BACKGROUND: Vision Zero aims to eliminate serious and fatal road injuries using a Safe System approach. Safe System principles establish that safety is a shared responsibility; this involves both multisector partners and community engagement. This descriptive study explored multisector partners and community engagement in the development of municipal Vision Zero plans.
METHODS: We reviewed all first edition Vision Zero plans published by US municipalities from 2014 to 2022. Using a structured coding tool, we abstracted partner involvement and community engagement strategies used in the development of Vision Zero plans.
RESULTS: We identified, reviewed and abstracted 64 plans. The average number of partner groups per plan was 11.5 (12.0 for municipalities with a population ≥150 000; 10.1 for municipalities <150 000) and was higher for later plans (11.9 for plans published 2019-2022; 10.0 for plans published 2014-2018). Common partner groups engaged in the plan were law enforcement (85.9% of plans), local transportation planning (78.3%), mayor/city council/city manager (78.1%), engineering/public works (78.1%) and schools (73.4%). Community engagement strategies were reported in 71.9% of the plans and were more frequent among municipalities with a population ≥150 000 (76.1%) compared with a population <150 000 (61.1%), and in those with more recent plans (82.1%) versus earlier ones (56.0%). The most common community engagement strategies were public meetings, online surveys and map mark-ups.
CONCLUSIONS: These findings highlight the extent to which Vision Zero plans were aligned with core Safe System tenets regarding diverse partner involvement and community engagement. Plan developers should consider the translation of Safe System principles in Vision Zero plan development.

Elderly pedestrians are involved in disproportionately more vehicle-pedestrian crashes than younger age groups. Training programs have been found to be effective in training children in pedestrian behaviours that improve their safety, however there is no consensus on whether older adults benefit from training. This systematic review aimed to identify whether training is effective for older adult pedestrians through analysis of training type, modalities, and the lasting effects of training. A systematic search of Medline, PsycINFO, and Scopus was conducted in March 2022 and updated in September 2023. Eight studies met the criteria all of which were high quality. Four distinct training types were found: physical (e.g., training physical strength or balance), behavioural (e.g., training specific pedestrian safety behaviours), cognitive (e.g., training reaction time and executive functioning), and educational (training knowledge about pedestrian safety behaviours). Physical training types were found to be most effective, followed by behavioural, cognitive, and educational respectively. Twelve pedestrian behaviours were measured across the eight studies. Reaction time was the most effectively trained outcome, followed by missed crossing opportunities. Errors of stimuli, median accepted time gap, initiation time and crossing were not effectively trained. The effects of training were maintained at follow-up for missed crossing opportunities only. There was preliminary evidence of potential efficacy of training for specific pedestrian safety behaviours, however, the long-term efficacy of training was not promising. Theory-driven research is needed to better understand why some behaviours are more trainable than others. More research is also needed to determine the real-world generalisability if training is to be recommended for older adult pedestrians.

As vulnerable road users, pedestrians and cyclists are facing a growing number of injuries and fatalities, which has raised increasing safety concerns globally. Based on the crash records collected in the Australian Capital Territory (ACT) in Australia from 2012 to 2021, this research firstly establishes an extended crash dataset by integrating road network features, land use features, and other features. With the extended dataset, we further explore pedestrian and cyclist crashes at macro- and micro-levels. At the macro-level, random parameters negative binomial (RPNB) model is applied to evaluate the effects of Suburbs and Localities Zones (SLZs) based variables on the frequency of pedestrian and cyclist crashes. At the micro-level, binary logit model is adopted to evaluate the effects of event-based variables on the severity of pedestrian and cyclist crashes. The research findings show that multiple factors are associated with high frequency of pedestrian total crashes and fatal/injury crashes, including high population density, high percentage of urban arterial road, low on-road cycleway density, high number of traffic signals and high number of schools. Meanwhile, many factors have positive relations with high frequency of cyclist total crashes and fatal/injury crashes, including high population density, high percentage of residents cycling to work, high median household income, high percentage of households with no motor vehicle, high percentage of urban arterial road and rural road, high number of bus stops and high number of schools. Additionally, it is found that more severe pedestrian crashes occur: (i) at non-signal intersections, (ii) in suburb areas, (iii) in early morning, and (iv) on weekdays. More severe cyclist crashes are observed when the crash type is overturned or struck object/pedestrian/animal; when more than one cyclist is involved; and when crash occurs at park/green space/nature reserve areas.

Pedestrian fatalities comprise a quarter of all traffic deaths in Low-and-Middle-Income Countries (LMICs). The use of safer modes of transport such as buses can reduce road trauma as well as air pollution and traffic congestion. Although travelling by bus is safer than most other modes, accessing bus stops can be risky for pedestrians. This paper systematically reviews factors contributing to the safety of pedestrians near bus stops in countries of differing income levels. The review included forty-one studies from high (20), upper-middle (13) and lower-middle income countries (8) during the last two decades. The earliest research was conducted in high-income countries (HICs), but research has spread in the last decade. The factors influencing pedestrian safety fell into three groups: (a) characteristics of road users, (b) characteristics of bus stops and (c) characteristics of the road traffic environment. Pedestrians near bus stops are frequently exposed to a high risk of collisions and fatalities due to factors such as unsafe pedestrian behaviours (e.g., hurrying to cross the road), lack of bus stop amenities such as safe footpaths, high traffic speeds and traffic volumes, multiple lanes, and roadside hazards (e.g., parked cars obscuring pedestrians). Road crash statistics are commonly used to identify unsafe bus stops in HICs but the unavailability and unreliability of data have prevented more widespread use in LMICs. Future research is recommended to focus on surrogate safety measures to identify hazardous bus stops for pedestrians.

UNASSIGNED: Child pedestrian injuries represent a significant public health challenge. Understanding the most complex cognitive skills required to cross streets helps us understand, improve, and protect children in traffic, as underdeveloped cognitive skill likely impacts children\'s pedestrian safety. One complex component of street-crossing is the cognitive-perceptual task of judging time-to-arrival of oncoming traffic. We examined capacity of 7- and 8-year-olds to judge time-to-arrival for vehicles approaching from varying distances and speeds, as well as improvement in those judgments following intensive street-crossing training in a virtual reality (VR) pedestrian simulator.
UNASSIGNED: 500 seven- and eight-year-olds participated in a randomized trial evaluating use of a large kiosk VR versus smartphone-based VR headset to teach street-crossing skills. Prior to randomization into VR training condition and also prior to initiation of any training, children engaged in a video-based vehicle approach estimation task to assess ability to judge traffic time-to-arrival. They then engaged in multiple VR-based pedestrian safety training sessions in their randomly assigned condition until achieving adult functioning. Soon after training and again 6 months later, children repeated the vehicle estimation task.
UNASSIGNED: Prior to randomization or training, children were more accurate judging time to arrival for closer versus farther traffic, and rapidly-moving versus slower-moving traffic, but those results were subsumed by a speed x distance interaction. The interaction suggested distance cues were used more prominently than speed cues, and speed had varying effects at different distances. Training group had minimal effect on learning and all children became significantly better at judging vehicle arrival times following training.
UNASSIGNED: Children tend to underestimate vehicle arrival times. Distance cues are more impactful on time-to-arrival judgments than speed cues, but children\'s estimations based both on manipulations of vehicle speed and manipulations of vehicle distance improved post-training. Improvements were retained six months later. This finding is consistent with psychophysics research suggesting vehicle approach judgments rely on optical size and looming, which are impacted both by vehicle speeds and distances. Implementation of VR-based training for child pedestrian safety is recommended, as it may improve children\'s judgment of vehicle time-to-arrival, but it must be conducted cautiously to avoid iatrogenic effects.

Virtual reality (VR) simulation offers a proactive, cost effective, immersive, and low risk platform for studying pedestrian safety. Within immersive virtual environments (IVEs), existing and alternative design conditions and intelligent transportation systems (ITS) technologies can be directly compared, prior to real-world implementation, to assess the impacts alternatives may have on pedestrian safety, perception, and behavior. Environmental factors can be controlled within IVEs so that test trials are replicable and directly comparable. Coupled with stated preference feedback, participants\' observed preferences and behavior provide a comprehensive understanding of the impacts of proposed design alternatives. This research presents a case study of pedestrian behavior with three different mid-block crossing safety treatments modeled within a one-to-one scale IVE replication of a real-world location in Charlottesville, Virginia. The three safety treatments consider both passive and active collision avoidance designs and technologies, including (1) the existing painted crosswalk, (2) the addition of rectangular rapid flashing beacons (RRFBs), and (3) a pedestrian to everything (P2X) ITS phone application. Additionally, this paper demonstrates a VR simulation experimental design and framework for testing pedestrian safety treatments within naturalistic and replicable IVEs to assess both stated and observed preferences and behaviors of pedestrians. Repeated measures ANOVA indicated changes in both accepted gap size (p = 0.001) and crossing speed (p < 0.001) with alternative safety treatments. Generalized mixed models showed that pedestrians waited for statistically larger gap sizes (p = 0.02) without the assistance of alternative safety technologies (RRFBs and P2X application) and pedestrians crossed the street significantly faster (p = 0.001) without the alternative safety technologies, leading to unsafe dashing behavior. Through post-experiment surveys, it was found that participants perceived the As Built environment to be the least safe of the three treatments and that their sense of risk within the IVE was realistic. Considering both the observed crossing behavior and stated feedback, pedestrians exhibited intentionally unsafe darting behavior without assistive safety technology. This study demonstrates how VR simulation may be leveraged to study both stated preferences and observed behavior for understanding the safety implications of alternative roadway designs, providing a proactive approach for assessing and designing for pedestrian safety.

BACKGROUND: Pedestrian and bicycling injuries may be less likely to be captured by traffic injury surveillance relying on police reports. Non-collision injuries, including pedestrian falls and single bicycle crashes, may be more likely than motor vehicle collisions to be missed. This study uses healthcare records to expand the ascertainment of active transportation injuries and evaluate their demographic and clinical features.
METHODS: We identified pedestrian and bicyclist injuries in records of deaths, hospitalisations and emergency department visits in Ontario, Canada, between 2002 and 2017. We described the most common types of clinical injury codes among these records and assessed overall counts and proportions of injury types captured by each ascertainment definition. We also ascertained relevant fall injuries where the location was indicated as \'street or highway\'.
RESULTS: Pedestrian falls represented over 50% of all pedestrian injuries and affected all age groups, particularly non-fatal falls. Emergency department records indicating in-traffic bicycle injuries not involving a collision with motor vehicles increased from 14% of all bicycling injury records in 2003 to 34% in 2017. The overall number of injuries indicated by these ascertainment methods was substantially higher than official counts derived from police reports.
CONCLUSIONS: The use of healthcare system records to ascertain bicyclist and pedestrian injuries, particularly to include non-collision falls, can more fully capture the burden of injury associated with these transportation modes.

In the real world, the severity of traumatic injuries is measured using the Abbreviated Injury Scale (AIS) and is often estimated, in finite element human computer models, with the maximum principal strains (MPS) tensor. MPS can predict when a serious injury is reached, but cannot provide any AIS measures lower and higher from this. To overcome these limitations, a new organ trauma model (OTM2), capable of calculating the threat to life of any organ injured, is proposed. The OTM2 model uses a power method, namely peak virtual power, and defines brain white and grey matters trauma responses. It includes human age effect (volume and stiffness), localised impact contact stiffness and provides injury severity adjustments for haemorrhaging. The focus, in this case, is on real-world pedestrian brain injuries. OTM2 model was tested against three real-life pedestrian accidents and has proven to reasonably predict the post mortem (PM) outcome. Its AIS predictions are closer to the real-world injury severity than the standard maximum principal strain (MPS) methods currently used. This proof of concept suggests that OTM2 has the potential to improve forensic predictions as well as contribute to the improvement in vehicle safety design through the ability to measure injury severity. This study concludes that future advances in trauma computing would require the development of a brain model that could predict haemorrhaging.

UNASSIGNED: The objective of this study was to describe fatal pedestrian injury patterns in youth aged 15 to 24 years old and correlate them with motor vehicle collision (MVC) dynamics and pedestrian kinematics using data from medicolegal death investigations of MVCs occurring in the current Canadian motor vehicle (MV) fleet.
UNASSIGNED: Based on a systematic literature review, MVC-pedestrian injuries were collated in an injury data collection form (IDCF). The IDCF was coded using the Abbreviated Injury Scale (AIS) 2015 revision. The AIS of the most frequent severe injury was noted for individual body regions. The Maximum AIS (MAIS) was used to define the most severe injury to the body overall and by body regions (MAISBR). This study focused on serious to maximal injuries (AIS 3-6) that had an increasing likelihood of causing death. The IDCF was used to extract collision and injury data from the Office of the Chief Coroner for Ontario (OCCO) database of postmortem examinations done at the Provincial Forensic Pathology Unit (PFPU) in Toronto, Canada, and other provincial facilities between 2013 and 2019. Injury data were correlated with data about the MVs and MV dynamics and pedestrian kinematics.The study was approved by the Western University Health Science Research Ethics Board (Project ID: 113440; Lawson Health Research Institute Approval No. R-19-066).
UNASSIGNED: There were 88 youth, including 54 (61.4%) males and 34 (38.6%) females. Youth pedestrians comprised 13.1% (88/670) of all autopsied pedestrians. Cars (n = 25/88, 28.4%) were the most frequent type of vehicle in single-vehicle impacts, but collectively vehicles with high hood edges (i.e., greater distance between the ground and hood edge) were in the majority. Forward projection (n = 34/88, 38.6%) was the most frequent type of pedestrian kinematics. Regardless of the type of vehicle, there was a tendency in most cases for the median MAISBR ≥ 3 to involve the head and thorax. A similar trend was seen in most of the pedestrian kinematics involving the various frontal impacts. Of the 88 cases, at least 63 (71.6%) were known to be engaged in risk-taking behaviors (e.g., activity on roadway). At least 12 deaths were nonaccidental (8 suicides and 4 homicides). Some activities may have been impairment related, because 26/63 (41.3%) pedestrians undertaking risk-taking behavior on the roadway were impaired. Toxicological analyses revealed that over half of the cases (47/88, 53.4%) tested positive for a drug that could have affected behavior. Ethanol was the most common. Thirty-one had positive blood results.
UNASSIGNED: A fatal dyad of head and thorax trauma was observed for pedestrians struck by cars. For those pedestrians hit by vehicles with high hood edges, which were involved in the majority of cases, a fatal triad of injuries to the head, thorax, and abdomen/retroperitoneum was observed. Most deaths occurred from frontal collisions and at speeds more than 35 km/h.

BACKGROUND: The COVID-19 pandemic policy response dramatically changed local transportation patterns. This project investigated the impact of COVID-19 policies on motor vehicle collision (MVC)-related emergency department (ED) visits and hospitalisations in Ontario.
METHODS: Data were collected on MVC-related ED visits and hospitalisations in Ontario between March 2016 and December 2022. Using an interrupted time series design, negative binomial regression models were fitted to the pre-pandemic data, including monthly indicator variables for seasonality and accounting for autocorrelation. Extrapolations simulated expected outcome trajectories during the pandemic, which were compared with actual observed outcome counts using the overall per cent change and mean monthly difference. Data were modelled separately for vehicle occupants, pedestrians and cyclists (MVC and non-MVC injuries).
RESULTS: There was a 31.5% decrease in observed ED visits (95% CI -35.4 to -27.3) and a 6.0% decrease in hospitalisations (95% CI -13.2 to 1.6) among vehicle occupants, relative to expected counts during the pandemic. Results were similar for pedestrians. Among cyclist MVCs, there was an increase in ED visits (12.8%, 95% CI -8.2 to 39.4) and hospitalisations (46.0%, 95% CI 11.6 to 93.6). Among non-MVC cyclists, there was also an increase in ED visits (47.0%, 95% CI 12.5 to 86.8) and hospitalisations (50.1%, 95% CI 8.2 to 101.2).
CONCLUSIONS: We observed fewer vehicle occupant and pedestrian collision injuries than expected during the pandemic. By contrast, we observed more cycling injuries than expected, especially in cycling injuries not involving motor vehicles. These observations may be attributable to changes in transportation patterns during the pandemic and increased uptake of recreational cycling.