Exploring pedestrian Bluetooth and WiFi detection at public transportation terminals

Shlayan, Neveen; Kurkcu, Abdullah; Özbay, Kaan

doi:10.1109/itsc.2016.7795559

Cited by 32 publications

(17 citation statements)

References 7 publications

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“…This was not possible as the arrival time of bus passengers at the stop cannot be determined due to the check-in hardware being located inside the bus. However, recent research efforts have enabled the use of new technology such as capturing waiting passengers using Bluetooth or Wi-Fi detection from smartphones (Shlayan et al, 2016).…”

Section: Discussion and Study Limitationsmentioning

confidence: 99%

Passenger arrival and waiting time distributions dependent on train service frequency and station characteristics: A smart card data analysis

Ingvardson

Nielsen

Raveau

et al. 2018

Transportation Research Part C: Emerging Technologies

107

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Section: Discussion and Study Limitationsmentioning

confidence: 99%

Passenger arrival and waiting time distributions dependent on train service frequency and station characteristics: A smart card data analysis

Ingvardson

Nielsen

Raveau

et al. 2018

Transportation Research Part C: Emerging Technologies

107

View full text Add to dashboard Cite

“…Crowd data collection and monitoring (Abedi, Bhaskar, & Chung, 2013), and combining data from both sensor types (WiFi and Bluetooth), results in useful insights into pedestrian dynamics (Heuvel, Ton, & Hermansen, 2016). Previous research has also used WiFi and Bluetooth data of pedestrians inside terminals (Shlayan, Kurkcu, & Ozbay, 2016). This research attempts to detect moving pedestrians and their behavioral patterns within the terminal and to create an origin-destination motion matrix.…”

Section: Wifi and Non-passenger Data Experimentsmentioning

confidence: 99%

“…Pedestrian data can be estimated with the system to detect unknown MAC addresses of devices at short distances at fixed locations (Jackson et al, 2014) and the performance of the BT-WiFi method evaluated to identify these unknown MAC addresses (Lesani & Moreno, 2016). Lastly, the research uses WiFi devices paired permanently in strategic locations (Lesani et al, 2016), and the use of software installed on smartphones; it should be noted that most pedestrians do not make use of this software (Shlayan et al, 2016). This research will describe and explain WiFi scanner data in Obuse, a tourist spot in Japan.…”

Section: Wifi and Non-passenger Data Experimentsmentioning

confidence: 99%

Determine Non-Passenger Data from WiFi Scanner Data (MAC Address), A Case Study: Romango Bus, Obuse, Nagano Prefecture, Japan

Hidayat

Terabe

Yaginuma

2018

IRSPSD International

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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's 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.

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“…The research that detects human movement uses high Wi-Fi frequencies, connected with GPS so that the position of the MAC addresses or access points can be identified (Sapiezynski et al, 2015). The use of Wi-Fi and Bluetooth in public terminal transportation has also been applied in a high and wide frequency to capture MAC addresses so the travel behavior of pedestrian patterns can be identified and understood in terms of seconds and minutes (Shlayan et al, 2016). This public terminal transportation research considers high-frequency Wi-Fi detected data compared with Bluetooth data.…”

Section: Introductionmentioning

confidence: 99%

Time Travel Estimations Using Mac Addresses of Bus Passengers: A Point to Path-Qgis Analysis

Hidayat

Terabe

Yaginuma

2018

Geoplanning: Journal of Geomatics and Planning

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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.

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Exploring pedestrian Bluetooth and WiFi detection at public transportation terminals

Cited by 32 publications

References 7 publications

Passenger arrival and waiting time distributions dependent on train service frequency and station characteristics: A smart card data analysis

Passenger arrival and waiting time distributions dependent on train service frequency and station characteristics: A smart card data analysis

Determine Non-Passenger Data from WiFi Scanner Data (MAC Address), A Case Study: Romango Bus, Obuse, Nagano Prefecture, Japan

Time Travel Estimations Using Mac Addresses of Bus Passengers: A Point to Path-Qgis Analysis

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