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         Geoplanning 
  Vol 5, No. 2, 2018, 259-268                                                                                                                                                          Journal of Geomatics and Planning 

                                                                                                 E-ISSN: 2355-6544 
http://ejournal.undip.ac.id/index.php/geoplanning 

doi :10.14710/geoplanning.5.2. 259-268 

TIME TRAVEL ESTIMATIONS USING MAC ADDRESSES OF BUS, 
PASSENGERS: A POINT TO PATH-QGIS ANALYSIS 

A. Hidayata,b  , S. Terabea , H. Yaginumaa 

a Urban and Transportation Planning Laboratory, Department of Civil Engineering  
b Department of Civil Engineering, Universitas Teknologi Sulawesi, Jalan Talasalapang No.51 Makassar 90221, Indonesia 

 

Abstract: Currently, the development of Wi-Fi is proliferating. Especially in the field of 

transportation and smart cities. At the same time, Wi-Fi 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 Wi-Fi scanner to capture media access control (MAC) address data of bus passengers 
Wi-Fi devices and then identifies each MAC address travel time to confirm the bus passengers. 
The MAC address is a unique ID for aech device used suchh as moble phones, smartphones, 
laptops, tablets, and other Wi-Fi-enabled equipment. The Wi-Fi scanner was placed inside the 
bus to capture all tthe 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 
point. The procedure determined how many passengers traveled and explained which bus 
passengers used based on travel time.  

 

 Copyright © 2018 GJGP-UNDIP  

This open access article is distributed under a  

Creative Commons Attribution (CC-BY-NC-SA) 4.0 International license. 

How to cite (APA 6th Style):  Hidayat, A, Terabe ,S., & Yaginuma, H. (2018). Time Travel Estimations Using MAC Addresses of Bus, Passengers: A 

Point to Path-QGIS Analysis. Geoplanning: Journal of Geomatics and Planning, 5(2), 259-268. doi:10.14710/geoplanning.5.2.259-268. 

1. INTRODUCTION 
 Technology development is growing every day. This development also applies to information technology 
systems such as Wi-Fi. Wi-Fi is a network connection system that is currently used almost universally. The 
Wi-Fi connection is built into devices such as smartphones, laptops, tablets, and other devices that receive 
Internet signals or data (Hidayat et al., 2017a). The development of Wi-Fi and information technology 
systems has penetrated the transportation engineering sector. The use of Wi-Fi in transportation is 
currently advancing, especially in terms of the development of travel data related to origin-destination 
(OD), speed estimation, travel time, and the estimation of passengers. This advancement pertains to 
transportation sectors such as a pedestrian, motor vehicle, and others (Abedi, 2014; Al-Husainy & Fadhil, 
2013; Xia et al., 2014). Wi-Fi thrives because of its low cost, accessibility, energy efficiency, and mobile 
scanner capacity (non-static scanner). Furthermore, almost everyone uses Wi-Fi daily due to the easy data 
retrieval process. 
 Wi-Fi technology is based on IEEE 802.11 standards (including 802.11a, 802.11b, 802.11g, and 802.11n) 
(Cisco, 2008; Najafi et al., 2014). It is a popular method to provide Internet access for wireless users (Xu et 
al., 2013). The more common Wi-Fi mode of operation is 802.11, called the infrastructure mode, where 
stations communicate with other wireless stations and wired networks (typically Ethernet) through an 
access point. The access point bridges traffic between wireless stations through the lookup of the 
destination address in the 802.11 frame (Sridhar, 2008). The infrastructure mode supports smartphones, 
tablets, routers, and laptop, among others. A smartphone can be identified by its unique ID such as its 
international mobile equipment identity (IMEI) number or the media access control (MAC) address. The 
IMEI is received when the mobile device is registered on a network, whereas the MAC address is on every 
data packet sent by the Wi-Fi-enabled mobile handset. MAC addresses are designed to be persistent and 

OPEN ACCESS 

Article Info: 
Received: 26 July 2018 
in revised form: 29 August 2018 
Accepted: 20 Sept 2018 
Available Online: 25 Oct 2018  
 

Keywords:  
WiFi scanner, Point to Path, GIS, 
Travel Time, Procedure, MAC 
address 
 

Corresponding Author: 
Arief Hidayat 
Urban and Transportation 
Planning Laboratory-Department 
of Civil Engineering-Tokyo 
University of Science 
Email: 

ariefhidayat06@hotmail.com   
  

http://doi.org/10.14710/geoplanning.5.2.259-268
https://orcid.org/0000-0001-8845-6747
mailto:ariefhidayat06@hotmail.com


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globally unique (Martin et al., 2017). A MAC address is a 48-bit number used to identify a network interface 
(Cunche, 2014). The Wi-Fi connection for smartphones is designed to periodically transmit a probe-request-
frame to determine a known access point (Matte, 2017; Yaik et al., 2016). Probe requests are the active 
scans by the mobile device (Sun et al., 2017; Verbree et al., 2013). The probe request content includes the 
sender's MAC address (Musa & Eriksson, 2012). The Wi-Fi scanner probe request can load all MAC address 
data into a single log file. This system accesses the MAC address without connecting to the Internet and is a 
passive scanning activity that collects data. The Wi-Fi scanner as a probe request mode was developed to 
collect MAC addresses included in the infrastructure mode. This study uses a Wi-Fi scanner on a bus to 
collect MAC address data from bus passengers and non-passengers.   

Wi-Fi systems capture MAC address data from Wi-Fi device users (Abedi et al., 2015; Dunlap et al., 2016; 
Jackson et al., 2014). The MAC address is the same on each device that is Wi-Fi-enabled. MAC addresses are 
unique numbers and letters for each device and no device has two MAC address. In addition, one MAC 
address cannot be assigned to two devices (Asija, 2016; Hidayat et al., 2017a; Hidayat et al., 2018b; 
Sapiezynski et al., 2015; Shiravi, et al., 2016). In intersection estimation research today, there is a 
relationship between Wi-Fi data and travel time. Such intersection estimation research seeks to confirm the 
accuracy of the Bluetooth and Wi-Fi data on urban roads against reliable travel time results (Shiravi et al., 
2016). 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. The reliability of travel time using 
Bluetooth has been investigated to identify the Bluetooth ability to detect MAC addresses (Araghi, et al., 
2015). In such studies, data processing was conducted by dividing the detection zone and detection time. 
Another empirical evaluation of Wi-Fi was conducted on road transport. The method used was to detect 
“exit to exit” with a procedure filtering the data with time as the main variable (Abbott-jard et al., 2013). 
“Exit to exit” is intended to be the “beginning” and the “end” of each MAC address identification time.  
Other travel research using static equipment has been conducted for bicycle users with time and speed 
filtering data processing to confirm the penetration rate of Wi-Fi data (Böhm et al., 2016; Ryeng et al., 
2016). Research on the time travel estimation process is important in confirming the accuracy of the Wi-Fi 
data (Hidayat et al., 2018b).  

GIS software explains spatial data distribution, in the case of transportation, this is through the use of 
open-source QGIS software. There are several studies related to the use of GIS for transportation and travel 
time. The distribution of Wi-Fi spatialized data can be identified if the data have specific XY coordinates 
captured through the Wi-Fi scanner and GPS (Feng & Liu, 2012; Odiyo, 2014). The GIS is also more efficient 
for wireless data deployment in the development of trade and services or urban planning (Aldasouqi & 
Salameh, 2014). Furthermore, research on travel diary data has been conducted based on travel time using 
GIS. In such research, the analyses use “starting” and “ending” person-trip data in combination with 
spatiotemporal data (Yu & Shaw, 2004). An analysis of “day-to-day” variations in travel time using GPS 
connected to a notebook PC can capture vehicle movement over multiple days. One study describes the 
tracking of vehicle movements using a GPS device based on travel time and travel speed (Ohmori et al., 
2002). Path GIS research uses spatial trajectory analysis on all of the GIS data points obtained for vehicle 
movements (Zambrano et al., 2016). More specifically, examining the logical path of tourist movements 
using GPS data from tourism spots, the travel time data for each tourist (Meng-Lung Lin et al., 2009) or the 
fastest and shortest travel times can be classified using GIS modelling (Abousaeidi et al., 2016; Ilayaraja, 
2013).  

This research uses a Wi-Fi scanner to retrieve data from Wi-Fi users. For the study, the Wi-Fi scanner was 
placed on a bus to capture the MAC addresses of the bus users and non-bus users. The difference between 
this study and previous research is that this study uses non-static Wi-Fi placed on a moving vehicle. The 
data results are analyzed using a GIS procedure. An important variable in this research is “time,” which 
measures how long each MAC address is traveling. Travel time is essential for the identification and 
confirmation of the bus versus non-bus passengers. This study conducts a “point to path” procedure to 

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analyze the origin of destination or “beginning” and “end” of MAC address identification based on the 
travel time variable. The aim of a “point to path” analysis with a travel time variable is to estimate bus 
passenger usage. 
 

2. DATA AND METHODS 
2.1. Location  
 A survey was conducted in Obuse in the Nagano Prefecture in Japan (Figure 1). The Obuse bus is a tour 
bus that delivers passengers to a tourism spot in the town of Obuse. Obuse was selected for the Wi-Fi field 
test because Obuse is a tourism area with a hop-on-off bus system. This system is usually characterized as a 
per day, time pay system. 
 This field test was done by using a Wi-Fi scanner placed on the Obuse Bus from 09:50 to 17:10 (Hidayat 
et al., 2017a; Hidayat et al., 2017b; Hidayat at al., 2018a; Hidayat et al., 2018b; Terabe et al., 2017). The 
Obuse bus makes nine stops and the distance between each bus stop is about 500 meters or three minutes. 
The Obuse bus also makes seven loops, each called circulation time (CT), from bus stop one to bus stop nine 
and then starts back at one. Thus, the nine bus stops can categorize Obuse as a bus stop tourism spot. 
 

 
Figure 1. Obuse orientation map and bus route map 

 
2.2. Wi-Fi Scanner Equipment and Installation 
 As stated, the Wi-Fi scanner equipment captures MAC addresses of devices such as smartphones, 
laptops, tablets, computers, and other Wi-Fi-enabled devices. The MAC address only shows a unique 
identification for each device and it does not display personal data.  
 The Wi-Fi scanner equipment includes an antenna, GPS, and a mobile battery (Figure 2). This Wi-Fi 
scanner has an approximate range of about 200-300 meters and it detects bus passenger Wi-Fi-enabled 
devices, buildings, vehicles, and pedestrians (Hidayat et al., 2017a, Hidayat et al., 2017b, Hidayat et al., 
2018b). The scanner uses a mini Raspberry Pi computer. This is a quad-core processor-powered single board 
computer running at 900MHz and the system has 1 GB RAM capacity. It also has a USB port, a pole stereo 
output, a video port, and an HDMI port, plus a micro SD port for loading the operating system and storing 
data. The scanner includes GPS Tracking BU-353 with high frequency. In terms of electricity, it is powered 
by a mobile battery 30,000 MAH, so it can be active up to 12 hours.  
 The Wi-Fi scanner was placed inside the bus near the driver. After the survey ended, the scanner was 
turned off and the MAC address recorded data was downloaded for further analysis. The data became raw 
data that would be confirmed for each travel time. 
  

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Figure 2. Obuse bus, Wi-Fi scanner equipment, and Wi-Fi scanner approximate range 

 

3. RESULTS AND DISCUSSION 
 This section describes the processing of the raw data into travel time data. This procedure is the cleaning 
of the Wi-Fi data. The Wi-Fi log data are in the form of time and MAC addresses, while the GPS log data are 
in the form of time, latitude, and longitude. There are seven processing steps for the Wi-Fi data, which start 
with the raw data and include combining the GPS and Wi-Fi data, converting the coordinates, inserting 
“point to path” QGIS analysis, analyzing travel time, confirming bus passengers based on circulation time, 
and validating or comparing driver data and Wi-Fi confirmed data. Figure 3 presents a chart of the Wi-Fi 
cleaning process converting the data into travel times. This procedure is performed using Python and QGIS 
open-source software. The Python software makes it easier to analyze the amount of Wi-Fi data. All 
analyses use open-source Anaconda Python 3.0 and QGIS 3.0 Girona applications. 
 

 
 

Figure 3. Flowchart of data processing 
 
3.1. The First Step Combines Data 
 The Wi-Fi scanner provides the Wi-Fi log data and the GPS provides the GPS log data. These data are 
still distinct, so they need to be merged so that each MAC address has a position or coordinate. The position 
and coordinate easily track the MAC address. To merge these data, the Pandas Python package and the 
concept command are used, with the MAC address as the merging "key." The screenshot in Figure 4 shows 
the structure of combining the Wi-Fi and GPS log data.  
 

 
Figure 4. Screenshot Python - combining data 

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3.2. The Second Step Converts the Coordinate System 
The data that have been merged need to be converted to UTM from decimal degrees. This change is 

done so that it is easier to calculate the distance the MAC addresses travel in the next analysis. The 
coordinate data transformation uses the Python’s Geo-Pandas module. The Figure 5 screenshot shows the 
structure of the data once they are converted. These data should appear in the QGIS interface. The data are 
entered as CSV-shaped so that the "add" data is a “delimited” file and the set of X and Y coordinates are the 
spatial positions. The UTM reference used is UTM WGS 84 Zone 54N. The MAC address data appear on the 
QGIS interface as data points (Figure 6). 
 

 
Figure 5. Screenshot python – UTM conversion 

 

 
Figure 6. Wi-Fi data points 

 
3.3. The Third Step is “Point to Path (PtP)” Analysis 
 Point to path (PtP) is one tool in QGIS that "connects the dots" based on a common attribute and a 
sequence field. The attribute field determines which points should be grouped together into a line (QGIS, 
2011; Sherman, 2011). The sequence field determines the order in which the points will be connected. 
Before the analysis, PtP plugins must be installed first on the menu QGIS managed plugin. This tool has 
three variables: “group,” “begin,” and “end” (Sherman, 2011).  

• Group - the name of the ID/MAC address taken from the field, we chose as the group field 

• Begin - the time value of the first point order field used to create the path 

• End - the time value of the last point order field used to create the path 
 In PtP analysis, two crucial factors must be considered when entering “group” and “time.” “Group” 
represents specific data based on the merged data and “time” is the time input. The “group” data capture 
the MAC addresses, while the “time” data capture the time-shaped column: “hour,” “minute,” and 
“second.” The results show the OD line of the movement of each MAC address. New attributes of the 
output data include the “begin” (time journey begins) and “end” times (time end of trip). “Begin” and “end” 
for each MAC address are used to analyze the travel time of each MAC address (Figure 7 and 8). 
 

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Figure 7. Screenshot Python – PtP analysis 

 

 
Figure 8. PtP Results Analysis 

 

3.4. The Fourth Steo is Time Travel Analysis 
 Time data were analyzed in Python with the equation “travel time = end – begin,” and show the 
number of seconds and minutes for each MAC address. Furthermore, the MAC addresses are divided by 
using the time classification as shown in Table 1. Once divided by time classification, the numbers of MAC 
addresses are shown that can be confirmed as passengers. This study uses three minutes because the 
distance between each bus stop is about three minutes and the distance for one loop (from bus stop one to 
nine and back again) is about 40 minutes (Figure 9a).  

Tabel 1. Time Classification 

No Travel Time (minutes) Classification 

1 >40 Non-Passenger 

2 < 3 Non-Passenger 

3 3-40 Passenger 

 

3.5. The Fifth Step Confirms the Passengers 
 This stage confirms the passengers who use the bus that travels seven CTs from 9:50 to 17:10. In this 
process, the MAC address travel times are shown as well as the bus circulation times. The MAC addresses 
outside the CT can be removed. MAC addresses can be defined as passengers who are in the zone of CT. 
The bus circulation times are taken from the Obuse bus timetable or the bus stops (Table 2) (Figure 9b). 

Tabel 2. Bus circulation time 

Bus Circulation Time CN Begin CN End 

CT 1 9:50 10:40 

CT 2 10:50 11:40 

CT 3 11:50 12:40 

CT 4 13:20 14:10 

CT 5 14:20 15:10 

CT 6 15:20 16:10 

CT 7 16:20 17:10 

 
 

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3.6. Discussion 
 The raw travel time result shows the amount of travel time in the range of 0-400 minutes. This result 
still has to be reclassified based on the bus circulation time. There are 2,000 MAC addresses selected from 
about 75,000 raw data points for the CT analysis. The Figure 9 histogram shows the frequency of the 
number of MAC addresses with their travel times (Sridhar, 2008). After confirmation, based on the bus CT, 
the MAC addresses that are estimated as passengers equate to almost 120 MAC addresses with their 
frequency distribution as in Figure 9. From the histogram, we can see that more than 60% of the travel time 
is from 3-15 minutes, with the rest in the range of 15-40 minutes. A ground count and analysis test the data 
validity. Furthermore, the ground “truth” results are taken from driver data. The PtP result compared with 
driver data show that the difference trend is not too significant between the PtP data and ground data. 
Between 10:50 am and 11:40 am and 11:50 am and 12:40 pm passenger data tends to be high. This is 
because the morning before noon is a better time to travel. At other times, the circulation tends to 
decrease such as in early morning and late afternoon. This analysis is in line with some previous results that  
provides an illustration of how the PtP procedure can be used to calculate the number of passengers on a 
bus using Wi-Fi data (see Abousaeidi et al., 2016; Ilayaraja, 2013). 
 

 

 
Figure 9. Travel time histogram of differences between before and after the process 

 

 
Figure 10. Comparison between driver data with a PtP procedure 

 

a) 1st Classification 

Raw Data – Justify Travel Time 

CT: Circulation Time 

DD: Driver Data/Ground Truth 

WJ: WiFi Justification 

DDW: Difference between DD and WJ 

b) 2nd Classification 

Justify Circulation Time 

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 In Figure 10, the comparison between the driver field data and the Wi-Fi procedure results shows that 
CT1, CT2, CT5, CT5, and CT7 have insignificant value differences compared with CT3 and CT4. During CT3 
and CT4, there is a significant number of visitors to Obuse using the bus or detected around the bus 
(Hidayat et al., 2017a, Hidayat et al., 2017b, Hidayat et al., 2018b). These time frames (circulation times) 
capture the highest number of passengers, which tends to be very significant from morning until noon. 
Passengers begin to decrease from noon into the afternoon. The difference in the number of passengers 
between the driver and Wi-Fi data is in the 5-10 passenger range.  
 

4. CONCLUSION 

The results imply that PtP analysis is advantageous (easy, instantaneous, intuitive) when used to process 
Wi-Fi scanner data, in particular, and Big Data in general. Travel time data can be categorized into two 
types: namely, passenger and non-passenger data. The analysis can be used as part of the development of 
smart city-based transportation and Big Data projects. Based on the comparison between driver data and 
Wi-Fi confirmed data, there was not a significant difference in the number value between these. The data 
cleaning process still needs to be developed with various additional analyses to get the confirmed Wi-Fi 
data closer to the field data. Further research can be fine-tuned to cover the non-passenger parts such as 
pedestrians, vehicles, and buildings and the making of an OD matrix for passenger data. The origin of the 
movement is still a straight line, which should be based on the route, so the distance calculations still 
include errors. 
 

5. ACKNOWLEDGMENTS 

 The authors would like to thank all those who have contributed to this paper. Thank you to members of 
the Transportation Planning Laboratory, the Department of Civil Engineering, and the Tokyo University of 
Science, which provided surveys, data, and extensive assistance in support of this paper. Thank you to my 
workplace Universities Technology Sulawesi-Indonesia, my scholarship from the Ministry of Research, 
Technology and Higher Education and the Indonesia Endowment Fund for Education (LPDP) Ministry of 
Finance, The Republic of Indonesia. Thank you also to all the reviewers who provided corrections for this 
paper. 

 

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