This study investigates pedestrian safety perception in Ho Chi Minh City under mixed traffic conditions by evaluating comfort,crash risk,and injury risk perceptions in two scenarios:walking along and crossing multilan...This study investigates pedestrian safety perception in Ho Chi Minh City under mixed traffic conditions by evaluating comfort,crash risk,and injury risk perceptions in two scenarios:walking along and crossing multilane roads.Using visual experiments with 510 participants,the study identifies how sidewalk quality,obstructions,crossing infrastructure,and traffic conditions shape pedestrian experiences.Statistical modeling reveals that protected sidewalks and comprehensive crossing features significantly enhance perceived safety and comfort.Findings emphasize the need for improved pedestrian infrastructure and traffic calming in dense urban settings to support safer,more inclusive mobility under mixed traffic conditions like Vietnam.展开更多
Due to multi-scale variations and occlusion problems,accurate traffic road pedestrian detection faces great challenges.This paper proposes an improved pedestrian detection method called Multi Scales Attention-YOLOv5x(...Due to multi-scale variations and occlusion problems,accurate traffic road pedestrian detection faces great challenges.This paper proposes an improved pedestrian detection method called Multi Scales Attention-YOLOv5x(MSA-YOLOv5x)based on the YOLOv5x framework.Firstly,by replacing the first convolutional operation of the backbone network with the Focus module,this method expands the number of image input channels to enhance feature expressiveness.Secondly,we construct C3_CBAM module instead of the original C3 module for better feature fusion.In this way,the learning process could achieve more multi-scale features and occluded pedestrian target features through channel attention and spatial attention.Additionally,a new feature pyramid detection layer and a new detection channel are embedded in the feature fusion part for enhancing multi-scale pedestrian detection accuracy.Compared with the baseline methods,experimental results on a public dataset demonstrate that the proposed method achieves optimal detection accuracy for traffic road pedestrian detection.展开更多
Pedestrian detection has been a hot spot in computer vision over the past decades due to the wide spectrum of promising applications,and the major challenge is false positives that occur during pedestrian detection.Th...Pedestrian detection has been a hot spot in computer vision over the past decades due to the wide spectrum of promising applications,and the major challenge is false positives that occur during pedestrian detection.The emergence of various Convolutional Neural Network-based detection strategies substantially enhances pedestrian detection accuracy but still does not solve this problem well.This paper deeply analyzes the detection framework of the two-stage CNN detection methods and finds out false positives in detection results are due to its training strategy misclassifying some false proposals,thus weakening the classification capability of the following subnetwork and hardly suppressing false ones.To solve this problem,this paper proposes a pedestrian-sensitive training algorithm to help two-stage CNN detection methods effectively learn to distinguish the pedestrian and non-pedestrian samples and suppress the false positives in the final detection results.The core of the proposed algorithm is to redesign the training proposal generating scheme for the two-stage CNN detection methods,which can avoid a certain number of false ones that mislead its training process.With the help of the proposed algorithm,the detection accuracy of the MetroNext,a smaller and more accurate metro passenger detector,is further improved,which further decreases false ones in its metro passenger detection results.Based on various challenging benchmark datasets,experiment results have demonstrated that the feasibility of the proposed algorithm is effective in improving pedestrian detection accuracy by removing false positives.Compared with the existing state-of-the-art detection networks,PSTNet demonstrates better overall prediction performance in accuracy,total number of parameters,and inference time;thus,it can become a practical solution for hunting pedestrians on various hardware platforms,especially for mobile and edge devices.展开更多
This study presents an interpretable surrogate framework for predicting pedestrian-leg injury severity that integrates high-fidelity finite-element(FE)simulations with a TabNet-based deep-learning model.We generated a...This study presents an interpretable surrogate framework for predicting pedestrian-leg injury severity that integrates high-fidelity finite-element(FE)simulations with a TabNet-based deep-learning model.We generated a parametric dataset of 3000 impact scenarios-covering ten vehicle types and various legform impactors-using automated FE runs configured via Latin hypercube sampling.After preprocessing and one-hot encoding of categorical features,we trained TabNet alongside Support-Vector Regression,Random Forest,and Decision-Tree ensembles.All models underwent hyperparameter tuning via Optuna’s Bayesian optimization coupled with repeated four-fold crossvalidation(20 trials per model).TabNet achieved the best balance of explanatory power and predictive accuracy,with an average R^(2)=0.94±0.01 and RMSE=0.14±0.02.On an independent test set,85%,88%,and 90%of predictions for tibial acceleration,knee-flexion angle,and shear displacement,respectively,fell within±20%of true peaks.SHAPbased analyses confirm that collision-point location and bumper geometry dominate injury outcomes.These results demonstrate TabNet’s capacity to deliver rapid,robust,and explainable injury predictions,offering actionable design insights for vehicle front-end optimization and regulatory assessment in early development stages.展开更多
Visual indoor positioning methods have the potential for widespread application in complex large-scale indoor environments,such as shopping centers and hospitals.However,during the visual positioning process,passing p...Visual indoor positioning methods have the potential for widespread application in complex large-scale indoor environments,such as shopping centers and hospitals.However,during the visual positioning process,passing pedestrians may cause occlusion in the visual image,leading to large deviations in the visual positioning results.Aiming at the problem of feature occlusion in visual images caused by pedestrians,this paper proposes a visual indoor positioning system that combines semantic segmentation and image restoration.The paper proposes a method called the fast image segmentation repair(FISR),which segments and rapidly repairs the selected image to eliminate the influence of pedestrians on image feature extraction and improve positioning accuracy.In addition,the paper proposes a method called local feature based bag-of-visual-words combined with high-level semantic information(LFHS)for image retrieval.LFHS uses both local features and high-level semantic information to obtain more comprehensive and accurate representations of image features.This approach improves the accuracy and robustness of image retrieval by harnessing the combined power of local features and high-level semantic information.Experimental results show that the proposed positioning method reduces the average positioning error by 0.35 m compared with NetVLAD and 0.49 m compared with MixVPR,significantly improving the performance of visual positioning technology.展开更多
The increasing availability of ubiquitous sensor data on the built environment holds great potential for a new generation of travel and mobility research.Bluetooth technology,for example,is already vastly used in vehi...The increasing availability of ubiquitous sensor data on the built environment holds great potential for a new generation of travel and mobility research.Bluetooth technology,for example,is already vastly used in vehicular transportation management solutions and services.Current studies discuss the potential of this emerging technology for pedestrian mobility research,but it has yet to be examined in a large urban setting.One of the main problems is detecting pedestrians from Bluetooth records since their behavior and movement patterns share similarities with other urban transportation modes.This study aims to accurately detect pedestrians using a network of 65 Bluetooth detectors located in Tel-Aviv,Israel,which record on average over 60,000 unique daily Bluetooth Media-Access-Control addresses.We propose a detection methodology that includes system calibration,effective travel time calculation,and classification by velocity that takes into consideration the probability of vehicular traffic jams.An evaluation of the proposed methodology presents a promising pedestrian detection accuracy rate of 89%.We showcase the results of pedestrian traffic analysis,together with a discussion on the data analysis challenges and limitations.To the best of our knowledge,this work is the first to analyze pedestrian records detection from a Bluetooth network employed in a dynamic urban environment setting.展开更多
Traffic intersections are incredibly dangerous for drivers and pedestrians. Statistics from both Canada and the U.S. show a high number of fatalities and serious injuries related to crashes at intersections. In Canada...Traffic intersections are incredibly dangerous for drivers and pedestrians. Statistics from both Canada and the U.S. show a high number of fatalities and serious injuries related to crashes at intersections. In Canada, during 2019, the National Collision Database shows that 28% of traffic fatalities and 42% of serious injuries occurred at intersections. Likewise, the U.S. National Highway Traffic Administration (NHTSA) found that about 40% of the estimated 5,811,000 accidents in the U.S. during the year studied were intersection-related crashes. In fact, a major survey by the car insurance industry found that nearly 85% of drivers could not identify the correct action to take when approaching a yellow traffic light at an intersection. One major reason for these accidents is the “yellow light dilemma,” the ambiguous situation where a driver should stop or proceed forward when unexpectedly faced with a yellow light. This situation is even further exacerbated by the tendency of aggressive drivers to inappropriately speed up on the yellow just to get through the traffic light. A survey of Canadian drivers conducted by the Traffic Injury Research Foundation found that 9% of drivers admitted to speeding up to get through a traffic light. Another reason for these accidents is the increased danger of making a left-hand turn on yellow. According to the National Highway Traffic Safety Association (NHTSA), left turns occur in approximately 22.2% of collisions—as opposed to just 1.2% for right turns. Moreover, a study by CNN found left turns are three times as likely to kill pedestrians than right turns. The reason left turns are so much more likely to cause an accident is because they take a driver against traffic and in the path of oncoming cars. Additionally, most of these left turns occur at the driver’s discretion—as opposed to the distressingly brief left-hand arrow at busy intersections. Drive Safe Now proposes a workable solution for reducing the number of accidents occurring during a yellow light at intersections. We believe this fairly simple solution will save lives, prevent injuries, reduce damage to public and private property, and decrease insurance costs.展开更多
Simulation of pedestrians’behavior in the hub can help decision-makers to formulate better evacuation strategies.With this aim,this study develops an improved cellular automata model considering pedestrian’s mass-fo...Simulation of pedestrians’behavior in the hub can help decision-makers to formulate better evacuation strategies.With this aim,this study develops an improved cellular automata model considering pedestrian’s mass-following psychology and competitive awareness,and based on this model,pedestrian’s evacuation process from the channel of the hub with two exits is simulated.Moreover,dynamic guidance information,e.g.,the realtime congestion situation of the evacuation routes,plays an important role during pedestrian evacuation processes in a hub,as the evaluation routes can be adjusted based on this information.That is,the congestion situation during the evaluation can be improved.Thus,dynamic signs are incorporated into the proposed model to study the influence of dynamic guidance information on pedestrian evacuation behavior.In simulation experiments,the influence of two parameters,namely the proportion of pedestrians unfamiliar with the hub and update interval of dynamic signs,on pedestrian evacuation behavior is studied.Results show that dynamic guidance information can improve the efficiency of pedestrian evacuation.In particular,the higher the proportion of pedestrians unfamiliar with the hub is,the more obvious the effect of dynamic guidance information is.Besides,different proportions of pedestrians unfamiliar with the hub lead to different update intervals of dynamic signs.Finally,the results of this study can provide some implications to the practical hub operation and evacuation,e.g.,to standardize the order of evacuation routes and improve the information service level in the hub.展开更多
Road traffic safety can decrease when drivers drive in a low-visibility environment.The application of visual perception technology to detect vehicles and pedestrians in infrared images proves to be an effective means...Road traffic safety can decrease when drivers drive in a low-visibility environment.The application of visual perception technology to detect vehicles and pedestrians in infrared images proves to be an effective means of reducing the risk of accidents.To tackle the challenges posed by the low recognition accuracy and the substan-tial computational burden associated with current infrared pedestrian-vehicle detection methods,an infrared pedestrian-vehicle detection method A proposal is presented,based on an enhanced version of You Only Look Once version 5(YOLOv5).First,A head specifically designed for detecting small targets has been integrated into the model to make full use of shallow feature information to enhance the accuracy in detecting small targets.Second,the Focal Generalized Intersection over Union(GIoU)is employed as an alternative to the original loss function to address issues related to target overlap and category imbalance.Third,the distribution shift convolution optimization feature extraction operator is used to alleviate the computational burden of the model without significantly compromising detection accuracy.The test results of the improved algorithm show that its average accuracy(mAP)reaches 90.1%.Specifically,the Giga Floating Point Operations Per second(GFLOPs)of the improved algorithm is only 9.1.In contrast,the improved algorithms outperformed the other algorithms on similar GFLOPs,such as YOLOv6n(11.9),YOLOv8n(8.7),YOLOv7t(13.2)and YOLOv5s(16.0).The mAPs that are 4.4%,3%,3.5%,and 1.7%greater than those of these algorithms show that the improved algorithm achieves higher accuracy in target detection tasks under similar computational resource overhead.On the other hand,compared with other algorithms such as YOLOv8l(91.1%),YOLOv6l(89.5%),YOLOv7(90.8%),and YOLOv3(90.1%),the improved algorithm needs only 5.5%,2.3%,8.6%,and 2.3%,respectively,of the GFLOPs.The improved algorithm has shown significant advancements in balancing accuracy and computational efficiency,making it promising for practical use in resource-limited scenarios.展开更多
Pedestrian positioning system(PPS)using wearable inertial sensors has wide applications towards various emerging fields such as smart healthcare,emergency rescue,soldier positioning,etc.The performance of traditional ...Pedestrian positioning system(PPS)using wearable inertial sensors has wide applications towards various emerging fields such as smart healthcare,emergency rescue,soldier positioning,etc.The performance of traditional PPS is limited by the cumulative error of inertial sensors,complex motion modes of pedestrians,and the low robustness of the multi-sensor collaboration structure.This paper presents a hybrid pedestrian positioning system using the combination of wearable inertial sensors and ultrasonic ranging(H-PPS).A robust two nodes integration structure is developed to adaptively combine the motion data acquired from the single waist-mounted and foot-mounted node,and enhanced by a novel ellipsoid constraint model.In addition,a deep-learning-based walking speed estimator is proposed by considering all the motion features provided by different nodes,which effectively reduces the cumulative error originating from inertial sensors.Finally,a comprehensive data and model dual-driven model is presented to effectively combine the motion data provided by different sensor nodes and walking speed estimator,and multi-level constraints are extracted to further improve the performance of the overall system.Experimental results indicate that the proposed H-PPS significantly improves the performance of the single PPS and outperforms existing algorithms in accuracy index under complex indoor scenarios.展开更多
Urban intersections without traffic signals are prone to accidents involving motor vehicles and pedestrians.Utilizing computer vision technology to detect pedestrians crossing the street can effectively mitigate the o...Urban intersections without traffic signals are prone to accidents involving motor vehicles and pedestrians.Utilizing computer vision technology to detect pedestrians crossing the street can effectively mitigate the occurrence of such accidents.Faced with the complex issue of pedestrian occlusion at signal-free intersections,this paper proposes a target detection model called Head feature And ENMS fusion Residual connection For CNN(HAERC).Specifically,the model includes a head feature module that detects occluded pedestrians by integrating their head features with the overall target.Additionally,to address the misselection caused by overlapping candidate boxes in two-stage target detection models,an Extended Non-Maximum Suppression classifier(ENMS)with expanded IoU thresholds is proposed.Finally,leveraging the CityPersons dataset and categorizing it into four classes based on occlusion levels(heavy,reasonable,partial,bare),the HAERC model is experimented on these classes and compared with baseline models.Experimental results demonstrate that HAERC achieves superior False Positives Per Image(FPPI)values of 46.64%,9.59%,9.43%,and 6.78%respectively for the four classes,outperforming all baseline models.The study concludes that the HAERC model effectively identifies occluded pedestrians in the complex environment of urban intersections without traffic signals,thereby enhancing safety for long-range driving at such intersections.展开更多
Pedestrian self-organizing movement plays a significant role in evacuation studies and architectural design.Lane formation,a typical self-organizing phenomenon,helps pedestrian system to become more orderly,the majori...Pedestrian self-organizing movement plays a significant role in evacuation studies and architectural design.Lane formation,a typical self-organizing phenomenon,helps pedestrian system to become more orderly,the majority of following behavior model and overtaking behavior model are imprecise and unrealistic compared with pedestrian movement in the real world.In this study,a pedestrian dynamic model considering detailed modelling of the following behavior and overtaking behavior is constructed,and a method of measuring the lane formation and pedestrian system order based on information entropy is proposed.Simulation and analysis demonstrate that the following and avoidance behaviors are important factors of lane formation.A high tendency of following results in good lane formation.Both non-selective following behavior and aggressive overtaking behavior cause the system order to decrease.The most orderly following strategy for a pedestrian is to overtake the former pedestrian whose speed is lower than approximately 70%of his own.The influence of the obstacle layout on pedestrian lane and egress efficiency is also studied with this model.The presence of a small obstacle does not obstruct the walking of pedestrians;in contrast,it may help to improve the egress efficiency by guiding the pedestrian flow and mitigating the reduction of pedestrian system orderliness.展开更多
With the development of positioning technology,loca-tion services are constantly in demand by people.As a primary location service pedestrian navigation has two main approaches based on radio and inertial navigation.T...With the development of positioning technology,loca-tion services are constantly in demand by people.As a primary location service pedestrian navigation has two main approaches based on radio and inertial navigation.The pedestrian naviga-tion based on radio is subject to environmental occlusion lead-ing to the degradation of positioning accuracy.The pedestrian navigation based on micro-electro-mechanical system inertial measurement unit(MIMU)is less susceptible to environmental interference,but its errors dissipate over time.In this paper,a chest card pedestrian navigation improvement method based on complementary correction is proposed in order to suppress the error divergence of inertial navigation methods.To suppress atti-tude errors,optimal feedback coefficients are established by pedestrian motion characteristics.To extend navigation time and improve positioning accuracy,the step length in subsequent movements is compensated by the first step length.The experi-mental results show that the positioning accuracy of the pro-posed method is improved by more than 47%and 44%com-pared with the pure inertia-based method combined with step compensation and the traditional complementary filtering com-bined method with step compensation.The proposed method can effectively suppress the error dispersion and improve the positioning accuracy.展开更多
Multispectral pedestrian detection technology leverages infrared images to provide reliable information for visible light images, demonstrating significant advantages in low-light conditions and background occlusion s...Multispectral pedestrian detection technology leverages infrared images to provide reliable information for visible light images, demonstrating significant advantages in low-light conditions and background occlusion scenarios. However, while continuously improving cross-modal feature extraction and fusion, ensuring the model’s detection speed is also a challenging issue. We have devised a deep learning network model for cross-modal pedestrian detection based on Resnet50, aiming to focus on more reliable features and enhance the model’s detection efficiency. This model employs a spatial attention mechanism to reweight the input visible light and infrared image data, enhancing the model’s focus on different spatial positions and sharing the weighted feature data across different modalities, thereby reducing the interference of multi-modal features. Subsequently, lightweight modules with depthwise separable convolution are incorporated to reduce the model’s parameter count and computational load through channel-wise and point-wise convolutions. The network model algorithm proposed in this paper was experimentally validated on the publicly available KAIST dataset and compared with other existing methods. The experimental results demonstrate that our approach achieves favorable performance in various complex environments, affirming the effectiveness of the multispectral pedestrian detection technology proposed in this paper.展开更多
Traffic accidents involving pedestrians and drivers pose significant public health and safety concerns.Understanding the differential influences of road physical design attributes on crash frequencies for these two gr...Traffic accidents involving pedestrians and drivers pose significant public health and safety concerns.Understanding the differential influences of road physical design attributes on crash frequencies for these two groups is critical for developing targeted safety interventions.Considering that the zero-truncated characteristic of the data is uncertain,the results of the zero-truncated negative binomial models and traditional negative binomial models are calculated to seek the better model.The result revealed that the road surface conditions and vertical and horizontal curvature have greater influence on both pedestrian and driver compared to number of lanes and speed limit.And speed limits were more pronounced for pedestrian crash frequency than driver group.Conversely,the effect of different types of intersections was stronger for driver crash frequency.The differential influences of road physical design attributes on traffic crash frequencies for pedestrians versus drivers highlight the importance of adopting a user-centric approach to transportation safety planning and infrastructure design.Tailoring interventions to address the unique needs and vulnerabilities of different road user groups can lead to more effective safety improvements and better overall traffic safety outcomes.展开更多
Walkability is an essential aspect of urban transportation systems. Properly designed walking paths can enhance transportation safety, encourage pedestrian activity, and improve community quality of life. This, in tur...Walkability is an essential aspect of urban transportation systems. Properly designed walking paths can enhance transportation safety, encourage pedestrian activity, and improve community quality of life. This, in turn, can help achieve sustainable development goals in urban areas. This pilot study uses wearable technology data to present a new method for measuring pedestrian stress in urban environments and the results were presented as an interactive geographic information system map to support risk-informed decision-making. The approach involves analyzing data from wearable devices using heart rate variability (RMSSD and slope analysis) to identify high-stress locations. This data-driven approach can help urban planners and safety experts identify and address pedestrian stressors, ultimately creating safer, more walkable cities. The study addresses a significant challenge in pedestrian safety by providing insights into factors and locations that trigger stress in pedestrians. During the pilot study, high-stress pedestrian experiences were identified due to issues like pedestrian-scooter interaction on pedestrian paths, pedestrian behavior around high foot traffic areas, and poor visibility at pedestrian crossings due to inadequate lighting.展开更多
The anthropometric differences between European/American and Chinese population are remarkable and have significant influences on pedestrian kinematics and injury response in vehicle crashes.Therefore,the current stud...The anthropometric differences between European/American and Chinese population are remarkable and have significant influences on pedestrian kinematics and injury response in vehicle crashes.Therefore,the current study aims to develop and validate a Finite Element(FE)human body model representing the anthropometry of Chinese 50th percentile adult male for pedestrian safety analysis and development of Chinese ATDs(Anthropomorphic Test Devices).Firstly,a human body pedestrian model,named as C-HBM(Chinese Human Body Model),was developed based on the medical image data of a volunteer selected according to both anthropometry and anatomy characteristics of 50th percentile Chinese adult male.Then,the biofidelity of the C-HBM pedestrian model was validated against cadaver impact test data reported in the literature at the segment and full-body level.Finally,the validated C-HBM pedestrian model was employed to predict Chinese pedestrian injuries in real world vehicle crashes.The results indicate that the C-HBM pedestrian model has a good capability in predicting human body mechanical response in cadaver tests and Chinese leg and thorax injuries in vehicle crashes.Kinematic analysis shows that the C-HBM pedestrian model has less sliding on the hood surface,shorter movement in the horizontal direction,and higher pelvis displacement in the vertical direction than cadavers and the pedestrian model in the anthropometry of westerner due to anthropometric differences in the lower limbs.The currently developed C-HBM pedestrian model provides a basic tool for vehicle safety design and evaluation in China market,and for development of Chinese ATDs.展开更多
This study explores the challenges posed by pedestrian detection and occlusion in AR applications, employing a novel approach that utilizes RGB-D-based skeleton reconstruction to reduce the overhead of classical pedes...This study explores the challenges posed by pedestrian detection and occlusion in AR applications, employing a novel approach that utilizes RGB-D-based skeleton reconstruction to reduce the overhead of classical pedestrian detection algorithms during training. Furthermore, it is dedicated to addressing occlusion issues in pedestrian detection by using Azure Kinect for body tracking and integrating a robust occlusion management algorithm, significantly enhancing detection efficiency. In experiments, an average latency of 204 milliseconds was measured, and the detection accuracy reached an outstanding level of 97%. Additionally, this approach has been successfully applied in creating a simple yet captivating augmented reality game, demonstrating the practical application of the algorithm.展开更多
The rapid growth of impervious areas in urban basins worldwide has increased the number of impermeable surfaces in cities,leading to severe flooding and significant economic losses for civilians.This trend highlights ...The rapid growth of impervious areas in urban basins worldwide has increased the number of impermeable surfaces in cities,leading to severe flooding and significant economic losses for civilians.This trend highlights the urgent need for methodologies that assess flood hazards and specifically address the direct impact on pedestrians,which is often overlooked in traditional flood hazard analyses.This study aims to evaluate a methodology for assessing the risk to pedestrians from hydrodynamic forces during urban floods,with a specific focus on Cúcuta,Colombia.The methodology couples research outcomes from other studies on the impact of floodwaters on individuals of different ages and sizes with 1D/2D hydrological modeling.Advanced computational algorithms for image recognition were used to measure water levels at 5-s intervals on November 6,2020,using drones for digital elevation model data collection.In Cúcuta,where flood risk is high and drainage infrastructure is limited,the PCSWMM(Computer-based Urban Stormwater Management Model)was calibrated and validated to simulate extreme flood events.The model incorporated urban infrastructure details and geomorphological parameters of Cúcuta's urban basin.Four return periods(5,10,50,100),with extreme rainfall of 3 h,were used to estimate the variability of the risk map.The output of the model was analyzed,and an integrated and time-varying comparison of the results was done.Results show that the regions of high-water depth and high velocity could vary significantly along the duration of the different extreme events.Also,from 5 to 100 years return period,the percentage of area at risk increased from 9.6%to 16.6%.The pedestrian sensitivity appears much higher than the increase in velocities or water depth individually.This study identified medium to high-risk locations,which are dynamic in time.We can conclude dynamics are spatiotemporal,and the added information layer of pedestrians brings vulnerability information that is also dynamic.Areas of immediate concern in Cúcuta can enhance pedestrian safety during flash flood events.The spatiotemporal variation of patterns requires further studies to map trajectories and sequences that machine learning models could capture.展开更多
When arranging the pedestrian infrastructure,one of the most important components that make a tangible contribution to the safety of pedestrians is to organize the safe road crossing.In cities,pedestrians often cross ...When arranging the pedestrian infrastructure,one of the most important components that make a tangible contribution to the safety of pedestrians is to organize the safe road crossing.In cities,pedestrians often cross a road in the wrong place due to established routes or inadequate location of crosswalks.Accidents with the participation of pedestrians who crossed the road neglecting the traffic rules,make up a significant part of the total amount of road accidents.In this paper,we propose a method that allows us,on the basis of the results of a computer simulation of pedestrian traffic,to obtain predicted routes for road crossing and to indicate optimal locations for crosswalks that take into account established pedestrian routes and increase their safety.The work describes an extension for the existing AntRoadPlanner simulation algorithm,which searches for and clusters points where pedestrians cross the roadway and suggests locations for new crosswalks.This method was tested on the basis of a comparative simulation of several territories before and after its application,as well as on the basis of a field study of the territories.The developed algorithm can also be used to search for other potentially dangerous places for pedestrians on plans of districts,for example,crossings in places with limited visibility.展开更多
文摘This study investigates pedestrian safety perception in Ho Chi Minh City under mixed traffic conditions by evaluating comfort,crash risk,and injury risk perceptions in two scenarios:walking along and crossing multilane roads.Using visual experiments with 510 participants,the study identifies how sidewalk quality,obstructions,crossing infrastructure,and traffic conditions shape pedestrian experiences.Statistical modeling reveals that protected sidewalks and comprehensive crossing features significantly enhance perceived safety and comfort.Findings emphasize the need for improved pedestrian infrastructure and traffic calming in dense urban settings to support safer,more inclusive mobility under mixed traffic conditions like Vietnam.
文摘Due to multi-scale variations and occlusion problems,accurate traffic road pedestrian detection faces great challenges.This paper proposes an improved pedestrian detection method called Multi Scales Attention-YOLOv5x(MSA-YOLOv5x)based on the YOLOv5x framework.Firstly,by replacing the first convolutional operation of the backbone network with the Focus module,this method expands the number of image input channels to enhance feature expressiveness.Secondly,we construct C3_CBAM module instead of the original C3 module for better feature fusion.In this way,the learning process could achieve more multi-scale features and occluded pedestrian target features through channel attention and spatial attention.Additionally,a new feature pyramid detection layer and a new detection channel are embedded in the feature fusion part for enhancing multi-scale pedestrian detection accuracy.Compared with the baseline methods,experimental results on a public dataset demonstrate that the proposed method achieves optimal detection accuracy for traffic road pedestrian detection.
文摘Pedestrian detection has been a hot spot in computer vision over the past decades due to the wide spectrum of promising applications,and the major challenge is false positives that occur during pedestrian detection.The emergence of various Convolutional Neural Network-based detection strategies substantially enhances pedestrian detection accuracy but still does not solve this problem well.This paper deeply analyzes the detection framework of the two-stage CNN detection methods and finds out false positives in detection results are due to its training strategy misclassifying some false proposals,thus weakening the classification capability of the following subnetwork and hardly suppressing false ones.To solve this problem,this paper proposes a pedestrian-sensitive training algorithm to help two-stage CNN detection methods effectively learn to distinguish the pedestrian and non-pedestrian samples and suppress the false positives in the final detection results.The core of the proposed algorithm is to redesign the training proposal generating scheme for the two-stage CNN detection methods,which can avoid a certain number of false ones that mislead its training process.With the help of the proposed algorithm,the detection accuracy of the MetroNext,a smaller and more accurate metro passenger detector,is further improved,which further decreases false ones in its metro passenger detection results.Based on various challenging benchmark datasets,experiment results have demonstrated that the feasibility of the proposed algorithm is effective in improving pedestrian detection accuracy by removing false positives.Compared with the existing state-of-the-art detection networks,PSTNet demonstrates better overall prediction performance in accuracy,total number of parameters,and inference time;thus,it can become a practical solution for hunting pedestrians on various hardware platforms,especially for mobile and edge devices.
基金sponsored by the National Natural Science Foundation of China(No.U21A20165,No.52072057).
文摘This study presents an interpretable surrogate framework for predicting pedestrian-leg injury severity that integrates high-fidelity finite-element(FE)simulations with a TabNet-based deep-learning model.We generated a parametric dataset of 3000 impact scenarios-covering ten vehicle types and various legform impactors-using automated FE runs configured via Latin hypercube sampling.After preprocessing and one-hot encoding of categorical features,we trained TabNet alongside Support-Vector Regression,Random Forest,and Decision-Tree ensembles.All models underwent hyperparameter tuning via Optuna’s Bayesian optimization coupled with repeated four-fold crossvalidation(20 trials per model).TabNet achieved the best balance of explanatory power and predictive accuracy,with an average R^(2)=0.94±0.01 and RMSE=0.14±0.02.On an independent test set,85%,88%,and 90%of predictions for tibial acceleration,knee-flexion angle,and shear displacement,respectively,fell within±20%of true peaks.SHAPbased analyses confirm that collision-point location and bumper geometry dominate injury outcomes.These results demonstrate TabNet’s capacity to deliver rapid,robust,and explainable injury predictions,offering actionable design insights for vehicle front-end optimization and regulatory assessment in early development stages.
基金Supported by the National Natural Science Foundation of China(No.61971162,61771186)the Natural Science Foundation of Heilongjiang Province(No.PL2024F025)+2 种基金the Open Research Fund of National Mobile Communications Research Laboratory Southeast University(No.2023D07)the Outstanding Youth Program of Natural Science Foundation of Heilongjiang Province(No.YQ2020F012)the Fundamental Scientific Research Funds of Heilongjiang Province(No.2022-KYYWF-1050).
文摘Visual indoor positioning methods have the potential for widespread application in complex large-scale indoor environments,such as shopping centers and hospitals.However,during the visual positioning process,passing pedestrians may cause occlusion in the visual image,leading to large deviations in the visual positioning results.Aiming at the problem of feature occlusion in visual images caused by pedestrians,this paper proposes a visual indoor positioning system that combines semantic segmentation and image restoration.The paper proposes a method called the fast image segmentation repair(FISR),which segments and rapidly repairs the selected image to eliminate the influence of pedestrians on image feature extraction and improve positioning accuracy.In addition,the paper proposes a method called local feature based bag-of-visual-words combined with high-level semantic information(LFHS)for image retrieval.LFHS uses both local features and high-level semantic information to obtain more comprehensive and accurate representations of image features.This approach improves the accuracy and robustness of image retrieval by harnessing the combined power of local features and high-level semantic information.Experimental results show that the proposed positioning method reduces the average positioning error by 0.35 m compared with NetVLAD and 0.49 m compared with MixVPR,significantly improving the performance of visual positioning technology.
文摘The increasing availability of ubiquitous sensor data on the built environment holds great potential for a new generation of travel and mobility research.Bluetooth technology,for example,is already vastly used in vehicular transportation management solutions and services.Current studies discuss the potential of this emerging technology for pedestrian mobility research,but it has yet to be examined in a large urban setting.One of the main problems is detecting pedestrians from Bluetooth records since their behavior and movement patterns share similarities with other urban transportation modes.This study aims to accurately detect pedestrians using a network of 65 Bluetooth detectors located in Tel-Aviv,Israel,which record on average over 60,000 unique daily Bluetooth Media-Access-Control addresses.We propose a detection methodology that includes system calibration,effective travel time calculation,and classification by velocity that takes into consideration the probability of vehicular traffic jams.An evaluation of the proposed methodology presents a promising pedestrian detection accuracy rate of 89%.We showcase the results of pedestrian traffic analysis,together with a discussion on the data analysis challenges and limitations.To the best of our knowledge,this work is the first to analyze pedestrian records detection from a Bluetooth network employed in a dynamic urban environment setting.
文摘Traffic intersections are incredibly dangerous for drivers and pedestrians. Statistics from both Canada and the U.S. show a high number of fatalities and serious injuries related to crashes at intersections. In Canada, during 2019, the National Collision Database shows that 28% of traffic fatalities and 42% of serious injuries occurred at intersections. Likewise, the U.S. National Highway Traffic Administration (NHTSA) found that about 40% of the estimated 5,811,000 accidents in the U.S. during the year studied were intersection-related crashes. In fact, a major survey by the car insurance industry found that nearly 85% of drivers could not identify the correct action to take when approaching a yellow traffic light at an intersection. One major reason for these accidents is the “yellow light dilemma,” the ambiguous situation where a driver should stop or proceed forward when unexpectedly faced with a yellow light. This situation is even further exacerbated by the tendency of aggressive drivers to inappropriately speed up on the yellow just to get through the traffic light. A survey of Canadian drivers conducted by the Traffic Injury Research Foundation found that 9% of drivers admitted to speeding up to get through a traffic light. Another reason for these accidents is the increased danger of making a left-hand turn on yellow. According to the National Highway Traffic Safety Association (NHTSA), left turns occur in approximately 22.2% of collisions—as opposed to just 1.2% for right turns. Moreover, a study by CNN found left turns are three times as likely to kill pedestrians than right turns. The reason left turns are so much more likely to cause an accident is because they take a driver against traffic and in the path of oncoming cars. Additionally, most of these left turns occur at the driver’s discretion—as opposed to the distressingly brief left-hand arrow at busy intersections. Drive Safe Now proposes a workable solution for reducing the number of accidents occurring during a yellow light at intersections. We believe this fairly simple solution will save lives, prevent injuries, reduce damage to public and private property, and decrease insurance costs.
基金the National Natural Science Foundation of China(No.61873190)。
文摘Simulation of pedestrians’behavior in the hub can help decision-makers to formulate better evacuation strategies.With this aim,this study develops an improved cellular automata model considering pedestrian’s mass-following psychology and competitive awareness,and based on this model,pedestrian’s evacuation process from the channel of the hub with two exits is simulated.Moreover,dynamic guidance information,e.g.,the realtime congestion situation of the evacuation routes,plays an important role during pedestrian evacuation processes in a hub,as the evaluation routes can be adjusted based on this information.That is,the congestion situation during the evaluation can be improved.Thus,dynamic signs are incorporated into the proposed model to study the influence of dynamic guidance information on pedestrian evacuation behavior.In simulation experiments,the influence of two parameters,namely the proportion of pedestrians unfamiliar with the hub and update interval of dynamic signs,on pedestrian evacuation behavior is studied.Results show that dynamic guidance information can improve the efficiency of pedestrian evacuation.In particular,the higher the proportion of pedestrians unfamiliar with the hub is,the more obvious the effect of dynamic guidance information is.Besides,different proportions of pedestrians unfamiliar with the hub lead to different update intervals of dynamic signs.Finally,the results of this study can provide some implications to the practical hub operation and evacuation,e.g.,to standardize the order of evacuation routes and improve the information service level in the hub.
文摘Road traffic safety can decrease when drivers drive in a low-visibility environment.The application of visual perception technology to detect vehicles and pedestrians in infrared images proves to be an effective means of reducing the risk of accidents.To tackle the challenges posed by the low recognition accuracy and the substan-tial computational burden associated with current infrared pedestrian-vehicle detection methods,an infrared pedestrian-vehicle detection method A proposal is presented,based on an enhanced version of You Only Look Once version 5(YOLOv5).First,A head specifically designed for detecting small targets has been integrated into the model to make full use of shallow feature information to enhance the accuracy in detecting small targets.Second,the Focal Generalized Intersection over Union(GIoU)is employed as an alternative to the original loss function to address issues related to target overlap and category imbalance.Third,the distribution shift convolution optimization feature extraction operator is used to alleviate the computational burden of the model without significantly compromising detection accuracy.The test results of the improved algorithm show that its average accuracy(mAP)reaches 90.1%.Specifically,the Giga Floating Point Operations Per second(GFLOPs)of the improved algorithm is only 9.1.In contrast,the improved algorithms outperformed the other algorithms on similar GFLOPs,such as YOLOv6n(11.9),YOLOv8n(8.7),YOLOv7t(13.2)and YOLOv5s(16.0).The mAPs that are 4.4%,3%,3.5%,and 1.7%greater than those of these algorithms show that the improved algorithm achieves higher accuracy in target detection tasks under similar computational resource overhead.On the other hand,compared with other algorithms such as YOLOv8l(91.1%),YOLOv6l(89.5%),YOLOv7(90.8%),and YOLOv3(90.1%),the improved algorithm needs only 5.5%,2.3%,8.6%,and 2.3%,respectively,of the GFLOPs.The improved algorithm has shown significant advancements in balancing accuracy and computational efficiency,making it promising for practical use in resource-limited scenarios.
基金supported by the National Natural Science Foundation of China under(Grant No.52175531)in part by the Science and Technology Research Program of Chongqing Municipal Education Commission under Grant(Grant Nos.KJQN202000605 and KJZD-M202000602)。
文摘Pedestrian positioning system(PPS)using wearable inertial sensors has wide applications towards various emerging fields such as smart healthcare,emergency rescue,soldier positioning,etc.The performance of traditional PPS is limited by the cumulative error of inertial sensors,complex motion modes of pedestrians,and the low robustness of the multi-sensor collaboration structure.This paper presents a hybrid pedestrian positioning system using the combination of wearable inertial sensors and ultrasonic ranging(H-PPS).A robust two nodes integration structure is developed to adaptively combine the motion data acquired from the single waist-mounted and foot-mounted node,and enhanced by a novel ellipsoid constraint model.In addition,a deep-learning-based walking speed estimator is proposed by considering all the motion features provided by different nodes,which effectively reduces the cumulative error originating from inertial sensors.Finally,a comprehensive data and model dual-driven model is presented to effectively combine the motion data provided by different sensor nodes and walking speed estimator,and multi-level constraints are extracted to further improve the performance of the overall system.Experimental results indicate that the proposed H-PPS significantly improves the performance of the single PPS and outperforms existing algorithms in accuracy index under complex indoor scenarios.
基金Beijing Natural Science Foundation(9234025)National Social Science Fund Project of China(21FGLB014)Humanity and Social Science Youth Foundation of Ministry of Education of China(21YJC630094).
文摘Urban intersections without traffic signals are prone to accidents involving motor vehicles and pedestrians.Utilizing computer vision technology to detect pedestrians crossing the street can effectively mitigate the occurrence of such accidents.Faced with the complex issue of pedestrian occlusion at signal-free intersections,this paper proposes a target detection model called Head feature And ENMS fusion Residual connection For CNN(HAERC).Specifically,the model includes a head feature module that detects occluded pedestrians by integrating their head features with the overall target.Additionally,to address the misselection caused by overlapping candidate boxes in two-stage target detection models,an Extended Non-Maximum Suppression classifier(ENMS)with expanded IoU thresholds is proposed.Finally,leveraging the CityPersons dataset and categorizing it into four classes based on occlusion levels(heavy,reasonable,partial,bare),the HAERC model is experimented on these classes and compared with baseline models.Experimental results demonstrate that HAERC achieves superior False Positives Per Image(FPPI)values of 46.64%,9.59%,9.43%,and 6.78%respectively for the four classes,outperforming all baseline models.The study concludes that the HAERC model effectively identifies occluded pedestrians in the complex environment of urban intersections without traffic signals,thereby enhancing safety for long-range driving at such intersections.
基金Project supported by the National Natural Science Foundation of China(Grant No.71603146).
文摘Pedestrian self-organizing movement plays a significant role in evacuation studies and architectural design.Lane formation,a typical self-organizing phenomenon,helps pedestrian system to become more orderly,the majority of following behavior model and overtaking behavior model are imprecise and unrealistic compared with pedestrian movement in the real world.In this study,a pedestrian dynamic model considering detailed modelling of the following behavior and overtaking behavior is constructed,and a method of measuring the lane formation and pedestrian system order based on information entropy is proposed.Simulation and analysis demonstrate that the following and avoidance behaviors are important factors of lane formation.A high tendency of following results in good lane formation.Both non-selective following behavior and aggressive overtaking behavior cause the system order to decrease.The most orderly following strategy for a pedestrian is to overtake the former pedestrian whose speed is lower than approximately 70%of his own.The influence of the obstacle layout on pedestrian lane and egress efficiency is also studied with this model.The presence of a small obstacle does not obstruct the walking of pedestrians;in contrast,it may help to improve the egress efficiency by guiding the pedestrian flow and mitigating the reduction of pedestrian system orderliness.
文摘With the development of positioning technology,loca-tion services are constantly in demand by people.As a primary location service pedestrian navigation has two main approaches based on radio and inertial navigation.The pedestrian naviga-tion based on radio is subject to environmental occlusion lead-ing to the degradation of positioning accuracy.The pedestrian navigation based on micro-electro-mechanical system inertial measurement unit(MIMU)is less susceptible to environmental interference,but its errors dissipate over time.In this paper,a chest card pedestrian navigation improvement method based on complementary correction is proposed in order to suppress the error divergence of inertial navigation methods.To suppress atti-tude errors,optimal feedback coefficients are established by pedestrian motion characteristics.To extend navigation time and improve positioning accuracy,the step length in subsequent movements is compensated by the first step length.The experi-mental results show that the positioning accuracy of the pro-posed method is improved by more than 47%and 44%com-pared with the pure inertia-based method combined with step compensation and the traditional complementary filtering com-bined method with step compensation.The proposed method can effectively suppress the error dispersion and improve the positioning accuracy.
基金supported by the Henan Provincial Science and Technology Research Project under Grants 232102211006,232102210044,232102211017,232102210055 and 222102210214the Science and Technology Innovation Project of Zhengzhou University of Light Industry under Grant 23XNKJTD0205+1 种基金the Undergraduate Universities Smart Teaching Special Research Project of Henan Province under Grant Jiao Gao[2021]No.489-29the Doctor Natural Science Foundation of Zhengzhou University of Light Industry under Grants 2021BSJJ025 and 2022BSJJZK13.
文摘Multispectral pedestrian detection technology leverages infrared images to provide reliable information for visible light images, demonstrating significant advantages in low-light conditions and background occlusion scenarios. However, while continuously improving cross-modal feature extraction and fusion, ensuring the model’s detection speed is also a challenging issue. We have devised a deep learning network model for cross-modal pedestrian detection based on Resnet50, aiming to focus on more reliable features and enhance the model’s detection efficiency. This model employs a spatial attention mechanism to reweight the input visible light and infrared image data, enhancing the model’s focus on different spatial positions and sharing the weighted feature data across different modalities, thereby reducing the interference of multi-modal features. Subsequently, lightweight modules with depthwise separable convolution are incorporated to reduce the model’s parameter count and computational load through channel-wise and point-wise convolutions. The network model algorithm proposed in this paper was experimentally validated on the publicly available KAIST dataset and compared with other existing methods. The experimental results demonstrate that our approach achieves favorable performance in various complex environments, affirming the effectiveness of the multispectral pedestrian detection technology proposed in this paper.
基金Projects(52102407,52472354)supported by the National Natural Science Foundation of China。
文摘Traffic accidents involving pedestrians and drivers pose significant public health and safety concerns.Understanding the differential influences of road physical design attributes on crash frequencies for these two groups is critical for developing targeted safety interventions.Considering that the zero-truncated characteristic of the data is uncertain,the results of the zero-truncated negative binomial models and traditional negative binomial models are calculated to seek the better model.The result revealed that the road surface conditions and vertical and horizontal curvature have greater influence on both pedestrian and driver compared to number of lanes and speed limit.And speed limits were more pronounced for pedestrian crash frequency than driver group.Conversely,the effect of different types of intersections was stronger for driver crash frequency.The differential influences of road physical design attributes on traffic crash frequencies for pedestrians versus drivers highlight the importance of adopting a user-centric approach to transportation safety planning and infrastructure design.Tailoring interventions to address the unique needs and vulnerabilities of different road user groups can lead to more effective safety improvements and better overall traffic safety outcomes.
文摘Walkability is an essential aspect of urban transportation systems. Properly designed walking paths can enhance transportation safety, encourage pedestrian activity, and improve community quality of life. This, in turn, can help achieve sustainable development goals in urban areas. This pilot study uses wearable technology data to present a new method for measuring pedestrian stress in urban environments and the results were presented as an interactive geographic information system map to support risk-informed decision-making. The approach involves analyzing data from wearable devices using heart rate variability (RMSSD and slope analysis) to identify high-stress locations. This data-driven approach can help urban planners and safety experts identify and address pedestrian stressors, ultimately creating safer, more walkable cities. The study addresses a significant challenge in pedestrian safety by providing insights into factors and locations that trigger stress in pedestrians. During the pilot study, high-stress pedestrian experiences were identified due to issues like pedestrian-scooter interaction on pedestrian paths, pedestrian behavior around high foot traffic areas, and poor visibility at pedestrian crossings due to inadequate lighting.
基金supported by the National Natural Science Foundation of China(Grant No.52275286)Hunan Outstanding Youth Fund(Grant No.2023JJ10010)+2 种基金Key Research and Development Program of Hunan Province(Grant NO.2022SK2105)Shenzhen Science and Technology Program(Grant NO.JCYJ20230807122004009)Sanming Project of Medicine in Shenzhen(Grant NO.SZZYSM202311006).
文摘The anthropometric differences between European/American and Chinese population are remarkable and have significant influences on pedestrian kinematics and injury response in vehicle crashes.Therefore,the current study aims to develop and validate a Finite Element(FE)human body model representing the anthropometry of Chinese 50th percentile adult male for pedestrian safety analysis and development of Chinese ATDs(Anthropomorphic Test Devices).Firstly,a human body pedestrian model,named as C-HBM(Chinese Human Body Model),was developed based on the medical image data of a volunteer selected according to both anthropometry and anatomy characteristics of 50th percentile Chinese adult male.Then,the biofidelity of the C-HBM pedestrian model was validated against cadaver impact test data reported in the literature at the segment and full-body level.Finally,the validated C-HBM pedestrian model was employed to predict Chinese pedestrian injuries in real world vehicle crashes.The results indicate that the C-HBM pedestrian model has a good capability in predicting human body mechanical response in cadaver tests and Chinese leg and thorax injuries in vehicle crashes.Kinematic analysis shows that the C-HBM pedestrian model has less sliding on the hood surface,shorter movement in the horizontal direction,and higher pelvis displacement in the vertical direction than cadavers and the pedestrian model in the anthropometry of westerner due to anthropometric differences in the lower limbs.The currently developed C-HBM pedestrian model provides a basic tool for vehicle safety design and evaluation in China market,and for development of Chinese ATDs.
文摘This study explores the challenges posed by pedestrian detection and occlusion in AR applications, employing a novel approach that utilizes RGB-D-based skeleton reconstruction to reduce the overhead of classical pedestrian detection algorithms during training. Furthermore, it is dedicated to addressing occlusion issues in pedestrian detection by using Azure Kinect for body tracking and integrating a robust occlusion management algorithm, significantly enhancing detection efficiency. In experiments, an average latency of 204 milliseconds was measured, and the detection accuracy reached an outstanding level of 97%. Additionally, this approach has been successfully applied in creating a simple yet captivating augmented reality game, demonstrating the practical application of the algorithm.
基金University of PamplonaColombian School of Engineering Julio Garavito。
文摘The rapid growth of impervious areas in urban basins worldwide has increased the number of impermeable surfaces in cities,leading to severe flooding and significant economic losses for civilians.This trend highlights the urgent need for methodologies that assess flood hazards and specifically address the direct impact on pedestrians,which is often overlooked in traditional flood hazard analyses.This study aims to evaluate a methodology for assessing the risk to pedestrians from hydrodynamic forces during urban floods,with a specific focus on Cúcuta,Colombia.The methodology couples research outcomes from other studies on the impact of floodwaters on individuals of different ages and sizes with 1D/2D hydrological modeling.Advanced computational algorithms for image recognition were used to measure water levels at 5-s intervals on November 6,2020,using drones for digital elevation model data collection.In Cúcuta,where flood risk is high and drainage infrastructure is limited,the PCSWMM(Computer-based Urban Stormwater Management Model)was calibrated and validated to simulate extreme flood events.The model incorporated urban infrastructure details and geomorphological parameters of Cúcuta's urban basin.Four return periods(5,10,50,100),with extreme rainfall of 3 h,were used to estimate the variability of the risk map.The output of the model was analyzed,and an integrated and time-varying comparison of the results was done.Results show that the regions of high-water depth and high velocity could vary significantly along the duration of the different extreme events.Also,from 5 to 100 years return period,the percentage of area at risk increased from 9.6%to 16.6%.The pedestrian sensitivity appears much higher than the increase in velocities or water depth individually.This study identified medium to high-risk locations,which are dynamic in time.We can conclude dynamics are spatiotemporal,and the added information layer of pedestrians brings vulnerability information that is also dynamic.Areas of immediate concern in Cúcuta can enhance pedestrian safety during flash flood events.The spatiotemporal variation of patterns requires further studies to map trajectories and sequences that machine learning models could capture.
基金This work was financially supported by Russian Science Foundation with co-financing of Bank Saint Petersburg[Agreement#17-71-30029].
文摘When arranging the pedestrian infrastructure,one of the most important components that make a tangible contribution to the safety of pedestrians is to organize the safe road crossing.In cities,pedestrians often cross a road in the wrong place due to established routes or inadequate location of crosswalks.Accidents with the participation of pedestrians who crossed the road neglecting the traffic rules,make up a significant part of the total amount of road accidents.In this paper,we propose a method that allows us,on the basis of the results of a computer simulation of pedestrian traffic,to obtain predicted routes for road crossing and to indicate optimal locations for crosswalks that take into account established pedestrian routes and increase their safety.The work describes an extension for the existing AntRoadPlanner simulation algorithm,which searches for and clusters points where pedestrians cross the roadway and suggests locations for new crosswalks.This method was tested on the basis of a comparative simulation of several territories before and after its application,as well as on the basis of a field study of the territories.The developed algorithm can also be used to search for other potentially dangerous places for pedestrians on plans of districts,for example,crossings in places with limited visibility.
