing FSI versus total crashes. The proposed framework presents an approach for dynamically extracting distance matrices of varying sizes tailored to the specific dataset, providing a fresh method to incorporate spatial impacts into the development of machine learning models. Additionally, the framework extends a feature selection technique to enhance machine learning models that typically lack comprehensive feature selection capabilities.

Aggression of electric bike riders in China: comparisons of the Cycling Anger Expression Inventory toward drivers, pedestrians, and other riders

ABSTRACT Electric bikes (e-bikes) account for an unexpected proportion of traffic crash injuries and deaths in China. There is an urgent need to study the factors influencing the aggressive behaviours of e-bike riders and reduce their potential involvement in crashes. In addition, aggression varies when directed at different types of road users, particularly when expressed by vulnerable groups. This study aimed to validate the Cycling Anger Expression Inventory (CAX) for e-bikers and explore whether the expression of aggression in e-bikers differs depending on the recipients, such as drivers, pedestrians, or other riders. The analysis examined the aggression specificity of e-bikers by employing tests of measurement invariance (MI). These findings indicate that adaptive/constructive expression has emerged as a mainstream strategy for managing e-biker anger. E-bikers exhibited the highest levels of aggression toward drivers. These results provide a strong basis for the prevention and intervention of aggression and accidents among Chinese e-bikers.

The epidemiology and outcomes of severe road traffic injury by road user type in Korea: A nationwide retrospective observational study

ObjectivesRoad traffic injuries (RTIs) pose a significant public health burden, and more than half of these fatalities are attributed to vulnerable road users (VRUs). This study aimed to evaluate the epidemiology and outcomes of severe RTIs in Korea by focusing on different types of road users. MethodsThis is nationwide retrospective observational study. Using data from the Korean Nationwide Severe Trauma Registry, this study analyzed severe RTI cases from 2016 to 2020. The study included EMS-treated severe trauma patients, defining severe RTI as cases with an injury severity score (ISS) ≥16 or out-of-hospital cardiac arrest (OHCA). The main variable of interest was the road user type, classified as motor vehicle occupants (MVOs), pedestrians, motorcyclists, and bicyclists. Trends and injury characteristics by road user type were analyzed, and multivariate logistic regression was conducted to calculate the adjusted odds ratios (AORs) and 95 % confidence intervals (CIs) of road user type for in-hospital mortality. ResultsOf the 143,021 EMS-treated severe trauma cases, 24,464 were included in this study. Pedestrians represented the largest group (n = 8,782; 35.9 %). More than half of the patients died (n = 12,620, 51.6 %), and a high proportion of patients had OHCA (n = 10,048, 41.1 %). There was no significant change in the overall severe RTI numbers from 2016 to 2020, but a decrease in pedestrian cases and an increase in motorcyclist cases were noted (both p for trend<0.05). Low usage of safety devices was observed (28.2 % of motor vehicle occupants used seat belts, 35.9 % of motorcyclists used helmets, and 9.6 % of bicyclists used helmets). Head injuries were most common, particularly among bicyclists (77.0 %) and motorcyclists (69.8 %). Compared to motor vehicle occupants, pedestrians (AOR [95 % CI] 1.12 [1.04–1.20]) and others (AOR [95 % CI] 1.30 [1.02–1.65]) had higher odds of mortality, while motorcyclists (AOR [95 % CI] 0.64 [0.59–0.69]) and bicyclists (AOR [95 % CI] 0.68 [0.60–0.76]) had lower odds of mortality. ConclusionWe found varying trends and injury characteristics in severe RTIs according to road user type. Adapting prevention strategies for evolving road user patterns, with particular attention to increasing safety device usage and addressing the high mortality associated with severe RTIs are warranted.

Injury severity and injury patterns in older participants in road traffic

Older people are or remain increasingly mobile for longer and participate in road traffic as car drivers or passengers, cyclists, and pedestrians. Regardless of their role in causing accidents, they are more likely to be seriously injured due to their higher vulnerability. If they are involved in an accident they suffer increasingly more from severe injuries, which consequently leads to longer hospitalization times. These aspects are even more applicable for persons aged75 years or more than for persons aged 65-74 years. From aGerman in-depth accident study (GIDAS) analysis of the individual injuries of different types of road users, the most frequently severely injured body regions as well as the leading injuries can be derived. Primarily head and thorax injuries are of importance and secondarily also injuries to the lower extremities (especially for cyclists and pedestrians). The majority of the presented results confirm findings from comparable studies; however, this study was conducted for the first time on the basis of the abbreviated injury scale (AIS) 2015 and some individual injuries (especially commotio cerebri, which dominates in almost all age and road user groups) were upgraded from AIS1 to AIS2 in the latest AIS revision. As aresult, the current results partly show significant increases in injury severity, especially for the head, compared to earlier studies based on the AIS 2008.

Shared space multi-modal traffic modeling using LSTM networks with repulsion map and an intention-based multi-loss function

Shared space is an innovative, yet controversial concept of traffic regulation that features the coexistence of different types of road users (cars, motorcycles, bicycles, pedestrians, etc.), with the same priority (no traffic lights or signs) and in the same, unsegregated environment. The effect on traffic conditions, as well as on safety, has not yet been systematically documented due to the lack of dedicated traffic models and simulation tools. In this paper, we develop a variation of the Occupancy LSTM network, enhanced with spatial and temporal proximity aspects and a sophisticated, intention-based loss function that models interactions in a shared space environment and predicts the trajectories of vehicle and pedestrian users. The model achieves a significant prediction accuracy, which allows its exploitation for simulating shared space conditions. Moreover, a simulation experiment is designed to evaluate the concept of shared space in terms of efficiency and safety. Results indicate that the interactions that take place in such an environment would lead to a slight increase of travel time for both types of users (compared to free flow conditions) and a low number of possible conflicts.

The long-memory temporal dependence of traffic crash fatality for different types of road users

This study investigates the long-memory auto-correlations within daily fatality time series data of the driver, passenger, bicyclist, and pedestrian, and the long-memory cross-correlations between the fatality series of different road users. The analysis is based on the traffic crash fatalities in Florida during the period 1975–2015. A detrended fluctuation analysis (DFA) and a detrended cross-correlation analysis (DCCA) are used for capturing the long-memory auto-correlations and the cross-correlations, respectively. The investigation is conducted through the following steps: (1) identifying the periodicity of the traffic fatalities series via traditional signal processing methods; (2) removing the season effects identified by simple average value analysis and principal component analysis (PCA) from the original time series; (3) based on the original and processed series, exploring the long-memory auto-correlations and cross-correlations in the time series data of different road users. The results of DFA indicate that the auto-correlations of the fluctuations in the daily fatalities are positive for all road users regardless of the seasonal effects. The results of DCCA indicate that the fluctuation in the fatalities for any type of road user is positively associated with that for other road users. The fluctuation in traffic fatalities for any road user is therefore not random-walk but correlated to their fluctuations in the last time unit and that of other road users during the same period. The findings of this study add to the understanding of the long-memory property in traffic fatalities and provide implications for the development of time series models in the traffic safety field.

Hello, world! VIVA+: A human body model lineup to evaluate sex-differences in crash protection.

Finite element Human Body Models are increasingly becoming vital tools for injury assessment and are expected to play an important role in virtual vehicle safety testing. With the aim of realizing models to study sex-differences seen in the injury- and fatality-risks from epidemiology, we developed models that represent an average female and an average male. The models were developed with an objective to allow tissue-based skeletal injury assessment, and thus non-skeletal organs and joints were defined with simplified characterizations to enhance computational efficiency and robustness. The model lineup comprises female and male representations of (seated) vehicle occupants and (standing) vulnerable road users, enabling the safety assessment of broader segments of the road user population. In addition, a new workflow utilized in the model development is presented. In this workflow, one model (the seated female) served as the base model while all the other models were generated as closely-linked derivative models, differing only in terms of node coordinates and mass distribution. This approach opens new possibilities to develop and maintain further models as part of the model lineup, representing different types of road users to reflect the ongoing transitions in mobility patterns (like bicyclists and e-scooter users). In this paper, we evaluate the kinetic and kinematic responses of the occupant and standing models to blunt impacts, mainly on the torso, in different directions (front, lateral, and back). The front and lateral impacts to the thorax showed responses comparable to the experiments, while the back impact varied with the location of impact (T1 and T8). Abdomen bar impact showed a stiffer load-deflection response at higher intrusions beyond 40 mm, because of simplified representation of internal organs. The lateral shoulder impact responses were also slightly stiffer, presumably from the simplified shoulder joint definition. This paper is the first in a series describing the development and validation of the new Human Body Model lineup, VIVA+. With the inclusion of an average-sized female model as a standard model in the lineup, we seek to foster an equitable injury evaluation in future virtual safety assessments.

Impact of comorbidities on survival following major injury across different types of road users

BackgroundWhile comorbidities and types of road users are known to influence survival in people hospitalised with injury, few studies have examined the association between comorbidities and survival in people injured in road traffic crashes. Further, few studies have examined outcomes across different types of road users with different types of pre-existing comorbidities. This study aims to examine differences in survival within 30 days of admission among different road user types with and without different pre-existing comorbidities. MethodRetrospective cohort study using data for all major road trauma cases were extracted from the NSW Trauma Registry Minimum Dataset (1 January 2013 – 31 July 2019) and linked to the NSW Admitted Patient Data Collection, and the NSW Registry of Births, Deaths and Marriages - death dataset. Pre-existing comorbidities and road user types were identified by the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision, Australian Modification (ICD-10-AM) codes and Charlson Comorbidity Index in the Trauma Registry, hospital admission, and death datasets. Logistic regression was used to assess the associations between six types of road users (pedestrian, pedal cycle, two- and three-wheel motorcycle, car and pick-up truck, heavy vehicle and bus, and other types of vehicle) and death within 30 days of hospital admission while controlling for comorbidities. All models used ‘car and pick-up truck driver/passenger’ as the road user reference group and adjusted for demographic variables, injury severity, and level of impaired consciousness. ResultsWithin 6253 traffic injury person-records (all aged ≥15 years old, ISS>12), and in final models, injured road users with major trauma who had a history of cardiovascular diseases (including stroke), diabetes mellitus, and higher Charlson Comorbidity Index score, were more likely to die, than those without pre-existing comorbidities. Furthermore, in final models, pedestrians were more likely to die than car occupants (OR: 1.68 – 1.77, 95CI%: 1.26 – 2.29 depending on comorbidity type). ConclusionsThis study highlights the need to prioritize enhanced management of trauma patients with comorbidities, given the increasing prevalence of chronic medical conditions globally, together with actions to prevent pedestrian crashes in strategies to reach Vision Zero.

Speed behaviour of right‐turn drivers at signalized intersections in China

Ensuring human-like driving performance is fundamental to the widespread adoption and acceptance of intelligent driving systems. The speed behaviour of right-turn drivers at signalized intersections was studied to improve the human-like driving capability. The study was conducted using natural driving data and 545 data for drivers turning right at signalized intersections were extracted. YOLOv4 was used to identify the different types of road users. A one-way ANOVA was used to study the influence of the scene complexity, number of lanes, and intersection shape on the speed selection behaviour. Linear regression and binary logistic regression analyses were used to study the influence of road users, number of lanes, intersection shape, and vehicle motion state on the speed control behaviour. The main results indicate the following: (1) Drivers drove slower when there were more traffic participants or fewer lanes. (2) The intersection shape did not have any effect on the speed behaviour of right-turn drivers, either speed selection behaviour or speed control behaviour. (3) The reaction distance was affected only by the approach speed in this study; the greater the speed, the longer the reaction distance. (4) In addition to the vehicle motion state, external environmental factors also play a critical role in the braking characteristics, that is, pedestrians, cyclists, lateral traffic, and number of lanes after the turn. (5) The driver's intention to brake after entering the intersection stemmed from road users who were in collision conflict with the host vehicle and fewer lanes after the turn, rather than the speed at the stop line. This research has a guiding significance for the design of human-like driving of right-turn driving assistance systems. The target classification method and the measurement method of the driving environment complexity also help improve the human-like perception of the right-turn driving assistance system.

Addressing inequal risk exposure in the development of automated vehicles

Automated vehicles (AVs) are expected to operate on public roads, together with non-automated vehicles and other road users such as pedestrians or bicycles. Recent ethical reports and guidelines raise worries that AVs will introduce injustice or reinforce existing social inequalities in road traffic. One major injustice concern in today’s traffic is that different types of road users are exposed differently to risks of corporal harm. In the first part of the paper, we discuss the responsibility of AV developers to address existing injustice concerns regarding risk exposure as well as approaches on how to fulfill the responsibility for a fairer distribution of risk. In contrast to popular approaches on the ethics of risk distribution in unavoidable accident cases, we focus on low and moderate risk situations, referred to as routine driving. For routine driving, the obligation to distribute risks fairly must be discussed in the context of risk-taking and risk-acceptance, balancing safety objectives of occupants and other road users with driving utility. In the second part of the paper, we present a typical architecture for decentralized automated driving which contains a dedicated module for real-time risk estimation and management. We examine how risk estimation modules can be adjusted and parameterized to redress some inequalities.

Sustainable Safety in The Netherlands Creating a Road Environment where People on Foot and on Bikes are as Safe as People in Cars

Road crashes claim over one million lives each year worldwide, overwhelmingly in low- and middle-income countries. A handful of higher-income countries have made great progress in reducing traffic fatalities and are moving toward Vision Zero. The goal of this study is to evaluate how one such country, the Netherlands, has cut its traffic fatalities by over 90%. The results show that the Dutch have virtually eliminated the concept of “vulnerable road users” in that the risk of fatality for pedestrians, bicyclists and vehicle occupants has all converged at a low level. This is an amazing achievement, especially when compared with countries like the U.S. where the risk of fatality for non-vehicle occupants is 5–8 times that of vehicle occupants. In this paper, we assess the evolution of risk for different types of road users in the Netherlands since 1970. We also review critical events, advocacy, policies, and programs that were implemented in the Netherlands over the last five decades to address the issue of traffic safety. This analysis demonstrates that the Dutch used protests and advocacy campaigns to garner support for policies and programs that promoted non-motorized transportation as routine mobility choice. Furthermore, the governing body for safety in the Netherlands was an early adopter (in the 1990s) of a systems-based approach to traffic safety called Sustainable Safety. A 2020 FHWA webinar highlights that this systems-based approach is now beginning to take hold in the U.S.

Road users’ evaluations and perceptions of road infrastructure, trip characteristics, and daily trip experiences across countries

Governments in every country have made attempts to encourage road safety, and road users’ perceptions of these attempts are crucial to understanding community needs and the effectiveness of road safety initiatives. However, while many studies compare countries with respect to fatality rates or possible causative factors related to these fatalities, little is known about how road users in different countries evaluate and perceive their tangible road environments from their own points of view. Therefore, the purpose of this study is to statistically explore drivers’-motorcyclists’, pedestrians’, cyclists’ and public transport users’ evaluations and perceptions of road infrastructure, trip characteristics and daily trip experiences in Estonia, Greece, Kosovo, Russia, and Turkey. In total, 1221 participants completed a questionnaire battery that included different sets of questions for different types of road users. Chi-Square analysis was used to examine the similarities and differences in the evaluations and perceptions of different road users across five countries. The main findings suggest that all road users across the five countries evaluated environmental characteristics significantly differently from each other. No similar trend could be observed within and between road users and countries. This is a quantitative study that provides descriptive information about road users’ perceptions and evaluations across countries, which can be used for both conceptual and practical purposes. In future studies, this study finding can be used for system-based interventions and may inform government regulation on human or human-environmental interactive factors in order to improve regional and overall road safety.

GIS aided sustainable urban road management with a unifying queueing and neural network model

With the tide of electrifying urban transportation systems by introducing electric vehicles, the differences between fuel vehicles and electric vehicles in driving styles and strategies to achieve eco-driving have become a burden for efficient operations of urban transportation systems. Most of the previous energy management strategies have sought to achieve system optimisation at a single-vehicle or multi-vehicles level, and failed to consider the vehicle-to-vehicle and vehicle-to-infrastructure effects in a global optimisation manner. Furthermore, as a typical human-in-the-loop cyber–physical system, the mobility behaviours of road users undoubtedly play a vital role in the cooperative and green operations of urban transportation systems. Yet little research has dedicated to develop means to incentivise energy-saving behaviours in transportation systems. Hence, in this paper, we propose a unifying queueing and neural network model to calculate the time and energy efficient course of actions and routes for different types of road users within an urban road network in a real time manner. The lower-level queueing model captures the interactive dynamics of road users and solves the optimal flow ratio at each intersection while the upper-level neural network model further customises desired routes for different types of road users. In addition, an incentive mechanism is proposed to encourage road users to follow the optimal actions via publishing various types of reward-gaining tasks. A case study in a designated area of Beijing shows that the use of the bi-level optimisation algorithm can reduce the average travel time by approximately 20% and decrease the energy consumption by 10% in comparison with the realistic trip data.

Classification of road traffic injury collision characteristics using text mining analysis: Implications for road injury prevention.

Road traffic injuries are a leading cause of morbidity and mortality globally. Understanding circumstances leading to road traffic injury is crucial to improve road safety, and implement countermeasures to reduce the incidence and severity of road trauma. We aimed to characterise crash characteristics of road traffic collisions in Victoria, Australia, and to examine the relationship between crash characteristics and fault attribution. Data were extracted from the Victorian State Trauma Registry for motor vehicle drivers, motorcyclists, pedal cyclists and pedestrians with a no-fault compensation claim, aged > = 16 years and injured 2010-2016. People with intentional injury, serious head injury, no compensation claim/missing injury event description or who died < = 12-months post-injury were excluded, resulting in a sample of 2,486. Text mining of the injury event using QDA Miner and Wordstat was used to classify crash circumstances for each road user group. Crashes in which no other was at fault included circumstances involving lost control or avoiding a hazard, mechanical failure or medical conditions. Collisions in which another was predominantly at fault occurred at intersections with another vehicle entering from an adjacent direction, and head-on collisions. Crashes with higher prevalence of unknown fault included multi-vehicle collisions, pedal cyclists injured in rear-end collisions, and pedestrians hit while crossing the road or navigating slow traffic areas. We discuss several methods to promote road safety and to reduce the incidence and severity of road traffic injuries. Our recommendations take into consideration the incidence and impact of road trauma for different types of road users, and include engineering and infrastructure controls through to interventions targeting or accommodating human behaviour.

Routes’ Safety Evaluation Application Development

Pedestrians and cyclists are the most vulnerable road users. One of the ways to increase their safety is the safest route selection. Nowadays, there are a lot of navigation systems and route planners. However, these routes are not assessed from the viewpoint of their safety. The article describes one of the possible system alternatives to assess the safety of route for different types of road users. The developed system is based on the multifactorial analysis of information. Correction factors allow considering special features of each type of road users. The developed algorithm is implemented as an application.

The Intersection of the Future

One of the keys to smarter cities is sensors that can, not only reliably detect and classify different types of road users at intersections, but can also broadcast that information to other road users, making intersections safer for all participants

Observational Study of Pedestrian and Cyclist Interactions at Intersections in Vancouver, BC and Montréal, QC

As cycling and walking for transportation continues to become more popular in urban settings, there is increased potential for interactions between different types of road users, including between pedestrians and cyclists. However, because of limited data, we know relatively little about the frequency and nature of pedestrian-cyclist interactions. In this observational study we aimed to quantify the extent of pedestrian crossings that involved an interaction with a cyclist at 10 intersections in Vancouver, British Columbia (BC) and Montréal, Quebec (QC), and identify road user and crossing environment characteristics associated with these interactions. Of the 3,884 pedestrians we observed, 562 (14%) were involved in an interaction with a cyclist. The interaction rate was slightly higher in Montréal (16.5%) than Vancouver (13.4%), but varied considerably across intersections (range from 0.9% to 35.8%). Men were slightly more likely to be involved in an interaction with a cyclist, as well as pedestrians crossing at a slower pace at the beginning of the crossing, and at mid-crossing. Contrary to common thought, distracted pedestrians (either using a cell phone or wearing headphones) were not more likely to be involved in an interaction. When considering the street crossing environment, interactions were more likely at crossings with cycle tracks, stop or yield signs, three-way intersections, crossings that had no pedestrian ground markings, and longer crosswalks. Our study provides insight into interactions between pedestrians and cyclists, a well-known gap in transportation safety, and can help identify which urban design features are needed to ensure safe and comfortable pedestrian crossings.

Zone-Specific Interaction Modeling of Pedestrians and Cars in Shared Spaces

In shared space environments, different types of road users share the urban space and frequently interact with each other, e.g., to negotiate priority. Instead of traffic rules, their interactions are governed by social protocols such as courtesy behavior and by informal rules like the rule of the road. Modeling the movement behavior of road users in shared places is crucial to evaluate the safety and efficiency of such environments. Based on the observation that this behavior differs based on environment topology, in this paper, we investigate two types of shared space topology: intersection zones and road zones. We use real-world data to investigate how road users behave differently in different shared space zones and model zone-specific movement behavior of road users. We validate our model by simulating various zone-specific scenarios involving pedestrian-to-pedestrian, multiple cars to pedestrians and also car-to-car interactions. The results indicate that our simulation model produces realistic behaviors.

Epidemiological profile and pattern of injuries in road traffic accidents in Moradabad: A prospective study

Background: In many countries, motor vehicle accidents rank first among all fatal accidents. RTAs have got multi-factorial causation. It is a part of the price we pay for our technological progress. Regional differences exist in the pattern of injury sustained by different types of road users that can have significant implications in the development of prevention policies. The objective of the Study: To understand the profile and pattern of injuries sustained and circumstances leading to RTAs in and around Moradabad, U.P. Materials and Methods: After ethical clearance for the study, all RTA victims presenting to the Casualty of TMU Moradabad, U.P. were taken for this study. All relevant details of RTIs were recorded and data collected, entered on a predesigned proforma and then tabulated, analyzed and interpreted statistically. Results: Out of 730 patients included in this study, most common victims were pedestrians (33.01%), most common site of injury was head (86.71%), most common external injury was lacerated wound (46.75%), and intoxication was seen in 24.11%, protective/safety measures used by 10.23%. Conclusion: RTAs continue to be a speedily rising problem, causing heavy loss of manpower and resources. Road users should be properly trained by authorized centers; driving licenses should be issued after strict testing of driving skills. Trauma centers with integrated facility of surgical, orthopedic, neurosurgical, anesthetic experts with modern investigative procedures like USG, CT-Scan and facility of blood-bank is the best solution for RTA victims who are severely injured.

Cyclists' Anger As Determinant of Near Misses Involving Different Road Users.

Road anger constitutes one of the determinant factors related to safety outcomes (e.g., accidents, near misses). Although cyclists are considered vulnerable road users due to their relatively high rate of fatalities in traffic, previous research has solely focused on car drivers, and no study has yet investigated the effect of anger on cyclists’ safety outcomes. The present research aims to investigate, for the first time, the effects of cycling anger toward different types of road users on near misses involving such road users and near misses in general. Using a daily diary web-based questionnaire, we collected data about daily trips, bicycle use, near misses experienced, cyclist’s anger and demographic information from 254 Spanish cyclists. Poisson regression was used to assess the association of cycling anger with near misses, which is a count variable. No relationship was found between general cycling anger and near misses occurrence. Anger toward specific road users had different effects on the probability of near misses with different road users. Anger toward the interaction with car drivers increased the probability of near misses involving cyclists and pedestrians. Anger toward interaction with pedestrians was associated with higher probability of near misses with pedestrians. Anger toward cyclists exerted no effect on the probability of near misses with any road user (i.e., car drivers, cyclists or pedestrians), whereas anger toward the interactions with the police had a diminishing effect on the occurrence of near misses’ involving all types of road users. The present study demonstrated that the effect of road anger on safety outcomes among cyclists is different from that of motorists. Moreover, the target of anger played an important role on safety both for the cyclist and the specific road users. Possible explanations for these differences are based on the difference in status and power with motorists, as well as on the potential displaced aggression produced by the fear of retaliation by motorized vehicle users.