![]() Manufacturing enterprises are the most vulnerable group of enterprises to this kind of change, especially those where human resources are particularly important. The criteria for control and management of a remote work process by law and the European Parliament have not been strictly and clearly regulated therefore, this research is particularly relevant. The issue of support and regulation of labor relations during remote work is especially acute. The support of enterprise personnel during the active implementation of remote work is very important. The procedure determined how many passengers traveled and explained which bus passengers used based on travel time. This procedure, using point to path-GIS, produced 70,000-80,000 raw data points cleaned into 100-130 new data points. The paper describes the procedure of the time travel estimation for each MAC address using the “point to path” analysis in QGIS open source software. The survey was conducted for one day (eight hours). The WiFi scanner was placed inside the bus to capture all the MAC addresses inside and around the bus. The MAC address is a unique ID for each device used such as mobile phones, smartphones, laptops, tablets, and other WiFi-enabled equipment. This paper describes our study, which first uses a WiFi scanner to capture media access control (MAC) address data of bus passengers’ WiFi devices and then identifies each MAC address travel time to confirm the bus passengers. At the same time, WiFi is a low-cost technology, which offers a longer survey time and is able to support the Big Data era. A comparison between passenger volumes obtained from the Wi-Fi data processing procedure and the data obtained using the ground truth procedure indicates the number of passengers determined using the Wi-Fi data acquisition and processing procedure is less than the number of passengers determined using the ground truth procedure.Ĭurrently, the development of WiFi is proliferating, especially in the field of transportation and smart cities. ![]() The approach developed in the proposed study is capable of producing outputs, such as an origin-destination (OD) matrix and passenger volume for a bus route section. This study also describes a new data cleaning procedure that is used to characterize bus passenger volume and travel trends using a combination of MAC address and GPS data. This Wi-Fi scanner is capable of engaging a probe request mode to capture MAC addresses from mobile devices or other Wi-Fi-enabled modalities without connecting to the internet. This study aims to obtain media access control (MAC) addresses of individual bus passengers by using a Wi-Fi scanner. Transportation data retrieval using information technology, such as Bluetooth, Wi-Fi, and smartcards, is prominent. Therefore, the cleaning procedure proposed in this study can effectively clean raw Wi-Fi data to extract passenger volume data.Ĭurrently, transport survey methods are very diverse. The correlation between Wi-Fi estimation and ground truth is 0.78, and the trend line in both methods is similar. A comparison of the passenger volume results obtained from Wi-Fi data and ground truth data indicates that the number of passengers determined from the former is less than that from the latter. The approach proposed in this study can yield the passenger volume outputs for various bus route sections. The Wi-Fi scanner, used as a tool to capture passenger device data, can engage in a probe request mode to capture the MAC addresses of mobile devices or other Wi-Fi-enabled modalities without connecting to the internet. We used a Wi-Fi scanner to detect the MAC addresses of individual bus passengers. The study proposes a new data cleaning procedure to characterize bus passenger volume using a combination of media access control (MAC) address and global positioning system (GPS) data. The main objective of this study is to estimate bus passenger volume based on a Wi-Fi scanner transportation survey.
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