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1.
Appl Ergon ; 106: 103878, 2023 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-36001925

RESUMO

As driving performance relies heavily on the interpretation of visual information, driving simulators require a visual display that can effectively communicate the virtual environment to the driver. Most high-fidelity visual displays include an expensive system of high-definition projectors and wraparound screens. To reduce the overall cost of a driving simulator while preserving the generalizability of results to naturalistic driving, head mounted displays (HMD) are being considered as a substitute visual cueing system. Recent innovations to virtual reality technologies are encouraging, however, differences between HMDs and more traditional visual displays have not been explored for all types of driving measures. In particular, while existing literature provides insight into the validity of HMDs as a substitute for higher fidelity visual displays in tests of driver behaviour and performance, there is a gap in the literature regarding differences in physiological responses. In the current study, upper body muscle activation and joint angle ranges were compared between an Oculus™ Rift Development Kit 2 HMD and a system of wrap around screens. Twenty-one participants each completed two simulated drives, one per display, in a counterbalanced order. During the simulation, drivers encountered unanticipated pedestrian crossings during which peak surface electromyography, root-mean-square of the surface electromyography signal and joint angles were determined bilaterally on the upper limbs. No significant differences (p ≤ 0.05) were observed between the Oculus™ Rift HMD and the wrap around screens for all dependent variables with the exception of left joint range of motion in female participants, suggesting that the HMD reduced field of view had a minimal effect on driver kinematics and no effect on muscle activation levels. Upper body bracing was observed during the hazard response time segments characterized by significantly increased muscle activity during hazard response time segments and minimal joint movement. Considering the lack of significant kinematic and muscle activation differences between the two visual inputs, HMD technology for hazard response may provide a suitable alternative to wrap around screens for studying kinematic responses during hazardous driving scenarios.


Assuntos
Condução de Veículo , Músculos , Óculos Inteligentes , Feminino , Humanos , Masculino , Condução de Veículo/psicologia , Fenômenos Biomecânicos , Desenho de Equipamento , Músculos/fisiologia , Pedestres , Tempo de Reação/fisiologia
2.
Sci Total Environ ; 857(Pt 1): 159406, 2023 Jan 20.
Artigo em Inglês | MEDLINE | ID: mdl-36243067

RESUMO

Research combining the measurement of objective variables with surveys of people's perception of noise on city streets is useful in terms of understanding the impact of urban noise on the population and improving the environment. Although previous investigations have analysed the factors that may influence the noise annoyance of citizens, it is usually considered as a global aspect. This paper presents research based on in situ surveys and objective variables (urban, meteorological and noise indicators) to evaluate some specific effects of noise on pedestrians in urban environments where road traffic is the main source of sound. The results show significant relationships of the effects of noise and perceptions of how noisy urban environments are with variables such as building height, road category and temperature, with correlation coefficients ranging from 0.37 to 0.64. Significant correlations between these subjective variables and the acoustic variables were also found, with explanations of variability that reached values of up to 50 %. A multivariate analysis revealed that both urban variables (especially the category of street) and environmental variables can be an alternative or a complement to models predicting the effects and perception of environmental noise based only on acoustic variables.


Assuntos
Ruído dos Transportes , Pedestres , Humanos , Ruído dos Transportes/efeitos adversos , Exposição Ambiental , Som , Acústica
3.
Sensors (Basel) ; 22(21)2022 Oct 25.
Artigo em Inglês | MEDLINE | ID: mdl-36365858

RESUMO

Among existing wireless and wearable indoor pedestrian tracking solutions, the ultra-wideband (UWB) and inertial measurement unit (IMU) sensors are the popular options due to their accurate and globally referenced positioning, and low-cost and compact size, respectively. However, the UWB position accuracy is compromised by the indoor non-line of sight (NLOS) and the IMU estimation suffers from orientation drift as well as requiring position initialization. To overcome these limitations, this paper proposes a low-cost foot-placed UWB and IMU fusion-based indoor pedestrian tracking system. Our data fusion model is an improved loosely coupled Kalman filter with the inclusion of valid UWB observation detection. In this manner, the proposed system not only adjusts the consumer-grade IMU's accumulated drift but also filters out any NLOS instances in the UWB observation. We validated the performance of the proposed system with two experimental scenarios in a complex indoor environment. The root mean square (RMS) positioning accuracy of our data fusion model is enhanced by 60%, 53%, and 27% compared to that of the IMU-based pedestrian dead reckoning, raw UWB position, and conventional fusion model, respectively, in the single-lap NLOS scenario, and by 70%, 34%, and 12%, respectively, in the multi-lap LOS+NLOS scenario.


Assuntos
Pedestres , Humanos , Algoritmos ,
4.
Sensors (Basel) ; 22(21)2022 Oct 27.
Artigo em Inglês | MEDLINE | ID: mdl-36365927

RESUMO

We propose a pedestrian trajectory prediction algorithm based on the bimodal extended Kalman filter. With this filter, we are able to make full use of the dual-state nature of the pedestrian movement, i.e., the pedestrian is either moving or remains stationary. We apply the dual-mode probability model to describe the state of the pedestrian. Based on this model, we construct the proposed bimodal extended Kalman filter to estimate pedestrian state distribution. The filter obtains the state distribution for each pedestrian in the scene, respectively, and use that state distribution to predict the future trajectories of all the people in the scene. This prediction method estimates the prior probability of each parameter of the model through the dataset and updates the individual posterior probability of the pedestrian state through the bimodal extended Kalman filter. Our model can predict the trajectory of every individual, by taking the social interaction of pedestrians as well as the surrounding physical obstacles into account, with less than fifty model parameters being used, while with the limited parameter, our model could be nearly accurate as other deep learning models and still be comprehensible for model users.


Assuntos
Pedestres , Humanos , Algoritmos , Probabilidade
5.
Sensors (Basel) ; 22(21)2022 Nov 04.
Artigo em Inglês | MEDLINE | ID: mdl-36366178

RESUMO

Object detection is a critical technology of environmental perception for autonomous driving vehicle. The Convolutional Neural Network has gradually become a powerful tool in the field of vehicle detection because of its powerful ability of feature extraction. In aiming to reach the balance between speed and accuracy of detection in complex traffic scenarios, this paper proposes an improved lightweight and high-performance vehicle-pedestrian detection algorithm based on the YOLOv4. Firstly, the backbone network CSPDarknet53 is replaced by MobileNetv2 to reduce the number of parameters and raise the capability of feature extraction. Secondly, the method of multi-scale feature fusion is used to realize the information interaction among different feature layers. Finally, a coordinate attention mechanism is added to focus on the region of interest in the image by way of weight adjustment. The experimental results show that this improved model has a great performance in vehicle-pedestrian detection in traffic scenarios. Experimental results on PASCAL VOC datasets show that the improved model's mAP is 85.79% and speed is 35FPS, which has an increase of 4.31% and 16.7% compared to YOLOv4. Furthermore, the improved YOLOv4 model maintains a great balance between detection accuracy and speed on different datasets, indicating that it can be applied to vehicle-pedestrian detection in traffic scenarios.


Assuntos
Condução de Veículo , Pedestres , Humanos , Acidentes de Trânsito , Algoritmos , Redes Neurais de Computação
6.
Comput Intell Neurosci ; 2022: 3154532, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-36337268

RESUMO

The interactive motion planning between unmanned vehicles and pedestrians in urban road environments is the key to realizing the autonomous motion of unmanned vehicles in hybrid traffic scenarios. The problem of human-vehicle interaction motion planning modeling at complex intersections is studied for an unmanned vehicle in this article. First, the motion planning of pedestrians and the unmanned vehicles is established according to the social force model and the behavioral dynamics model. Then, the autonomous vehicle is added to the crowd, and the human-vehicle interaction force is established. The virtual force is added to the social force model and the behavioral dynamics model, respectively, and the improved social force model and the behavioral dynamics model are used for the motion planning of pedestrians and unmanned vehicles. In this way, the established model solves the problems of simple pedestrian interaction motion planning in the social force model and single-body motion planning in the behavioral dynamics and thus provides a strong support for multibody motion planning. Finally, through the interactive motion planning trajectory of pedestrians and unmanned vehicles in different scenes, the vehicle and pedestrian motion planning trajectory can effectively avoid overlapping or crossing, so as to avoid the collision, which verifies the effectiveness and feasibility of the proposed model.


Assuntos
Acidentes de Trânsito , Pedestres , Humanos , Acidentes de Trânsito/prevenção & controle , Segurança , Caminhada
7.
Artigo em Inglês | MEDLINE | ID: mdl-36360662

RESUMO

This study aims to examine child pedestrian safety around parks by considering four rule-compliance measures: temporal, spatial, velocity and visual search compliance. In this regard, street crossing observations of 731 children were recorded at 17 crosswalks around four parks in Montreal, Canada. Information on child behaviors, road features, and pedestrian-vehicle interactions were gathered in three separate forms. Chi-square tests were used to highlight the individual, situational, behavioral and road environmental characteristics that are associated with pedestrian rule compliance. About half of our sampled children started crossing at the same time as the adults who accompanied them, but more rule violations were observed when the adult initiated the crossing. The child's gender did not have a significant impact on rule compliance. Several variables were positively associated with rule compliance: stopping at the curb before crossing, close parental supervision, and pedestrian countdown signals. Pedestrian-car interaction had a mixed impact on rule compliance. Overall, rule compliance among children was high for each of our indicators, but about two-thirds failed to comply with all four indicators. A few measures, such as longer crossing signals and pedestrian countdown displays at traffic lights, may help to increase rule compliance and, ultimately, provide safer access to parks.


Assuntos
Pedestres , Adulto , Criança , Humanos , Acidentes de Trânsito/prevenção & controle , Caminhada , Comportamento Infantil , Família , Segurança
8.
Artigo em Inglês | MEDLINE | ID: mdl-36360717

RESUMO

Street crime is a common social problem that threatens the security of people's lives and property. Understanding the influencing mechanisms of street crime is an essential precondition for formulating crime prevention strategies. Widespread concern has contributed to the development of streetscape environment features as they can significantly affect the occurrence of street crime. Emerging street view images are a low-cost and highly accessible data source. On the other hand, machine-learning models such as XGBoost (eXtreme Gradient Boosting) usually have higher fitting accuracies than those of linear regression models. Therefore, they are popular for modeling the relationships between crime and related impact factors. However, due to the "black box" characteristic, researchers are unable to understand how each variable contributes to the occurrence of crime. Existing research mainly focuses on the independent impacts of streetscape environment features on street crime, but not on the interaction effects between these features and the community socioeconomic conditions and their local variations. In order to address the above limitations, this study first combines street view images, an objective detection network, and a semantic segmentation network to extract a systematic measurement of the streetscape environment. Then, controlling for socioeconomic factors, we adopted the XGBoost model to fit the relationships between streetscape environment features and street crime at the street segment level. Moreover, we used the SHAP (Shapley additive explanation) framework, a post-hoc machine-learning explainer, to explain the results of the XGBoost model. The results demonstrate that, from a global perspective, the number of people on the street, extracted from street view images, has the most significant impact on street property crime among all the street view variables. The local interpretability of the SHAP explainer demonstrates that a particular variable has different effects on street crime at different street segments. The nonlinear associations between streetscape environment features and street crime, as well as the interaction effects of different streetscape environment features are discussed. The positive effect of the number of pedestrians on street crime increases with the length of the street segment and the number of crime generators. The combination of street view images and interpretable machine-learning techniques is helpful in better accurately understanding the complex relationships between the streetscape environment and street crime. Furthermore, the readily comprehensible results can offer a reference for formulating crime prevention strategies.


Assuntos
Pedestres , Humanos , Aprendizado de Máquina , Crime , Fatores Socioeconômicos , Coleta de Dados
9.
Artigo em Inglês | MEDLINE | ID: mdl-36360941

RESUMO

In the prioritized vehicle traffic environment, motorized transportation has been obtaining more spatial and economic resources, posing potential threats to the travel quality and life safety of non-motorized transportation participants. It is becoming urgent to improve the safety situation of non-motorized transportation participants. Most previous studies have focused on the psychological aspects of pedestrians and cyclists exposed to the actual road environment rather than quantifying the objective safety hazards, which has led to a non-rigorous evaluation of their basic safety situation. An integrated processing approach is proposed to comprehensively and objectively evaluate the overall safety level of non-motorized transportation participants on each road segment. Our main contributions include (1) the universal approach is established to automatically identify hazard scenarios related to non-motorized transportation and their direct causing factors from street view images based on multiple deep learning models; (2) a seed points spreading algorithm is designed to convert semantic images into target detection results with detail contour, which breaks the functional limitation of these two types of methods to a certain extent; (3) The safety situation of non-motorized transportation on various road sections in Gulou District, Nanjing, China has been evaluated and based on this, a series of suggestions have been put forward to guide the better adaptation among multiple transportation participants.


Assuntos
Aprendizado Profundo , Pedestres , Humanos , Acidentes de Trânsito , Meios de Transporte , China
10.
Artigo em Inglês | MEDLINE | ID: mdl-36361298

RESUMO

Red-light violations of pedestrians crossing at signal intersections is one of the key factors in pedestrian traffic accidents. Even though there are various studies on pedestrian behavior and pedestrian traffic conflicts, few focus on the risk of different crosswalks for the violating pedestrian group. Due to the spatio-temporal nature of violation risk, this study proposes a geographical and temporal risk evaluation method for pedestrian red-light violations, which combines actual survey and video acquisition. First, in the geographical-based risk evaluation, the pedestrian violation rate at signal intersections is investigated by Pearson correlation analysis to extract the significant influencing factors from traffic conditions, built environment, and crosswalk facilities. Second, in the temporal-based risk evaluation, the survival analysis method is developed to quantify the risk of pedestrian violation in different scenarios as time passes by. Finally, this study selects 16 typical signalized intersections in Suzhou, China, with 881 pedestrian crosswalk violations from a total size of 4586 pedestrians as survey cases. Results indicate that crossing distance, traffic volume on the crosswalk, red-light time, and crosswalk-type variables all contribute to the effect of pedestrian violation from a geographical perspective, and the installation of waiting refuge islands has the most significant impact. From the temporal perspective, the increases in red-light time, number of lanes, and traffic volume have a mitigating effect on the violations with pedestrian waiting time increases. This study aims to provide a development-oriented path by proposing an analytical framework that reconsiders geographical and temporal risk factors of violation. The findings could help transport planners understand the effect of pedestrian violation-related traffic risk and develop operational measures and crosswalk design schemes for controlling pedestrian violations occurring in local communities.


Assuntos
Pedestres , Humanos , Acidentes de Trânsito , Ambiente Construído , China/epidemiologia , Fatores de Risco , Caminhada , Segurança
11.
Artigo em Inglês | MEDLINE | ID: mdl-36429416

RESUMO

Pedestrian understanding of driver intent is key to pedestrian safety on the road and in parking lots. With the development of autonomous vehicles (AVs), the human driver will be removed, and with it, the exchange that occurs between drivers and pedestrians (e.g., head nods, hand gestures). One possible solution for augmenting that communication is an array of high-intensity light-emitting diodes (LEDs) to project vehicle-to-pedestrian (V2P) messages on the ground plane behind a reversing vehicle. This would be particularly beneficial to elderly pedestrians, who are at particular risk of being struck by reversing cars in parking lots. Their downward gaze and slower reaction time make them particularly vulnerable. A survey was conducted to generate designs, and a simulator experiment was conducted to measure detection and reaction times. The study found that elderly pedestrians are significantly more likely to detect an additional projected message on the ground than detect the existing brake light alone when walking in a parking lot.


Assuntos
Pedestres , Humanos , Idoso , Acidentes de Trânsito/prevenção & controle , Veículos Autônomos , Iluminação , Comunicação
12.
Sensors (Basel) ; 22(22)2022 Nov 09.
Artigo em Inglês | MEDLINE | ID: mdl-36433238

RESUMO

Pedestrian detection is an important research domain due to its relevance for autonomous and assisted driving, as well as its applications in security and industrial automation. Often, more than one type of sensor is used to cover a broader range of operating conditions than a single-sensor system would allow. However, it remains difficult to make pedestrian detection systems perform well in highly dynamic environments, often requiring extensive retraining of the algorithms for specific conditions to reach satisfactory accuracy, which, in turn, requires large, annotated datasets captured in these conditions. In this paper, we propose a probabilistic decision-level sensor fusion method based on naive Bayes to improve the efficiency of the system by combining the output of available pedestrian detectors for colour and thermal images without retraining. The results in this paper, obtained through long-term experiments, demonstrate the efficacy of our technique, its ability to work with non-registered images, and its adaptability to cope with situations when one of the sensors fails. The results also show that our proposed technique improves the overall accuracy of the system and could be very useful in several applications.


Assuntos
Condução de Veículo , Pedestres , Humanos , Teorema de Bayes , Cor , Algoritmos
13.
Sensors (Basel) ; 22(22)2022 Nov 10.
Artigo em Inglês | MEDLINE | ID: mdl-36433291

RESUMO

Surveillance video has been widely used in business, security, search, and other fields. Identifying and locating specific pedestrians in surveillance video has an important application value in criminal investigation, search and rescue, etc. However, the requirements for real-time capturing and accuracy are high for these applications. It is essential to build a complete and smooth system to combine pedestrian detection, tracking and re-identification to achieve the goal of maximizing efficiency by balancing real-time capture and accuracy. This paper combined the detector and Re-ID models into a single end-to-end network by introducing a new track branch to YOLOv5 architecture for tracking. For pedestrian detection, we employed the weighted bi-directional feature pyramid network (BiFPN) to enhance the network structure based on the YOLOv5-Lite, which is able to further improve the ability of feature extraction. For tracking, based on Deepsort, this paper enhanced the tracker, which uses the Noise Scale Adaptive (NSA) Kalman filter to track, and adds adaptive noise to strengthen the anti-interference of the tracking model. In addition, the matching strategy is further updated. For pedestrian re-identification, the network structure of Fastreid was modified, which can increase the feature extraction speed of the improved algorithm by leaps and bounds. Using the proposed unified network, the parameters of the entire model can be trained in an end-to-end method with the multi-loss function, which has been demonstrated to be quite valuable in some other recent works. Experimental results demonstrate that pedestrians detection can obtain a 97% mean Average Precision (mAP) and that it can track the pedestrians well with a 98.3% MOTA and a 99.8% MOTP on the MOT16 dataset; furthermore, high pedestrian re-identification performance can be achieved on the VERI-Wild dataset with a 77.3% mAP. The overall framework proposed in this paper has remarkable performance in terms of the precise localization and real-time detection of specific pedestrians across time, regions, and cameras.


Assuntos
Pedestres , Humanos , Algoritmos , Sistemas Computacionais
14.
Sensors (Basel) ; 22(22)2022 Nov 15.
Artigo em Inglês | MEDLINE | ID: mdl-36433434

RESUMO

Indoor pedestrian positioning has been widely used in many scenarios, such as fire rescue and indoor path planning. Compared with other technologies, inertial measurement unit (IMU)-based indoor positioning requires no additional equipment and has a lower cost. However, IMU-based indoor positioning has the problem of error accumulation, resulting in inaccurate positioning. Therefore, this paper proposes a cascade filtering algorithm to correct the accumulated error using only a small amount of map information. In the lower filter, the zero-velocity correction and the attitude-extended complementary filtering (ECF) algorithm are utilized to initially solve the pedestrian's trajectory. In the upper filter, a particle filter (PF) combined with the map information is adopted to correct the accumulated error of the heading and stride length. In the 2D positioning process, the root mean square error (RMSE) of the proposed algorithm is only 1.35 m. In the altitude correction, this paper proposes a method of clustering floor discrimination to deal with the instability of the barometer resulting from an uneven pressure and temperature. In the final 3D positioning experiment, with a total length of 536.5 m and including the process of going up and down the stairs, the end-point error is only 2.45 m by the proposed algorithm.


Assuntos
Pedestres , Humanos , Algoritmos , Projetos de Pesquisa , Análise por Conglomerados
15.
Sensors (Basel) ; 22(22)2022 Nov 19.
Artigo em Inglês | MEDLINE | ID: mdl-36433562

RESUMO

Infrared pedestrian detection has important theoretical research value and a wide range of application scenarios. Because of its special imaging method, infrared images can be used for pedestrian detection at night and in severe weather conditions. However, the lack of pedestrian feature information in infrared images and the small scale of pedestrian objects makes it difficult for detection networks to extract feature information and accurately detect small-scale pedestrians. To address these issues, this paper proposes an infrared pedestrian detection network based on YOLOv5, named IPD-Net. Firstly, an adaptive feature extraction module (AFEM) is designed in the backbone network section, in which a residual structure with stepwise selective kernel was included to enable the model to better extract feature information under different sizes of the receptive field. Secondly, a coordinate attention feature pyramid network (CA-FPN) is designed to enhance the deep feature map with location information through the coordinate attention module, so that the network gains better capability of object localization. Finally, shallow information is introduced into the feature fusion network to improve the detection accuracy of weak and small objects. Experimental results on the large infrared image dataset ZUT show that the mean Average Precision (mAP50) of our model is improved by 3.6% compared to that of YOLOv5s. In addition, IPD-Net shows various degrees of accuracy improvement compared to other excellent methods.


Assuntos
Pedestres , Humanos , Progressão da Doença , Coluna Vertebral , Tempo (Meteorologia)
16.
J Prim Care Community Health ; 13: 21501319221134851, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-36348573

RESUMO

BACKGROUND: Our study aimed to describe the variation in the frequency of correct mask use among pedestrians in the first and second waves of the COVID-19 pandemic in high-flow indoor public spaces from different geographic and social settings in Peru. METHODS: We carried out a cross-sectional exploratory study among pedestrians in Lima (the capital city) and other coastal and highland cities in Peru. Pedestrians were directly observed by trained medical students in 2 high-flow indoor areas at different times in November 2020 (first wave) and October 2021 (second wave). Primary outcomes included the frequencies of mask use and correct use. We applied multinomial logistic models and estimated crude and adjusted relative prevalence ratios for sex, age, obesity, and location. Additionally, we used binomial generalized linear models to estimate prevalence ratios in crude and adjusted models. RESULTS: We included 1996 participants. The frequency of mask use was similar in both years: 96.9% in 2020 and 95.5% in 2021. However, the frequency of correct mask use significantly decreased from 81.9% (95% CI, 79.4-84.3) in 2020 to 60.3% (95% CI, 57.2-67.3) in 2021. In 2020, we observed an increase in the probability of misuse in the cities of Lima (aRP: 1.42; P = .021) and Chiclayo (aPR: 1.62, P = .001), whereas, in 2021, we noted an increase in the probability of misuse in the cities of Lima (aRP: 1.72; P < .001) and Piura (aPR: 1.44; P < .001). CONCLUSIONS: The correct mask use decreased during the second wave, although no significant overall variations were observed in mask use in pedestrians between both periods. Also, we found regional differences in correct mask use in both periods.


Assuntos
COVID-19 , Pedestres , Humanos , COVID-19/epidemiologia , Pandemias , Estudos Transversais , Peru/epidemiologia
17.
Sud Med Ekspert ; 65(5): 30-33, 2022.
Artigo em Russo | MEDLINE | ID: mdl-36196837

RESUMO

The study objective is to analyze the results of forensic medical examination (FME) of fatal cases of falling off a bicycle (9 cases) and a similar non-fatal injury (18 cases). All cases were males aged 8 to 74 years. Of the 27 cases, 6 were children aged 8-17 years. Head and chest injuries were the most common. The nature and localization of these injuries, along with strip-like, linear and parallel abrasions on the skin of the anterolateral surface of the body, indicated that they were a result of collisions and slips of the victim's body on the road surface. A few cases of penetrating injury of the abdomen with internal organs damage were observed; they were related to the impact of the handlebars and were the most characteristic of this type of injury. The data presented may help distinguish the fall of bicycle riders from other types of accidents, in particular from collisions between moving vehicles and pedestrians. The nature of the identified injuries can be considered when providing medical care to the victims.


Assuntos
Ciclismo , Pedestres , Acidentes de Trânsito , Ciclismo/lesões , Criança , Feminino , Corpo Humano , Humanos , Masculino
18.
Comput Intell Neurosci ; 2022: 7700511, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-36275967

RESUMO

Current emergency management research mainly specifies the positions of evacuation guides from a knowledge base of experience, disregarding the subjective perceived decision-making of pedestrians caught in an emergency situation. Therefore, in this paper, a fuzzy inference system for pedestrians to select guides is designed from the perspective of pedestrians, and a crowd evacuation model with guides under limited vision is constructed. First, selecting the indoor evacuation of people with limited vision as the context, the number and optimal initial positions of guides are determined by a Gaussian fuzzy clustering algorithm. Next, a two-layer fuzzy inference system based on a multifactor pedestrian selection guide is established. Then, from the comprehensive perspective of managers and pedestrians, an improved social force evacuation model with guides is constructed. A comparison of the evacuation times and evacuation processes of known methods with different scene population distributions is analyzed through simulations. The results show that the guide setting scheme of the improved model is more conducive to reducing evacuation times and balancing exit utilizations. The model can provide a basis for emergency management decision-making departments to formulate more flexible guidance strategies.


Assuntos
Modelos Teóricos , Pedestres , Humanos , Análise por Conglomerados
19.
Sensors (Basel) ; 22(19)2022 Sep 30.
Artigo em Inglês | MEDLINE | ID: mdl-36236528

RESUMO

Pedestrian origin-destination (O-D) estimates that record traffic flows between origins and destinations, are essential for the management of pedestrian facilities including pedestrian flow simulation in the planning phase and crowd control in the operation phase. However, current O-D data collection techniques such as surveys, mobile sensing using GPS, Wi-Fi, and Bluetooth, and smart card data have the disadvantage that they are either time consuming and costly, or cannot provide complete O-D information for pedestrian facilities without entrances and exits or pedestrian flow inside the facilities. Due to the full coverage of CCTV cameras and the huge potential of image processing techniques, we address the challenges of pedestrian O-D estimation and propose an image-based O-D estimation framework. By identifying the same person in disjoint camera views, the O-D trajectory of each identity can be accurately generated. Then, state-of-the-art deep neural networks (DNNs) for person re-ID at different congestion levels were compared and improved. Finally, an O-D matrix based on trajectories was generated and the resident time was calculated, which provides recommendations for pedestrian facility improvement. The factors that affect the accuracy of the framework are discussed in this paper, which we believe could provide new insights and stimulate further research into the application of the Internet of cameras to intelligent transport infrastructure management.


Assuntos
Pedestres , Simulação por Computador , Aglomeração , Humanos , Processamento de Imagem Assistida por Computador , Redes Neurais de Computação
20.
Sensors (Basel) ; 22(19)2022 Oct 02.
Artigo em Inglês | MEDLINE | ID: mdl-36236575

RESUMO

Due to the potential economic benefits, pedestrian flow is considered an essential indication of public spaces. Pedestrian flow prediction is designed to assist operators in making decisions (such as shopping center owners). Operators hold certain events, such as sales promotions, to attract surrounding pedestrians; we refer to this type of event as a business event. Business events attract pedestrian flows, which means business opportunities for the merchants. Moreover, their placement will affect the distributions of the pedestrian flows. However, deciding which route is chosen for a specified event is difficult. To the best of our knowledge, we are the first to consider business events when predicting pedestrian flow. In this paper, we investigate two problems: one is pedestrian flow prediction with business events, and the other is route recommendation for business events. First, we propose an Attraction-Based Matrix Factorization model (ABMF) to efficiently predict the pedestrian flow with business events, which introduces the attraction index of different categories to pedestrians in matrix factorization. Second, we leverage the Skip-gram mode to learn the latent representations and improve the pair-wise ranking loss to a flow-aware-based method (SG-FWARP), which aims to learn events' latent representations for route recommendation. Compared with other state-of-the-art methods, the experimental results show ABMF can predict pedestrian flow matrix with a similarity of over 0.9 compared with the ground truth, and SG-FWARP can recommend routes for business events with high accuracy.


Assuntos
Pedestres , Acidentes de Trânsito , Tomada de Decisões , Humanos , Caminhada
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