Sustainable repurpose of end-of-life fiber reinforced polymer composites: A new circular pedestrian bridge concept.

In response to global challenges in resource supply, many industries are adopting the principles of the Circular Economy (CE) to improve their resource acquisition strategies. This paper introduces an innovative approach to address the environmental impact of waste Glass Fiber Reinforced-Polymer (GFRP) pipes and panels by repurposing them to manufacture structural components for new bicycle and pedestrian bridges. The study covers the entire process, including conceptualization, analysis, design, and testing of a deck system, with a focus on the manufacturing process for a 7-m-long prototype bridge. The study shows promising results in the concept of a sandwich structure utilizing discarded GFRP pipes and panels, which has the flexibility to account for variabilities in dimensions of incoming products while still meeting mechanical requirements. The LCA analysis shows that the transportation of materials is the governing contributing factor. It was concluded that further development of this concept should be accompanied by a business model that considers the importance of the contributions from the whole value chain.

A novel model for real-time risk evaluation of vehicle-pedestrian interactions at intersections.

Safety decisions for vehicles at an intersection rely on real-time, objective and continuous assessment of risks in vehicle-pedestrian interactions. Existing surrogate safety models, constrained by ideal assumptions of constant current speed and reliant on interaction points, often misjudge risks, and show inefficiency, inaccuracy and discontinuity. This work proposes a novel model for evaluation of those risks in vehicle-pedestrian interactions at intersections, which abstracts the pedestrian distribution density around a vehicle into a generalized model of driver-pedestrian interaction preferences. The introduction of two conceptions: 'driving risk index' and 'driving risk gradient,' facilitates the delineation of driving spaces for identifying safety-critical events. By means of the trajectory data from three intersections, model parameters are calibrated and a multidimensional vehicle-pedestrian interaction risk (VPIR) model is proposed to adapt the complex and dynamic characteristics of vehicle-pedestrian interactions at intersections. Commonly used surrogate safety models, such as Time to Collision (TTC), are selected as benchmark models. Results show that the proposed model overcomes the limitations of the existing interaction-point-based models, and offers a ideal assessment of driving risks at intersections. Finally, the model is illustrated with a case study that assesses the risks in vehicle-pedestrian interactions in varied scenarios and the case study indicates that the VPIR model works well in evaluating vehicle-pedestrian interaction risks. This work can facilitate humanoid learning in the autonomous driving domain, and achieve an ideal evaluation of vehicle-pedestrian interaction risks for safe and efficient vehicle navigation through an intersection.

Meteorological gaps in audits of pedestrian environments: a scoping review.

BACKGROUND: Weather and season are determinants of physical activity. Therefore, it is important to ensure built environments are designed to mitigate negative impacts of weather and season on pedestrians to prevent these losses. This scoping review aims to identify built environment audits of pedestrian environments developed for use during a specific weather condition or season. Secondly, this review aims to investigate gaps in the inclusion of relevant weather mitigating built environment features in pedestrian environment audit tools. METHODS: Following a standard protocol, a systematic search was executed in CINAHL, Medline and Web of Science to identify built environment audit tools of pedestrian spaces. These databases were chosen since they are well-known to comprehensively cover health as well as multi-disciplinary research publications relevant to health. Studies were screened, and data were extracted from selected documents by two independent reviewers (e.g., psychometric properties and audit items included). Audit items were screened for the inclusion of weather mitigating built environment features, and the tool's capacity to measure temperature, precipitation, seasonal and sustainability impacts on pedestrians was calculated. RESULTS: The search returned 2823 documents. After screening and full text review, 27 articles were included. No tool was found that was developed specifically for use during a specific weather condition or season. Additionally, gaps in the inclusion of weather mitigating items were found for all review dimensions (thermal comfort, precipitation, seasonal, and sustainability items). Poorly covered items were: (1) thermal comfort related (arctic entry presence, materials, textures, and colours of buildings, roads, sidewalk and furniture, and green design features); (2) precipitation related (drain presence, ditch presence, hazards, and snow removal features); (3) seasonal features (amenities, pedestrian scale lighting, and winter destinations and aesthetics); and (4) sustainability features (electric vehicle charging stations, renewable energy, car share, and bike share facilities). CONCLUSIONS: Current built environment audit tools do not adequately include weather / season mitigating items. This is a limitation as it is important to investigate if the inclusion of these items in pedestrian spaces can promote physical activity during adverse weather conditions. Because climate change is causing increased extreme weather events, a need exists for the development of a new built environment audit tool that includes relevant weather mitigating features.

Identifying the key characteristics, trends, and seasonality of pedestrian traffic injury at a major trauma center in Saudi Arabia: a registry-based retrospective cohort study, 2017-2022.

BACKGROUND: Pedestrian traffic injuries are a rising public health concern worldwide. In rapidly urbanizing countries like Saudi Arabia, these injuries account for a considerable proportion of trauma cases and represent a challenge for healthcare systems. The study aims to analyze the key characteristics, seasonality, and outcomes of pedestrian traffic injuries in Riyadh, Saudi Arabia. METHODS: This study was a retrospective cohort analysis of all pedestrian traffic injuries presented to King Saud Medical City, Riyadh, and included in the Saudi Trauma Registry (STAR) database between August 1, 2017, and December 31, 2022. The analysis of metric and nominal variables was reported as mean (standard deviation, SD) or median (interquartile range, IQR) and frequencies (%), respectively. A logistic regression analysis was performed to examine the influence of patients' pre-hospital vitals and key characteristics on arrival at the ED on the need for mechanical ventilation and in-hospital mortality. RESULTS: During the study period, 1062 pedestrian-injured patients were included in the analysis, mostly males (89.45%) with a mean (SD) age of 33.44 (17.92) years. One-third (35.88%) of the patients were Saudi nationals. Two-thirds (67.04%) of the injuries occurred from 6 p.m. until 6 a.m. Compared to other years, a smaller % of injury events (13.28%) were noticed during the COVID-19 pandemic (2020). Half (50.19%) of the patients were transported to the emergency department by the Red Crescent ambulance, and 19.68% required intubation and mechanical ventilation. Most of the patients (87.85%) were discharged home after completion of treatment, and our cohort had a 4.89% overall mortality. The logistic regression analysis showed the influence of patients' pre-hospital vitals and key characteristics on arrival at the ED on the need for mechanical ventilation (Chi2 = 161.95, p < 0.001) and in-hospital mortality (Chi2 = 63.78, p < 0.001) as a whole significant. CONCLUSION: This study details the demographic, temporal, and clinical trends of pedestrian traffic injuries at a major Saudi trauma center. Identifying high-risk individuals and injury timing is crucial for resource allocation, targeting road safety interventions like public awareness campaigns and regulatory reforms, and improving prehospital care and patient outcomes.

Pilot study of a pedestrian collision detection test for a multisite trial of field expansion devices for hemianopia.

SIGNIFICANCE: Performance-based outcome measures are crucial for clinical trials of field expansion devices. We implemented a test simulating a real-world mobility situation, focusing on detection of a colliding pedestrian among multiple noncolliding pedestrians, suitable for measuring the effects of homonymous hemianopia and assistive devices in clinical trials. PURPOSE: In preparation for deploying the test in a multisite clinical trial, we conducted a pilot study to gather preliminary data on blind-side collision detection performance with multiperiscopic peripheral prisms compared with Fresnel peripheral prisms. We tested the hypothesis that detection rates for colliding pedestrians approaching on a 40° bearing angle (close to the highest collision risk when walking) would be higher with 100Δ oblique multiperiscopic (≈42° expansion) than 65Δ oblique Fresnel peripheral prisms (≈32° expansion). METHODS: Six participants with homonymous hemianopia completed the test with and without each type of prism glasses, after using them in daily mobility for a minimum of 4 weeks. The test, presented as a video on a large screen, simulated walking through a busy shopping mall. Colliding pedestrians approached from the left or the right on a bearing angle of 20 or 40°. RESULTS: Overall, blind-side detection was only 23% without prisms but improved to 73% with prisms. For multiperiscopic prisms, blind-side detection was significantly higher with than without prisms at 40° (88 vs. 0%) and 20° (75 vs. 0%). For Fresnel peripheral prisms, blind-side detection rates were not significantly higher with than without prisms at 40° (38 vs. 0%) but were significantly higher with prisms at 20° (94 vs. 56%). At 40°, detection rates were significantly higher with multiperiscopic than Fresnel prisms (88 vs. 38%). CONCLUSIONS: The collision detection test is suitable for evaluating the effects of hemianopia and prism glasses on collision detection, confirming its readiness to serve as the primary outcome measure in the upcoming clinical trial.

Gaze Scanning on Mid-Block Sidewalks by Pedestrians With Homonymous Hemianopia With or Without Spatial Neglect.

Purpose: The purpose of this study was to investigate gaze-scanning by pedestrians with homonymous hemianopia (HH) when walking on mid-block sidewalks. Methods: Pedestrians with right homonymous hemianopia (RHH), and left homonymous hemianopia (LHH) without and with left spatial neglect (LHSN) walked on city streets wearing a gaze-tracking system. Gaze points were obtained by combining head movement and eye-in-head movement. Mixed-effects regression models were used to compare horizontal gaze scan magnitudes and rates between the side of the hemi-field loss (BlindSide) and the seeing side (SeeingSide), among the three subject groups, and between mid-block walking and street crossing segments. Results: A total of 7021 gaze scans were obtained from 341 minutes of mid-block walking videos by 19 participants (6 with LHH, 7 with RHH, and 6 with LHSN). The average gaze magnitude and scanning rate in mid-block segments were significantly higher towards the BlindSide than the SeeingSide in LHH (magnitude larger by 1.9° (degrees), P = 0.006; scan rate higher by 4.2 scans/minute, P < 0.001) and RHH subjects (magnitude larger by 3.3°, P < 0.001; scan rate higher by 3.2 scans/minute, P = 0.002), but they were not significantly different in LHSN subjects. The scanning rate, in terms of scans/minute (mean, 95% confidence interval [CI]) was significantly lower in LHSN subjects (mean = 6.9, 95% CI = 5.6-8.7) than LHH (mean = 10.2, 95% CI = 8.0-13.1; P = 0.03) and RHH (mean = 11.1, 95% CI = 9.0-13.7; P = 0.007) subjects. Compared to street-crossings, the scan rate during the mid-block segments was lower by 3.5 scans/minute (P < 0.001) and the gaze magnitude was smaller by 3.8° (P < 0.001) over the 3 groups. Conclusions: Evidence of compensatory scanning suggests a proactive, top-down mechanism driving gaze in HH. The presence of spatial neglect (SN) appeared to negatively impact the top-down process.

Evaluating current and future pedestrian mid-block crossing safety treatments using virtual reality simulation.

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.

Analysis of pedestrian-two-wheeler conflicts at green light digital countdown signals: A random parameter ordered logit model approach.

The rising prevalence of e-bikes and shared bikes in transportation modes adds complexity to pedestrian movement at intersections. The conflict technique is a substitute for collisions in analyzing pedestrian safety at digital countdown signal intersections. Pedestrian and two-wheeler trajectories were obtained using Unmanned Aerial Vehicle (UAV) and T-Analyst software. The severity of pedestrian-two-vehicle conflicts was assessed using indicators such as Time to Collision (TTC), Post Encroachment Time (PET), and Yaw Rate Ratio (YRR), along with the fuzzy C-mean clustering method. An analysis of the impact of pedestrian characteristics, cyclist characteristics, and road conflict factors on severity was conducted using a random parameter ordered logit model. A total of 630 valid conflicts were identified, comprising 105 potential conflicts, 242 minor conflicts, and 283 serious conflicts. More minor and serious conflicts occurred in Signal 1 and Signal 2. Serious conflicts mainly occurred in road Zone 2, Zone 3, and Zone 5, while minor conflicts were more frequent in Zone 4 and Zone 5. Pedestrian crossing at Signal 2 increased the conflict severity, and the refuge island had a similar effect. Cyclists passing the conflict point first reduced the probability of serious conflicts. Older adults are safer at countdown signal intersections than young people. It is essential to enhance the awareness of digital countdown signals among youth. Managers should consider diverting two-wheelers during peak hours and encourage cyclists to walk through crosswalks.

Estimating the effectiveness of marked sidewalks: An application of the spatial causality approach.

Various safety enhancements and policies have been proposed to enhance pedestrian safety and minimize vehicle-pedestrian accidents. A relatively recent approach involves marked sidewalks delineated by painted pathways, particularly in Asia's crowded urban centers, offering a cost-effective and space-efficient alternative to traditional paved sidewalks. While this measure has garnered interest, few studies have rigorously evaluated its effectiveness. Current before-after studies often use correlation-based approaches like regression, lacking effective consideration of causal relationships and confounding variables. Moreover, spatial heterogeneity in crash data is frequently overlooked during causal inference analyses, potentially leading to inaccurate estimations. This study introduces a geographically weighted difference-in-difference (GWDID) method to address these gaps and estimate the safety impact of marked sidewalks. This approach considers spatial heterogeneity within the dataset in the spatial causal inference framework, providing a more nuanced understanding of the intervention's effects. The simplicity of the modeling process makes it applicable to various study designs relying solely on pre- and post-exposure outcome measurements. Conventional DIDs and Spatial Lag-DID models were used for comparison. The dataset we utilized included a total of 13,641 pedestrian crashes across Taipei City, Taiwan. Then the crash point data was transformed into continuous probability values to determine the crash risk on each road segment using network kernel density estimation (NKDE). The treatment group comprised 1,407 road segments with marked sidewalks, while the control group comprised 3,097 segments with similar road widths. The pre-development program period was in 2017, and the post-development period was in 2020. Results showed that the GWDID model outperformed the spatial lag DID and traditional DID models. As a local causality model, it illustrated spatial heterogeneity in installing marked sidewalks. The program significantly reduced pedestrian crash risk in 43% of the total road segments in the treatment group. The coefficient distribution map revealed a range from -22.327 to 2.600, with over 95% of the area yielding negative values, indicating reduced crash risk after installing marked sidewalks. Notably, the impact of crash risk reduction increased from rural to urban areas, emphasizing the importance of considering spatial heterogeneity in transportation safety policy assessments.

A proof of concept model to calculate white and grey matter AIS injuries in pedestrian collisions.

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.

Investigating the influence of streetscape environmental characteristics on pedestrian crashes at intersections using street view images and explainable machine learning.

Examining the relationship between streetscape features and road traffic accidents is pivotal for enhancing roadway safety. While previous studies have primarily focused on the influence of street design characteristics, sociodemographic features, and land use features on crash occurrence, the impact of streetscape features on pedestrian crashes has not been thoroughly investigated. Furthermore, while machine learning models demonstrate high accuracy in prediction and are increasingly utilized in traffic safety research, understanding the prediction results poses challenges. To address these gaps, this study extracts streetscape environment characteristics from street view images (SVIs) using a combination of semantic segmentation and object detection deep learning networks. These characteristics are then incorporated into the eXtreme Gradient Boosting (XGBoost) algorithm, along with a set of control variables, to model the occurrence of pedestrian crashes at intersections. Subsequently, the SHapley Additive exPlanations (SHAP) method is integrated with XGBoost to establish an interpretable framework for exploring the association between pedestrian crash occurrence and the surrounding streetscape built environment. The results are interpreted from global, local, and regional perspectives. The findings indicate that, from a global perspective, traffic volume and commercial land use are significant contributors to pedestrian-vehicle collisions at intersections, while road, person, and vehicle elements extracted from SVIs are associated with higher risks of pedestrian crash onset. At a local level, the XGBoost-SHAP framework enables quantification of features' local contributions for individual intersections, revealing spatial heterogeneity in factors influencing pedestrian crashes. From a regional perspective, similar intersections can be grouped to define geographical regions, facilitating the formulation of spatially responsive strategies for distinct regions to reduce traffic accidents. This approach can potentially enhance the quality and accuracy of local policy making. These findings underscore the underlying relationship between streetscape-level environmental characteristics and vehicle-pedestrian crashes. The integration of SVIs and deep learning techniques offers a visually descriptive portrayal of the streetscape environment at locations where traffic crashes occur at eye level. The proposed framework not only achieves excellent prediction performance but also enhances understanding of traffic crash occurrences, offering guidance for optimizing traffic accident prevention and treatment programs.

Injury patterns in motor vehicle collision-youth pedestrian deaths.

OBJECTIVE: 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. METHODS: 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). RESULTS: 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. CONCLUSION: 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.

Applicability of the Madymo Pedestrian Model for forensic fall analysis.

Forensic reconstruction and scenario evaluation are crucial in investigations of suspicious deaths related to falls from a height. In such cases, distinguishing between accidental falls, being pushed or jumping is an important but difficult task, since objective methods to do so are currently lacking. This paper explores the possibility of repurposing a passive rigid body model of a human from commercially available crash simulation software for forensic reconstruction and scenario evaluation of humans dropping from heights. To use this approach, a prerequisite is that the human body model can produce realistic movements compared to those of a real human, given similar environmental conditions. Therefore, this study assessed the validity of the commercially available Simcenter Madymo Pedestrian Model (MPM) for simulating human fall movements. Experimental kinematic and kinetic data was collected from nine participants, who dropped from a height in three different ways: passively tilting over, getting pushed, and jumping. Next, the performance of the MPM in reproducing the kinematics of the experimental falls was assessed by comparing the orientation of the body 0.3 s after platform release. The results show that the MPM currently does not consistently reproduce the experimentally recorded falling movements across multiple falling conditions and outcome measures. The MPM must therefore be adapted if to be used for forensic reconstruction and scenario evaluation, for example by implementing active movement.

Generation of nighttime pedestrian fatal precrash scenarios at junctions in Tamil Nadu, India, using cluster correspondence analysis.

OBJECTIVE: Modern transportation amenities and lifestyles have changed people's behavioral patterns while using the road, specifically at nighttime. Pedestrian and driver maneuver behaviors change based on their exposure to the environment. Pedestrians are more vulnerable to fatal injuries at junctions due to increased conflict points with vehicles. Generation of precrash scenarios allows drivers and pedestrians to understand errors on the road during driver maneuvering and pedestrian walking/crossing. This study aims to generate precrash scenarios using comprehensive nighttime fatal pedestrian crashes at junctions in Tamil Nadu, India. METHODS: Though numerous studies were available on identifying pedestrian crash patterns, only some focused on identifying crash patterns at junctions at night. We used cluster correspondence analysis (CCA) to address this research gap to identify the patterns in nighttime pedestrian fatal crashes at junctions. Further, high-risk precrash scenarios were generated based on the positive residual means available in each cluster. This study used crash data from the Road Accident Database Management System of Tamil Nadu State in India from 2009 to 2018. Characteristics of pedestrians, drivers, vehicles, crashes, light, and roads were input to the CCA to find optimal clusters using the average silhouette width, Calinski-Harabasz measure, and objective values. RESULTS: CCA found 4 clusters with 2 dimensions as optimal clusters, with an objective value of 3.3618 and a valence criteria ratio of 80.03%. Results from the analysis distinctly clustered the pedestrian precrash behaviors: Clusters 1 and 2 on pedestrian walking behaviors and clusters 3 and 4 on crossing behaviors. Moreover, a hidden pattern was observed in cluster 4, such as transgender drivers involved in fatal pedestrian crashes at junctions at night. CONCLUSION: The generated precrash scenarios may be used to train drivers (novice and inexperienced for nighttime driving), test scenario creation for developing advanced driver/rider assistance systems, hypothesis creation for researchers, and planning of effective strategic interventions for engineers and policymakers to change pedestrian and driver behaviors toward sustainable safety on Indian roads.

Temporal instability of the determinants of fatal/severe elderly pedestrian injury outcomes in intersections and non-intersections before, during, and after the COVID-19 pandemic.

This study examines the variability in the impacts of factors influencing injury severity outcomes of elderly pedestrians (age >64) involved in vehicular crashes at intersections and non-intersections before, during, and after the COVID-19 pandemic. To account for unobserved heterogeneity in the crash data, a random parameters logit model with heterogeneity in the means approach is utilized to analyze vehicle-elderly pedestrian crash data from Seoul, South Korea, occurring between 2018 and 2022. Preliminary transferability tests revealed instability in factor impacts on injury severity outcomes, highlighting the need to estimate individual models across various road segments and time periods. Thus, the dataset was segregated by crash location (intersection/non-intersection) and period (before, during, and after COVID-19), with individual models estimated for each group. Results obtained from the analyses revealed that back injuries positively influenced fatalities at non-intersections after the pandemic and was negatively associated with fatalities at intersections before the pandemic. Additionally, several indicators demonstrated significant instability in their impact magnitudes across different road segments and crash years. During the pandemic, head injuries increased the probability of fatalities higher at non-intersections. After the pandemic, crosswalk locations decreased the possibility of fatalities more at intersections. Compared to intersection segments, the female indicator reduced the likelihood of fatal injuries at non-intersections more before, during, and after the pandemic. Before the pandemic, much older pedestrians experienced a greater decline in fatalities at intersections than non-intersections. This instability could be attributed to altered mobility patterns stemming from the COVID-19 pandemic. Overall, the study findings highlight the variability of determinants of fatal/severe injury outcomes among elderly pedestrians across various road segments and years, with the underlying cause of this fluctuation remaining unclear. Furthermore, the findings revealed that accounting for heterogeneity in the means of random parameters enhances model fit and provides valuable insights for safety professionals. The factor impact variability in the estimated models carries significant implications for elderly pedestrian safety, especially in scenarios where precise projections of the effects of alternative safety measures are essential. Road safety experts can leverage these findings to refine or update current policies to enhance elderly pedestrian safety at intersections and non-intersections.

Study on the risk assessment of Pedestrian-Vehicle conflicts in channelized Right-Turn lanes based on the Hierarchical-Grey Entropy-Cloud model.

Channelized right-turn lanes (CRTLs) in urban areas have been effective in improving the efficiency of right-turning vehicles but have also presented negative impacts on pedestrian movement. Pedestrians experience confusion regarding the allocation of road space when crossing crosswalks within these areas, leading to frequent conflicts between pedestrians and motor vehicles. In this paper, considering the characteristics of pedestrian-vehicle conflicts at channelized right-turn lanes as well as the ambiguity and uncertainty of the causes, a comprehensive assignment combined with a cloud model is proposed as a risk evaluation model for pedestrian-vehicle conflicts. The study established a risk indicator system based on three aspects of the transportation system: pedestrians, motor vehicles, and the road environment. Combining the analytic hierarchy process (AHP), grey relational analysis (GRA), and entropy weighting method (EWM) to get the weights of indicator combinations, and then using the cloud model to realize quantitative and qualitative language transformation to complete the risk evaluation. This study employs specific road segments in Qingdao as a validation case for model analysis. The results indicate that the model's evaluation outcomes exhibited a significant level of agreement with the findings from field investigations during both peak and off-peak periods. It is demonstrated that the model has good performance for the safety assessment of pedestrian-vehicle conflicts at CRTL, and it also reflects the ability of the model to assess fuzzy randomness problems. It provides participation value for urban pedestrian-vehicle safety problems as well as applications in other fields.

Effects of driver's braking behavior by the real-time pedestrian scale warning system.

A driver warning system can improve pedestrian safety by providing drivers with alerts about potential hazards. Most driver warning systems have primarily focused on detecting the presence of pedestrians, without considering other factors, such as the pedestrian's gender and speed, and whether pedestrians are carrying luggage, that can affect driver braking behavior. Therefore, this study aims to investigate how driver braking behavior changes based on the information about the number of pedestrians in a crowd and examine if a developed warning system based on this information can induce safe braking behavior. For this purpose, an experiment scenario was conducted using a virtual reality-based driving simulator and an eye tracker. The collected driver data were analyzed using mixed ANOVA to derive meaningful conclusions. The research findings indicate that providing information about the number of pedestrians in a crowd has a positive impact on driver braking behavior, including deceleration, yielding intention, and attention. Particularly, It was found that in scenarios with a larger number of pedestrians, the Time to Collision (TTC) and distance to the crosswalk were increased by 12%, and the pupil diameter was increased by 9%. This research also verified the applicability of the proposed warning system in complex road environments, especially under conditions with poor visibility such as nighttime. The system was able to induce safe braking behavior even at night and exhibited consistent performance regardless of gender. In conclusion, considering various factors that influence driver behavior, this research provides a comprehensive understanding of the potential and effectiveness of a driver warning system based on information about the number of pedestrians in a crowd in complex road environments.

How does street environment affect pedestrian crash risks? A link-level analysis using street view image-based pedestrian exposure measurement.

Street space plays a critical role in pedestrian safety, but the influence of fine-scale street environment features has not been sufficiently understood. To analyze the effect of the street environment at the link level, it is essential to account for the spatial variation of pedestrian exposure across street links, which is challenging due to the lack of detailed pedestrian flow data. To address these issues, this study proposes to extract link-level pedestrian exposure from spatially ubiquitous street view images (SVIs) and investigate the impact of fine-scale street environment on pedestrian crash risks, with a particular focus on pedestrian facilities (e.g., crossing and sidewalk design). Both crash frequency and severity are analyzed at the link level, with the latter incorporating two distinct aggregation metrics: maximum severity and medium severity. Using Hong Kong as a case study, the results show that the link-level pedestrian exposure extracted from SVIs can lead to better model fit than alternative zone-level measurements. Specifically, higher pedestrian exposure is found to increase the total pedestrian crash frequency, while reducing the risk of serious injuries or fatalities, confirming the "safety in numbers" effect for pedestrians. Pedestrian facilities are also shown to influence pedestrian crash frequency and severity in different ways. The presence of crosswalks can increase crash frequency, but denser crosswalk design mitigates this effect. In addition, two-side sidewalks can increase crash frequency, while the absence of sidewalks leads to higher risks of crash severity. These findings highlight the importance of fine-scale street environment and pedestrian facility design for pedestrian safety.

Adversarial Infrared Curves: An attack on infrared pedestrian detectors in the physical world.

Deep neural network security is a persistent concern, with considerable research on visible light physical attacks but limited exploration in the infrared domain. Existing approaches, like white-box infrared attacks using bulb boards and QR suits, lack realism and stealthiness. Meanwhile, black-box methods with cold and hot patches often struggle to ensure robustness. To bridge these gaps, we propose Adversarial Infrared Curves (AdvIC). Using Particle Swarm Optimization, we optimize two Bezier curves and employ cold patches in the physical realm to introduce perturbations, creating infrared curve patterns for physical sample generation. Our extensive experiments confirm AdvIC's effectiveness, achieving 94.8% and 67.2% attack success rates for digital and physical attacks, respectively. Stealthiness is demonstrated through a comparative analysis, and robustness assessments reveal AdvIC's superiority over baseline methods. When deployed against diverse advanced detectors, AdvIC achieves an average attack success rate of 76.2%, emphasizing its robust nature. We conduct thorough experimental analyses, including ablation experiments, transfer attacks, adversarial defense investigations, etc. Given AdvIC's substantial security implications for real-world vision-based applications, urgent attention and mitigation efforts are warranted.

Impact of urban tree arrangement on pedestrian exposure to the size-fractional particulate matter in a city boulevard.

Trees act as natural filters that mitigate roadside air pollution. However, the filtration impact of different tree arrangements on traffic pollutants with different particle diameters has rarely been analysed in real street canyon environments. To quantify how roadside tree arrangements impact pedestrian exposure to particle number concentrations (PNCs) of different diameters (0.25-32 µm), in situ field measurements were carried out in a boulevard-type street canyon in the city of Xi'an, China. This study analysed the experimental data of PNCs collected along segments of a pedestrian lane under four typical tree arrangements: open space without trees, a sparse-spaced tree arrangement, a medium-spaced tree arrangement, and a dense-spaced tree arrangement in a street canyon. Our results reveal that the effect of tree arrangement on PNCs depended on the particle diameter. In general, trees can significantly reduce coarse PNC (particles with diameters >2.5 µm) but not the fine PNC. Quantitative analysis showed that a medium-spaced tree arrangement, in which tree crowns are adjacent to each other but do not overlap, is the most capable of reducing PNC, followed by a sparse-spaced tree arrangement, while a the dense-spaced tree arrangement has the least impact. The attenuation effect of trees on the PNCs increased with increasing particle diameter. Moreover, the presence of trees altered the local microclimate, which also affected how exposure to PNCs changed. Our empirical findings further highlight the complexity of how trees affect particulate pollutants in street canyons and provide timely insights for enhancing tree-planning management in cities from the perspective of air quality improvement.