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In the rapidly evolving realm of transportation technology, the dynamic relationship between pedestrians and technological innovations has attained unprecedented importance. The complex social-technological intersection surrounding pedestrian road crossings has emerged as an attention for traffic safety. What distinguishes the contemporary urban environment is the rapid assimilation of intelligent transportation systems (ITS) into the transportation infrastructure, including technological elements such as autonomous vehicles, advanced surveillance systems, and smart infrastructure. To investigate how pedestrians perceive risks, trust technology, and make decisions in this era of technological progress, we designed a video-based questionnaire utilizing the stated preference (SP) methodology. We collected SP data from 589 Chinese pedestrians and employed an integrated choice and latent variable (ICLV) model to quantify the influence of risk perception and trust in autonomous vehicle (trust in AV), treated as latent variables, on their crossing decisions. Our findings indicate that the presence of autonomous vehicles significantly affects pedestrian crossing decisions. Specifically, an increase in the approaching vehicle speed and a decrease in the approaching vehicle distance increase the pedestrians' tendency to choose not to cross the road, and the latent variables of risk perception and trust in AV strongly predict this phenomenon. The results of the scenario analysis show that, compared with overall pedestrians, middle-aged pedestrians and high-risk perception-level pedestrians are more conservative in their crossing decisions, but high levels of trust in AV improve pedestrians' willingness to cross the street. Additionally, the pedestrian-related findings of this study at the social-technological intersection provide better understanding of the decision process and contribute to the planning and development of urban intelligent transportation systems. • Relationship between ITS facilities, pedestrian risk perception, trust in AV, and crossing decisions are investigated. • A video-based SP questionnaire was design. • The ICLV model was used to predict pedestrians' propensity to cross the street. • The presence of autonomous vehicles can have a significant impact on pedestrian crossing decision. • Pedestrians who are middle-aged, and have high levels of risk perception are more conservative in their crossing decisions. [ABSTRACT FROM AUTHOR]

Cell phone use while walking escalates pedestrian safety risks, particularly among students, given their heavy reliance on these devices. Psychological factors wield significant influence on individual behavior, particularly among adolescents who navigate a blend of rational and social-reactive pathways in decision-making. Therefore, this paper employs the Prototype Willingness Model (PWM) to scrutinize psychological determinants of students' cell phone use while walking, drawing on data from 368 valid samples. Structural equation modeling (SEM) dissects the impact of psychological constructs on this behavior, highlighting the significance of both the social response and reasoning pathways within PWM. The study comprehensively analyzes direct, indirect, and overall effects of exogenous variables on behavioral intention and behavior. [ABSTRACT FROM AUTHOR]

The paper presents a field study analysing how pedestrians walked for leisure purposes in a familiar urban area, and why they walked as they did. Using mobile-based GPS, the walking trajectories of 44 participants were recorded, followed by the administration of a post-walk survey. Results show that participants gravitated towards five distinct paths that were similar in length (three shorter and two longer paths), yet observably different with respect to specific street features. Correspondingly, participants rated street features associated with these differences as factors that either attracted them towards, or repelled them away, from walking in specific streets. [ABSTRACT FROM AUTHOR]

Asian cities have promoted TOD to increase MRT use and decrease private vehicle use. A crucial factor in achieving this goal is improving the pedestrian environment, which can increase people's willingness to walk to MRT stations. This paper analyses how Asian cities have improved the pedestrian environment around MRT stations and the factors influencing walking willingness by reviewing literature and taking Taipei as the empirical area. The results demonstrated that reducing obstacles in pedestrian space, evening the pavement, using transparent window displays in stores along the pavement, and constructing rain shelters can increase people's willingness to walk. The simulations of improved environments can provide references for countries improving the pedestrian environment. The contribution and innovation are to point out the differences in the motivation and goals of TOD in Asian and Western cities and propose improvements to increase people's willingness to walk to achieve TOD's goals. [ABSTRACT FROM AUTHOR]

Social behavior in crowds, such as herding or increased interpersonal spacing, is driven by the psychological states of pedestrians. Current macroscopic crowd models assume that these are static, limiting the ability of models to capture the complex interplay between evolving psychology and collective crowd dynamics that defines a "social crowd". This paper introduces a novel approach by explicitly incorporating an "activity" variable into the modeling framework, which represents the evolving psychological states of pedestrians and is linked to crowd dynamics. To demonstrate the role of activity, we model pedestrian egress when this variable captures stress and awareness of contagion. In addition, to highlight the importance of dynamic changes in activity, we examine a scenario in which an unexpected incident necessitates alternative exits. These case studies demonstrate that activity plays a pivotal role in shaping crowd behavior. The proposed modeling approach thus opens avenues for more realistic macroscopic crowd descriptions with practical implications for crowd management. [ABSTRACT FROM AUTHOR]

We study the validity of the dissipative Aw–Rascle system as a macroscopic model for pedestrian dynamics. The model uses a congestion term (a singular diffusion term) to enforce capacity constraints in the crowd density while inducing a steering behavior. Furthermore, we introduce a semi-implicit, structure-preserving, and asymptotic-preserving numerical scheme which can handle the numerical solution of the model efficiently. We perform the first numerical simulations of the dissipative Aw–Rascle system in one and two dimensions. We demonstrate the efficiency of the scheme in solving an array of numerical experiments, and we validate the model, ultimately showing that it correctly captures the fundamental diagram of pedestrian flow. [ABSTRACT FROM AUTHOR]

Road traffic collisions disproportionately impact Ghana and other low- and middle-income countries. This study explored road user perspectives regarding the magnitude, contributing factors, and potential solutions to road traffic collisions, injuries, and deaths. We designed a qualitative study of 24 in-depth interviews with 14 vulnerable road users (pedestrians, occupants of powered 2- and 3-wheelers, cyclists) and ten non-vulnerable road users in four high-risk areas in November 2022. We used a mixed deductive (direct content analysis) and inductive (interpretive phenomenological analysis) approach. In the direct content analysis, a priori categories based on Haddon's Matrix covered human, vehicle, socioeconomic environment, and physical environment factors influencing road traffic collisions, along with corresponding solutions. We used inductive analysis to identify emerging themes. Participants described frequent and distressing experiences with collisions, and most often reported contributing factors, implementation gaps, and potential solutions within the human (road user) level domain of Haddon's Matrix. Implementation challenges included sporadic enforcement, reliance on road users' adherence to safety laws, and the low quality of the existing infrastructure. Participants expressed that they felt neglected and ignored by road safety decision-makers. This research emphasizes the need for community input for successful road safety policies in Ghana and other low- and middle-income countries, calling for greater governmental support an action to address this public health crisis. We recommend the government collaborates with communities to adapt existing interventions including speed calming, footbridges, and police enforcement, and introduces new measures that meet local needs. [ABSTRACT FROM AUTHOR]

A survey was carried out in Rome to investigate whether street luminance affects pedestrians’ comfort, visibility and perception. Forty people walked five roads and assessed on a questionnaire their walking comfort, visibility of people and background, perception of lighting intensity and uniformity. Results indicated that walking comfort was affected by mean luminance of whole scene, of sidewalk, of lateral wall and of human body; luminance also influenced the perception of street lighting intensity and uniformity and, to a lesser extent, the visibility of other people and of background. [ABSTRACT FROM AUTHOR]

The continuous downward movement exhibited by the Lashagou landslide group in recent years poses a significant threat to the safety of both vehicles and pedestrians traversing the highway G310. By integrating geomorphological interpretation using multi-temporal optical images, interferometric synthetic aperture radar (InSAR) measurements, and continuous global navigation satellite system (GNSS) observations, this paper traced the formation period of the Lashagou landslide group, and explored its kinematic behavior under external drivers such as rainfall and snowmelt. The results indicate that the formation period can be specifically categorized into three periods: before, during, and after the construction of highway G310. The construction of highway G310 is the direct cause and prerequisite for the formation of the Lashagou landslide group, whereas summer precipitation and spring snowmelt are the external driving factors contributing to its continuous downward movement. Additionally, both the long-term seasonal downslope movement and transient acceleration events are strongly controlled by rainfall, and there is a time lag of approximately 1–2 days between the transient acceleration and heavy rainfall events. This study highlights the benefits of leveraging multi-source remote sensing data to investigate slow-moving landslides, which is advantageous for the implementation of effective control and engineering intervention to mitigate potential landslide disasters. [ABSTRACT FROM AUTHOR]

To address the issue of tracking drift and failures in thermal infrared (TIR) tracking tasks caused by target occlusion, this study proposes an anti‐occlusion TIR target tracker named AODiMP‐TIR. This approach involves an anti‐occlusion strategy that relies on target occlusion status determination and trajectory prediction. This enables the prediction of the target's current position when it is identified as occluded, ensuring swift recapture upon reappearance. A criterion is introduced for occlusion status determination based on the classification response map of SuperDiMP. Additionally, a trajectory mapping module designed to decouple target motion from camera motion is presented, enhancing the precision of trajectory prediction. Comparative experiments with other state‐of‐the‐art trackers are conducted on the large‐scale high‐diversity thermal infrared object tracking benchmark (LSOTB‐TIR), LSOTB‐TIR100, and thermal infrared pedestrian tracking benchmark (PTB‐TIR) datasets. The results indicate that the AODiMP‐TIR performs well across all three datasets, particularly exhibiting outstanding performance in occlusion sequences. Furthermore, ablation study experiments confirm the effectiveness of the anti‐occlusion strategy, occlusion determination criterion and trajectory mapping module. [ABSTRACT FROM AUTHOR]

Accurately predicting the future trajectory of pedestrians is critical for tasks such as autonomous driving and robot navigation. Previous methods for pedestrian trajectory prediction dealt with social interaction and pedestrian movement factors either concurrently or sequentially, neglecting the link between them. Therefore, a spatial–temporal interaction-aware graph convolution network (STIGCN) is proposed for pedestrian trajectory prediction. STIGCN considers the correlation between social interaction and pedestrian movement factors to achieve more accurate interaction modeling. Specifically, we first constructed spatial and temporal graphs to model social interactions and movement factors. Then, we designed the spatial–temporal interaction-aware learning to utilize the spatial interaction features of each moment to assist the temporal interaction modeling and utilize the temporal interaction features of each pedestrian to assist the spatial interaction modeling, resulting in more accurate interaction modeling. Finally, a time-extrapolator pyramid convolution neural network (TEP-CNN) is designed to jointly estimate the two-dimensional Gaussian distribution parameters of future trajectories by combining the prediction features from multiple layers. Experimental results on two benchmark pedestrian trajectory prediction datasets show that our proposed method outperforms existing methods in terms of average displacement error and final displacement error and achieves more accurate predictions for pedestrian motions such as convergence and encounter. [ABSTRACT FROM AUTHOR]

Comprehending crowd dynamics on staircases is imperative for preventing accidents, particularly in emergency scenarios. In this context, our study delves into bi-directional pedestrian flow. When confronted with limited staircase space, the occurrence of two distinct lanes—one for upstairs and another for downstairs—is a common observation. However, there has been no quantitative investigation conducted to understand this phenomenon. To facilitate such an analysis, we employ a velocity-based personal space model that accurately reproduces the formation of upstairs and downstairs lanes in bi-directional staircases. The study of lane formation mechanisms and the exploration of factors influencing deadlock are essentially two sides of the same coin. This is due to the fact that, the occurrence of deadlock signifies a disruption in the anticipated lane behavior during counter flow. As a result, we have devised various scenarios to meticulously analyze the factors contributing to both deadlock formation and its subsequent performance. This involves manipulating parameters such as speed, speed dispersion, pedestrian count, initial density, right-hand preference weight, minimum personal space size, same-direction following intensity, and time step. The findings hold the potential to enhance the overall quality of service in stairway movement and offer significant contributions to the understanding and management of pedestrian behavior in such settings. [ABSTRACT FROM AUTHOR]

Background: Developmental coordination disorder (DCD) affects movement coordination, but little is known about how the condition impacts the behaviours of car drivers and pedestrians. Aims: This study examined the self-reported driving and pedestrian behaviours of adults with Developmental Coordination Disorder (DCD). Methods and procedures: One hundred and twenty-eight participants (62 adults with DCD vs. 66 TD adults) responded to an online survey asking them about their perceptions of confidence and self-reported driving and pedestrian behaviours in the real-world. Outcomes and results: Results suggested that adults with DCD felt less confident and reported more lapses in attention (e.g., forgetting where their car was parked) and errors (e.g., failing to check their mirrors prior to a manoeuvre) when driving compared to typically developed (TD) adults. Adults with DCD also reported feeling less confident and reported less adherence to road traffic laws (e.g., not waiting for a green crossing signal before crossing the road) when walking as pedestrians. Conclusions and implications: These results offer some much-needed insight into the behaviours of those with DCD outside of the laboratory environment and underline the need for research investigating the driving and pedestrian behaviours of individuals with DCD in 'real-world' contexts. [ABSTRACT FROM AUTHOR]

Background: According to social-ecological models, the built and natural environment has the potential to facilitate or hinder physical activity (PA). While this potential is well researched in urban areas, a current systematic review of how the built and natural environment is related to PA in rural areas is lacking. Methods: We searched five databases and included studies for adults (18–65 years) living in rural areas. We included quantitative studies investigating the association between any self-reported or objectively measured characteristic of the built or natural environment and any type of self-reported or objectively measured PA, and qualitative studies that reported on features of the built or natural environment perceived as barriers to or facilitators of PA by the participants. Screening for eligibility and quality assessment (using the Standard Quality Assessment Criteria for Evaluating Primary Research Papers from a Variety of Fields) were done in duplicate. We used a narrative approach to synthesize the results. Results: Of 2432 non-duplicate records, 51 quantitative and 19 qualitative studies were included. Convincing positive relationships were found between the availability and accessibility of places for exercise and recreation and leisure-time PA as well as between the overall environment and leisure-time PA. Possible positive associations were found between the overall environment and total and transport-related PA, between greenness/natural environment and total PA, between cycling infrastructure and aesthetics and MVPA, and between pedestrian infrastructure and total walking. A possible negative relationship was found between safety and security and total walking. Qualitative studies complemented several environmental facilitators (facilities for exercise and recreation, sidewalks or streets with low traffic, attractive natural environment) and barriers (lack of facilities and destinations, lack of sidewalks, speeding traffic and high traffic volumes, lack of street lighting). Conclusions: Research investigating the relationship between the built and natural environment and PA behaviors of adults living in rural areas is still limited and there is a need for more high-quality and longitudinal studies. However, our most positive findings indicate that investing in places for exercise and recreation, a safe infrastructure for active transport, and nature-based activities are possible strategies that should be considered to address low levels of PA in rural adults. Trial registration: PROSPERO: CRD42021283508. [ABSTRACT FROM AUTHOR]

LiDAR-based 3D object detection and localization are crucial components of autonomous navigation systems, including autonomous vehicles and mobile robots. Most existing LiDAR-based 3D object detection and localization approaches primarily use geometric or structural feature abstractions from LiDAR point clouds. However, these approaches can be susceptible to environmental noise due to adverse weather conditions or the presence of highly scattering media. In this work, we propose an intensity-aware voxel encoder for robust 3D object detection. The proposed voxel encoder generates an intensity histogram that describes the distribution of point intensities within a voxel and is used to enhance the voxel feature set. We integrate this intensity-aware encoder into an efficient single-stage voxel-based detector for 3D object detection. Experimental results obtained using the KITTI dataset show that our method achieves comparable results with respect to the state-of-the-art method for car objects in 3D detection and from a bird's-eye view and superior results for pedestrian and cyclic objects. Furthermore, our model can achieve a detection rate of 40.7 FPS during inference time, which is higher than that of the state-of-the-art methods and incurs a lower computational cost. [ABSTRACT FROM AUTHOR]

With the continuous advancement of autonomous driving and monitoring technologies, there is increasing attention on non-intrusive target monitoring and recognition. This paper proposes an ArcFace SE-attention model-agnostic meta-learning approach (AS-MAML) by integrating attention mechanisms into residual networks for pedestrian gait recognition using frequency-modulated continuous-wave (FMCW) millimeter-wave radar through meta-learning. We enhance the feature extraction capability of the base network using channel attention mechanisms and integrate the additive angular margin loss function (ArcFace loss) into the inner loop of MAML to constrain inner loop optimization and improve radar discrimination. Then, this network is used to classify small-sample micro-Doppler images obtained from millimeter-wave radar as the data source for pose recognition. Experimental tests were conducted on pose estimation and image classification tasks. The results demonstrate significant detection and recognition performance, with an accuracy of 94.5%, accompanied by a 95% confidence interval. Additionally, on the open-source dataset DIAT-μRadHAR, which is specially processed to increase classification difficulty, the network achieves a classification accuracy of 85.9%. [ABSTRACT FROM AUTHOR]

In outdoor environments after dark, pedestrians may experience discomfort from glare caused by lighting. Several models to predict discomfort from glare have been proposed or extended for pedestrian applications; these models use different luminous and geometrical quantities to predict discomfort. Consistent measurements and reporting in studies of discomfort from glare are important for identifying best performing models; however, previous studies proposing a new model tended to only report the performance of the new model and its quantities. This practice makes it difficult to evaluate how a new model performs compared to other existing models. To promote more consistent and complete reporting, this research note proposes measuring and reporting all relevant quantities that are used in existing models. This can make it easier for researchers to use a study dataset to compare the performance of several models or to combine datasets from several studies to address between-study variance. [ABSTRACT FROM AUTHOR]

Previous studies have gained data from automated counters to compare the influence of ambient light level on the numbers of people walking and cycling. This paper reports an exploratory study using instead in-person counting, the advantage being that the apparent age and gender of each pedestrian and cyclist can also be recorded. The analysis compares travel counts in case and control periods, with case periods in daylight and darkness, to isolate the effect of change in ambient light level. As expected, the results reveal that there are fewer people walking and cycling after dark. What was unexpected was that for pedestrians, the deterrence of darkness was similar for males and females, which disagrees with previous studies capturing stated preferences suggesting that darkness is a greater deterrent for females than for males. [ABSTRACT FROM AUTHOR]

The optimal illuminance from road lighting for pedestrian reassurance after dark is that beyond which further increase in illuminance has no significant effect on reassurance. Previous studies have not revealed a precise estimate of optimal illuminance. The current study investigates the use of segmented regression for defining optimal illuminance, applied to 25 662 reassurance evaluations by 380 test participants who recorded their reassurance assessments in 253 locations in three cities in Israel. Segmented regression led to models which better fit the empirical data than their unsegmented counterparts and offered precise estimates of optimal illuminance, these ranging from 8.9 lx to 26 lx, depending on location. [ABSTRACT FROM AUTHOR]

This study concerns road lighting for pedestrians. Many experiments have been conducted to determine how changes in lighting affect the ability to make interpersonal evaluations, usually considering variations in light level or light spectrum. Here, we consider an alternative approach, predicting performance using an existing model, Relative Visual Performance. The results show that face evaluation ability is affected by adaptation luminance, pavement surface reflectance, observer age, and skin tone of the observed person. Previous experimental studies have tended to use young test participants to evaluate Caucasian or Asian faces: if the situation instead involved an elderly person evaluating a face of South African skin tone, then the current analysis predicts that for optimal performance the light level would need to be doubled. [ABSTRACT FROM AUTHOR]

Person Re-Identification falls within the scope of computer vision, acting a technique to ascertain the presence of a specified pedestrian within a video or image library. The related research is of great significance in real-world environments such as criminal investigation and statistical analysis of commercial foot traffic and has received extensive attention from the academic community. However, traditional methods such as manual extraction cannot adapt to largescale data volumes, and deep learning-based methods at this stage suffer from interference in complex environments such as similar costumes, perspective changes, and occlusion. Therefore, in this paper, we investigate the above problems. Firstly, we expand the dataset by introducing random erasure-based preprocessing of pedestrian images to enhancing the robustness and generalization capability of neural networks. Secondly, a composite attention mechanism is introduced after the network residual layer to enhance the spatial information capability and feature expression. Finally, the union loss composed of Circle Loss, Ternary Loss, and Cross Entropy Loss was chosen for network training in the loss optimization phase. Findings from the experiments reveal that the improved method proposed in this experiment achieves 96.0%Rank-1 and 88.3%mAP in Market1501, which reflects the validity of the approach proposed in this manuscript, and provides valuable reference suggestions for Person Re-Identification related research. [ABSTRACT FROM AUTHOR]

Traffic-calming measures (TCMs) are physical modifications of the road network aimed at making the roads safer. Although researchers have reported reductions in numbers of road crashes and injuries tied to the presence of TCMs, such studies have been criticized for their pre-/post- designs. In this study, we aimed to complement our knowledge of TCMs' effectiveness by assessing their impact using a longitudinal design. The implementation of 8 TCMs, including curb extensions and speed humps, was evaluated at the intersection and census tract levels in Montreal, Quebec, Canada, from 2012 to 2019. The primary outcome was fatal or serious collisions among all road users. Inference was performed using a Bayesian implementation of conditional Poisson regression in which random effects were used to account for the spatiotemporal variation in collisions. TCMs were generally implemented on local roads, although most collisions occurred on arterial roads. Overall, there was weak evidence that TCMs were associated with study outcomes. However, subgroup analyses of intersections on local roads suggested a reduction in collision rates due to TCMs (median incidence rate ratio, 0.31; 95% credible interval: 0.12, 0.86). To improve road safety, effective counterparts of TCMs on arterial roads must be identified and implemented. [ABSTRACT FROM AUTHOR]

Intensive urban development has resulted in the degradation of the urban thermal environment in most regions. There is a growing consensus on the need to enhance urban thermal comfort through well-designed forms, especially in open spaces like urban canyons. To address this, our study focuses on Xi'an's commercial pedestrian streets, employing K-means clustering analysis to create 32 representative models based on actual scenes, capturing their textural characteristics. Simultaneously, 11 geometric indicators (2D/3D) were chosen to quantify the canyon's geometric form. We assessed the spatial and temporal distribution differences in the thermal environment across these models using Envi-met simulation. Finally, Spearman correlation analysis was employed to examine the correlation and significance of the two sets of indicators, culminating in formulating an ideal model. The findings reveal that (1) wind conditions are predominantly influenced by the canyon's geometric form, followed by solar radiation and temperature, with the lowest relative humidity change amplitude among the assessed thermal parameters. (2) Among the 11 geometric form indicators, 3D indicators correlate more significantly with thermal environment parameters than 2D indicators. Specifically, street orientation significantly impacts the thermal environment, Build-To-Line Rat holds greater significance than interface density, and both building shape coefficient and block surface ratio are significantly correlated with air temperature and wind speed, with a weaker correlation to solar radiation. (3) In the Xi'an region, courtyards oriented north-south demonstrate a more favorable trend in the thermal environment. [ABSTRACT FROM AUTHOR]

Several researchers have modified the original Social Force Model (SFM) for pedestrians by including vehicles and simulating interaction behaviors in shared space layouts. The present study is conducted at a signalized intersection where the performance and functionality are defined by spontaneous order. The SFM was modified, and two-wheelers were added along with cars and pedestrians to analyze their interactions. The Hybrid Calibration Method was adopted to calibrate the interaction parameters utilizing an optimization technique and real-time data from the study site. It can be concluded from the parameter values that the strength of interaction reduced as the range between the road agents increased. The findings can aid urban planners in visualizing the interaction behavior of pedestrians, cars, and two-wheelers. Moreover, the feasibility of introducing the shared space concept in India as an alternative to the conventional traffic planning system can also be determined during the designing phase. [ABSTRACT FROM AUTHOR]

Many enclosed public spaces with multiple destinations often feature obstacles, such as columns within the building space and dense crowds, which adversely affect the recognition and guidance of pedestrian sign. To mitigate this issue, enhancing traffic efficiency necessitates the implementation of auxiliary guidance signs. However, experience-based schemes for positioning these signs often require iterative adjustments to achieve the desired effect. Thus, this paper endeavors to address this challenge by analyzing the relationship between obstacles and pedestrians' visual blind areas. It establishes a mathematical model to describe pedestrian visual perception and its correlation with crowd density and individual visual field function. The model employs the effective aggregation area of the population exceeding a certain density threshold as its primary variable and enhances this variable's function. Subsequently, it formulates an effect function that yields a substantial improvement in various combinations and permutations of auxiliary guidance signs. To exemplify this research, a large subway transfer hall with multiple destinations serves as a simulation model. The study calculates the optimal combination of auxiliary guidance sign placements that align with the enhancement effect function. Based on this combination, an optimized layout plan is derived, resulting in a significant improvement. The results demonstrate that the auxiliary guidance sign layout scheme proposed in this study markedly enhances traffic efficiency in enclosed public spaces. This research serves as a valuable reference for decision-makers seeking to optimize traffic flow in such spaces. [ABSTRACT FROM AUTHOR]

Green spaces are important elements of cities due to their benefits to residents' physical health, mental health and development, and social connections. Residents are more likely to experience green space benefits when they use them frequently and usage increases when amenities are accessible. Because lower-income households often have less reliable access to automobiles, examining access solely on the road network is inadequate. Do households across the socioeconomic spectrum have access to green space on pedestrian and bicycle networks? After estimating residential locations (origins) from U.S. Census data and park and other green space locations (destinations) from the City of Albuquerque data, we connect the two using service area network analyses in a geographic information system (GIS). Findings suggest that overall green space access is a concern since less than 50% of Albuquerque residents have access to a green space within 400 m, indicating levels below those recommended by the World Health Organization. Park accessibility is relatively equally distributed for low-, middle-, and high-income households across both pedestrian and road networks. Access to other green space is a source of possible inequity with high-income households having more than five times the access to non-park green spaces than low-income households. The bike network presents another source of possible inequity as 39% of high-income households have access to the bike network versus only 30% of low-income households and an expansion of the bike network is a primary recommendation for increasing access in an equitable fashion. [ABSTRACT FROM AUTHOR]

This paper addresses the global optimal signal plan of a fixed time control for an isolated signalized intersection with central pedestrian refuges considering both vehicle delays and pedestrian delays. A signal phase plan, a pedestrian signal setting scheme, and a signal timing plan are all part of the signal plan presented here. Pedestrian delays are explicitly formulated with signal timing parameters considering signal phase plans and pedestrian signal setting schemes. Integer nonlinear combinatorial programming models are proposed to minimize average and total delays. Numerical experiments show that the proposed optimization models outperform the existing ones, especially when traffic demands between phases are unbalanced. The analysis of the results shows that separate pedestrian signalization settings, and adjacent (or nonadjacent) allowed crossing phases for pedestrians at central islands are superior signal plan options when the minimum phase time is insufficient (or sufficient) to accommodate the minimum green time for crosswalks. [ABSTRACT FROM AUTHOR]

Objective: This study used a virtual environment to examine how older and younger pedestrians responded to simulated augmented reality (AR) overlays that indicated the crossability of gaps in a continuous stream of traffic. Background: Older adults represent a vulnerable group of pedestrians. AR has the potential to make the task of street-crossing safer and easier for older adults. Method: We used an immersive virtual environment to conduct a study with age group and condition as between-subjects factors. In the control condition, older and younger participants crossed a continuous stream of traffic without simulated AR overlays. In the AR condition, older and younger participants crossed with simulated AR overlays signaling whether gaps between vehicles were safe or unsafe to cross. Participants were subsequently interviewed about their experience. Results: We found that participants were more selective in their crossing decisions and took safer gaps in the AR condition as compared to the control condition. Older adult participants also reported reduced mental and physical demand in the AR condition compared to the control condition. Conclusion: AR overlays that display the crossability of gaps between vehicles have the potential to make street-crossing safer and easier for older adults. Additional research is needed in more complex real-world scenarios to further examine how AR overlays impact pedestrian behavior. Application: With rapid advances in autonomous vehicle and vehicle-to-pedestrian communication technologies, it is critical to study how pedestrians can be better supported. Our research provides key insights for ways to improve pedestrian safety applications using emerging technologies like AR. [ABSTRACT FROM AUTHOR]

This exploratory study aimed to evaluate the preferences, expectations, and sense of safety of blind or visually impaired persons regarding three types of pedestrian phasing with audible pedestrian signals configurations that exist in Quebec City (Canada). These include: 1) exclusive phasing with non-directional audible pedestrian signals; 2) exclusive phasing with directional audible pedestrian signals; and 3) concurrent phasing with directional audible pedestrian signals. Thirty-two blind or visually impaired persons were asked to complete a survey. Their preferences and expectations regarding audible pedestrian signals were documented through a series of simulations. Their sense of safety regarding the three existing configurations were also documented. Subsequently, semi-directed, individual interviews with 11 of the individuals who had completed the survey were conducted to build off the collected information. No formal consensus regarding many of the issues discussed were established as participants' responses varied too significantly. However, findings suggest that the exclusive phasing with directional audible pedestrian signals configuration is perceived to be the safest option by the participants. This study may have practical implications on the design of intersections (e.g., selection of a type of pedestrian phasing with audible pedestrian signal) and the training of blind or visually impaired pedestrians. The addition of audible pedestrian signals to pedestrian signals heightens the sense of safety of blind or visually impaired persons. This study may have practical implications on the design of intersections with audible pedestrian signals and the selection of a type of audible pedestrian signals based on intersection characteristics. Since many participants reported a lower sense of safety when faced with concurrent phasing, it is recommended that more intensive orientation and mobility interventions be provided to blind or visually impaired pedestrians who use this type of traffic signals. Educating road users about blind or visually impaired pedestrians appears also essential. [ABSTRACT FROM AUTHOR]

Background: Active commuting to school can be a meaningful contributor to overall physical activity in children. To inform better micro-level urban design near schools that can support active commuting to school, there is a need for measures that capture these elements. This paper describes the adaptation of an observational instrument for use in assessing micro-scale environments around urban elementary schools in the United States. Methods: The Micro-scale Audit of Pedestrian Streetscapes for Safe Routes to School (MAPS-SRTS) was developed from existing audit instruments not designed for school travel environments and modifications for the MAPS-SRTS instrument include the structure of the audit tool sections, the content, the observation route, and addition of new subscales. Subscales were analyzed for inter-rater reliability in a sample of 36 schools in Austin, TX. To assess reliability for each subscale, one-way random effects single-measure intraclass correlation coefficients (ICC) were used. Results: Compared to the 30 original subscales, the adapted MAPS-SRTS included 26 (86.6%) subscales with revised scoring algorithms. Most MAPS-SRTS subscales had acceptable inter-rater reliability, with an ICC of 0.97 for the revised audit tool. Conclusions: The MAPS-SRTS audit tool is a reliable instrument for measuring the school travel environment for research and evaluation purposes, such as assessing human-scale determinants of active commuting to school behavior and documenting built environment changes from infrastructure interventions. [ABSTRACT FROM AUTHOR]

It has become urgent to formulate the physical vulnerability of pedestrians exposed to the compound hazard of strong wind and flooding in an urban landscape and identify the pedestrian risk characteristics within urban building arrays. In this study, the physical vulnerability of pedestrians subject to a joint effect of wind and floodwater is quantified theoretically based on a mechanical analysis in terms of floodwater velocity, depth and wind speed. Laboratory experiment regarding the instability of a dummy is performed to calibrate this vulnerability formula. The formula is adopted to quantify the pedestrian risk rating within different urban building array configurations in conjunction with computational fluid dynamics simulation, including the urban block layout, building form, and building array skyline. It is found that (i) the reverse wind reduces the area of the extreme instability risk zone of pedestrians but improves the safety zone area in comparison to the forward wind; (ii) in comparison to the determinant layout, the enclosed layout is indeed favorable to pedestrian safety; however, the staggered layout causes pedestrian safety to deteriorate; (iii) either corner modification or a change in building form leads to a large reduction in pedestrian risk; and (iv) For a parallel building skyline array, a low street aspect ratio (i.e., the ratio of building height to street width) yields an increased pedestrian risk when the aspect ratio is smaller than unity; however, a large aspect ratio produces an increased pedestrian risk when the aspect ratio exceeds unity. The protuberant skyline leads to more high-risk zones than the parallel skyline, while a concave skyline does not alter the risk distribution except for the last row of buildings. The ascending skyline configuration leads to a more complex and more uniform risk distribution pattern, while the descending skyline does not change the general risk distribution trend. These results might be referenced by engineers and managers to implement tailored measures (for example, optimizing urban array configurations) for mitigating and reducing pedestrian risk in urban regions. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to evaluate the general characteristics of geriatric cases aged ≥65 years, who presented at the Forensic Medicine Clinic due to trauma, and the severity of injury using trauma scoring systems. Methods: The study included all trauma cases over 65 years with a forensic report prepared in the Forensic Medicine Clinic between 2015 and 2021. Evaluations were made using the chi-square, Mann-Whitney U, and Kruskal-Wallis (post-hoc: Dunn-Bonferroni) tests. A value of p<0.05 was considered statistically significant. Results: Two-thirds of the cases were male (67.6%) and mean age was 73.23±6.25 years. More than half of the cases were exposed to battery-related trauma (54%). There was an injury in more than one region in 37.4% (n=52) of the cases. The median injury severity score (ISS) of the cases was 2 (1.5) and the median newinjury severity score (NISS) was 3 (1.8). The scores of ISS and NISS in cases aged >75 years were higher than in those aged ≤75 years (p<0.05). The score severity of ISS and NISS of the cases injured in traffic accidents and other accidents was higher than in those injured due to battery (p<0.001). Almost half (48.1%) of the traffic accident-related cases were pedestrians, and it was seen that pedestrians suffered more severe trauma. Conclusion: Increasing the necessary safety measures in traffic, especially pedestrian safety, and taking measures to make daily life easier for the elderly may help protect this vulnerable population from the effects of severe trauma. [ABSTRACT FROM AUTHOR]

There has been a global increase in the number of vehicles in use, resulting in a higher occurrence of traffic accidents. Advancements in computer vision and deep learning enable vehicles to independently perceive and navigate their environment, making decisions that enhance road safety and reduce traffic accidents. Worldwide accidents can be prevented in both driver-operated and autonomous vehicles by detecting living and inanimate objects such as vehicles, pedestrians, animals, and traffic signs in the environment, as well as identifying lanes and obstacles. In our proposed system, road images are captured using a camera positioned behind the front windshield of the vehicle. Computer vision techniques are employed to detect straight or curved lanes in the captured images. The right and left lanes within the driving area of the vehicle are identified, and the drivable area of the vehicle is highlighted with a different color. To detect traffic signs, pedestrians, cars, and bicycles around the vehicle, we utilize the YOLOv5 model, which is based on Convolutional Neural Networks. We use a combination of study-specific images and the GRAZ dataset in our research. In the object detection study, which involves 10 different objects, we evaluate the performance of five different versions of the YOLOv5 model. Our evaluation metrics include precision, recall, precision-recall curves, F1 score, and mean average precision. The experimental results clearly demonstrate the effectiveness of our proposed lane detection and object detection method. [ABSTRACT FROM AUTHOR]

The article focuses on the accessibility challenges faced by visually impaired individuals, particularly at street intersections, and the role of Accessible Pedestrian Signals (APS) in addressing these challenges. It discusses the legal framework provided by the Americans with Disabilities Act (ADA) and related statutes, examines relevant court cases, and advocates for the implementation of APS as a solution to enhance the independent navigation of blind and low-vision pedestrians.

Point cloud object detection is gradually playing a key role in autonomous driving tasks. To address the issue of insensitivity to sparse objects in point cloud object detection, we have made improvements to the voxel encoding and 3D backbone network of the PVRCNN++. We have introduced adaptive pooling operations during voxel feature encoding to expand the point cloud information within each voxel, followed by the utilization of multi-layer perceptrons to extract richer point cloud features. On the 3D backbone network, we have employed adaptive sparse convolution operations to make the backbone network's channel count more flexible, allowing it to accommodate a wider range of input data types. Furthermore, we have integrated Focal Loss to tackle the issue of class imbalance in detection tasks. Experimental results on the public KITTI dataset demonstrate significant improvements over the PVRCNN++, particularly in pedestrian and bicycle detection tasks. Specifically, we have observed 1% increase in detection accuracy for pedestrians and 2.1% improvement for bicycles. Our detection performance also surpasses that of other comparative detection algorithms. [ABSTRACT FROM AUTHOR]

Ensuring real-time performance while leveraging pedestrian detection is a crucial prerequisite for intelligent driving technology. The development of lightweight models with good detection accuracy is also crucial. This work proposes a novel method, the Attention Digital Filter with Anchor-Free Feature Pyramid Learning Model (ADFAFPLM), to meet these needs. The suggested method consists of combining two networks: one is a digital filter based on an attention network that eliminates noise and other picture distortions. The attention-based residual network digital filters are chosen for their enhanced filtering performance, adaptability, efficient learning through residual connections, noise suppression, interpretability, and generalization capabilities. Next, from the input crowded and occluded photos, the pedestrian is identified using an anchor-free feature pyramid network. The Eurocity person dataset was used to train the model, and it was also tested on other datasets like CityPersons, INRIA, PennFudan, and Eurocity. The investigation was expanded to include images in hazy, noisy, and occlusion environments, among other environmental conditions. The image resolutions were also considered for analysis and it was observed that with increasing image resolution, the mAP increases. Based on the ablation study, the ADF-AFPLM adopted YOLOv8n with batch size 16, and image size 640 is considered for efficient result with different testing datasets. The model achieved a mean average precision (mAP) of approx. 87% and shows its efficacy over state-of-art models. [ABSTRACT FROM AUTHOR]

Person re-identification relies on discriminative features. However, most researches focus on extracting features from the high-layer of network while ignoring the middle-layer features, some important details are overlooked frequently. To address this issue, we propose a Multi-Scale and Multi-Patch Feature Fusion Network(MSPF). We employ modified OSFA to extract, align, and fuse the feature maps in the middle-layer of network, which can compensate for the lack of detailed information in the high-level network features. To obtain richer detailed global features of pedestrian, we construct a multi-patch feature fusion module(MPF). We concatenate the global features extracted from modified OSFA and MPF to obtain global features with richer detailed representations. Cross-entropy loss, triplet loss and center loss are combined to constrain our model. We evaluate the performance of our model on Market-1501, CUHK03_labeled and DukeMTMC. The results prove that our method is superior to the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Pedestrian trajectory prediction plays a crucial role in autonomous driving, as its accuracy directly affects the autonomous driving system's comprehension of the environment and subsequent decision-making processes. Current trajectory prediction methods tend to oversimplify pedestrians to mere point coordinates, utilizing positional information to infer interactions among individuals while overlooking the temporal correlations between them, thereby excessively simplifying pedestrian characteristics. To address the aforementioned issues, this paper proposes a trajectory prediction model for autonomous driving applications, that takes into account both pedestrian motion characteristics and scene interaction. The model optimizes the LSTM unit structure twice, serving to learn correlations among long trajectories of pedestrians and to integrate multiple forms of information into the neighborhood interaction module. Furthermore, our model introduces dual attention mechanisms for individuals and scenes, focusing on the key motion points of individual pedestrians and their interactive behavior with others in busy scenarios. The efficacy of the model was validated on the MOT16 pedestrian dataset and the Daimler pedestrian path prediction dataset, outperforming mainstream methods with 8% and 10% reductions in Average Displacement Error and Final Displacement Error respectively. [ABSTRACT FROM AUTHOR]

Pedestrian detection plays a crucial role in ensuring traffic safety within the domain of computer vision. However, accurately detecting pedestrians in complex environments proves to be a challenge due to issues such as occlusion. To address this issue, this paper presents an end-to-end pedestrian detection model founded on the DEtection TRansformer (DETR) architecture, effectively managing occlusion scenarios. The proposed model comprises a backbone Convolutional Neural Network (CNN) and a Transformer network. The backbone CNN incorporates variable convolution and U-Net design principles to enhance feature extraction capabilities, particularly for occluded pedestrians. Additionally, our innovative Adaptive Occlusion-Aware Attention Mechanism (AOAM) is embedded within the Transformer network, allowing the model to dynamically adjust attention weights and enhance the localization and identification of occluded pedestrians. Extensive experiments on the Caltech and ETH datasets demonstrate the superior performance of our model compared to state-of-the-art approaches across four key evaluation metrics. This study provides effective methodologies and theoretical foundations for pedestrian detection in complex environments. [ABSTRACT FROM AUTHOR]

Numerous technologies and systems, including autonomous vehicles, surveillance systems, and robotic applications, rely on the capability to accurately detect pedestrians to ensure their safety. As the demand for realtime object detection continues to rise, many researchers have dedicated their efforts to developing effective and trustworthy algorithms for pedestrian recognition. By integrating learning complexity-aware cascades with an enhanced you only look once (YOLO) algorithm, the paper presents a real-time system for identifying both items and pedestrians. The performance of the proposed approach is evaluated using the Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) pedestrian dataset across both the v4 and v8 versions of the YOLO framework. Prioritizing both speed and accuracy, the enhanced YOLO algorithm outperforms its baseline counterpart. The demonstrated superiority of the suggested technique on the KITTI pedestrian dataset underscores its effectiveness in real-world contexts. Furthermore, the complexity-aware learning cascades contribute to a streamlined detection model without compromising performance. When applied to scenarios requiring real-time identification of objects and individuals, the proposed method consistently delivers promising outcomes. [ABSTRACT FROM AUTHOR]

We propose an extension of the anisotropic interaction model which allows for collision avoidance in pairwise interactions by a rotation of forces (Totzeck (2020) Kinet. Relat. Models 13 (6), 1219–1242.) by including the agents' body size. The influence of the body size on the self-organisation of the agents in channel and crossing scenarios as well as the fundamental diagram is studied. Since the model is stated as a coupled system of ordinary differential equations, we are able to give a rigorous well-posedness analysis. Then we state a parameter calibration problem that involves data from real experiments. We prove the existence of a minimiser and derive the corresponding first-order optimality conditions. With the help of these conditions, we propose a gradient descent algorithm based on mini-batches of the data set. We employ the proposed algorithm to fit the parameter of the collision avoidance and the strength parameters of the interaction forces to given real data from experiments. The results underpin the feasibility of the method. [ABSTRACT FROM AUTHOR]

Nowadays, with the rapid development of consumer Unmanned Aerial Vehicles (UAVs), utilizing UAV platforms for visual surveillance has become very attractive, and a key part of this is remote vision-based pedestrian attribute recognition. Pedestrian Attribute Recognition (PAR) is dedicated to predicting multiple attribute labels of a single pedestrian image extracted from surveillance videos and aerial imagery, which presents significant challenges in the computer vision community due to factors such as poor imaging quality and substantial pose variations. Despite recent studies demonstrating impressive advancements in utilizing complicated architectures and exploring relations, most of them may fail to fully and systematically consider the inter-region, inter-attribute, and region-attribute mapping relations simultaneously and be stuck in the dilemma of information redundancy, leading to the degradation of recognition accuracy. To address the issues, we construct a novel Mask-Relation-Guided Transformer (MRG-T) framework that consists of three relation modeling modules to fully exploit spatial and semantic relations in the model learning process. Specifically, we first propose a Masked Region Relation Module (MRRM) to focus on precise spatial attention regions to extract more robust features with masked random patch training. To explore the semantic association of attributes, we further present a Masked Attribute Relation Module (MARM) to extract intrinsic and semantic inter-attribute relations with an attribute label masking strategy. Based on the cross-attention mechanism, we finally design a Region and Attribute Mapping Module (RAMM) to learn the cross-modal alignment between spatial regions and semantic attributes. We conduct comprehensive experiments on three public benchmarks such as PETA, PA-100K, and RAPv1, and conduct inference on a large-scale airborne person dataset named PRAI-1581. The extensive experimental results demonstrate the superior performance of our method compared to state-of-the-art approaches and validate the effectiveness of mask-relation-guided modeling in the remote vision-based PAR task. [ABSTRACT FROM AUTHOR]

Predicting human trajectories poses a significant challenge due to the complex interplay of pedestrian behavior, which is influenced by environmental layout and interpersonal dynamics. This complexity is further compounded by variations in scene density. To address this, we introduce a novel dataset from the Festival of Lights in Lyon 2022, characterized by a wide range of densities (0.2–2.2 ped/ m 2 ). Our analysis demonstrates that density-based classification of data can significantly enhance the accuracy of predictive algorithms. We propose an innovative two-stage processing approach, surpassing current state-of-the-art methods in performance. Additionally, we utilize a collision-based error metric to better account for collisions in trajectory predictions. Our findings indicate that the effectiveness of this error metric is density-dependent, offering prediction insights. This study not only advances our understanding of human trajectory prediction in dense environments, but also presents a methodological framework for integrating density considerations into predictive modeling, thereby improving algorithmic performance and collision avoidance. [ABSTRACT FROM AUTHOR]

In the pedestrian navigation system, researchers have reduced measurement errors and improved system navigation performance by fusing measurements from multiple low-cost inertial measurement unit (IMU) arrays. Unfortunately, the current data fusion methods for inertial sensor arrays ignore the system error compensation of individual IMUs and the correction of position information in the zero-velocity interval. Therefore, these methods cannot effectively reduce errors and improve accuracy. An error compensation method for pedestrian navigation systems based on a low-cost array of IMUs is proposed in this paper. The calibration method for multiple location-free IMUs is improved by using a sliding variance detector to segment the angular velocity magnitude into stationary and motion intervals, and each IMU is calibrated independently. Compensation is then applied to the velocity residuals in the zero-velocity interval after zero-velocity update (ZUPT). The experimental results show a significant improvement in the average noise performance of the calibrated IMU array, with a 3.01-fold increase in static noise performance. In the closed-loop walking experiment, the average horizontal position error of a single calibrated IMU is reduced by 27.52% compared to the uncalibrated IMU, while the calibrated IMU array shows a 2.98-fold reduction in average horizontal position error compared to a single calibrated IMU. After compensating for residual velocity, the average horizontal position error of a single IMU is reduced by 0.73 m, while that of the IMU array is reduced by 64.52%. [ABSTRACT FROM AUTHOR]

• We examined the impact of peer presence on adolescent pedestrian risk-taking intentions whereby a traffic scenario-based experimental research. • Adolescents' behavioral intentions in traffic were safer when there were peers present than when there were no peers present. • Adolescents were less safe when crossing the street with familiar peer(s) than with unfamiliar peer(s). • Peer familiarity didn't effect adolescents' safety when walking on the sidewalk. • Adolescents' safety in traffic was higher with multiple familiar peers than with multiple unfamiliar peers. Adolescence 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. 607 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. (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. Adolescents 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. [ABSTRACT FROM AUTHOR]

• A virtual reality experiment exploring pedestrians' initial interactions with automated vehicles. • Investigates impacts of social context on pedestrians' understanding of and reaction to eHMIs. • Pedestrians' crossing decisions are influenced by other pedestrians next to them. • Behaviors of other pedestrians contribute to individual's difficulty in understanding eHMIs. Automated vehicles (AVs) are going to enter public roads in the near future and will inevitably encounter more than one pedestrian on roads. Very little is known about how multiple pedestrians will interact with AVs and their external human–machine interfaces (eHMIs). This study investigates the social influence of others on the pedestrian decision-making process during their initial encounters with AVs. In a virtual reality experiment, 26 male and 55 female participants in three experimental conditions made crossing decisions – either alone or next to other simulated pedestrians – in front of AVs equipped with abstract eHMIs showing yielding or non-yielding intentions. The results show that the behaviors of others significantly influenced pedestrian crossing decisions. While the appropriate behaviors of others contributed to faster crossing decision-making, misleading behaviors of others led to riskier crossing decisions and greater difficulty in understanding the eHMIs. However, the impacts from others on pedestrian crossing decisions disappeared once participants became more familiar with eHMIs. Qualitative data revealed that participants acquired knowledge about AVs and eHMIs through direct experience, observing others, and drawing from their past experiences. Additionally, there was a tendency among certain pedestrians to overly rely on and misuse eHMIs. To ensure the safe deployment of AVs, it is therefore essential to adequately educating the public about this novel technology and to optimize eHMI designs for effective communication with multiple pedestrians. [ABSTRACT FROM AUTHOR]