USING BIG DATA OF AUTOMATED FARE COLLECTION …engineering.nyu.edu/citysmart/trbpaper/15-5262.pdf1 USING BIG DATA OF AUTOMATED FARE COLLECTION SYSTEM FOR ANALYSIS AND IMPROVEMENT OF

USING BIG DATA OF AUTOMATED FARE COLLECTION SYSTEM FOR ANALYSIS 1

AND IMPROVEMENT OF BRT- BUS RAPID TRANSIT LINE IN ISTANBUL 2

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Ilgin Gokasar, Assistant Professor 5

Bogazici University 6

34342 Bebek/Istanbul Turkiye 7

Tel: +90(212) 359-7278 Fax: +90(212) 287-2457; Email: [email protected] 8

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Kevser Simsek, Corresponding Author 10

Istanbul Metropolitan Municipality Belbim Inc., Bogazici University 11

Hava Limani Karsisi Istanbul Dunya Ticaret Merkezi A3 Blok Kat:2-3 34149 12

Yesilkoy/ISTANBUL 13

Tel: +90(212) 468-0000 Fax: +90(212) 465-5193; Email: [email protected] 14

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Kaan Ozbay, Professor 16

New York University 17

1 MetroTech Center, 19th Floor, Brooklyn, NY 11201 18

Tel: +1(646) 997-0552; Email: [email protected] 19

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Word count: 4,452 words text + 12 tables/figures x 250 words (each) = 7,452 words 23

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Submission Date: 2014-07-3130

TRB 2015 Annual Meeting Paper revised from original submittal.

Gokasar, Simsek, Ozbay 2

ABSTRACT 1

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Istanbul´s smart card fare collection system generates large amounts of operational data from the 3

BRT-Bus Rapid Transit line. In addition to ridership, it captures system-wide transaction data and 4

provides comprehensive data records on usage. Processing and analysis of these data open new 5

opportunities in transportation and travel behavior research. This paper presents a qualitative 6

analysis of smart card (Istanbulkart) activity for the BRT-Bus Rapid Transit and investigates its 7

potential for understanding complexities of the system and characterizing travel behavior. In this 8

paper, an assessment of spatial and temporal travel behavior of commuters including mode 9

choice, travel, and waiting times, is performed. As a result of this qualitative analysis an 10

evaluation of the automated fare collection system with some recommendations for improving 11

the planning and management of the BRT-Bus Rapid Transit line are also provided. 12

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Keywords: Smart card (Istanbulkart); Automated fare collection; Transportation planning; BRT-17

Bus Rapid Transit 18

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I INTRODUCTION 1

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Automated Fare Collection (AFC) - Smart card (Istanbulkart) Technology 3

Since their invention in the 1969 with the improvement of smart card technology, automated fare 4

collection systems have become the most common collection method used by public transit 5

authorities (1). 6

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Istanbul first used the Akbil (ancient anonymous ticket ) for fare collection in 1994, and then, 8

with the improvement of contactless transport systems, which respond to the requirements of 9

operators and end-users alike, the smart card, appropriately named the Istanbulkart, was put into 10

service in Istanbul’s public transportation system in 2004. The operators of Istanbulkart 11

technology reduced the system’s fraud and maintenance costs by replacing the inefficient paper-12

based fare collection system, and the data collection and reporting capacity has been improved. 13

Today, the Istanbulkart can be used for payment for all modes of public transport. 14

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Istanbulkart is similar in look and size to a credit card, and each has a unique serial number. A 16

card can be assigned to a specific individual or it can be anonymous, and each unique card ID 17

represents one single person. This enables analysis of individual itineraries and opens new ways 18

for understanding people’s travel behavior on short as well as long term scales. 19

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In Istanbul, the fare charge for each customer is based on travel distance, transport mode, and 21

certain demographic attributes, such as prioritized rates for elderly people, children, students, 22

senior citizens, government employees, etc. Currently, for example, 37 different media types and 23

69 different fare types are provided by the Istanbulkart (Table 1). 24

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Fares are collected by an automated reader (Validator) next to the driver or at a turnstile before 26

one boards a transport vehicle. The Validator is a smart device that does validity checks, collects 27

fares in accordance with specified tariffs, and records the result of all transactions. Data collected 28

from the stations are transferred from the stations’ data transfer computers to the automated fare 29

collection server located in the data center by means of an established external network. 30

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TABLE 1 Istanbul Public Transportation System Ticket Types 32

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Percentages of fare collection methods for the BRT line are Istanbulkart 95%, Limited Usage 46

Card 3%, and Akbil 2%. (2) The BRT-Bus Rapid Transit pricing system depends on the trip 47

Ticket Types Definition

Fro

m I

stan

bul

AF

C

Akbil Ancient anonymous ticket

Limited Usage Tickets

birGec 1 Journey Ticket

ikiGec 2 Journeys Ticket

besGec 5 Journeys Ticket

onGec 10 Journeys Ticket

Stored Value Tickets

Istanbulkart

Anonymous Anonymous Ticket

Discounted Reduced Fare Tickets

Free Free Entry Tickets

Function Function Cards



distance. When a traveler first uses his or her card, the full price is charged; then, at the 1

destination station, he or she uses the card again, either at a refund machine or on another transit 2

vehicle. There are refund machines at the exits from BRT- Bus stations. These machines 3

recognize the cards of travelers who have used only a portion of the line and credits them with 4

refunds up to 46% of the full fare. First and last usage dates (by mining more than 6,000,000 5

different passenger data per day) were recorded to obtain mean travel time and origin-destination 6

station data. Hence, the trip time of each traveler can be calculated from information taken from 7

the refund machines (Figure 1). The graphics are obtained for approximately 46% of all travelers 8

because only some travelers receive refunds to their Istanbulkarts (3).Thus, besides of the 9

information on boarding time and location, the data collected from Istanbulkart cards contain 10

detailed records of alighting times and destination location for Bus Rapid Transit (BRT) stations. 11

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FIGURE 1 Flow chart to determine BRT- Destination station 32

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Typically, the date and time of the transaction, status of payment (transfer, acceptance, or 34

refusal), card ID, fare type (student, adult, senior), route ID, and other related data are stored in 35

the Validator and the central server (4). Because these data allow for more detailed assessment of 36

travel behavior and mobility patterns they are invaluable for transit planning, long term planning, 37

and daily management of the transportation system. Massive amount of data are collected and 38

stored, complicating the process of analyzing it. 39

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In brief, by using a smart card system, transportation authorities have access to: 41

1. personal travel data of millions of people, 42

2. information about each card and/or traveler, 43

3. continuous trip data including refund information, 44

4. identity of user and frequency of use. 45

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Despite the accumulation of so much information, it is difficult to improve the Istanbulkart’s 1

usability and accessibility for the following reasons: 2

1. Its prevalent purpose is not to monitor performance of the transportation system; hence, 3

additional passenger trip information such as destinations and delays cannot be directly 4

retrieved. 5

2. Each passive data collection method has its disadvantages, and processing usually requires 6

additional knowledge. Interoperating and mining heterogeneous datasets would enhance 7

both the depth and reliability of transportation studies. 8

3. The amount of data obtained is increasing tremendously (approximately 6 million distinct 9

Istanbulkart data every day) and traditional data processing methods might not be equal to 10

the task. 11

12

Such data barriers make the development of a large-scale transportation performance monitoring 13

system cumbersome and slow (5). 14

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Istanbul BRT –Bus Rapid Transit 16

The Bus Rapid Transit system (BRT) of Istanbul began service in 2007. Istanbul-BRT is a bus-17

based mass transit system, and aims to combine the capacity and speed of light rail with the 18

flexibility, lower cost and simplicity of a bus system. BRT buses operate their journey within a 19

fully dedicated right of way to avoid traffic congestion. In addition, it has an alignment in the 20

centre of the road to avoid typical curb-side delays, and stations with off-board fare collection to 21

reduce boarding and alighting delay related to paying the driver. 22

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The line connects the Asian and European sides of Istanbul. IT is the only intercontinental BRT 24

system in the world. Thanks to this connection, the duration of the 52 kilometer, 44 stations 25

journey from Beylikduzu on the European side to Sogutlucesme on the Asian side has been 26

reduced to 98 minutes (Figure 2). 27

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The salient features of BRT-Bus Rapid Transit are 29

1. fast transportation (30 seconds service frequency and average speed of 34.8km/h); 30

2. appropriate for metropolitan area with high population; 31

3. environmentally friendly; 32

4. comfortable alternative transportation. 33

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FIGURE 2 BRT-Bus Rapid Transit Line and Route Map (6). 4

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The yearly and monthly increase of ridership on the BRT-Bus Rapid Transit is illustrated in 6

Figure 3. The increase is due mainly to improvement of the transfer points and the addition of 7

new lines and stations. 8

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10 FIGURE 3 Number of BRT Passengers per month. 11



2.LITERATURE REVIEW 1

2

There are several BRT applications similar to Istanbul BRT line in other cities of the world, such 3

as Guangzhou, China, Bogotá, Colombia, Rio de Janeiro, Brazil, Lima, and Peru. (7) There is an 4

increasing number of studies and thus published articles, and papers on smart card data analysis 5

of BRT systems. A comprehensive survey on this topic is offered by CALSTART (2005) reflects 6

what the transit operators are saying regarding the effect of BRT vehicles on ridership. Specific 7

issues addressed in these studies include 1) whether the vehicles are branded and/or styled 8

differently than the communities’ regular buses, 2) whether the BRT vehicles themselves were 9

responsible for changes in ridership levels and 3) the effect of the vehicles on community 10

acceptance of the BRT system. The survey results indicate that ridership levels increased after 11

BRT system implementation, and, in some cases, up to one third of the new riders came were 12

new to transit and an additional third were riding more often. (8) Moreover previous work with 13

focus on Case Studies in Bus Rapid Transit identifies the potential range of bus rapid transit 14

(BRT) applications, and provides planning and implementation guidelines for BRT was released 15

in January 2004 (9). Chu et al. (2010) presented a methodology for characterization of trips 16

based on socio-demographic characteristics, multiday travel patterns and association of travel 17

with specific locations. (10) 18

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Main motivation of this paper is the analysis of smart card (Istanbulkart) data generated by the 20

BRT-Bus Rapid Transit in Istanbul and the investigation of its potential for understanding 21

complexities of the system and characterizing travel behavior. An assessment of spatial and 22

temporal travel behavior, including mode choice, travel, and waiting times based on smart card is 23

another goal of this paper. 24

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3. PRESENTATION AND ANALYSIS OF BRT-BUS RAPID TRANSIT DATA 26

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In this paper, we adopt the definitions used by Istanbul Metropolitan Municipality Department of 28

Transportation, and define a journey as “one-way travel from one activity to another”. Each 29

journey consists of one or more consecutive journey stages or trips on the BRT line or a different 30

transportation mode. To deal with the problems of analyzing this big data, we perform 31

visualization and analysis of transportation performance measures by using Rstudio and 32

Microsoft Excel to highlight connections among heterogeneous transportation data sets, 33

including Istanbulkart data (11). Functions in Rstudio provide a data-rich visualization platform 34

to monitor transit network performance for purposes of planning and operations. 35

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Single trips of each customer were aggregated to journeys according to fare rules and stations 37

(2014). For further processing of the data, we used MySQL open-source database in combination 38

with MS Excel.csv file format and Rstudio software to extract passenger origin information from 39

the Istanbulkart data. All BRT stations are geocoded using information provided by Department 40

of Transportation. The original aggregated one-day data set contains ca. 800,000 journey records 41

and ca. 460,000 unique card IDs. In the process of preparation and reviewing of these data, about 42

4,000 journeys (1.1%) were removed from the data set because of errors, illogical journey routes, 43

and missing values. 44

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A variety of methods can be used to characterize and analyze public transport usage on BRT 46

lines. Description in spatial and temporal dimensions are often used by transportation planning 47



authorities as indicators of performance and quality of service. The objective of this section is 1

not to provide comprehensive statistical description of the BRT line system but to show how 2

Istanbulkart data can be specifically used as indicators of region- and culture-specific travel 3

behavior and external factors that influence people’s travel decisions and waiting times. 4

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In the analysis of times, the time at which people decide to start their travel can provide valuable 6

data. Figure 4 shows arrivals and departures. Red lines show direction to the Asian side, blue 7

lines show direction to the European side. 8

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FIGURE 4 Arrivals (gray)/ departures (dark gray) /Asian (red) and European (blue) side 12

per stations (2013). 13

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In the European side, Zincirlikuyu (Station number 37) is the main transit station that traveler 15

ends its journey or continues towards to the Asian side or European side. When we look at the 16

direction data of this station we observe peak values from Istanbulkart data in 8:00 and 18:00 as 17

travelers are traveling from / to the European and Asian sides. In the night, travelers are mostly 18

passing towards to the European side. There is a sharp increase between 17:00 and 20:00 19

followed with a decrease after 20:00 as shown in Figure 5. 20

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FIGURE 5 BRT journeys towards Asian side (red) and European side (blue) (2013) 3

(Zincirlikuyu Main Transit Station). 4

5

Figure 6 shows the temporal distribution of boarding times at the first stop or station of the 6

journey for all BRT line journeys during 24 hours. The sharp peaks in the morning and evening 7

hours are noteworthy. The evening peak shows a high number of journey starts concentrated 8

within a 25-45 minutes period. The fact that many people start their journeys almost 9

synchronously during the morning as well as the evening peak hours suggests that there is not 10

much flexibility in the travel time choice for commuting, probably as a result of commonly 11

inflexible working hours in Istanbul. Additionally, it is worth noting that the small peak around 12

1:00 p.m. in the afternoon might be the result of lunch time travel, part-time workers going to or 13

leaving work, and class hours in educational institutions. 14

15

The representation of trip distribution highlights the main advantages of analysis based on AFC 16

data as opposed to the commonly used self-report surveys. High temporal resolution and 17

reliability of records allow aggregation on a minute by minute basis and thus the detection of 18

sharp peaks, which are clearly visible in Figure 6 and similar to the Figure 5 continental transit 19

values. 20

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6.604

22.37533.883

20.284 20.77928.367

38.447

70.774

110.919

74.750

40.268

27.11317.364

15.728

54.852

34.59829.167

37.135

49.111

59.305

92.530

135.937

99.223

65.797

47.918

30.029

0

50000

100000

150000

200000

250000

300000

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23

EuropeanSide

Asian Side

To

tal

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T T

ran

sit

Tra

vel

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Per

Ho

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Transit Traveler value per hour



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FIGURE 6 Percent of Average trip start distribution during 24h (2013) 3

4

Identification of Waiting Times 5

Another important temporal measure, which has implications on travel behavior and route 6

choice, is waiting time, i.e., the amount of time between a passenger’s arrival at a station or stop 7

and the boarding of a vehicle. No information on the waiting time of bus passengers can be 8

obtained from Istanbulkart data, since the smart card readers, on which passengers tap their 9

Istanbulkarts as they board a bus, are located inside the bus. However, Istanbulkarts are tapped 10

when a passenger enters and leaves a BRT station, not on board. This means that waiting time is 11

a part of total recorded travel time and can be extracted from Istanbulkart data for BRT. 12

13

In this paper we use the term “waiting time” to describe the amount of time a passenger spends 14

inside a station, which includes time spent walking in the station and actual waiting time. In order 15

to extract this time for each station, all BRT trips from any one station to other stations on the same 16

BRT line are considered. Hence only direct, uninterrupted trips without interchanges are used for 17

the calculation of waiting times. Travel times of passengers traveling between each pair of stations 18

in each direction are extracted. It is assumed that the fastest passenger arrived at the platform just 19

in time and did not have to wait for the bus at all. That passenger’s waiting time is considered to 20

be zero and is used as a benchmark. By subtracting this benchmark time from the travel times of 21

all other customers travelling between the same two stations, waiting times of these passengers are 22

obtained (Figure 7). 23

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1

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3

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FIGURE 7 Average waiting times (in-station times) per stations for West-East BRT Line 5

during 24h (2013) 6

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Dependent on the focus of the analysis of waiting times at BRT stations, information on 8

significant service frequency changes during the day as well as potentially overcrowded buses 9

can be gained. However, it is important to note that because of the method for defining and 10

identifying waiting time, the absolute time values do not precisely indicate the service frequency 11

at a particular station. Large hub stations with several access points tend to have larger variations 12

and higher averages of waiting times, which reflects different lengths of access routes from 13

different entry points. In Figure 7 the average waiting times per station for Istanbul’s West-East 14

BRT line are shown. As expected, large hub stations with long underground access routes such as 15

Saadetdere, BRT Station 9, show longer waiting times. Notably the longest waiting time is found 16

for Uzuncayir, BRT station 42. This exceptionally high value in comparison with other stations 17

can be explained by high passenger volumes at the first station on the Asian side. Next to the 18

common waiting time distribution around 7 minutes, a second peak of waiting times ranging 19

from 8 to 9 minutes is clearly visible from the Figure. This peak results from passengers who 20

take BRT in the opposite direction towards the last stop at Zincirlikuyu and then stay on board to 21

secure a seat during the morning peak hour. This observation leads to an interesting conclusion: 22

the value of having a seat is exceptionally high and for some people it is worth about 6 minutes 23

of additional travel time. This finding is also an indication of high passenger volumes and long 24

BRT journeys. In the case of journeys from the Zincirlikuyu station, the average distance to the 25

destination station for passengers taking the detour over the Edirnekapi Station is 7 stations 26

longer compared to passengers taking the direct service. 27

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Station Numbers (1-44)

Av

era

ge

wait

ing

tim

e (m

) 9.0

7.0

5.0

3.0



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FIGURE 8 Waiting times at the Uzuncayir BRT Station for journeys starting between 3

7am-10am (Station number 42) 4

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Analysis of activities for BRT travelling 6

People usually travel from one place to another because they want to conduct activities, such as 7

work, education, leisure, or social intercourse, that cannot be performed in the desired way at 8

their current location. Hence, in order to understand people’s travel behavior in terms of mobility 9

patterns and traffic volumes, it is necessary to look at their daily activities and locations. In the 10

absence of comprehensive data sources such as census data, one of the main challenges in 11

modeling travel behavior results from a lack of verified data of high spatial resolution on 12

people’s home and particularly work locations (12). However such information is important not 13

only for transport planning and implementation of agent-based transport models, but is also 14

invaluable in the areas of urban development and land-use planning. Travel patterns observed in 15

smart card data from public transport can provide important information on people’s primary 16

activity locations and can help to verify and refine existing models and assumptions. 17

18

To describe the activities of a particular person, the recorded daily trip chain of that person must 19

be consistent. Consistency in the context of AFC smart card data means that the person who 20

arrived at an activity location by public transport has to leave at the end of the activity by public 21

transport; otherwise the duration of the activity cannot be extracted. The assumption of 22

consistency based only on AFC data is hard to verify since the use of any means of transport 23

other than public transport, e.g., walking, cannot be detected. However, obvious cases of 24

inconsistency can be detected by analyzing the distances between the alighting location of the 25

last journey and the boarding location of the following journey (Figures 9a and 9b). 26

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1 2

FIGURE 9a Total Transit BRT Trips 3

4

5 6

FIGURE 9b BRT most common boarding stations. 7

5,46%

63,17%

17,32%

0,70%

13,37%

0175.000350.000525.000700.000875.000

1.050.0001.225.0001.400.0001.575.0001.750.0001.925.0002.100.0002.275.0002.450.0002.625.0002.800.0002.975.0003.150.0003.325.0003.500.000

Bus BRT

Public Buses Private Buses

Land Transport Marine Transport Rail Transport

Transportation types and percentages of BRT transit travelers

Tota

l Tra

nsi

ts o

f B

RT

tra

vele

rs

0

50.000

100.000

150.000

200.000

250.000

BRT Public Buses

Private Buses

MarineTransport

Land Transport Rail Transport

01.Tuyap Congress Center

05.Beylikduzu

12. Avcılar

20. Yenibosna

21.Sirinevler

24. Zeytinburnu

26.Cevizlibag

29. Edirnekapı

36.Mecidiyekoy

37. Zincirlikuyu

38.Bosphorus Bridge

41. Acıbadem

BRT- Most common transit boarding stations

Tota

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per

sta

tio

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The analysis of distances between Istanbulkart journeys is shown in the form of a cumulative 1

relative frequency graph in Figure 10. Only persons with more than one journey recorded in the 2

one-day Istanbulkart record were evaluated. The graph shows that 90.2% of journeys following a 3

previous journey start less than 2 km away from the previous alighting location. This indicates 4

that the majority of public transport users do not switch to other transport modes between public 5

transport journeys. 6

7

A slightly different picture is obtained by looking at the distances between the first boarding and 8

the last alighting station of the day; if they are nearby they are likely to be in the vicinity of 9

passengers’ home locations. As shown in the cumulative relative frequency graph of these 10

distances for all persons in the Istanbulkart data, including those with one journey (Figure 10), 11

only 73% return to a station within a radius of 1 km from their first departure station of the day. 12

This observation is largely due to the fact that 27% of BRT journeys are one-way journeys. To 13

better understand mode choice and identify transportation modes used in combination with 14

public transport in Istanbul, in the following section we refer to previous annual records of the 15

Istanbul Metropolitan Municipality Land Transportation Authority (IETT) (2010-2014). 16

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FIGURE 10 Cumulative and relative frequency graph of distances between journey stages 20



Looking at the distances between starting point and final destination of the day, we observe that 1

more than 92% of all persons return to their starting location at the end of the day. Analysis of 2

the alternative modes of transport used by BRT users reveals that a total of 64.7% of reported 3

journeys are conducted completely on public buses, 7% are on ships, and 29.3% are on rail. We 4

cannot detect other transit modes such as taxi, private car, cycle, foot, or some unknown mode in 5

instances when a new journey starts more than 2 km away from a previous alighting location or 6

more than 2 hours later. Almost all BRT journeys are made as the first or last journey of the day, 7

which indicates the high usage of private buses for travel to and from work or academic locations 8

(Figures 9 and 10). 9

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Another important indicator of activity type is the regularity and frequency of trips to and from a 11

particular activity area. In order to be able to conduct such analysis, a longer term Istanbulkart 12

record of at least several consecutive days is obtained. This enables the analysis of multi-day 13

travel behavior and identification of principal activity spaces. The regularity of activities 14

performed there would give a strong indication of type. Additionally, analysis of activities can be 15

based on demographic factors and differentiation of work activities for adults and educational 16

activities for students (13). In this way, the accuracy and reliability of estimated home and work 17

locations and their densities can be much improved. 18

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4. CONCLUSION 21

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Improving the data-mining process while reducing the time needed for data processing has 23

become an important problem for transportation decision makers who now have access to 24

unprecedented amounts of operational data. In this paper, Istanbul´s automated fare collection 25

system and pricing policies, operational sources of big data, have been introduced, and 26

approaches for the use of public transport smart card fare payment data for characterizing and 27

analyzing travel behavior have been presented. Even though examination of this data source 28

alone is not enough to measure the full potential of Istanbulkart data for planning purposes, the 29

processed data used in this study reveal the potential for solving short and long term problems 30

the users of the system. .In this study, we used the Rstudio to analyze this large amount of usage 31

data. Some of the immediate research questions that can lead to the improvement of BRT-Bus 32

Rapid Transit line planning and operations that we were able to identify as a result of the current 33

study can be summarized as follows: 34

1. Deployment of efficient data mining techniques to analyze large data sets for the purpose 35

of discovering patterns of persistent problems; 36

2. Development of fast and easy to implement visualization tools for the analysis of this type 37

of big data; 38

3. Use of the Istanbulkart data to devise data-driven algorithms to generate products for 39

operational and planning purposes including time- dependent OD tables and travel time 40

estimates. 41

42

In this paper, the link between public transport usage and specific activities as a reason for travel 43

was also established. Methods for identifying primary activity locations were described. This is a 44

first step towards an activity location model based on the public journeys record extracted from 45

Istanbul BRT-line data. The disaggregated, multi-day data record, which is becoming accessible 46

for research, will allow expansion and implementation of methods presented in this work. 47



1

Furthermore, models of work locations based on Istanbulkart data can be compared with existing 2

models used by transportation planners. Multi-day data open the door for further analysis of 3

travel patterns and behavior, for the identification of demand profiles for bus routes, stations, and 4

interchanges; and for development of public transport route-choice model. Such future research 5

possibilities add additional value to Istanbulkart data records and contribute to the development 6

of methods for processing and analyzing smart card data records for the purpose of transportation 7

planning. 8

9

In the second stage of this study, an integrated data fusion procedure that models the travel 10

patterns and regularities of transit riders along the BRT-Bus Rapid Transit line will be 11

developed. This procedure will incorporate transit riders' trip chains based on their temporal and 12

spatial characteristics and effectively capture their historical travel patterns. We will examine big 13

data in the rate of streaming data; and “veracity,” the relative certainty of data. (14) Then, 14

through examination of travel patterns and transfer data, rider-level destinations can be estimated 15

from multi-day observations and “agent-based micro-simulations”. (15) In these simulations, 16

travelers and vehicles are modeled through agents that interact with the public transport system 17

according to their individual goals. 18

19

In the future, we will also use numerical simulation models to test pricing strategies . We believe 20

that an improved version of our visualization and analysis methods and use of agent-based 21

micro-simulations can improve the design of demand management systems, including origin-22

destination based schemes, in the realm of public transport. 23

24

5. ACKNOWLEDGEMENT 25 26

The authors thank Istanbul Metropolitan Municipality – Belbim Inc. for supporting this research 27

and providing the data. 28

29

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31

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