The one meter rail station Implementing physical distanciation at Paris Nord and North Shenzen railway stations

Since the pandemy, the carrying capacity of major railway stations has been restricted to respect the distance. In Asian metropolises, a tech-driven flow management system reinforces the surveillance of travellers and help the maintenance of a top affluence in station's buildings. In France, an attempt is made to space out travelers with less intrusive processes, such as signage and boarding protocol adaptation. A comparison between these two responses leads us to question what physical distancing does, here and there, to the practices and places of transit and to the economic injonction to densify flows in and around stations until now. Articulating the approaches of crowding science and health regulation in transit environments, the article presents a transcontinental comparison, and then questions the status of major station as post pandemic urbanism showroom.

Monitoring of the Social Distance between Passengers in Real-time through Video Analytics and Deep Learning in Railway Stations for Developing the Highest Efficiency

Near the end of December 2019, the globe was hit with a major crisis, which is nothing but the coronavirus-based pandemic. The authorities at the train station should also keep in mind the need to limit the spread of the covid virus in the event of a global pandemic. When it comes to controlling the COVID-19 epidemic, public transportation facilities like train stations play a pivotal role because of the proximity of so many people who may be exposed to the virus. Using common place CCTV cameras and deep learning with simple online and real-time (DeepSORT) methods, this study develops social distance monitoring using a YOLOv4 identification of a Surveillance Object Model. Based on experiments conducted with a minicomputer equipped with an Intel 11th Gen Intel(R) Core(TM) i3-1115G4 at 3.00GHz, 2995 Mhz, two Core(s), four Logical processor, four gigabytes of random-access memory (RAM), this paper makes use of CCTV surveillance, which was put into practice at the Guindy railway station, Chennai, Tamilnadu in India in order to detect the violation of social distancing. © 2022 IEEE.

Multi-robot Sanitization of Railway Stations Based on Deep Q-Learning

Sanitizing railway stations is a relevant issue especially due to the recent evolution of the Covid-19 pandemic. In this work, we propose a multi-robot approach to sanitize railway stations based on a distributed Deep Q-Learning technique. The framework relies on anonymous information from existing WiFi networks to localize passengers inside the station and to develop a map of possible risky areas to be sanitized. Starting from this map, a swarm of cleaning robots, each one endowed with a robot-specific convolutional neural network, learns how to on-line cooperate inside the station in order to maximize the sanitized area depending on the presence of the passengers. © 2021 Copyright for this paper by its authors.

COVID-19: Automatic Social Distancing Rule Voilation Detection using PP-Yolo Tensorflow in OpenCV

In this critical situations where people are fighting with dangerous pandemic disease;it is required to maintain the situation by indulging with social distancing or it can also be pronounced as physical distancing. Social or physical distancing may reflects to reduce the virus from spreading. There are several places where it should be followed properly to stop spreading COVID-19 like railway stations, malls, marts, airports and many more. It is advised to maintain at least 6 feet of social distancing as per the WHO guidelines. Various researches have been done to automatically detect the physical distancing violations but an ideal system should be available to detect it effectively with high level of accuracy. Here the system is based on PP-Yolo (PaddlePaddle - You only look once) and Tensorflow library. Tensorflow is an object detection or pattern recognition tool through which pedestrian can be detected automatically and then PP-Yolo classifies the distance between the pedestrians or classifying whether persons are following the physical distancing rule or not. Violation detection is bit challenging for any system because a crowd may have uncertain structures that can hardly classified distance among them. This challenge can be accepted through various researchers but not met the desired precision. Proposed system is intended to detect the physical distancing rule violations effectively and acquiring high level of accuracy with minimal false alarm rate. © 2022 IEEE.

Travel demand forecast of a new high-speed railway station during COVID 19

Passenger flow at a new high-speed railway station presents significant uncertainty during COVID-19, which brings a huge challenge to the daily management and operation of the station. To detect the future development trend of demand and reduce the impact of its fluctuation on the daily operation of the station, three classical forecast methods are applied to predict the passenger flow in and out of the station during workdays in this paper. Furthermore, the performance of these methods is compared by conducting a case study of Huairou South Station. The results show that the ARIMA model (autoregressive integrated moving average model) shows better performance than the neural network model and Bass model (Bass diffusion model). Finally, a revised ARIMA model is introduced to predict the passenger flow of the National Day. © 2021 IEEE.

Emotional analysis of safeness and risk perception of London and Rome railway stations during the COVID-19 pandemic

The evaluation of perceived safeness and risk by persons is particularly valuable for safety and security handling. Each individual is influenced by others’ opinions on safety and security and the Internet embodies the place where these opinions are mostly pursued, acquired, and estimated. From this point of view, social networks play a considerable influential role. For this reason, Opinion Mining and Sentiment Analysis have found noteworthy uses in a variety of situations and one of the most interesting is embodied by public safety and security. The goal of this work is to examine the perception of safety and risk within the railway stations of London (UK) and Rome (Italy) during COVID-19 pandemic. In particular, the railway stations of London Victoria and Waterloo and the railway stations of Rome Termini and Tiburtina were considered, from March 23 to July 9, 2020, highlighting the emotional components in three distinct pandemic phases of the considered period in the two countries, by means of the semantic analysis of the textual contents present in Twitter. © 2021 IEEE.

Design of low cost voice operated vending machine using V3 module

In this project, a low-cost voice operated vending machine (VOVM) is designed using a V3 voice recognition module which is a touchless base vending system. The basic vending machine represents an automatic seller machine where the buyer puts the money in terms of coin or banknote into the vending machine. Then, the required product will be vending out from the machine after selecting the item which is store inside the system. After analysing several vending machines, it is found that the proposed VOVM is more advanced than the other existing vending machine as it can select the items without any contact with the system. This VOVM may also be useful to this current pandemic situation of COVID-19 because of its unique features. The main objective is to upgrade the touchbased vending machine to voice operated system as one can select any items in the vending machine using voice command. This system will make it easier for the user to interact with the machine for everyone and also it can be deployed in every corner of the workplace, hospital, airport, schools, railway station, etc. for selling different kinds of products efficiently. © 2021 IEEE.

Real-Time Face Mask Detection in Deep Learning using Convolution Neural Network

COVID-19 Pandemic has affected human being without distinguishing the region and religion around the world. Even though vaccine is available now, we should still be use precautionary steps to avoid being affected by this virus. Use of face mask will help to stop the spread of the virus. COVID - 19 face mask detector will help in detecting whether a person is wearing mask or not by the use of its on face mask net with deep learning techniques. With suggested technique of deep learning get an accuracy of 98 percent. Our suggested technique also works with still pictures and give effective results with live video stream captured using the CCTV cameras and able to identify whether person is using face mask or not. It is very useful at the place of the mass gathering and screening like shopping mall and railway station and shopping streets and colleges. By using this tool for the face mask will help in regulating the use of it and if person found not using it will be easily warned by the regulator. © 2021 IEEE.

Face Mask Detection System Using Deep Learning

In the present scenario due to Covid-19, there are no efficient face mask detection applications which are now in high demand for transportation means, densely populated areas, residential districts, large-scale manufacturers and other enterprises to ensure safety. This system can therefore be used in real-time applications which require face-mask detection for safety purposes due to the outbreak of Covid-19. This project can be integrated with embedded systems for application in airports, railway stations, offices, schools, and public places to ensure that public safety guidelines are followed.

Recognition of Human Face Regions under Adverse Conditions—Face Masks and Glasses—In Thermographic Sanitary Barriers through Learning Transfer from an Object Detector

The COVID-19 pandemic has detrimentally affected people’s lives and the economies of many countries, causing disruption in the health, education, transport, and other sectors. Several countries have implemented sanitary barriers at airports, bus and train stations, company gates, and other shared spaces to detect patients with viral symptoms in an effort to contain the spread of the disease. As fever is one of the most recurrent disease symptoms, the demand for devices that measure skin (body surface) temperature has increased. The thermal imaging camera, also known as a thermal imager, is one such device used to measure temperature. It employs a technology known as infrared thermography and is a noninvasive, fast, and objective tool. This study employed machine learning transfer using You Only Look Once (YOLO) to detect the hottest temperatures in the regions of interest (ROIs) of the human face in thermographic images, allowing the identification of a febrile state in humans. The algorithms detect areas of interest in the thermographic images, such as the eyes, forehead, and ears, before analyzing the temperatures in these regions. The developed software achieved excellent performance in detecting the established areas of interest, adequately indicating the maximum temperature within each region of interest, and correctly choosing the maximum temperature among them.