WIFI SCANNER FOR OBTAINING PEDESTRIAN DATA

Recently, many technologies to estimate pedestrian data to know about pedestrian travel behavior. Wifi is one of the most useful technologies that can be used in counting pedestrian data. This paper described using of WiFi scanner which carried out seven times circulated the bus. The method used WiFi and GPS are to counting MAC address as raw data from pedestrian smartphone or WiFi devices nearfrom the bus as long as the bus going around the route, generate and processing to be pedestrian data. There are five processes to make pedestrian data from raw data. The purpose of this study is to calculate, obtain and estimate the number of pedestrian data divide circulation number and road segmentation.

(Section A: Planning Strategies and Design Concepts)

WiFi is one of the most useful technologies that can be used for detecting and counting MAC addresses. Many previous studies have interpreted MAC address data into other forms for use in infrastructure development and urban transport. This study uses onboard WiFi scanners, circulated on the "Romango Bus", a hop-on-hop-off bus that has nine bus stops with roaming time from 09.50 to 17.50. The method uses WiFi and GPS MAC addresses as raw data from WiFi devices, collected during the time the bus goes around the route. WiFi scanner devices are placed on two different buses for comprehensive monitoring of the route\u27s operating hours. Raw data obtained in the form of WiFi data and GPS data is combined and processed through five steps to produce non-passenger data. The results are displayed on a map that contains MAC address data, and that specifies non-passenger data categorized into pedestrians, vehicles, and buildings. Obuse is a tourist area that has many tourist attractions, and the results of WiFi at stopover locations shows a high number of pedestrians, especially at Obuse Park and Obuse Station

Counting and locating people in outdoor environments: a comparative experimental study using WiFi-based passive methods

WiFi-based passive methods are becoming a common tool to count, estimate, and/or locate people. One area of applicability is the development of intelligent control system for traffic management in urban areas, so that these systems are able to take into account not only vehicles’ behaviors but also pedestrians’, as important actors in the road scenario. In this work, we present the performance evaluation in terms of accuracy of a WiFi-based passive method used to identify pedestrians, classify them as moving pedestrians or static pedestrians, and for the latter, to locate them in a traffic intersection. The proposed algorithm is implemented in a low-cost development board and tested through several experiments in a real outdoor scenario. Our proposal is compared with several classic Machine Learning (ML) algorithms, specifically with Binary Logistic Regression, Support Vector Classification, Gaussian Naive Bayes, Random Forest, and k-Nearest Neighbors. Results show that despite the simplicity of our method, the outcomes are similar or better than most of the ML techniques, without the expected complexity or computational requirements that the latter required

Aparatos, sistema y método para el conteo de personas en un entorno urbano

Número de publicación: 2 750 688 Número de solicitud: 201930810Sistema y procedimiento para el conteo de personas en un entorno urbano, donde el sistema comprende un subsistema de adquisición de datos con una pluralidad de aparatos capturadores distribuidos en dicho entorno urbano y capturan una señal WiFi emitida por dispositivos móviles que se ubican en dicho entorno; un subsistema de procesado de los datos y generación de información de conteo, categorización y localización con al menos un aparato capturador-procesador, que recibe datos de los aparatos capturadores; y un subsistema de toma de decisiones en base a la información obtenida con un aparato capturador-procesador o un aparato capturador-procesador-actuador, que envía los datos de forma inalámbrica a un servidor remoto; y donde a partir del uso de este sistema se consigue desarrollar un procedimiento con el que se puede conocer en tiempo real el estado de un entorno exterior y aplicar dicho conocimiento para la optimización de diferentes servicios.Universidad Politécnica de Cartagen