Pedestrian Safety Research Articles

Nonreciprocal interactions in crowd dynamics: Investigating the impact of moving threats on pedestrian speed preferences

Nonreciprocal interaction crowd systems, such as human–human, human–vehicle, and human–robot systems, often have serious impacts on pedestrian safety and social order. A more comprehensive understanding of these systems is needed to optimize system stability and efficiency. Despite the importance of these interactions, empirical research in this area remains limited. Thus, in our study we explore this underresearched area, focusing on scenarios where nonreciprocity plays a critical role, such as mass stabbings, which pose a substantial risk to public safety. We conducted the first experiments on this system and analysed high-accuracy data obtained from these experiments. Specifically, we conduct laboratory experiments on three scenarios: single exits, dual exits, and obscured chases. Then, we introduce an equation to describe the mechanism behind a direct threat zone, which significantly affects pedestrian behaviour. The extent of the direct threat zone is determined by the speed of the moving threat and the radius of danger occurrence. The equation can provide insights for different nonreciprocal interaction crowd systems, where the subjects of the nonreciprocal interactions can be a pedestrian, an autonomous robot or a vehicle. We further categorize potential threats into direct, adjacent, and rear-view zones, quantifying the level of threat for pedestrians. Our study revealed that a pedestrian’s desired velocity correlated positively with potential threat intensity, increasing until near the direct threat zone. An emerging steady state is observed when escape routes are blocked by moving threats. This deviation affects the density–velocity relationship, making it distinct from the general relationship. This deviation signifies unique pedestrian behaviour in the presence of moving threats. Additionally, the rate of change in the angle for pedestrian motion in various desired directions is synchronized. This indicates the emergence of collective intelligence in nonreciprocal interaction crowd systems. As a result, our study may constitute a pioneering step towards understanding nonreciprocal interactions in crowd systems through laboratory experiments. These findings may enhance pedestrian safety and inform not only government crowd management strategies but also individual self-protection measures.

How to detect occluded crosswalks in overview images? Comparing three methods in a heavily occluded area

Crosswalk presence data is crucial for pedestrian safety and urban planning. However, obtaining such data at a large scale is often challenging due to the high cost associated with traditional collection methods. While automated methods based on computer vision have been explored to detect crosswalks from aerial images, a major obstacle to their application is the handling of candidate crosswalks occluded by objects or shadows in the aerial imagery. To address this challenge, this study explores different deep learning-based solutions, including the aerial-view and street-view methods, which are commonly used, and a combination of the two − dual-perspective method. Deep learning models based on Convolutional Neural Networks with VGG16 architecture were trained using 16,815 images to automatically detect crosswalks from both aerial and street view images. To compare the performance of these methods in handling occlusions, 1,378 images from a heavily occluded area were processed separately by the three methods. The results showed that the aerial-view method suffered the most when dealing with images from a heavily occluded area, resulting in the lowest accuracy, precision, recall, and F1 score among the three methods. On the other hand, the street-view method outperformed the aerial-view method significantly. The dual-perspective method demonstrated the highest accuracy and precision values, indicating its superiority in accurately predicting the location of a crosswalk. However, the street-view method exhibited the highest recall value, highlighting its superior ability to recover an occluded crosswalk among all methods.

ANALYSIS OF FACTORS AFFECTING DRIVING IN THE DARK TIME OF DAY

An analysis of factors affecting road traffic safety from the point of view of driving a car in the dark was carried out, in particular, the technical condition of the car, tinting of the windows, lighting of the streets (roads), speed of movement, driver fatigue. It has been established that a significant number of factors require significantly greater concentration and esponsibility from drivers. Driving a car in the dark requires special knowledge, skills and attention. The technical condition of vehicles becomes a number of priority problems. Vehicle malfunctions that are not detected in time or not eliminated increase the likelihood of a Road Traffic Accident. The cause of a Road Traffic Accident, especially in the dark period of the day, may be insufficient visibility of the road situation by the driver, which is caused by the violation of the requirements for tinting the glass in the car. The driver will not be able to fully assess everything that happens on the road at night both in conditions of insufficient visibility and in the presence of bad weather conditions. At the same time, proper street lighting can help reduce the risk of road traffic accidents by improving pedestrian visibility, reducing the likelihood of accidents and providing safer road conditions for both drivers and pedestrians. Speeding and incorrect selection of the speed regime in specific traffic conditions is one of the main factors affecting both the number and severity of Road Traffic Accidents. Another reason for the occurrence of a Road Transport Accident may be the driver’s fatigue, which reduces his concentration and ability to drive safely.

Pedestrian Priorities

This article explores the contemporary shift in urban studies towards prioritizing walkable neighborhoods amidst growing concerns about urban sprawl and its environmental impacts. Focusing on Persiaran Bestari in Shah Alam, Malaysia, the research aims to address the lack of pedestrian-friendly infrastructure and the consequent reliance on vehicular transport in residential areas. This research utilizes a mixed-method approach incorporating quantitative and qualitative methodologies, combining space syntax analysis and behavioral mapping to examine pedestrian movement within the study area. The data collection involves gathering pedestrian infrastructure data through field surveys and pedestrian volume surveys. As the result, behavioral mapping observations revealed varying pedestrian activity levels along different road corridors, with significant pedestrian traffic concentrated around residential zones. Utilizing space syntax analysis, the research detected that Persiaran Bestari and Permai experienced the highest levels of social and commercial activity, indicating higher resident density in these areas. This research highlights the correlation between pedestrian comfort and safety and the availability of pedestrian infrastructure. It reveals that inadequate infrastructure leads to poor walkability, prompting pedestrians to favor safer routes, such as neighborhood or local roads with reduced and slower vehicle traffic.

Safety analysis of pedestrians distracted by mobile phones at street crossings: Field study in Nanjing

The growing public concern over traffic safety hazards caused by pedestrians’ distracted behavior, particularly related to mobile phone usage at pedestrian crossings. Through video recording of pedestrians’ street-crossing behaviors on 12 sidewalks across 9 urban road intersections in Nanjing city, 1778 valid pedestrian samples were collected. The study categorizes mobile phone use during the crossing into five distinct types: no use, voice call, screen gaze, screen gaze with operation, and listening to music with headphones. Then, the effects of gender, age and companion conditions on the mobile phone during the crossing were examined by chi-square tests. Utilizing binomial logistic and Relogit regression models, the study analyzed the impact and safety risks of distraction on crossing behavior. Additionally, a random parameters (RP) logit model with heterogeneity in means was used to investigate the determinants affecting mobile phone usage. Notably, individuals aged 18 to 30 years were identified as the random parameter, while factors such as being under 18 years old, having a companion without interaction, having a companion with interaction, weekdays, sidewalk length, and the total number of other pedestrians were identified as fixed parameters. The study also presented significant variables affecting the probability of mobile phone usage through marginal effects, highlighting the potential safety risks associated with mobile phone usage during street crossing. These findings emphasize the need for heightened pedestrian safety awareness and a reduction in distracted behaviors to enhance overall traffic safety.

Compilation method of a catalogue of reasonable worst-case rainfall series for flash flood simulations of short, convective rainstorms

In this study, a categorisation method was developed and applied to identify, select and rank reasonable worst-case rainfall storm events with time-series resolution of one minute and duration of up to one hour. The method yields a catalogue of 24 reasonable worst-case rainfall series, i.e. potentially catastrophic events that could be used for safe-to-fail flash flood model scenarios, thus encouraging the testing of critical infrastructure and pedestrian safety for people trapped in floods. The identified 24 events were shown to be considerably more extreme than corresponding design storms, with a return interval of 100 years. In the selection of the extremes, special care was taken to consider different hazard attributes, which are associated with intrinsically different observed hyetograph shapes, including rectangular, short and long triangle, and double peak shape categories. Hydrodynamic simulations were carried out using the 2D robust shallow water model hms++ to quantify the flooding impact of the 24 rainfall scenarios in a small urbanised catchment, which enabled the ranking of the scenarios based on different hazard attributes. Overall, the rectangular and long-triangle events showed the highest total discharge, and the rectangular events comprise both the longest duration for hazard velocity and deep flooding. The long-triangle events presented the highest peak discharge and peak water depths. The short-triangle ones showed the highest flashiness (shortest time to peak), whereas the double peak events yielded the longest time to peak. The ranking emphasised the importance of the hyetograph shape characteristics of extreme storms and which needs to be taken into account in flash flood management. The novel categorisation method is easily transferable to other regions, if high-resolution rain series are available.

Identification of the best machine learning model for the prediction of driver injury severity

Predicting the injury severities sustained by drivers engaged in road traffic accidents is a key topic of research in road traffic safety. The current study analyzed the driver injury severity (DIS) using twelve machine learning (ML) algorithms. These models were implemented using 0.70, 0.80, and 0.90 train ratios and 5-, 10- and 15-fold cross-validation. Ten years of accident data (from 2011 to 2020) was obtained from police department of Shillong, India. A total of 693 accidents were documented, with 68% being nonfatal and 32% being fatal. Precision, recall, accuracy, F1 score and area under the curve measures were used to compare the performance of all twelve ML models. Overall, the light gradient-boosting machine model was shown to be the best ML model for predicting the injury severities of drivers engaged in road traffic incidents. Finally, variable importance analysis results showed that cause of accident, collision type and types of vehicles were the most influencing factors in nonfatal and fatal driver accidents. The results also revealed that age and gender were slightly associated with DIS. The findings of the current research could be helpful to road safety agencies for the implementation of suitable countermeasures to increase driver safety in road accidents.

Evacuation strategies for wrecked pedestrians considering emotional contagion and safety officers

Evacuation in shipwrecks remains challenging, and there is a gap in research related to the consideration of emotional changes in pedestrians and the role of safety personnel on passenger ships. The present strategy may be the first work in the dual-model (SIS model and social force model) related research to synthesize the pedestrian's multi-emotional changes and the safety officer's role and movement rules in the shipwreck scenario. We employed a multifactorial two-model approach to enhance the realism of evacuation outcomes during re-sinking scenarios, including pedestrian mood, safety officer, and ship tilt. Our findings reveal critical thresholds for emotion transmission, except for exit width and pedestrian density, where a few people transmitting emotions have minimal effects. Negative emotions beyond a certain threshold can disrupt evacuation and lead to casualties, while positive emotions contribute to smoother evacuations with fewer casualties. Furthermore, optimizing the safety officer's position and action strategy can alleviate the negative feelings of pedestrians and facilitate an orderly evacuation, positively affecting the evacuation outcome, especially in the case of a sinking ship with an increasingly severe angle of inclination. This study provides a valuable framework for improving evacuation safety and minimizing casualties during maritime emergencies.

Assessment of the visibility of unprotected road users in pedestrian crossing

The article presents selected results of research on improving pedestrian traffic safety. Based on annually-updated accident statistics made available by the police, as well as the new pedestrian traffic regulations in force, detailed work was undertaken to assess the level of visibility of pedestrians by drivers in pedestrian crossing areas. The research was carried out by analyzing several characteristic cases of pedestrian crossings occurring in Poland, in which there was only dedicated lighting for crossings, only street lighting, and a variant of coexistence of both of the above lighting solutions. Illuminance measurements were made in the horizontal and vertical planes of pedestrian crossings, and the results were confronted with the relevant guidelines. The next step involved a complementary measurement of the luminance distribution of the vertical plane containing the pedestrian and a portion of the sub- and super-horizontal background. Visibility pedestrians was considered in positive and negative contrast variants, and was then related to the obtained results of the illumination distribution. The analysis of the results of the study indicated the possibility of limited visibility of pedestrians at the crossings despite the satisfactory results obtained from measurements of the illuminance distribution within the crossings.

A multiscale enhanced pavement crack segmentation network coupling spectral and spatial information of UAV hyperspectral imagery

Road pavement cracks are a critical factor affecting the health conditions of road pavements. Accurate crack detection contributes to providing data support for road maintenance measures. Compared to conventional crack detection algorithms, deep learning based crack segmentation methods have practical significance for road maintenance and traffic safety management due to their high precision and automation. However, existing deep learning methods often suffer from segmentation accuracy loss due to the varying crack sizes and the presence of stains on the pavement with spatial characteristics similar to cracks, leading to the misclassification of cracks. Therefore, this study proposed a multiscale enhanced road pavement crack segmentation network (MS-CrackSeg) by coupling spectral and spatial information to detect pavement cracks from unmanned aerial vehicle (UAV) hyperspectral imagery. MS-CrackSeg can simultaneously learn the spatial and rich spectral features of cracks in the hyperspectral imagery, improving the discrimination of those targets with similar spatial features to cracks compared to previous approaches. Moreover, the Multiscale Self-Attention-like Feature Extraction Module (MSSA) is introduced to extract and fuse multiscale crack features to enhance the crack detection. Experiments on a dataset consisting of 1031 hyperspectral images demonstrated the superior crack segmentation of the proposed method compared to the comparative methods. In particular, the proposed method achieved the highest F1-score of 0.74 and mean Intersection over Union of 0.79, indicating an exceptional performance. The developed approach offers improved data support for road maintenance measures and has validated the advantages of UAV hyperspectral imagery in road crack segmentation. The annotated hyperspectral dataset and the code for MS-CrackSeg network are available at https://github.com/williamchen-x/MS-CrackSeg.

Assessment of Public Transport Footway Facilities Bogor District

Along with the increasing need for travel and the high dependence on motorized vehicles, the growth of existing transportation facilities and infrastructure is ultimately unable to accommodate these needs and leads to traffic jams, such as pollution, a decrease in the quality of life of the community and the level of road safety. Studies conducted at stations and Cibinong Terminal to analyze pedestrian characteristics and facilities. The research method used is direct observation and data collection in the field. From the results of the analysis in the direction of jl. Raya Bogor to Pabuaran with a vehicle volume of 2,160 per / hour on Monday afternoons at LOS B, the direction of Bogor to Jakarta with a vehicle volume of 13,390 on Tuesday and Saturday with peak hours in the afternoon, there is LOS E, and in the direction of Ciriung to Cikaret there is LOS B from morning to evening. The number of pedestrians is based on age with the direction of entering and exiting stations, stops and terminals, such as Cibinong Terminal with adult male pedestrians 16-40 years old, with the volume of pedestrians from and to the station. The density occurs on Monday afternoons. Tuesday morning, noon and evening, and Terminal on Wednesday morning. with the results of interviews of respondents who are at the productive age of 18-45 years, with high school / vocational school graduation with an income of 3-6 million and an expense of 1-2 million, the traveling characteristics of most respondents use station facilities, respondents are willing to walk 501-600 m, the level of facility in the respondent's station is quite satisfied, in the terminal the respondent is not satisfied, especially with the safety of pedestrians. Special satisfaction of Taxi / Ojek / delivery users and parking lots, respondents are quite satisfied, especially in the ease of finding a vehicle, the satisfaction of users of public transportation of respondents is quite satisfied, especially in the ease of finding a vehicle. The results of this study are the construction of pedestrian facilities and zebra crossing facilities.

Strategies and Implications that Can be Used to Improve Walkability through Urban Design

Designing spaces for walking has become popular in the belief that it contributes positively to health status, environmental conservation, economic development, and social coherence. As such there is increasing need to assess how walkable our cities are so that we can plan better for their future. “Walkability” refers to how enjoyable it is to walk around an area and it forms an integral part of urban design that helps make a city healthy, sustainable and lively. This paper aims at exploring deeply into urban design’s role in promoting walkability within different settings with researches findings as well as real life examples being highlighted on various ways on how best pedestrian friendly environments can be developed. In addition to this it also examines the benefits realized by having more walk able towns in terms of environment efficiency and economy opportunities as well as drawbacks faced when bringing about this aspect. Besides linking past studies together through this paper provides insights on relevance of the above-mentioned concept on policy makers, planners in town planning fields and designers. It gives a detailed account behind telling us that we need “walkable cities” and goes further ahead explaining what makes our cities as they are given their architecture design implications for daily footsteps in each quadrant. Keywords: Walkability, Built Environment, Pedestrian Safety, Urban Design, Neighbourhood Design.

Enhanced Road Safety with Photoluminescent Pedestrian Crossings in Urban Contexts

The safety of roads in urban areas is a major concern for governments, demanding innovative solutions to enhance pedestrian safety. This paper introduces a novel approach to crosswalks by integrating resin with photoluminescent additives, offering a significant boost to road safety. A thorough methodology was employed to assess its effectiveness, covering mechanical, lighting, and vibroacoustic aspects, alongside a photogrammetric analysis of real-world experiments. The material exhibited noteworthy mechanical properties, displaying consistent tensile strength, load capacity, and strain values with a remarkable Shore A hardness. After 20 min, luminance values peaked at 68 mcd/m2, surpassing standard vehicle headlights at 100 m. Additionally, vibroacoustic analysis highlighted a noticeable relationship between vehicle speed and sound bandwidth, indicating the system’s potential to alert pedestrians. Tests revealed that the proposed system significantly decreased the average vehicle speed by 36.96% compared to conventional crosswalks, with a 27.80% reduction when drivers yielded to pedestrians. Furthermore, a survey involving 35 participants, focusing on the knowledge of road safety regulations, behavior, signage, and visibility, found positive results regarding accident reduction. The estimations indicate potential decreases of 26.26% in injuries and 35.4% in fatalities due to improved road conditions, 26.58% in injuries and 53.16% in fatalities resulting from reduced average speeds, and 52.56% in injuries and 79.91% in fatalities through enhanced road education. This underscores the multifaceted impact of the system on urban road safety.

Empowering Pedestrian Safety: Unveiling a Lightweight Scheme for Improved Vehicle-Pedestrian Safety

Rapid advances in technology and shifting tastes among motorists have reworked the contemporary automobile production sector. Driving is now much safer and more convenient than ever before thanks to a plethora of new technology and apps. Millions of people are hurt every year despite the fact that automobiles are networked and have several sensors and radars for collision avoidance. Each year, many of them are injured in car accidents and need emergency care, and sadly, the fatality rate is growing. Vehicle and pedestrian collisions are still a serious problem, making it imperative to advance methods that prevent them. This paper refines our previous efficient VANET-based pedestrian safety system based on two-way communication between smart cars and the cell phones of vulnerable road users. We implemented the scheme using C and NS3 to simulate different traffic scenarios. Our objective is to measure the additional overhead to protect vulnerable road users. We prove that our proposed scheme adds just a little amount of additional overhead and successfully satisfies the stringent criteria of safety applications.

Awareness and Practice on Road Safety among Adults in Selected Area of Jalandhar Cantt, Punjab

It has been estimated that one million death and 15 million road side accident occur on the road worldwide. Globally road side accidents are the 7th leading cause of death in all age groups. In India we have 1% of the world vehicle population, but 6% of the world accidents 1. The aim of the study is to assess the awareness and practice on road safety among adults in in selected area of Jalandhar Cantt, Punjab. A total of 108 samples were selected for the study and samples were the adults with in the age group of 18-60 years. The major findings of the study were as follows: The study analysis showed that majority (69%) of the samples were males and 60% belonged to the group of 18-30 years of age. 15% of samples were drivers by profession, 63.8% had a valid driving license. Majority of samples, (83.2%) had good awareness on road safety and 94% of samples had good practice on pedestrian road safety. The study showed that there was a low positive and significant correlation between awareness and practice among pedestrians (n=400) as the p value is .001 and spearman’s rho value is 0.269. And there was a low and significant correlation between awareness and practice of drivers the p value is .001 and spearman’s rho value for 0.322.

Modelling yielding dilemma zone for drivers at non-signalised crosswalks

In developing countries like China, non-signalised crosswalks represent a considerable risk to pedestrian safety. This study introduces a “yielding dilemma zone” model to tackle the decision-making challenges encountered by drivers at these crosswalks. Field data collected via drones at three non-signalised crosswalks in Xi'an (China) facilitated the analysis of 1687 instances of driver yielding behaviour. The study employed Support Vector Machine (SVM), Artificial Neural Network (ANN), and Binary Logistic Regression (BLR) models to establish the boundaries of the dilemma zone. Additionally, it utilized surrogate safety metrics, such as the deceleration rate required to stop (DRS) and time to crossing (TC), to compare risks between dilemma and non-dilemma zones. Each modelling approach provided insightful findings. The SVM and ANN models identified the speed-distance dilemma zones, while the BLR model determined the multifactorial dilemma zones by considering characteristics such as pedestrian, vehicle, and traffic condition. The findings underscore the significance of the “yielding dilemma zone” for understanding driver-pedestrian interactions at non-signalised crosswalks. The innovative multi-model approach uncovered that pedestrian speed, vehicle type, vehicle approach speed, adjacent vehicle number, driving directions, and pedestrian crossing stage significantly influence this interaction. indicated a higher probability of collisions within dilemma zones compared to non-dilemma zones across all models. The study suggests that the SVM model could be instrumental in creating passive physical warning signs, while the ANN and BLR models could significantly contribute to real-time pedestrian and driver assistance systems. The “yielding dilemma zone” model, with its comprehensive approach, presents a promising avenue for enhancing pedestrian safety, intelligent transportation systems, and traffic management at non-signalised crosswalks.

The more peers are present, the more adventurous? How peer presence influences adolescent pedestrian safety

ObjectiveAdolescence is a high-risk period for traffic injury. One factor that may impact adolescent safety in traffic is the presence of peers. We conducted a quasi-experimental research study to examine the impact of peer presence, peer familiarity, and peer group size on adolescent pedestrian risk-taking intentions in both sidewalk and street-crossing settings. Methods607 students aged 12–18 years from Nantong city, China, completed a questionnaire that presented 20 traffic scenarios. The scenarios varied based on a 3 (peer group size: no peer vs. one peer vs. multiple peers) x 2 (peer familiarity: familiar vs. unfamiliar) x 2 (traffic setting: crossing the street vs. walking on the roadside) experimental design. Adolescents’ responses indicated safer vs riskier intentions in each situation. Results(1) Adolescents were safer when walking on the sidewalk than when crossing the street; (2) Whether crossing the street or walking on the sidewalk, adolescents’ behavioral intentions were safer when there were peers present than when there were no peers present; (3) Adolescents’ safety tended to be higher overall with unfamiliar peers than with familiar peers; (4) Adolescents were less safe when crossing the street with familiar peer(s) than with unfamiliar peer(s), but no differences emerged when walking on the sidewalk. ConclusionsAdolescents report safer behavior when walking with a peer or peers compared with walking alone. Familiar peers reduce adolescents’ safety of behavior intentions in traffic, especially when crossing the street.

Small group pedestrian crossing behaviour prediction using temporal angular 2D skeletal pose

A pedestrian is classified as a Vulnerable Road User (VRU) because they do not have the protective equipment that would make them fatal if they were involved in an accident. An accident can happen while a pedestrian is on the road, especially when crossing the road. To ensure pedestrian safety, it is necessary to understand and predict pedestrian behaviour when crossing the road. We propose pedestrian intention prediction using a 2D pose estimation approach with temporal angle as a feature. Based on visual observation of the Joint Attention in Autonomous Driving (JAAD) dataset, we found that pedestrians tend to walk together in small groups while waiting to cross, and then this group is disbanded on the opposite side of the road. Thus, we propose to perform prediction with small group of pedestrians, based on pedestrian statistical data, we define a small group of pedestrians as consisting of 4 pedestrians. Another problem raised is 2D pose estimation is processing each pedestrian index individually, which creates ambiguous pedestrian index in consecutive frame. We propose Multi Input Single Output (MISO), which has capabilities to process multiple pedestrians together, and use summation layer at the end of the model to solve the ambiguous pedestrian index problem without performing tracking on each pedestrian. The performance of our proposed model achieves model accuracy of 0.9306 with prediction performance of 0.8317.

Domain Adaptive Subterranean 3D Pedestrian Detection via Instance Transfer and Confidence Guidance

With the exploration of subterranean scenes, determining how to ensure the safety of subterranean pedestrians has gradually become a hot research topic. Considering the poor illumination and lack of annotated data in subterranean scenes, it is essential to explore the LiDAR-based domain adaptive detectors for localizing the spatial location of pedestrians, thus providing instruction for evacuation and rescue. In this paper, a novel domain adaptive subterranean 3D pedestrian detection method is proposed to adapt pre-trained detectors from the annotated road scenes to the unannotated subterranean scenes. Specifically, an instance transfer-based scene updating strategy is designed to update the subterranean scenes by transferring instances from the road scenes to the subterranean scenes, aiming to create sufficient high-quality pseudo labels for fine-tuning the pre-trained detector. In addition, a pseudo label confidence-guided learning mechanism is constructed to fully utilize pseudo labels of different qualities under the guidance of confidence scores. Extensive experiments validate the superiority of our proposed domain adaptive subterranean 3D pedestrian detection method.

Supporting children’s participation in active travel: developing an online road safety intervention through a collaborative integrated knowledge translation approach

ABSTRACT Even though regular engagement in physical activity (PA) among children can support their development and encourage the adoption of healthy lifelong habits, most do not achieve their recommended guidelines. Active travel (AT), or any form of human-powered travel (e.g., walking), can be a relatively accessible, manageable, and sustainable way to promote children’s PA. One common barrier to children’s engagement in AT, however, is a reported lack of education and training. To support children’s participation in AT, this paper presents the development of a comprehensive 4-module online road safety education intervention designed to improve children’s knowledge and confidence regarding AT. Using a qualitative integrated knowledge translation (iKT) approach undertaken with community collaborators (n = 50) containing expertise in health promotion, public safety, school administration, and transportation planning, our inductive thematic analysis generated fourth themes which constituted the foundation of the intervention modules: Active Travel Knowledge: Awareness of Benefits and Participation; Pedestrian Safety and Skills: Roles, Responsibilities, and Rules; Signs and Infrastructure: Identification, Literacy, and Behaviour; Wheeling Safety and Skills: Technical Training and Personal Maneuvers. Each theme/module was then linked to an explicit learning objective and connected to complementary knowledge activities, resources, and skill development exercises. Implications for research and practice are discussed.