=Paper=
{{Paper
|id=Vol-2557/paper-03
|storemode=property
|title=An Analysis of Bus Ticket Sales in East Bangalore
|pdfUrl=https://ceur-ws.org/Vol-2557/paper-03.pdf
|volume=Vol-2557
|authors=Yogalakshmi Jayabal,Rajagopalan S
}}
==An Analysis of Bus Ticket Sales in East Bangalore==
https://ceur-ws.org/Vol-2557/paper-03.pdf
An Analysis of Bus Ticket Sales in East Bangalore
Yogalakshmi Jayabal S. Rajagopalan
j.yogalakshmi@iiitb.org raj@iiitb.ac.in
International Institute of Information Technology, Bangalore
Abstract
This paper investigates different aspects of demand modelling for bus
transport systems based on the data obtained from Electronic Ticketing
Machine(ETM). Nowadays, ETM0 s have been introduced by many
Public Transit Agencies as part of improving their operations and
services. The data used in this study is the ticket sales data from
the Bangalore Metropolitan Transport Corporation(BMTC)1 . BMTC
approximately makes 69000 vehicle trips with a traffic revenue of Rs5.17
crores everyday. The ETM data of BMTC has approximately 40 million
transactions per month. This ETM data can be utilized effectively to
understand passenger movement, identification of peak and off-peak
hours of the day, popular Origin-Destinations, operator’s efficiency in
terms of revenue generated, load-profiles at 1. route-level, 2.corridor-
level, 3. Origin-Destination(OD) wise etc across Bangalore. This paper
focuses on generation of Origin-Destination matrices from this ETM
data to understand the user behaviour between different ODpairs,
duration of peaks and off-peaks for the ODpairs across the different
times of the day. This OD data will help in understanding the spatio-
temporal bus ridership demand in Bangalore. The work presented
in this paper provides details on the methodology for generating the
ODmatrix and additional inferences that are possible from the ETM
data. This work also presents a number of analysis tasks that were
executed, to derive information from ETM data for travel demand
modelling and operational planning of public transit agencies. A major
finding is that while nearly two thirds of ticket sales happen during peak
period, peak periods themselves were a small fraction of the overall
operating hours.
1 Introduction
Urbanization has resulted in greater demand for movement of people and goods which mandates good mobility
within the city. Public Transport plays an important role in mobility in any city. Transportation Planners are
often required to analyze various parameters to ensure effective services. Bangalore Metropolitan Transport
Corporation(BMTC) is the public bus transit operator in Bangalore in India. There are around 6600 buses with
around 2500 routes that are operated in the city. These buses are equipped with automatic vehicle location
system(GPS) and electronic ticketing machines. To attract more people, public bus transport-the fleet operator
Copyright c 2020 by the paper’s authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY
4.0).
1 https://www.mybmtc.karnataka.gov.in/info-1/BMTC-Glance/en
should provide quality service to passengers. It is important to estimate the demand for public transit which
in turn affects the operational policies and strategies of the public transit agency. Appropriate estimation of
the peak and off-peak time, peak and off-peak loads leads to better understanding and modelling of the travel
demands and operations.
The ETM is a handheld device that records the transaction when a passenger requests a ticket. The introduction
of the Automated data collection source like Electronic Ticketing Machine(ETM) plays a vital role in the absence
of smart cards or travel cards in Bangalore. Hence, building tools to explore this ETM data and asking the right
analytic questions provides us with the better understanding of the passenger movement and therefore system’s
behaviour. Bangalore has two types of ticketing system: 1. trip based tickets, and 2. pass-tickets. The pass-
tickets can be one of the following: 1. Student pass, 2. Day pass, 3. Monthly pass, or 4. Senior Citizen. Every
transaction in BMTC-ETM captures data like Ticket id, waybil Id, waybill no, schedule no, trip no, etim no,
route no, route id etc. Using these, various key performance indicators like Total number of passengers [route-
level,Daily], High boarding/alighting stops, In-vehicle passenger volume or Occupancy, Occupancy ratio, Average
revenue per shift etc can be computed to know the effectiveness of the services provided.
2 Related Literature
There are many factors like speed of the bus, schedule adherence,passenger demand, travel time etc that affect
the effectiveness of public travel. One of them is passenger demand. Passenger demand modelling and estimation
is one of the important task in transport operations. The conventional method of data collection like household
surveys, travel surveys to understand the demand are both expensive and time consuming and hence they are
infrequent[Cui07]. Also, surveys would be conducted on few sample routes or links or zone and hence the
comprehensive view of existing demand of a city may not be understood completely. In contrast, there is a need
for frequent analysis and updating of real time traffic scenarios to improve the public transport operations. Hence,
the Automated Data Collection(ADC) systems have gained importance. The Automated Data Collection(ADC)
systems include Automatic Passenger Counting(APC)[Fur06], Automatic Vehicle Location(AVL)[Fur06] and
Automatic Fare Collection(AFC)[Nun17]. Smartcard data[Ort15], cellphone data [Dem17] and social media
data are some of the other data sources that are being used now-a-days to understand the travel demand. There
are a lot of literature available that explore ADC data to understand the system.
Yu et al[Sha16] have proposed to forecast bus passenger trip flow for transit route design and optimization.
They have used Aritificial Neural Network(ANN) to forecast the bus passenger trip flow and have validated
with a dataset from China. The ANN model is based on the influence factors like each traffic zone land use
(the proportion of residential, commercial and industrial traffic), accessibility to bus stations, area and distance
between zones etc. They have used the OD pairs as a base from a survey that was carried out to forecast
the passenger flow. Kinene [Kin09] employed Random Forest machine learning algorithm to predict the hourly
demand for buses along all routes in ´’Orebro city in Sweden. They have considered factors like day of the week,
weather season, time of the day, customer types etc for predicting the hourly demand for buses. Kinene also
suggests that these information can be used to decide the frequency on a given route considering these factors.
Cui[Cui07] in his thesis has developed an algorithm to estimate bus passenger ODmatrix using the data from
Automatic Vehicle location(AVL) and Automated Fare Collection(AFC). Initially, a single route ODmatrix is
estimated from a seed matrix that is derived from AFC data. Then Iterative Proportional Fitting and Maximum
Likelihood Estimation(MLE) techniques are used to estimate ODmatrix for single routes. Then network level
ODmatrix are estimated. Ji et al [Yji17] have proposed Hierarchical Bayesian model to estimate the trip-level OD
flows and a period-level OD flow from the samples OD flow data collected by the WIFIsensors and the fareboxes.
They have used bus load and average journey length to reflect indirectly on the accuracy of their proposed OD
estimation method. Li et al[Dli11] also have proposed an OD estimation matrix for each route using the data
collected from the farebox. They have presented an OD estimation model based on trajectory search algorithms
to track passenger trips, using the pre-processed smart card data. They have used one day smart card data from
Jinan city. They also suggest that the estimated ODpairs can be used to evaluate route network and optimize
bus scheduling. Janine[Jan08] has also proposed to construct Automated Bus Origin-Destination matrix using
farebox and AVL data.
Most of the works in literature for travel demand analysis are based on the Automatic Fare Collection(AFC) or
Farebox. The data that we have analyzed is from Electronic Ticket Machines(ETM) which has few more details
than that of Farebox. A few works are available in literature that analyze data from ETM machines. Cyril et
all[Cyr17] have analyzed ETM data of Kerala State Road Transport Corporation for 6 depots in Trivandrum
Figure 1: Bangalore map and Area of study-East Bangalore as outlined by red
city for modelling intercity public transport demand to predict the number of trips on a given day. Kalanidhi
et al[Kal13] have used ETM data along with OD pattern of travels taken from Chennai City Traffic Study of
Chennai Meteropolitan Development Authority, passenger opinion survey and GPS data to study the accessibility
of urban trnasportation networks and assessing its influence on the public transport ridership. Wang et al[Wan11]
have proposed a methodology to infer bus passenger travel behaviour, ODpair inference using the smart card
transactions and AVL data in London.
In this study the objective is to analyze the ODpairs to understand the passenger distribution and hence to
obtain the temporal and spatial variation in ridership and hence passenger travel characteristics. This paper
focuses on generating the passenger movement from the ETM data and some of the key performance indicators
like Total number of passengers [route-level,day-level], load profiles of routes, identification of peak and off-peak
hours based on the number of tickets sold.
3 East Bangalore - A case study
This section presents the area of study and provides details on the data collected and the methodology used
to generate the ODpairs. Bangalore is the fifth largest urban city in India with a population of about 8.5
million as of 2011 with an area of 709 sq km. The below map shows the boundary of Bangalore and the portion
highlighted in red is the study region which is East-Bangalore2 BMTC is the government agency that operates
public transport bus service in Bangalore. It has different types of services like 1. General, 2. Samartha, 3.
Suvarna, 4. BIG 10, 5. Big Circle, 6. Atal Sarige, 7. Vajra, 8.Vayu vajra, 9. Marcopolo and Corona AC, 10.Metro
Feeder and 11. Hop On Hop Off. These BMTC buses are operated from 48 depots3 within the city and are
numbered from 1 to 48. In some BMTC services, the tickets are issued using a Electronic ticket machine(ETM)
and in few other services, the manual(pre-printed) tickets are issued. This study analyzes both ETM data and
manual ticket data sold in buses operated from four depots 6, 25, 28 and 41, which cater to the East Bangalore
population. In the introduction, it was mentioned that two types of tickets - trip based tickets and pass tickets
are available in BMTC. This study focuses only on trip based tickets as the information about the travel made
by pass ticket holders in not available. It is assumed that the analysis results could be a representative of the
total public transit passengers. BMTC is the government agency that operates public transport bus service in
Bangalore. It has different types of services like 1. General, 2. Samartha, 3. Suvarna, 4. BIG 10, 5. Big Circle,
6. Atal Sarige, 7. Vajra, 8.Vayu vajra, 9. Marcopolo and Corona AC, 10.Metro Feeder and 11. Hop On Hop
Off. These BMTC buses are operated from 48 depots4 within the city and are numbered from 1 to 48. In some
BMTC services, the tickets are issued using a Electronic ticket machine(ETM) and in few other services, the
manual(pre-printed) tickets are issued. This study analyzes both ETM data and manual ticket data sold in buses
operated from four depots 6, 25, 28 and 41, which cater to the East Bangalore population. In the introduction,
2 East-Bangalore was identified as study region since ticket sales data was predominantly available for this region from the 4
depot’s data and is not exclusive of east bangalore region.
3 https://www.mybmtc.karnataka.gov.in/info-1/Depots/en
4 https://www.mybmtc.karnataka.gov.in/info-1/Depots/en
Figure 2: Caption
Table 1: Ticket Parameters Studied
Parameter Explanation
Ticket from stop id Origin stop for the ticket sold
Ticket till stop id Destination stop for the ticket sold
Schedule no Bus Schedule number
Trip no Trip number of the schedule
Vehicle no Vehicle number of the bus[KA01F9372
Ticket type short code Code for type of ticket sold [Trip start / Trip close
/ Passenger / Luggage / Group / Pass / Penalty /
Stage close / Toll pass etc]
Ticket sub type short code Subtype of the ticket sold like [Adult / Child /
Heavyweight / Lightweight / Daily Pass etc]
px count Number of passengers
Total ticket amount Amount of the ticket sold
Ticket from stop seq no Within the route, stop no from where the passenger
boards the bus
Ticket till stop seq no Within the route, stop no where the passenger
alights from the bus
Ticket printed flag Whether the ticket was printed
Ticket date Ticket issue date
Trip direc Trip direction whether it is forward(UP) or
backward(DN)
it was mentioned that two types of tickets - trip based tickets and pass tickets are available in BMTC. This
study focuses only on trip based tickets as the information about the travel made by pass ticket holders in not
available. It is assumed that the analysis results could be a representative of the total public transit passengers.
3.1 Electronic Ticketing Machine
The Electronic ticket machine(ETM) is a handheld device which weighs about 800gms. They are GPRS5 enabled
ETM which transmits ticket data to ITS server every 5minutes. The figure 2 shows both ETM and manual ticket.
When a ticket is issued using the ETM, there are as many details as 50 parameters, that are sent to the data
server that is placed in BMTC data center. The parameters that we have analyzed are given in Table 1.
3.2 Data Collection and Pre-processing
The ETM data along with data in manual tickets for the month of December 2018 and July 2019 from depots
6, 25, 28 and 41 was provided to us for analysis. Each data file size was between 250MB to 800MB. Each
data file had 59 parameters including: ticket id, waybil Id, waybill no, schedule no, trip no, etim no, route no,
route id, transaction no, ticket no, ticket type short code, ticket sub type short code, ticket from stop id,
5 General Packet Radio service https://www.gsmarena.com/glossary.php3?term=gprs
Figure 3: Route-level ticket sales data for route: 139
ticket from stop seq no, ticket till stop id, fare type, upload flag etc. Out of these only parameters mentioned
in Table 1 were required for our analysis.
3.3 Pre-Processing of data files
One data file for each depot(6, 25, 28, 41) was provided consisting of ticket sales of all the routes that operates
from the depot. This data file of each depot is processed to check for any inconsistent data type values, spurious
rows etc. The data processing steps followed are:
1. From each depot data, generate separate files for every route.
2. Simultaneously, extract only the required parameters of Table 1 for every route.
3. The route-level data files for each depot and month(December2018 and July2019) are extracted separately.
This extracted route file data size is of the order of few KBs and becomes the base data for further analysis.
A snapshot of the generated route-level ticket sales data of route 139 is shown in the figure 3
4 Data Analysis
The route-level data files extracted for each depot forms the base data for all our analysis tasks. The following
analysis were carried out on these data:
1. Total Number of Passengers route wise and day wise.
2. Hourly occupancy of passengers route wise, day wise and vehicle wise.
3. Load profile - Occupancy trip wise and by stop wise
4. Identification of the location and time of peaks and valleys in the distribution of ticket sales month wise
and hence check for any patterns.
5. Distribution of users based on identified Origin-Destination pairs.
4.1 Total number of passengers
The total number of passengers route-level trip-wise, schedule-wise and day-wise computed using a Python script.
The sample output for some of the routes are as shown in table 2:
Table 2: Sample output of computed Total number of Passengers
Route Vehicle no Ticket date Total Total Shift Trip Scheduled Scheduled
no no;of ticket no no Start End time
pass amount time
engers
SBS- KA01FA1881 12/12/2018 3 28 2 1 2018-12-12 2018-12-12
13K 12:09:27 13:06:39
SBS- KA01FA1881 12/13/2018 4 34 2 1 2018-12-13 2018-12-13
13K 11:47:51
SBS- KA01FA1881 12/18/2018 8 55 2 1 2018-12-18 2018-12-18
13K 11:15:17 12:40:11
SBS- KA01FA1881 12/18/2018 6 50 2 1 2018-12-22 2018-12-22
13K 12:04:44 12:32:31
500-QG KA57F1926 12/29/2018 29 471 2 1 2018-12-29 2018-12-29
07:25:45 08:01:52
500-QG KA57F1926 12/2/2018 17 281 2 1 2018-12-02 2018-12-02
15:46:47 17:06:30
4.2 Hourly Occupancy
The term occupancy of a bus is defined as the following. It is given by:
Occupancy = x + y, where (1)
x = Number of people who are inside the bus when it arrives at a stop,
y = Number of people boarding the bus at that stop − Number of people alighting at that stop
The occupancy at a route level helps to understand the passenger demand in the route at different times of
the day. It also helps to understand the peak and off-peak times of the given route. The figure 4 gives the
hourly occupancy of route:V−500D between December 3rd − 7th and figure 5 gives the hourly occupancy of
route:SBS−1K between December 3rd − 7th .
It could be observed that the route:V-500D have 2 clear peaks, one in morning between 8.30 A.M to 10.30
A.M and one in evening between 17.30 P.M to 20.30 P.M. Whereas, the route:SBS-1K has a sharp morning peak,
but the evening peak relatively blunt compared to the morning peak. These give the times when the routes are
most used. Another important factor to observe is that the peak(morning/evening) load is 2 − 3 times that of
the load in off-peak times. This pattern is consistent across all days in the week as shown. The similar pattern
is also observed across all weeks of the month. The figure 6 shows the hourly occupancy in V-500D for 4 weeks
in December 2018. We could compute Utilization by examining whether the occupancy is < 100% or > 100%.
This piece of information would be a valuable feedback to be considered by the operations and planning team of
public transit agency while scheduling.
4.3 Load Profile
The load profiles of a route gives a much detailed information such as the trip-wise, stop-wise and time-wise
occupancy. These also allow us to infer the trip times of various trips made through out the day and how they
vary in peak and off-peak hours of the day. The figure 7 show the different trips made by the route:335C in
December 2nd − 7th . It can be observed that the trips that start between 8.00 A.M and 10.30 A.M take slightly
little longer time to complete the trip compared to other trips made in the day.
Figure 4: Hourly occupancy on V-500D
Figure 5: Hourly Occupancy on SBS-1K
Figure 6: Hourly occupancy on V-500D on 4 weeks in December 2018
Figure 7: Load Profile of 335C between 2-7th in December 2018
Figure 8: Peak and off-peaks between different ODpairs
4.4 Generate Origin-Destination pairs
The Origin-Destination pairs from the route-level ticket sales data have to generated to understand the spatio-
temporal passenger distribution across Bangalore. This also helps in identification of the peaks and valleys in the
distribution. The steps followed to generate the ODpairs from the route-level ticket sales data are given below.
1. From the route-level ticket sales data, the distinct Origin-Destination(OD) pairs for every 15 minutes are
extracted along with the number of passengers and ticket amount.
2. The ODpairs (same ODpairs could occur in multiple routes) for every 15 minutes for each week(only for
weekdays) are generated in separate files.
3. The week-wise files from step no 2 are generated for the months of December 2018 and July 2019 separately.
There are four weeks in December 2018 : Week 1 : December 3rd to 7th , Week 2 : December 10th to 14th ,
Week 3 : December 17th to 21th and Week 4 : December 24th to 28th . There are five weeks in July 2019 :
Week 1 : July 1st to 5th , Week 2 : July 8th to 12th , Week 3 : July 15th to 19th , Week 4 : July 22nd to 26th
and Week 5 : July 29th to 31st .
4. Once the week-wise ODpairs for each depot are obtained, the same ODpairs across four depots in the same
week and in the same time interval(i.e.24 hours of the day are divided into 15 minutes) are combined.
5. From step no 4, one file for each week of December 2018 and July 2019 are output.
6. The passengers count of same ODpair across time intervals(i.e. every 15 mins) are summed up to get the
total number of passengers for that ODpair in that week.
7. The ODpair file from step 6 got for each week is sorted in descending order according to the total number
of passengers.
8. The sorted ODpair file is parsed to extract the top 100 ODpairs.
9. The top 100 ODpairs from step 8 are analyzed for duration of peaks and the total number of tickets sold in
the peak duration.
Top 5 of the generated ODpairs for the 2 weeks of December are shown in table 3. From table 3, it can
be observed that most of the ODpairs from Week 1 of December are occurring in other week of December as
well. This informs that passenger movement across weeks remain similar. The next step is to examine the ticket
sales in these top 100 ODpairs for the peak/off-peak times of ticket sales. The peak and off-peak times are
identified using a Python script. Though in many of the routes, there are only 2 peaks(morning and evening
peak) observed, in many other routes multiple peaks are observed. Also, since the maximum passenger count
Table 3: Top 5 ODpairs and their passenger counts, total ticket amount for 2 week in December 2018
From From stop name To bus To stop name Passenger Total ticket
bus stop count amount
stop id id
Week -1 : December 3rd to 7th
134 Kundalahalli Gate 1629 Marathahalli 6401 32316
6930 AECS Layout 2050 Sathya Sai Hospital 5341 74001
Cross
134 Kundalahalli Gate 154 NAL Manipal 4313 77013
Hospital
2280 Hope Farm 140 White Field Post 4210 21143
(Towards Office
Varthuru)
7030 Bellanduru 2619 Marathahalli 4171 79290
Bridge
Week - 2 December 10th to 14th
134 Kundalahalli Gate 1629 Marathahalli 6346 32093
6930 AECS Layout 2050 Sathya Sai Hospital 5285 73504
Cross
134 Kundalahalli Gate 8456 Kempegowda Bus 4439 113625
Station
2280 Hope Farm 140 White Field Post 4233 21219
(Towards Office
Varthuru)
134 Kundalahalli Gate 154 NAL Manipal 4105 72389
Hospital
between different ODpairs varies(as shown in figure 8), there is a need to systemically identify the peaks and
off-peaks between different ODpairs. The steps of the algorithm to identify the peak and off-peak times of the
day is given in 1.
Algorithm 1: Identification of peak time and duration
Data: Top 100 ODpairs data file, Weekdf = ODpair data file of a week
Result: ODpair, peaktime, peak duration, number of passengers in peak duration
1 Abbreviations: pxc = passenger count;
2 foreach odpair ∈ top100odpairs do
3 peak pxc = Weekdf . max passenger count ;
4 foreach row ∈ Weekdf do
5 Iterate through the ODpair week data file containing passenger count in 15 mins time interval, to
identify and retrieve all the time intervals at which the passenger count is atleast 70% of the
peak pxc.
6 end
7 Store for each ODpair = peaktime, peak duration, no;of passengers in peakduration ;
8 end
9 return ODpair, peaktime, peak duration, no;of passengers in peakduration;
The algorithm 1 is executed for four weeks ODpair data files of December and five weeks ODpair data files of
July. The algorithm 1 provides 3 outputs for each week. They are for each ODpair, the peak passenger count,
peak duration, time at which peak occurred. Additionally, the total travel time for every ODpair is computed
in every week. Using these outputs the following two ratios are computed for every ODpair for every week.
Average weekly peak passenger count
peak pxc ratio = (2)
Average weekly passenger count
T otal peak duration of week
peak time ratio = (3)
T otal travel time of week
The week 4 of December 24th to 28th being a holiday week and Week 5 of July 29th to 31st having only 3 days
are ignored for peak behaviour analysis. The sample output of peak pxc ratio for 10 ODpairs for all the weeks
considered for analysis in December 2018 and July 2019 are shown in table 4. From the table 4 the following
observations can be made.
1. The percentage of ticket sales in these ODpairs across weeks in both months are similar.
2. The variance in the peak ticket sales percentage is also less than or equal to 5%.
The travel time was then computed to examine the duration for which these peak ticket sales occurred. The
peak time ratio as in eqn:3 was computed. The peak time ratio across weeks also remains similar. They are as
shown in table 5. These peak time ratio are very low indicating that the time for which the peak occurs is very
small. This behaviour was observed across weeks in both the months. This also is an evidence that the peak
ticket sales are really high compared to the off-peak ticket sales. The top 10 ODpairs for which the peak ticket
sales was observed is presented in table 6.
5 Discussion
The Table 6 shows that more than 30% of ticket sales occurs in the peak times. The ticket sales in some of the
ODpairs goes as high as 60%. The column Mean under peak pxc ratio shows the mean of peak pxc ratio of 7
weeks(3 weeks in December and 4 weeks in July). Similarly the Mean under peak time ratio shows the mean
of peak time ratio of 7 weeks. The peak duration are very less compared to the total trips time. This behaviour
needs to be considered while scheduling. Jara Dı́az et al [Ser17] have provided an analytical explanation that in
urban cities − the number of buses and vehicle size is determined by the characteristics of demand during peak
period and adjusting frequencies for other off-peak period whose characteristics are very different from that of
the peak duration. They have shown numerically that minimizing social costs(operator and user) for the whole
day results in a larger fleet of smaller size buses than if only peak period is considered for determining the fleet
size and capacity.
Table 4: Sample Computed peak pxc ratio for some ODpairs
December 2018 July 2019
odpair id odpair stopnames peak pxc ratio(as in eqn:2)
W1 W2 W3 W1 W2 W3 W4
134 1629 Kundalahalli Gate Marathahalli 0.10 0.11 0.11 0.17 0.12 0.12 0.13
6930 2050 AECS Layout Cross Sathya Sai Hospital 0.23 0.26 0.24 0.27 0.22 0.23 0.26
7030 2619 Bellanduru Marathahalli Bridge 0.22 0.23 0.26 0.30 0.29 0.29 0.30
2280 140 Hope Farm (Towards Varthuru) White 0.15 0.13 0.13 0.15 0.17 0.13 0.14
Field Post Office
1218 1234 Sony World 80ft Road 0.23 0.23 0.27 0.28 0.24 0.18 0.22
Koramangala Dhoopanahalli
Table 5: Sample Computed pxc time ratio for some ODpairs
December 2018 July 2019
odpair id odpair stopnames peak time ratio(as in eqn:3)
W1 W2 W3 W1 W2 W3 W4
134 1629 Kundalahalli Gate Marathahalli 0.0404 0.0577 0.0361 0.0069 0.0299 0.0257 0.0201
6930 2050 AECS Layout Cross Sathya Sai 0.029 0.0193 0.0229 0.0194 0.0323 0.0301 0.0128
Hospital
7030 2619 Bellanduru Marathahalli Bridge 0.0307 0.0168 0.0064 0.0148 0.0148 0.0148 0.0174
2280 140 Hope Farm (Towards Varthuru) 0.0166 0.0161 0.0377 0.0371 0.0163 0.0366 0.0163
1218 1234 Sony World 80ft Road 0.0228 0.0058 0.0058 0.0056 0.0056 0.0232 0.0114
Koramangala Dhoopanahalli
Table 6: Top 10 bus stops with high peak pxc ratio in peak time and the variance in peak time is very small
December 2018 July 2019
odpair id odpair stopnames peak pxc ratio peak time ratio
Mean Variance Mean Variance
2092 6914 Kadugodi Bus Station Pattandur Agrahara 0.66 0.0277 0.0014 0.00001
Gate
2092 140 Kadugodi Bus Station White Field Post Office 0.46 0.0127 0.0029 0.00002
9010 9288 Police Station Indiranagara Military Bridge 0.43 0.0104 0.0057 0.00002
2092 154 Kadugodi Bus Station NAL Manipal Hospital 0.42 0.0068 0.0057 0.00002
2619 2581 Marathahalli Bridge Dodda Nekkundi 0.39 0.0055 0.0043 0.00002
(Towards Hebbala)
5557 2595 Kadabisanahalli Bellanduru City Light 0.33 0.0064 0.0043 0.00002
Appartment
7030 5228 Bellanduru Kadabisanahalli 0.32 0.008 0.0057 0.00002
403 6919 Pattandur Agrahara Gate Hope Farm 0.32 0.0081 0.0014 0.00001
(Towards Hoskote)
2055 6929 White Field TTMC (Vydehi Hospital) AECS 0.31 0.0024 0.0114 0.00007
Layout Cross
134 2595 Kundalahalli Gate Bellanduru City Light 0.31 0.0009 0.01 0.00006
Appartment
The analysis tasks based on the ticket sales data as shown in this paper also show that the peak behaviour is
very different from the off-peaks in the system. Hence, the process of planning and scheduling needs to consider
both the peak and the off-peaks in the urban transit system.
6 Conclusion
The use of automatic data collection techniques various advantages. This study investigates the potential of
ETM data and in general ticket sales data for the purposes of operations and planning. The ticket sales data can
provide insights into quantitative measures for operational performance. This paper has shown a methodology
for generating ODmatrices from ticket sales data along with various other analytical tasks. This paper also shows
the effectiveness of ticket sales data for understanding various important performance indicators of the public
transit agency. Future works involve coming up with schedule modelling based on Jara Dı́az study.
6.0.1 Acknowledgements
The authors thank BMTC for sharing their data to us for analysis. This research received funding from the
Netherlands Organisation for Scientific Research (NWO) in the framework of the Indo Dutch Science Industry
Collaboration programme [NWO, Den Haag, PO Box 93138,NL-2509 AC The Hague,The Netherlands]. We are
thankful to NWO, Royal Shell and Prof. Sebastian Meijer, the Principal Investigator of this project.
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