ITE Journal - January 2020 - 46

Data and Methodology
RapidKL is a company owned by another government-owned
company, Prasarana Berhad, which was formed in 2004 with the
objective of providing a solution for the public transport services
of Kuala Lumpur and the surrounding cities. Primary data sources
in this study are automatically collected raw data from automatic
vehicle location (AVL), automatic passenger counting (APC), and
automatic fare collection (AFC) systems.
Route U32 was selected for conducting this study. This route
passes through the most congested sections of Kuala Lumpur City
Centre (KLCC), providing a suitable environment for conducting a
study on running time variability (Figure 1).

Figure 1. Route U32 layout.
After extracting and collecting data from the RapidKL archive, a
sample consisting of 450,300 observations on key stops and segments
was prepared. These data sets had to be cleaned and aggregated to
various trips before using them for the study. After removing the
errors and double-recorded data and also aggregating data over the
days in the study period, 17,800 records remained for analysis.
This study proposes regression models to evaluate various
aspects of running time. The first model focuses on the factors
affecting the running time, and evaluates the significance level
of the factors employed in the research. The second multivariable
model estimates the running time variations according to changes
in the other variables. The coefficient of variation quantifies
variability (without units) relative to the typical running time. It is
the absolute variability, and not the relative variability, that affects
the amount of additional resources that are required to provide
a reliability buffer in transit operations. However, the relative
variability values are useful in comparing the variability in two sets
of running times having different typical running times. Table 1
presents the variables used in the models.

Linear Model of Median Running Time
Running Time Model. As explained before, the running time
model concentrates on the factors affecting the running time, and
evaluates the quality of available data. Run time model is defined
as follows:
Run time = f [number of stops, distance, AM peak, PM peak,
off-peak, boardings [Ons], alightings [Offs], passenger activity
46

J a n uar y 2020

i te j o u rn al

Table 1. Description of variables.
Variable
Running time (s)

Description
Time for travelling between two key stops or
time points
CV run time (s)
The coefficient of variation in running time
between two key stops
Distance (km)
Length of segment or actual distance between
two key stops
Number of actual
Actual number of stops between two key stops or
stops
time points
AM peak
A dummy variable that is equal to 1 if the run time
is observed between 6:00 a.m. and 9:00 a.m., and
zero otherwise
PM peak
A dummy variable that is equal to 1 if the run time
is observed between 4:00 p.m. and 7:00 p.m., and
zero otherwise
Boardings (Ons)
The number of passengers boarding at the studied
key stop or segment
Alightings (Offs)
The number of passengers alighting at the studied
key stop or segment
Passenger activity
The sum of the number of passengers boarding
and alighting along the route/segment or at the
studied key stop
The squared value of passenger activity (Ons + Offs)
(Ons + Offs)2
Passenger Load
Average on-board passengers during the studied
run time
Terminal departure The delay from schedule departure time from the
delay (TDD)
terminal in the studied segment
Lift
Lift usage related to the studied segment or along
the route
Driver experience
Length of operation experience for the
(month)
specific driver
Age (years)
Driver's age
Relief
A dummy variable that is equal to 1 if the driver
has a relief assignment, and zero otherwise
Extraboard
A dummy variable that is equal to 1 if the driver
has an extraboard assignment, and zero otherwise
CV Number of stops Coefficient of variation of number of actual stops
CV Boardings
Coefficient of variation of number of passengers
boarding the bus along the studied segment
CV Alightings
Coefficient of variation of number of passengers
alighting from the bus along the studied segment
CV Passenger
Coefficient of variation of number of passengers
activity
(boarding + alighting) in the bus along the
studied segment
CV Lift usage
Coefficient of variation of number of times the lift
was used
CV DDT
Coefficient of variation of late departure from
the terminal
CV Driver
Coefficient of variation of drivers' experience
experience



ITE Journal - January 2020

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