ITE Journal - January 2020 - 48

In other words, the coefficient of variation quantifies variability
(without units) relative to the typical running time. The coefficient
of variation of run time is defined as follows:
CV of running time = f [distance, AM peak, PM peak, CV of
number of actual stops, CV of lift usage, CV of boardings [Ons],
CV of alightings [Offs], CV of passenger activity [Ons + Offs], CV
of passenger load, CV of terminal departure delay, CV of driver
experience]. Table 3 presents the results of the regression model.
A relatively high R-squared value indicates a good fitting between
the model and real world data. Table 3 only presents the factors
that have statistically significant effect on run time variation at 0.05
level of significance and higher. The coefficient of variation model
estimates that the running time can be negatively affected by the
distance travelled between two key stops. Further, it shows that an
increase in variation in the number of stops by 1 percent would lead
to 6 percent variation in the running time. From these two values, it
can be inferred that designing the segments (distance between two
key stops) with longer distance and lower number of minor stops
can be an efficient "preventive strategy." The results of the model
also show a higher level of variation in run time in AM peak hours
compared to the running times in off-peak and PM peak hours.
While the variation in passenger activity (Ons + Offs) does
not have a statistically significant effect on run time variation,
the variance in average on-board load increases the running time
variability by 21 percent. Further, the variance in late departure
from the terminal has a significant positive effect on running time
variation. Finally, the results reveal that variation in driver experience
by 1 percent leads to 6 percent change in running time variation.

Conclusion
Factors affecting bus running time were evaluated in this study. The
schedules of this route must be revised, since the results prove that
the run times assigned to segments along this route are not sufficient.
The running time regression model indicated that almost all the
factors included in this model are statistically significant. Hence, bus
service companies and researchers must consider all these factors
in their analysis and operation planning. Moreover, the estimated
values prove the important role ridership plays in running time and
running time variation. Average onboard passengers and passenger
activities (alightings + boardings) add 20 sec. to the related run
time at each studied stop along a segment. Further, variance in the
average on-board load increases the running time variability by 21
percent. These values can cause a large variation in run time in the
scale of route and a day of operation. Accordingly, we suggested some
strategies to RapidKL, in order to reduce the boarding time, such as:
"back door only policies for alightings, front door only policies for
boardings, and low floor buses." According to the findings, operator
experience significantly decreases the running time. Therefore,
assigning operators with more experience can be an efficient strategy
48

J a n uar y 2020

i te j o u rn al

Table 3. Results of regression model for CV of running time.
Coefficient

Estimate

T-value

P-value

(Intercept)

0.68

1.32

0.19

Distance

-0.12

-3.14

0.0018

AM peak

0.113

4.12

0.0005

CV No. of stops

0.061

3.89

0.0001

CV average load

0.21

2.11

0.035

CV TDD

0.14

4.03

0.0001

CV driver experience

-0.06

-2.98

0.0032

Adjusted R2 = 0.69
N = 380
to control deviations from schedule during peak hours. Although late
departure from the terminal is a common operational mistake that
happens frequently, researchers neglected the effect of this factor on
running time variation. The output of the model indicated that on an
average, delay in departure from the terminal causes an increase of 3
sec. in running time. The maximum running time observed because
of late departure was 9.5 minutes longer that the scheduled run time,
during AM peak hours. The regression model for run time estimated
that both distance and the number of stops increase the running time
to a certain extent. An interesting result gained from the CV model
is that the variation in distance has a negative effect on run time
deviation, which implies that longer segments have lower variation
in run time. This provides a useful technical hint for service planners
and route designers-longer routes with less stops significantly
decreases the variation in service. Each scheduled stop adds 0.6
percent to the schedule deviation; when translated to seconds per trip
segment, this equals approximately 5 sec. of additional run time. itej

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3. Castellanos, J.C., F. Fruett. Embedded system to evaluate the passenger
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