Recently, a variety of technologies, such as the development of Bluetooth, WiFi, and smart cards, have been used to investigate the movements of passengers or vehicles. This study describes the use of a WiFi scanner that was installed on a bus and circulated around the bus’s route 14 times. The method presented here involves the use of WiFi and GPS to analyze passengers’ movements while the bus is running and while stopped at a bus stop. Nine steps were used to derive travel data from the raw data to estimate the number of movements of the passengers. The results of this study describe the travel data collected between bus stops no. 1 and no. 9 and compare the observer data with the WiFi data.

Recently, Wifi is one of the most useful technologies that can be used for detecting and counting MAC Address. 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 the pedestrian smartphone, bus passenger or WiFi devices near from the bus as long as the bus going around the route. There are seven processes to make map WiFi data.

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Understanding the actual condition of tourist' travel behavior is important for mitigating tourist congestion and marketing purposes. Typical travel survey methods have practical difficulties (e.g. necessity of staying at a certain period of investigation) for fully uncovering tourists' travel behavior. In this study, we extracted unique identifiers of Wi-Fi packets which presumably be from tourists in Okinawa Main Island by focusing on the entrance information about the entry into the islands (e.g. cruise ship terminal users, domestic air terminal users, international air terminal users and, low-cost career terminal users). After appropriately conducting the data cleaning, we analyzed tourists' travel behavior mainly in terms of travel flow patterns across different entry points into the island. Through the data analysis, it became clear that tourist' travel behavior is significantly different by means of transportation into Okinawa Main Island. Furthermore, we have confirmed the possibility of fusing Wi-Fi based tourist excursion data and probe-based vehicle trajectory data that can reveal the whole travel patterns of each tourist, though its matching rate is not enough large at present.

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Currently, the development of WiFi is proliferating, especially in the field of transportation and smart cities. 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. 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. The MAC address is a unique ID for each device used such as mobile phones, smartphones, laptops, tablets, and other WiFi-enabled equipment. The WiFi scanner was placed inside the bus to capture all the MAC addresses inside and around the bus. The survey was conducted for one day (eight hours). 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. This procedure, using point to path-GIS, produced 70,000-80,000 raw data points cleaned into 100-130 new data points. The procedure determined how many passengers traveled and explained which bus passengers used based on travel time.