Pedestrian Safety Research Articles

Pedestrian dynamics on slopes: Empirical analysis of level, uphill, and downhill walking

Pedestrian safety on inclined walking facilities merits greater attention than that on level surfaces. Crowd accidents and domino effects are more likely to occur on slope walkways, especially at high crowd densities, as in the tragic case of the 2022 Itaewon Halloween crowd crush in South Korea. However, experimental investigations of the pedestrian dynamics on slopes with varying inclinations are scarce. In this study, a controlled experiment involving 48 participants was conducted to investigate the differences in pedestrian movement characteristics during level, uphill, and downhill slope walking. A wide range of slope inclinations was considered, namely 0°, 3°, 7°, 12°, 17°, and 22°. The results demonstrated that walking speeds uphill are lower than those on flat terrains, and uphill speeds decrease with increasing inclination. In contrast, increased slope inclinations do not always correspond to reduced downhill speeds. Notably, at inclinations of 3° and 7°, the downhill speed exceeds that of level walking, suggesting that a mild downhill slope can facilitate faster movement. Compared with downhill walking, pedestrians exhibit more lateral motion and significantly lower speeds (p < 0.01) when walking uphill at the same inclination. However, the average flow rate during downhill walking is lower than those observed on flat ground and uphill terrain. This difference may be attributable to the lower pedestrian density during downhill walking to reduce the likelihood of falling. These findings can enhance the understanding of pedestrian dynamics on ramps and have significant implications for the design of safer inclined walking facilities and prevention of potential disasters on such walkways.

GoogleNet transfer learning with improved gorilla optimized kernel extreme learning machine for accurate detection of asphalt pavement cracks

Asphalt cracks are the initial indication and major pavement structural distress in the field of civil engineering because it might potentially intimidate road traffic and highway safety. Managing and maintaining pavement structures are the two crucial issues faced eventually by road administrators. It is essential to take immediate action regarding pavement structural damages to prevent its severity. In recent times, there established numerous deep learning-based image processing methods to detect pavement cracks, but due to the presence of interference factors such as noises and road markings in the images, those methods failed to provide high accuracy and efficiency. Therefore, with the aim to yield high accuracy of crack detection, this article designs a novel asphalt pavement crack identification system “GoogleNet transfer learning with improved gorilla optimized kernel extreme learning machine” (GNet TL with IGT-KELM). The road crack images used for perusal are acquired from NHA12D dataset, in which it consists of 40 asphalt pavement images with two different viewpoints. For the purpose of data balancing, some non-crack images are gathered by field survey. The images with large noise distortions and unwanted background information influence crack detection performance so that the preprocessing steps are executed before applying it to the prediction system. The most significant crack and non-crack features from the images are extracted using GNet TL model. With the extracted features, the KELM is well-trained to detect cracks and non-cracks separately. To increase crack detection performance of the classifier, an improved gorilla troop optimizer is introduced to optimize the KELM parameters. The experimental finding reveals that the proposed detection mechanism achieves crack recognition accuracy of about 98.93%, which is considerably greater than compared baseline approaches.

A model for aircraft cabin evacuation considering passenger type

Pedestrian safety evacuation in aircraft cabins has been a challenging problem because of the aircraft's unique characteristics, such as the diversity of passengers and the restricted evacuation environment. It is difficult to reproduce evacuation activities in aircraft cabin due to safety concerns and cost constraints. To fill this gap, an improved cellular automaton model of crowd evacuation for aircraft cabin is established by incorporating the characteristics of cabin space structures and passenger attributes. Passengers are divided into healthy individual passengers and disabled-healthy group passengers, whose movement mechanisms are quantified. Based on the constructed model, simulation experiments are conducted using the configuration cabin layout of B737-800 as an example. The results show that the evacuation time is prolonged with increased passenger density and the number of disabled passengers. Moreover, the overall evacuation time is insignificantly affected by whether disabled-healthy group passengers' seats are close to the aisle or window, and the evacuation efficiency is best when their seats are evenly distributed in the cabin. The evacuation time is the shortest when all cabin doors are open, and pedestrians are evacuated the slowest when the central emergency doors are closed. This study provides valuable insights into effective strategies for pedestrian evacuation and crowd emergency management of civil aircraft.

Can we trust our eyes? Interpreting the misperception of road safety from street view images and deep learning

Road safety is a critical concern that impacts both human lives and urban development, drawing significant attention from city managers and researchers. The perception of road safety has gained increasing research interest due to its close connection with the behavior of road users. However, safety isn't always as it appears, and there is a scarcity of studies examining the association and mismatch between road traffic safety and road safety perceptions at the city scale, primarily due to the time-consuming nature of data acquisition. In this study, we applied an advanced deep learning model and street view images to predict and map human perception scores of road safety in Manhattan. We then explored the association and mismatch between these perception scores and traffic crash rates, while also interpreting the influence of the built environment on this disparity. The results showed that there was heterogeneity in the distribution of road safety perception scores. Furthermore, the study found a positive correlation between perception scores and crash rates, indicating that higher perception scores were associated with higher crash rates. In this study, we also concluded four perception patterns: “Safer than it looks”, “Safe as it looks”, “More dangerous than it looks”, and “Dangerous as it looks”. Wall view index, tree view index, building view index, distance to the nearest traffic signals, and street width were found to significantly influence these perception patterns. Notably, our findings underscored the crucial role of traffic lights in the “More dangerous than it looks” pattern. While traffic lights may enhance people's perception of safety, areas in close proximity to traffic lights were identified as potentially accident-prone regions.

A field evaluation of an in-ground lighting intervention for safety at a road crossing

There are limited numbers of naturalistic studies of behaviours in response to safety interventions, such as novel lighting products at road crossings. The study used a theory-based approach to evaluate behavioural responses to a novel, ground-mounted lighting intervention at a zebra crossing at a university campus. An evaluation framework was developed to explore the extent to which the intervention was implemented in practice and collect a range of data types to assess the outcomes and impacts of the lighting intervention, in terms of the responses and behaviours of crossing users.The study was primarily observational, collecting and analysing video recordings of the road crossing episodes for people alone or in small groups (n = 269). This was supported by a small consultation exercise to help understand user-perceptions, achieved through an online questionnaire of crossing users (n = 59).The observations contributed to understanding crossing behaviours in this location, generally demonstrating safe crossing behaviours, such as looking for traffic and walking within the marked crossing features. There was no observable indication of users noticing or responding overtly to the lighting. However, 29% of the small sample responding to the questionnaire stated that the lights had influenced the way that they used the crossing. The survey responses indicated that users understood the intention of the lighting and cited positive attributes and perceived benefits, which may improve behaviours and safety for both pedestrians and drivers. It is concluded that the lighting intervention has the potential to improve safety-related behaviours at crossings, but further investigation of its efficacy would be recommended.Guidance is provided on how to respond to various methodological and situational challenges that have been encountered in this study, such as how to improve observational data collection for field studies in this type of context. Future development of the intervention might involve sensor-based activation and variation in the lighting (e.g., differing flash rates and colours).

Driver yield and safe child pedestrian crossing behavior promotion by a school traffic warden program at primary school crossings: A cluster-randomized trial

Objective To determine the effect of a school traffic warden program on increasing driver yield and safe child pedestrian crossing behavior in Kampala, Uganda. Methods We designed and implemented a school traffic warden program in specific school zones in Kampala, Uganda. We randomly assigned 34 primary schools in Kampala, in a 1:1 ratio, using a computer-generated randomization sequence, to control or intervention arms in a cluster randomized trial. Each school in the intervention group received one trained adult traffic warden stationed at roads adjacent to schools to help young children safely cross. The control schools continued with the standard of care. We extracted and coded outcome data from video recordings on driver yield and child crossing behavior (defined as waiting at the curb, looking both ways for oncoming vehicles, not running while crossing, and avoiding illegal crossing between vehicles) at baseline and after 6 months. Using a mixed effect modified Poisson regression model, we estimated the prevalence ratio to assess whether being in a school traffic warden program was associated with increased driver yield and safe crossing behavior. Results A higher proportion of drivers yielded to child pedestrians at crossings with a school traffic warden (aPR 7.2; 95% CI 4.42–11.82). Children were 70% more likely to demonstrate safe crossing behavior in the intervention clusters than in control clusters (aPR 1.7; 95% CI 1.04–2.85). A higher prevalence was recorded for walking while crossing (aPR 1.2; 95% CI 1.08–1.25) in the intervention clusters. Conclusion The school traffic warden program is associated with increased driver yield and safe child pedestrian crossing behavior, i.e., stopping at the curb, walking while crossing, and not crossing between vehicles. Therefore, the school traffic warden program could be promoted to supplement other road safety measures, such as pedestrian safety road infrastructure, legislation, and enforcement that specifically protects children in school zones.

Analysis of long-term observational data on pedestrian road crossings at unmarked locations

Crossing roads is dangerous for pedestrians. Roads can be crossed at controlled locations, where traffic lights or zebra crossings regulate the behaviour of all traffic participants, or at unmarked locations, where pedestrians typically do not have priority. Technological advances mean observational data on pedestrian road crossing behaviour from public roads can now be recorded almost continuously. Here, we report on such a data collection campaign in Bristol, UK. We record the movement paths of traffic participants within the field of view of commercial camera-based sensors at two unmarked crossing locations. Between January and April 2022, we detect over 30,000 pedestrian road crossings across the two locations. We first explore the time series of hourly crossing counts, finding pronounced and regular temporal patterns that differ between locations. We then investigate the relationship of crossing numbers with road traffic characteristics and extraneous factors, such as university term dates, confirming previous findings on traffic volume reducing crossing frequency and the differences between our study sites. Finally, by studying the timing and distance between consecutive crossings we find evidence for social crossing behaviour, such as groups crossing synchronously. In addition to the specific findings on road crossing behaviour of our study, a key contribution of our work is a case study for how to work with large-volume, low-fidelity observational data on pedestrian behaviour that is becoming increasingly available and has the potential to transform pedestrian road safety research.

Enhancing pedestrian perceived safety through walking environment modification considering traffic and walking infrastructure.

Urban policies have recently been formulated, following the increasing interest in pedestrian-friendly cities, people-centered safety, and accessibility. Despite the research efforts on physical walking safety, safety evaluations centered on pedestrian perception have been under-reported. Investigating the factors affecting pedestrian subjective safety perception is critical to promoting walking intention because pedestrians forgo walking if they feel unsafe. This study explored the relationship between various walking environmental factors and pedestrians' psychological perception of safety by surveying 99 pedestrians' perceptions at nine study sites and conducting a field investigation. Because of the multifaceted nature of pedestrian perception, mediation effect analyses were also conducted to understand the relationship between walking environment factors and perceived safety in depth, considering the role of the perception of traffic characteristics and walking infrastructure. This study found that walking environmental factors closely related to physical safety (e.g., traffic safety facilities and crosswalks) may not greatly contribute to perceived safety and demonstrated that maintaining infrastructure quality is essential for enhancing perceived safety, considering the mediating effect of the perception of infrastructure on perceived safety. The results imply that to improve the walking environment, it is necessary to consider both the physical safety and the perceived safety of pedestrians. This requires comprehensive planning for enhancing traffic safety facilities as well as ensuring user comfort and pleasure through quality infrastructure. This study can provide a basis for enhancing pedestrian-centered safety and promoting residents' walking intention for public health while increasing their perceptions of safety.

Relationship Between General Health Domains and Pedestrian Safety Behavior: A Cross-Sectional Study

Background: Traffic injuries are one of the top ten leading causes of mortality worldwide that impose a substantial burden on the health and economy of nations. Among all traffic users, pedestrians are the most vulnerable, accounting for a significant share of injuries and fatalities resulting from traffic accidents. Numerous factors contribute to pedestrian traffic accidents, with unsafe pedestrian behavior being one of the most critical factors. Objectives: Various factors can influence the traffic behavior of pedestrians. Therefore, this study aimed to examine the relationship between demographic characteristics, the general health status of pedestrians, and their traffic behavior. Methods: In this cross-sectional study, after obtaining ethical approval and participants’ consent, data were collected from eligible individuals. The data included participants’ demographic information and their responses to the 28-question General Health Questionnaire (GHQ-28) and the Pedestrian Behavior Questionnaire (PBQ). Linear regression analysis was performed to assess the relationships between demographic variables, general health domains, and the total score obtained from the PBQ. Results: Among the 1 421 participants in the study, 720 (50.7%) were female and 1 310 (92.2%) were under the age of 60. In terms of marital status, 979 individuals were married (69.5%), and 368 (26.2%) were single. The overall score of the general health of the participants was high in 623 individuals (43.8%), moderate in 666 individuals (46.9%), low in 122 individuals (8.6%), and very low in 10 individuals (0.7%). The mean (standard deviation) score of pedestrian behavior was also 57.89 (±11.9). There was a significant relationship between female gender, age of 15-29 years, being single, and domains of general health (somatic symptoms, anxiety assessment, social dysfunction assessment, and depression assessment) with the overall score of pedestrian behavior. Additionally, a significant relationship was found between the overall score of the participants’ general health and the overall score of their pedestrian behavior. Conclusions: There is a significant relationship between demographic characteristics and various domains of the general health of pedestrians and their behavior. Paying attention to this issue in policymaking and conducting further studies to identify other factors affecting pedestrian traffic behavior to prevent traffic injuries seems necessary.

Comprehensive Safety Index for Road Safety Management System

A safety-index-based road safety management system (RSMS) is a tool to help identify locations where safety intervention is needed. To date, various safety indices have been developed and utilized, but it is rare to consider the plan–do–check–act structure in an RSMS when studying the decision-making methodology. In this study, 36 indicators and a system of evaluation indicators were selected based on the major classifications of performance, effect, and improvement. Performance was categorized by safety system components and effect was reflected in the safety status, such as the number of injuries. The indicators were validated, and a classification methodology for safety groups was proposed through cluster analysis. It was found that there was no correlation between the indicators and the population, budget, or road area by administrative district. It was also found that no particular indicators had a significant impact on the overall result in the major category or the overall index. It was determined that the developed indicators were suitable for administrative district-specific safety monitoring. It is expected that these indicators will be continuously utilized and enhanced in the national evaluation of road traffic safety.

Evaluation of traffic signs information volume at highway tunnel entrance zone based on the visual sample entropy of novice and experienced drivers

Objective Traffic signs play a crucial role in ensuring road traffic safety, particularly in high-risk driving zones like the entrance zone of highway tunnels. However, the impact of traffic signs information volume (TSIV) on the performance of experienced and novice drivers in this specific zone remains unclear. This study aims to investigate the influence of TSIV on the visual sample entropy (SampEn) of both experienced and novice drivers in the entrance zone of highway tunnels. Methods A real vehicle driving trial was conducted with 60 participants approaching tunnels under various TSIV conditions. Fixation duration SampEn, saccade duration SampEn, and saccade amplitude SampEn were analyzed for both driver groups. Results As both driver groups approached the tunnel entrance, fixation duration SampEn, saccade duration SampEn, and saccade amplitude SampEn gradually increased. Initially, visual SampEn declined, followed by an increase as the TSIV level increased. The lowest value of visual SampEn was observed under the T3 TSIV condition (48.31 bits). Experienced drivers exhibited lower visual SampEn compared to novice drivers under similar TSIV conditions. Conclusions TSIV and driving experience significantly influence the visual SampEn of drivers approaching the tunnel entrance. Optimal visual search patterns and performance were observed under the T3 TSIV condition (48.31 bits), which is critical for ensuring driving safety in the entrance zone of highway tunnels. Additionally, experienced drivers demonstrate better adaptation to tunnel environments and TSIV, while novice drivers may benefit from additional training to enhance their visual perception and performance.

Evaluation of Pedestrian Safety for Wave Overtopping by Ship-Induced Waves in Waterfront Revetment

Evaluation of Pedestrian Safety for Wave Overtopping by Ship-Induced Waves in Waterfront Revetment

Surrogate safety evaluation of curve sections on an interurban highway under heterogeneous traffic conditions in India

&lt;p class="H1"&gt;Road traffic safety is a global concern. Conventionally, road safety evaluation is carried out by analyzing historical crash data, which is a reactive approach to safety analysis. The safety analyst has to wait for sufficient time to accumulate crash data before taking up any safety analysis as a sample of substantial crashes is required for analysis. But in safety analysis with a proactive approach no crash data is required, which is replaced with surrogate “conflicts”, which can be obtained from the new techniques of traffic conflict technique and surrogate safety parameters. Other approaches can be applied in traffic safety evaluation in anticipation of the crash occurrence. The advent of traffic conflict techniques, i.e., use of traffic conflicts in place of crashes and microsimulation methods like modeling of the traffic flow and pattern in traffic streams on a road network, started to apply as a method of analyzing microscopic simulation models and traffic conflict techniques to determine the safety issues in traffic systems and correlate them to the probable incidences of collisions. In this regard, surrogate safety parameters have been used to determine the level of safety on the typical curve sections of an interurban highway namely, Faridabad-Gurgaon four-lane divided highway in Haryana, India. This is accomplished by the use of vehicle trajectory data extracted through microscopic simulation in Verkher in Staedten Simulation (VISSIM) and analysis in Surrogate Safety Assessment Model (SSAM) for the above-referred corridor. Further, efforts have been made to present the intensity of traffic conflicts happening at the curved sections. The surrogate safety measures time to collision (TTC), deceleration rate (DR), and change in velocity (&lt;em&gt;ΔV&lt;/em&gt;), as well as conflicting vehicle speed (Max &lt;em&gt;S&lt;/em&gt;), are obtained by analysis from the SSAM model for all the curve sections and are validated using the reported crash data on the curve sections of the candidate corridor for 3 years. With the help of statistical elaboration, the critical threshold for TTC with the heterogeneous traffic movement is found 1.6 s, meaning any conflict occurring less than this time would invariably lead to a fatal crash. Similarly, the critical deceleration rate is observed as 0.569 m/s&lt;sup&gt;2&lt;/sup&gt;, implying that any conflict with more than this value may lead to a fatal crash. Further, the Delta&lt;em&gt;V&lt;/em&gt; values deduced for the study corridor on interurban curve sections catering to heterogeneous traffic movement is 4.1 m/s. Again, any conflict more than this value can turn into a potential crash.&lt;/p&gt;

Safety assessment of pedestrian-vehicle interaction at signalized intersections: An observational study

Road safety is a crucial aspect of global policies and management. Surrogate Safety Measures (SSMs) have gained attention in the study of pedestrian safety. This study aims to establish an effective SSM methodology to analyze driver-pedestrian interactions. The analysis relies on SSM indicators, without the need for an initial classification of driver-pedestrian interactions into specific interaction patterns. The proposed methodology offers several advantages, including the accurate identification of conflicts through an affordable approach making it easily accessible for public administrations and authorities to assess pedestrian safety at road intersections. A dataset comprising 270 driver-pedestrian interactions, observed at three road intersections in Rome, Italy, was examined. The severity level of each event was assessed through a preliminary classification of each interaction into three patterns: high, low, and none. Subsequently, the severity levels were evaluated using three methods, employing Time-to-Collision (TTC), Post-Encroachment Time (PET), and a combination of TTC and PET. A comparison between the severity levels identified by the two approaches was conducted. The findings reveal that Method 2, utilizing PET, consistently identifies conflicts. Additionally, a binomial logistic regression analysis was performed to identify the variables that influence the likelihood of an interaction escalating into a conflict. The results demonstrate that the probability of conflict increases with the duration of a red signal, particularly for younger pedestrians.

Assessment of the impact of road technical parameters on the probability of an accident

The article presents a brief overview of the development of research in the field of road traffic safety, starting from traditional vehicles to the implementation of highly automated vehicles (HAVs). Road traffic safety is one of the key tasks of modern society, aimed at reducing the number of road traffic accidents (RTAs) and minimizing their consequences. With the development of technologies and the emergence of new types of vehicles, such as highly automated vehicles (HAVs), the relevance of research in this area only increases. Keywords highly automated vehicle, road infrastructure, road surface, navigation systems, adaptive control systems, road traffic safety

PED-AI: Pedestrian Detection for Autonomous Vehicles using YOLOv5

Pedestrian detection is crucial for autonomous vehicles, surveillance, and pedestrian safety. This abstract introduces a novel pedestrian detection method using the YOLOv5 algorithm, known for its real-time object detection prowess. The approach aims to enhance pedestrian detection accuracy across diverse lighting conditions. Methodologically, the process involves data preparation, YOLOv5 model training, and subsequent evaluation. The architecture of YOLOv5, which employs anchor boxes and a single-pass convolutional neural network, allows for quick and accurate pedestrian identification. YOLOv5's design, which includes anchor boxes and a single-pass convolutional neural network, enables speedy and accurate pedestrian recognition. Study tests confirm the efficacy of the YOLOv5-based approach. In the first scenario, the model detected pedestrians in daylight with 75% accuracy, but it also produced 11 false negatives or a 25% miss. Although Scenario 2's accuracy was higher at 85%, there were still 11 false negatives, which suggested that there was a persistent detection gap. In spite of these outcomes, the YOLOv5 model demonstrates the possibility of accurate pedestrian detection in real-world settings. While it greatly improves applications like self-driving cars and pedestrian safety, lowering false negatives remains a primary goal for increasing overall accuracy. The investigation's findings show that YOLOv5 can function in a variety of lighting conditions, but also highlight the necessity for further work in order to meet stringent detection requirements.

Nonlinear and Interactive Effects of Multi-Scale Built Environment on Road Traffic Safety

Nonlinear and Interactive Effects of Multi-Scale Built Environment on Road Traffic Safety

Influence of drug and alcohol consumption on road traffic safety

Konzumacija lijekova i alkohola može značajno utjecati na sigurnost cestovnog prometa. Lijekovi, posebice oni koji uzrokuju pospanost, smanjuju koncentraciju ili imaju nuspojave poput vrtoglavice, mogu umanjiti sposobnost pravilnog reagiranja vozača u različitim prometnim situacijama. Alkohol, s druge strane, dokazano smanjuje kognitivne funkcije, motoričke sposobnosti te povećava vrijeme reakcije. Čak i umjerena konzumacija alkohola može znatno utjecati na vozačku sposobnost, a konzumacija alkohola iznad zakonom dopuštenih granica predstavlja ozbiljan rizik za sigurnost cestovnog prometa. Vozači koji kombiniraju lijekove i alkohol izloženi su dodatnim rizicima jer se nuspojave lijekova mogu pojačati alkoholom te tako narušiti sposobnost upravljanja vozilom. Stoga je važno da vozači budu svjesni potencijalnih nuspojava lijekova koje koriste te da izbjegavaju konzumaciju alkohola prije vožnje. Edukacija, preventivne kampanje i stroga provedba zakona ključni su elementi u smanjenju utjecaja konzumacije lijekova i alkohola na sigurnost cestovnog prometa.

Validating multi-sensor object tracking in Heavy-Duty Trucks with extended trailer dynamics for road traffic situations

Multi-sensing technologies in vehicles have the potential to improve road and pedestrian safety. Freighting trucks with extended trailer face challenges in urban maneuvering due to limited visibility. In this paper, we evaluate and validate extended trailer dynamics for Heavy-Duty Trucks in road traffic situations with multi-sensor object tracking emphasizing trailer dynamics, back-end parameters, and characteristics that influence decision-making strategies. A custom in-vehicle compute unit, in combination with the onboard truck’s computer, facilitated this research.

Encouraging Factors of Walkability Among Students in the Tropical Campus

Recent studies on campus walkability have focused on understanding the factors that contribute to walkability and the impact it has on student life, health, and overall campus sustainability. This study explores the factors encouraging walkability among students at the Universiti Malaysia Kelantan (UMK) campus in Bachok, Kelantan. The research employs both quantitative and qualitative methods to gain comprehensive insights into the determinants of pedestrian activity. Quantitative data were collected through a survey distributed to 300 students at UMK Bachok Campus using purposive sampling. The data collection period spanned from May 2024 to June 2024 and were analysed by using SPSS 28.0. Findings from exploratory factor analysis indicate that there are three main factors that encourage the walkability among students in the campus, i) Pedestrian Safety and Comfort, ii) Pedestrian and Cyclist Infrastructure, and iii) Amenities and Environmental Factors. These factors collectively promote a secure, pleasant, and inclusive environment that motivates students to walk more. The study concludes with recommendations for campus planners and policymakers to further enhance walkability, thereby fostering a healthier, more sustainable, and connected campus environment in Bachok, Kelantan.