Safety Of Vulnerable Road Users Research Articles

Improving Truck Driver and Vulnerable Road User Interactions Through Driver Training: An Interview Study With Canadian Subject Matter Experts

Collisions between trucks and vulnerable road users (VRUs) represent one of the most severe types of road collisions. Research initiatives to mitigate truck–VRU collisions include vehicle redesign, infrastructure improvements, and driver warning systems. Truck driver training is a complementary solution that can lead to improved driving behavior and collision reduction. We aimed to understand the perceptions of subject matter experts of current truck driver training in Ontario, Canada, to identify gaps and potential improvements, particularly targeting VRU safety in urban areas. Further, we investigated the degree to which VRU safety is covered and the potential to incorporate it in training through simulators. We conducted semi-structured interviews with 21 truck driver trainees, novice and experienced truck drivers, a driving instructor, and road safety professionals. The participants highlighted a notable gap between current training and real-world truck driving. VRU safety and hazard anticipation training emerged as a missing component in training. Nine out of 11 participants who received simulator training perceived it positively and recommended it for its safe and relatively realistic environment. The main topics that should be incorporated in VRU safety training, as recommended by interviewees, include training on difficult truck maneuvers with presence of VRUs, anticipating hazardous VRU actions, and navigating difficult infrastructure components. This work presents subject matter expert perceptions of current truck driver training in Ontario, Canada, and identifies gaps and improvements targeting VRU safety. Although the study is based on Ontario, the results can apply across Canada and beyond, where trucks pose dangers to VRUs.

Do Traffic Countermeasures Improve the Safety of Vulnerable Road Users at Signalized Intersections? A Combination of Case-Control and Cross-Sectional Studies Using Video-Based Traffic Conflicts

Driven by the vision of eliminating road fatalities, Vision Zero initiatives have been widely adopted by many cities around the world, with significant investment of resources in various safety countermeasures. However, there is still a lack of reliable quantitative evidence on the effectiveness of those countermeasures on the traffic conflict frequency at intersections. This research attempts to address this challenge with a combination of case-control and cross-sectional studies, aiming at quantifying the safety effects of three commonly applied Vision Zero countermeasures, namely, leading pedestrian interval, no right turn on red, and installation of a dedicated bicycle lane. A case study was conducted using video trajectory data from 10 signalized intersections in the City of Toronto, Canada. The traffic interactions between vehicles and vulnerable road users were extracted using a video data processing platform, and two surrogate measures of safety, including post-encroachment time and conflict speed, were obtained, and then used to classify the conflict severity into different levels. A comparative analysis using mixed-effects negative binomial regression was conducted to quantify the impacts of different treatments on the frequency of traffic conflicts under specific road weather and traffic conditions. The results show that these three types of traffic countermeasures can effectively reduce the frequency of high-risk and moderate-risk traffic conflicts, moderated by various traffic exposure and weather and environmental conditions and accessible pedestrian signals. These findings could help road safety engineers and decision makers make better informed decisions on their road safety initiatives and projects.

Before-after safety analysis of a shared space implementation

Shared spaces aim to reduce the dominance of motor vehicles by promoting pedestrian/cyclist activity and minimizing segregation between road users.Despite the intended scope to improve the safety of vulnerable road users, only few works in the literature focused on before-after safety evaluations, mainly analyzing changes in users’ trajectories and speeds, traffic volumes, and conflict counts, which, while useful, cannot univocally quantify road safety.Here, we propose a more advanced methodology, based on surrogate measures of safety and Extreme Value Theory, to assess road safety before and after the implementation of a shared space. The aim is to produce a crash risk estimation in different scenarios, obtaining a quantitative and comprehensive indicator, useful to practitioners for evaluating the safety of urban design solutions.A real-world case study illustrates the proposed procedure. Video data were collected on two separate days, before and after a shared space implementation, and were semi-automatically processed to extract road users’ trajectories. Analysis of traffic volumes, trajectories, speeds and yield ratios allowed to understand the spatial behavior of road users in the two scenarios. Traffic conflicts, identified with an innovative surrogate measure of safety called “time to avoided collision point” (TTAC), were then used to estimate a Lomax distribution, and therefore to model the probabilistic relationship between conflicts and crashes, eventually retrieving a crash risk estimate. Results show that the analyzed shared space was able to significantly reduce the risk of crashes, and these findings are consistent with the observed changes in users speed and spatial behavior. The analyzed case study and its limitations were useful in highlighting the methodology main features and suggesting practical prescriptions for practitioners.

How do drivers allocate visual attention to vulnerable road users when turning at urban intersections?

IntroductionDrivers turning at urban intersections pose a high risk to Vulnerable Road Users (VRUs), such as cyclists and pedestrians. In vehicle collisions with VRUs, driver attention misallocation is considered a leading contributor. While previous naturalistic studies have examined driver gaze behaviors at intersections, findings are limited to general gaze directions obtained through video analysis, meaning specific areas to which drivers attend cannot be determined. MethodWe present a secondary analysis of an on-road instrumented vehicle dataset collected in 2019 which offers eye-tracking and video data from 26 experienced drivers (13 cyclists and 13 non-cyclists). Three coders jointly examined eye-tracking footage from four right-signalized turns (n = 96) to quantify drivers’ glance distributions to various areas of interest, including those most relevant to VRU safety when drivers turn. Individual temporal glance patterns and general attention allocation trends are presented and described. Results(1) Relevant pedestrians were the top objects of glance irrespective of signal status, and (2) at red light turns, driver attention was heavily skewed toward leftward traffic. ConclusionsThis analysis provides a detailed report of driver glance distributions toward scene-specific areas (as opposed to general directions) at urban intersections and discusses how these patterns may influence VRU safety. Practical applicationsThis study provides important information regarding the human factors challenges of vehicle-VRU collisions and their prevention.

Opinions of active transportation users on policies to ensure their perceived safety in the era of autonomous vehicles

Active transportation users, including pedestrians and bicyclists, have expressed significant safety concerns over the co-existence of autonomous vehicles (AVs) on the road in the future. However, insufficient emphasis has been paid to exploring the demands of safety-related public policies with empirical evidence, and even much of the previous literature has not fully incorporated the viewpoints of the active transportation users. Thus, this study explored the opinions of active transportation users on five safety-related policies in the era of AVs: (1) a speed limit restriction (policy 1), (2) human-occupied AVs at all times (policy 2), (3) active engagement of human drivers in school zones (policy 3), (4) trip-related data sharing (policy 4), and (5) AV technology-related data sharing (policy 5). This study developed five multinomial logit models using the 2019 BikePGH survey data collected in Pittsburgh, Pennsylvania, and revealed the following main findings. First, active transportation users who reported feeling extremely safe when sharing the road with AVs were significantly more likely to believe that none of the five policies were necessary. However, those who believed AVs would harm traffic injuries and fatalities favored policies 2 and 3. Also, those negatively affected by the fatal crash in Arizona expressed the need for policies 1 and 2. Additionally, those who have shared the road with AVs believed that policies 1, 2, and 3 were crucial. This research will benefit AV stakeholders, particularly transportation officials and planners, as it will offer guidelines to ensure the perceived safety of vulnerable road users and make roads safer for them in the AV era.

Identifying Factors Related to Crash Injury Levels Involving Bicyclists at Different Locations Through Crash Data Analysis

The safety of vulnerable road users, including bicyclists, has become an increasing societal concern. Factors characterizing bicyclists’ crashes with motor vehicles may affect bicyclists’ injuries differently depending on the location of these crashes. The purpose of this paper is to provide a comprehensive analysis for identifying significant factors that affect bicyclists’ injury levels from crashes occurring at travel lanes and at non-travel lanes (e.g., crosswalks and bicycle lanes). For this purpose, this study applied Multinomial Logistic Regression on the Crash Report Sampling System data for three consecutive years. Bicyclists’ injuries were categorized into three levels: (1) Possible, (2) Moderate, and (3) Severe. The study found that running a separate model for each location provided better performances than running an aggregated model for both locations. Results showed common factors significantly associated with an increased likelihood of moderate and/or severe injuries at both locations. Five unique factors were associated with higher likelihoods of these moderate and/or severe injuries to bicyclists in the Travel Lane model, whereas two unique factors were found related to increased odds of these injuries to bicyclists in the Non-Travel Lane model. The results of this study contribute to a better understanding of bicyclists’ crash scenarios and the development of potential countermeasures by alternating some circumstances characterizing these crashes, when possible, to reduce potential injuries.

Using reaction times and accident statistics for safety impact prediction of automated vehicles on road safety of vulnerable road users

The global effort to make automated vehicles a common reality on the roads of the world is intensifying in recent years and the challenges of automated driving are being investigated in ever more detail and variety. Among the foremost promises of using automated vehicles (AVs) in daily commute is their assumed benefit on road safety, which should offer additional safety to the most vulnerable road users (pedestrians, cyclists), as well as other vehicles.In this article we aim to derive a functional relationship (dose–response curve) between the proportion of automated vehicles on the road (penetration rate) and the expected accident numbers/fatalities of interactions with vulnerable road users. Our approach is built upon two fundamental components: Firstly, based on an analysis of current accident causes, we can make a projection of which causes of accidents between cars and vulnerable road users could ideally be mitigated by AVs and which not. Secondly, for the accidents that are not mitigated, we still assume a potential for reduction of accident occurrence and accident severity, based on the assumed reaction time of an automated vehicle, compared to the reaction time of a human driver.Based on statistics, braking distances and reaction times, as well as the power model, we derive an estimate of the potential reductions in accidents and fatalities in the presence of automated vehicles, ultimately expressed as a relationship between the proportion of automated vehicles (penetration rate) and the accident numbers/fatality rates of accidents between motorized vehicles and vulnerable road users.

Smart urban traffic zones – demonstration of three smart zones in Sweden based on geofencing and sensor data

Starting September 2020 and ending September 2022, the project “Smart Urban Traffic Zones” has demonstrated three different set-ups of smart urban traffic zones in the Cities of Stockholm and Gothenburg, Sweden. These zones are based on geofencing technology, smart sensors and V2X communication. The pilots have mainly been aimed at improving road safety for vulnerable road users, increasing transport efficiency in the urban environment, and creating a more flexible use of public space in the city. The results show that it is possible to set-up smart zones with this purpose and that the main challenges for scale-up are related to legal requirements, processes for data sharing and information management.

SECREDAS: Safe and (Cyber-) Secure Cooperative and Automated Mobility

Infrastructure-to-Vehicle (I2V) and Vehicle-to-Infrastructure (V2I) communication is likely to be a key-enabling technology for automated driving in the future. Using externally placed sensors, the digital infrastructure can support the vehicle in perceiving surroundings that would otherwise be difficult to perceive due to, for example, high traffic density or bad weather. Conversely, by communicating on-board vehicle measurements, the environment can more accurately be perceived in locations which are not (sufficiently) covered by digital infrastructure. The security of such communication channels is an important topic, since malicious information on these channels could potentially lead to a reduction in overall safety. Collective perception contributes to raising awareness levels and an improved traffic safety. In this work, a demonstrator is introduced, where a variety of novel techniques have been deployed to showcase an overall architecture for improving vehicle and vulnerable road user safety in a connected environment. The developed concepts have been deployed at the Automotive Campus intersection in Helmond (NL), in a field testing setting.

Characterization of a C-ITS based VRU safety system: Methodology towards scenarios selection

SAFE-UP is a research project funded by the European Commission, that aims to proactively address the novel safety challenges of future road mobility environment, by developing tools and innovative safety methods leading to improvements in road traffic safety, which are addressed by four demonstrators. This paper focuses on Demonstrator 4, which develops a safety system based on Cooperative Intelligent Transport Systems (C-ITS) technology that provides enhanced communication between vehicles, road infrastructure and vulnerable road users (VRU). This demonstrator aims at: (i) the provision of timely warnings which could support in avoiding safety-critical situations between passenger cars and VRUs, (ii) the engagement of an active safety system in cases where the reaction time is limited. This paper presents the method followed to characterize the selected scenarios for Demonstrator 4.

A Sensing, Communication and Computing Approach for Vulnerable Road Users Safety

Most fatal road accidents in urban areas involve vulnerable road users. New solutions for fighting against these accidents can be considered by leveraging connected, intelligent vehicles and smart cities connecting all parts of an urban environment. This work proposes a multi-sensing and communication approach to prevent potential accidents between vehicles and VRUs, by predicting and notifying both about potential collisions before they happen. This approach leverages and aggregates information from smart city sensors, dispersed in the vehicles (and aggregated by the On-Board Units, OBUs), in the VRUs (e.g., smartphones and smartwatches), and on the road itself (e.g. video cameras, radars, lidars). These elements communicate through several message standards and wireless access technologies (e.g. ITS-G5, C-V2X, LTE, 5G and, in the future, 6G). Using both sensing and communication, fusion and collision detection algorithms, this system predicts and notifies potential hazardous situations involving Vulnerable Road User (VRU)s and vehicles. The results in a real scenario with sensors, VRUs and vehicles on the road show that the system predicts potential collisions with high accuracy and low delay. Results also point to some vital deployment decisions that must be made to ensure proper notification timings, such as the usage of multi-homing, 5G and edge computing.

5G-enabled V2X communications for vulnerable road users safety applications: a review

Intelligent Transportation System (ITS) is continuously evolving alongside communication technologies and autonomous driving, giving way to new applications and services. Considering the significant rise in traffic casualties, protecting vulnerable road users (VRU), such as pedestrians, cyclists, motorcycles, animals, etc., has become ever more critical. That said, technological advances alone can not meet the requirements of such crucial applications. Therefore, combining them with architectural revolutions, particularly cloud, fog, and edge computing, is essential. In this review, we scrutinize the VRU safety application with regard to technological evolution. This review establishes the foundations for designing resilient, more reliable, end-to-end VRU protection services. It illustrates the possibility of combining the performance of different technologies through exploiting 5G architectural advantages (function placement, direct/indirect communication, etc.) for the intended application. In the context of 5G architecture, collision avoidance systems consider network and application-related challenges and solutions. This survey provides standardization, studies, and project efforts related to the use case and considers the different types of messages in the V2VRU communication-based safety application. We investigate how adapting the application parameters to the network state and devices’ available resources can use network resources efficiently and provide reliable services.

A method for predicting ITS cooperative applications performances

Vehicular cooperation taking place in Intelligent Transportation Systems (ITS) aims to solve traffic issues or to improve road safety for vehicles and vulnerable road users. Such cooperation is achieved through distributed applications running in the vehicular network (VANET), composed of communicating units in vehicles and infrastructure. For safety and legal reasons, it is expected that ITS services reach a satisfactory level of efficiency in the context they are deployed. However, predicting the behavior of distributed applications running in dynamic topologies remains a complex problem, which limits their deployment. In this paper, we propose a method able to predict the performances of cooperative ITS (C-ITS) applications in dynamic vehicular networks. It concerns applications relying on messages exchanges between close vehicles or between vehicles and infrastructure. The proposed method is based on an analysis of the application with respect to the vehicular traffic for determining the conditions ensuring the success of its underlying distributed algorithms and protocols. This is formalized as performances properties depending on the network dynamic. It becomes then easy to check whether these conditions are fulfilled or not in a given road traffic, either captured (and anonymized) for an existing road or expected for a new one. Thanks to an appropriate modeling, the conditions are sought and the performances of the C-ITS applications are predicted. We believe that our approach fills a gap between formal studies, VANET simulations and road tests. It will be of particular interest for the deployment of C-ITS applications. This is illustrated by several use-cases studied in realistic conditions.

Modeling the Frequency of Pedestrian and Bicyclist Crashes at Intersections: Big Data-driven Evidence From Maryland

This study leverages big location-based service data collected from mobile devices in 2019 to conduct a pedestrian and bicyclist safety analysis. Statistical models are estimated for pedestrian and bicyclist crash frequency at Maryland intersections using location-based service data as risk exposure data. The analysis is performed by employing prominent frequency modeling methodologies, including Poisson, negative binomial, zero-inflated Poisson, and zero-inflated negative binomial regression techniques. The findings indicate that inclusion of big location-based service exposure data in the analysis improves the performance of the models. Further, the results suggest that key contributing factors to pedestrian and bicyclist crashes at Maryland intersections include the following: (i) intersection design- and traffic-related attributes, such as the number of intersection legs, presence of a traffic signal, and average level of traffic stress rating, as well as safety risk exposure measures, such as the average daily pedestrian, bicyclist, and vehicle volumes at the intersection; (ii) travel-related attributes, including public transportation and nonmotorized mode shares within the intersection’s census block group; (iii) land use and built environment attributes, such as road network density, activity density, and extent of walkability within the census block group; (iv) socioeconomic and sociodemographic attributes, including the percentage of low-income workers, households with no vehicles, African American population, and senior population within the census block group. The findings of the study show how big location-based service exposure data can be utilized to identify pedestrian and bicyclist safety risks and guide data-driven, evidence-based policy decision-making to improve the safety of vulnerable road users.

Existence of the safety-in-numbers effect in the aspect of injury severity: A macroscopic analysis for bicyclists and pedestrians

Objective: Several studies have confirmed the existence of a safety-in-numbers effect in relation to vulnerable road users. The safety-in-numbers effect refers to a phenomenon wherein the number of bicyclists/pedestrians on a road is higher, and consequently, the risk of each bicyclist/pedestrian being involved in a crash is lower. Nevertheless, the existence of the safety-in-numbers effect in the aspect of injury severity in traffic crashes has not yet been investigated. Thus, this study aimed to explore whether traffic injuries are more (less) severe with fewer (more) pedestrians/bicyclists at the county level. Method: Using two fractional split multinomial logit models, the relationships between the number of bicyclists/pedestrians and the proportion of crashes involving bicyclists/pedestrians based on crash severity were investigated at the county level using crash data from Florida. In other words, we explored whether differing number of bicyclists/pedestrians could change the distribution of traffic injury severity levels. Results: The modeling results clearly revealed a lower proportion of severe injuries caused to bicyclists/pedestrians at a higher level of daily bicycle/pedestrian flows, indicating existence of the safety-in-numbers effect. Several variables (e.g., the percentage of people aged 65 years and older, the percentage of commuters using public transportation, and the proportion of recreational land use) were found to have a significant effect on the distribution of traffic injury severity among bicyclists/pedestrians. Conclusion: This study proves that a safety-in-numbers effect exists in the aspect of injury severity among bicyclists and pedestrians. Practical applications: These findings are expected to provide recommendations for promoting the use of active transportation, which will improve the safety of vulnerable road users in future.

Toward Context Awareness for Cooperative Vulnerable Road User Collision Avoidance: Incorporating Related Contextual Information

Recent approaches for increasing road safety for vulnerable road users (VRUs), like pedestrians or cyclists, propose the usage of mobile devices for exchanging movement and context information. This additional information about VRUs allows one, for example, to track their movements even in non-line-of-sight (NLOS) conditions. However, the usage of mobile devices for cooperative approaches also poses new challenges, like the high energy demand for exchanging and transmitting movement information or the inadequate position accuracy of current smartphones. Recent publications for cooperative VRU collision avoidance have successfully proven to address those challenges by using additional VRU-related contextual information. This article provides an in-depth analysis of how various kinds of VRU-related contextual information can support cooperative VRU collision avoidance and mitigate limitations that arise from using mobile devices. As one use case for using contextual information, we evaluate how a curb-detection module can increase the VRU’s position accuracy and thereby significantly decrease the false alarm probability in a near-miss scenario.

Interventions to improve pedestrian and cyclist safety in Latin America: a systematic review and metasummary

Road user fatalities account for one of the leading causes of preventable death in Latin America with pedestrians and bicyclists at higher risk for more extensive injuries as compared to other road users. Despite these vulnerable road user (VRU) risks, encouraging individuals to walk and cycle is an important public health strategy for addressing the region's obesity epidemic through promoting regular physical activity via active transportation (AT). However, in order to promote AT as a viable source of physical activity, safety of the VRU must be considered. Interventions to improve VRU safety are critical but a current understanding of the impact of these interventions in the region is lacking. The purpose of this systematic review and metasummary is to describe the effectiveness of interventions that have been implemented in Latin America to improve pedestrian and bicyclist safety. A systematic literature search of public health, policy and engineering databases was completed using search terms generated through the PICO method. The PRISMA framework was used to screen articles for inclusion and analysis. Eight articles detailing nine interventions across four countries were included for final synthesis and organized according to the Three E’s Model of Injury Prevention (Education, Engineering and Enforcement). Interventions assessed outcomes related to VRU safety ranging from attitudes and behaviors to fatal injuries, with only enforcement-based interventions reporting on the latter. Evidence-based interventions to inform the design of programs to promote AT and VRU safety in the region are lacking and should be of urgent public health priority.

Vulnerable road user safety and freight vehicles: A case study in North Carolina and Tennessee

Increasing freight traffic has posed greater road safety threats to Vulnerable Road Users (VRUs), including pedestrians and bicyclists, in urban communities. Although there are some disaggregated studies on truck-related crashes, the literature offers limited knowledge of how crash-level factors influence crashes related to urban freight. Our study uses crash data from North Carolina (2007–2019) and Tennessee (2009–2019) to explore the relationship between the crash-level factors and the occurrence and injury severity of crashes involving freight vehicles and VRUs. Crash-level factors include socio-demographics of VRUs and freight vehicle drivers, driving behaviors, temporal and weather effects, and environmental factors. The results indicate that freight-related VRU crashes, compared to nonfreight-related VRU crashes, are more likely to occur at private properties and parking areas and have a higher probability of causing severe injuries or deaths. The results also show that the involvement of larger vehicles and the occurrence at midblock segments are positively associated with more severe injury outcomes. The research results could help community and transport designers to increase attention to the safety impacts of growing freight traffic in urban communities.

Integrating Vulnerable Road User Safety Criteria into Transportation Asset Management to Prioritize Budget Allocation at the Network Level

Safety is a major worldwide concern due to the increasing number of fatalities of vulnerable road users (VRUs). VRUs are pedestrians, bicyclists, and motorcyclists, and their safety is a priority when making infrastructure management decisions. Traditionally, transportation agencies have adopted transportation asset management (TAM) practices based on performance measures to assess the physical condition of transport infrastructure. This paper describes a framework to incorporate VRU’s safety into the TAM decision-making process. The main objective of the VRU-TAM framework is to mitigate pedestrian fatalities by improving the decision-making process at the strategic and operational management level. The VRU-TAM framework is composed of four phases: assessment, prioritization, scenario analysis, and results. It includes a prioritization methodology using a safety-weighted effectiveness ratio (SWER) to consider pedestrian safety factors and costs in the decision-making process. In a broader perspective, pedestrian safety is affected by driver, demographic, pedestrian, infrastructure, and policy related factors. The decision-making criteria reflected in SWER considers the asset importance, location, pedestrian safety risk, costs, and remaining life in the budget allocation process, and adopts the dynamic bubble up (DBU) technique for budget prioritization at the network level. An example is presented for crosswalks to demonstrate the applicability of the methodology to evaluate different budget-driven scenarios for funding allocation. As a conclusion, it is demonstrated that the outcomes of the budget-driven scenarios following the method that incorporates safety criteria, with tangible metrics, offer a deeper understanding of the effects of budgetary constraints on backlog costs and the remaining life of infrastructure assets.

Development of a Systematic Methodology to Enhance the Safety of Vulnerable Road Users in Developing Countries

Development of a Systematic Methodology to Enhance the Safety of Vulnerable Road Users in Developing Countries