34

CHAPTER 5

DATA PREPARATION & PRELIMINARY ANALYSIS

5.1 DATA USED FOR TRIP GENERATION MODELLING

The data used for modelling can be broadly classified as

I. Travel data

II. Socio-economic data

5.1.1 Travel Data

The household interviews done for the Colombo Urban Transport Study Stage 1

(CUTS-1) surveys in 1995 are used to estimate the trip rates per household for each DSD

by the trip purpose and trip mode. Thereafter, the total trips per DSD were estimated by

multiplying the trip rates by the total number of households of that DSD.

Surveys have been carried out in 22 DSD's and a total number of 3,517

households have been interviewed. A household has been defined as an entity where

residents cook and eat together. Thus boarders and lodgers have been considered as

separate individual households. Therefore it is possible to have records of more than one

household for a single dwelling unit. According to the survey report, the enumerators

have attempted to select the sample such that it is representative of the houses in the

zone. A list of survey locations and sample size is given in Table 5.1.

The type of data collected include number of households in dwelling unit, number

of persons in a household, vehicle ownership details, occupation and educational details,

type of trips made by different modes and the trip purpose. A specimen survey form (SF

83) is given in Appendix 1.

35

AGA Division Households Interviewed AGA Division Households

Interviewed

Ratmalana 92 Gampaha 72

Wellawatta 90 Homagama 142

Dehiwela 83 Kollupitiya 105

Kotte 89 Minuwangoda 89

Maradana 115 Bandaragama 164

Kaduwela 73 Fort 90

Nugegoda 128 Negombo 113

Kolonnawa 91 Kesbewa 183

Wattala 98 Kochchikade 119

Moratuwa 160 Mahara 136

Borella 88 Maharagama 138

Ja-Ela 109 Kelaniya 54

Panadura 157 Mattakkuliya 93

Bambalapitiya 75 Grand Pass 90

Katana 92 Biyagama 74

Horana 131 Kalutara 117

Town Hall 67 Total 3,517

Table 5.1: Locations of Household Interviews and Sample Size

Since this particular survey was carried out for a different purpose, not all data is

useful for trip generation modelling. Further, since the surveys haven't been done on all

the DSD's within CMR, it is not possible to calculate the trip generations for the DSD's

where the data is not available. But when the models are calibrated, it is possible to

estimate the trip generations for the DSD's for which the data is not available, using the

models.

36

5.1.1.1 Sampling and Survey Errors

There are a number of erroneous and confusing entries in the survey data files.

These may be attributed to incorrect data entry at the field and at data coding. Some of

these errors include unusually high trip rates per person, incorrect household size, and

partly completed or missing records. The survey itself has been conducted in an

inconsistent manner. In some locations, the trip data of people have been collected for 5

previous weekdays whereas in other locations, it is for 2 days and at some places in was

for a single day. The survey report does not elaborate on these. Further, there were no

details on sampling method. It was only through the observation of the total reported trip

rates that it was able to establish the number of days the surveys have been done. Table

5.2 gives the number of days for which travel data from the household occupants have

been gathered at different survey locations. Upon identification of erroneous data, every

attempt was made to rectify the errors and include it in the analysis as omission of data

reduces the sample size.

AGA Division

Number of Days AGA Division Number

of Days AGA

Division Number of Days

Ratmalana 5 Ja-Ela 2 Fort 2

Wellawatta 2 Panadura 2 Negombo 2

Dehiwela 5 Bambalapitiya 2 Kesbewa 2

Kotte 5 Katana 2 Kochchikade 2

Maradana 5 Horana 2 Mahara 2

Kaduwela 5 Town Hall 2 Maharagama 1

Nugegoda 2 Gampaha 2 Kelaniya 1

Kolonnawa 2 Homagama 2 Mattakkuliya 1

Wattala 2 Kollupitiya 2 Grand Pass 2

Moratuwa 2 Minuwangoda 2 Biyagama 2

Borella 2 Bandaragama 2 Kalutara 2

Table 5.2: Travel Information Collection Duration at Different AGA's

37

5.1.2 Socio-Economic Data

Socio-economic variables that explains the trip generation such as population,

number of households, licensed vehicles and employment data were gathered from

publications by Department of Census and Statistics and Transport Database of the

University of Moratuwa.

In addition, the vehicle ownership was estimated using the household interview

data. The operational vehicle fleet data as compiled in the Transport Database of

University of Moratuwa is different from the interview based vehicle fleet as the

operational fleet also includes the vehicles owned by government institutions and

companies. This enables to select the most appropriate vehicle fleet estimation using two

different values. The percentage distribution of population by employment type was also

estimated using the information gathered from the household interviews.

5.2 DATA PREPARATION

Before commencing the model calibration, the household data and socio­

economic data need to be arranged such that data is in the required format for calibration.

The following sections will elaborate on the procedure adopted for the preparation of data

for model calibration.

5.2.1 Household Trip Rates

The steps in the calculation of household trip rates are given below.

I. Checking of the household survey data files for incorrect entries.

II. Using Microsoft Excel formula option, selecting entries that record bus,

rail and private vehicle trip information separately.

III. Getting totals of households in the sample and the one-way trips for

different modes.

38

IV. If the trip data is available for more than one day, estimating the total trips

for one day by dividing total trips by the number of days during which

these trips have been made.

V. Calculating the one-way trip rate per household by dividing the total trips

by the number of households in the sample.

VI. The above steps should be repeated for the all required modes of travel for

each survey location.

VII. Formulation trip generation rate table.

Table 5.3 gives a sample of the outputs in the above mentioned calculation steps.

Calculated trip rates for different trip purposes are tabulated in Appendix 2 while total

trip generations by the purpose of the trip are given in Appendix 3.

DSD

(1)

Location

(2)

# of sample Households (corrected)

(3)

# o f days of survey

(4)

# of HBWT by bus

(corrected) (5)

# of trips per day

(6) (5)/(4)

HBWT per HH

(3)/(6)

Colombo Borella 104 2 64 32 0.308

Ja Ela Ja Ela 222 2 108 54 0.243

Kaduwela Kaduwela 85 5 133 27 0.318

Kelaniya Kelaniya 74 1 70 70 0.946

Table 5.3: Sample of Trip Generation Rate Calculation from Household Interviews

5.2.2 Socio-Economic Variables

The following soio-economic variables were considered for model calibration. All

these are at DSD level.

I. Population

II. Households

III. Vehicle ownership - motor cycles and car/van

IV. Population by employment categories

39

As the above data is not collected annually, it is necessary to extrapolate the available

data to the required year. For this purpose, suitable growth rates need to be applied.

Statistical abstracts published by the Central Bank and recent reports such as Colombo

Metropolitan Regional Structure Plan (CMRSP) carry the growth rates for the socio­

economic variables under different economic scenarios. But these growth rates are

available at district level only. As such it is not feasible to estimate the growth of soio-

economic indices at DSD. Therefore the growth rates of the parent district are applied to

the constituent DSD's. The annual growth rates used (CMRSP, 1998) for different socio­

economic variables are given in Table 5.4.

Socio-Economic Variable

District Socio-Economic Variable Colombo Gampaha Kalutara

Population 1.28 2.24 2.40

Household 2.25 2.00 1.50

Table 5.4: Annual Growth Rates Applied for Socio-economic Variables

The following table shows the calculated values of socio-economic variables for

some of the DSD's for the base year 1998. The year on which primary data available is

also given in the Table 5.5.

40

DSD District Population 1994

Households 1994

Population 1998

Households 1998

Maharagama Colombo 120,466 26,618 126,753 29,096

Homagama Colombo 140,825 32,687 148,175 35,730

Gampaha Gampaha 143,688 31,502 157,002 34,099

Negombo Gampaha 145,813 31,695 159,323 34,308

Horana Kalutara 108,112 25,158 118,870 26,702

Panadura Kalutara 140,793 28,655 154,804 30,413

Table 5.5: Calculation of Socio-economic Variables for the Base Year = 1998

5.2.3 Categorization of Zonal Population by Employment Type

The employment categories identified for the population over 5 years of age are listed

below. Population below five years of age are not considered in this classification as their

contribution to trip generation is insignificant. In addition, they are not in a position to

generate trips on their own.

I. School/university education

II. Professionals

III. Clerical

IV. Business

V. Agricultural

VI. Self Employment

VII. Unemployed

VIII. Retired

The data collected in the household interviews was used for the above classification

process. When the demand models are calibrated, which forecast trip generation as DSD

level, it was assumed that the population distribution by employment in the sample

represents that of the DSD. If the sample chosen is biased, (i.e. it does not represent the

41

general employment distribution of the population of the particular DSD then the above

assumption may lead to errors in the models.

5.2.4 Rail Station Density

This variable can be used as a measure on the accessibility to the rail for people. It

is calculated by dividing the total number of rail stations in a DSD by the area of that

DSD. Another way of representing the effect of rail mode is to give the rail line length

per unit area. But this does not give a measure of accessibility to rail, as accessibility is

represented by the availability of stations rather than the availability of rail line.

Station density variable has certain shortcomings such as depending on the

location of the stations within the DSD, sometimes stations may serve neighbouring

DSD's as well. In addition, the level of service of the rail service is not represented by

this variable. Despite the above-mentioned shortcomings, this variable is retained for the

trip generation models, as they are only concerned about the factors that influence the trip

generation. If a trip distribution analysis is carried out then it is necessary to include

supply statistics as well. It should be mentioned that this variable is analogous with

vehicle ownership variable. The private vehicle ownership variable also does not identify

how often the owners use the vehicles for travelling. Therefore, both of these variables

are semi-static in their form as modal share variables. Table 5.6 gives the DSD's that have

rail stations and their rail station density.

42

DSD Area (km2) Rail Stations Station Density

(1) (2) (3) (3/2)

Colombo 40.5 12 0.30

Homagama 77.5 5 0.06

Maharagama 17.0 4 0.24

Nugegoda 20.0 1 0.05

Dehiwela 20.5 3 0.15

Moratuwa 20.0 5 0.25

Gampaha 96.0 5 0.05

Katana 108.0 6 0.06

Negombo 22.0 4 0.18

Wattala 63.0 1 0.02

Ja Ela 61.5 9 0.15

Kelaniya 20.0 3 0.15

Mahara 99.0 1 0.01

Kalutara 76.0 3 0.04

Panadura 46.0 3 0.07

Table 5.6: Rail Station Distribution Data for CMR

5.3 PRELIMINARY ANALYSIS

Before the commencement of calibration of models, a set of preliminary analysis

was carried out to identify the relationship between dependent and independent variables.

The types of analysis performed are scatter plots, bar charts and pie charts. Analysis of

this nature are extremely useful as they give an insight to the type of relationship that

exist between the variable such as linear or exponential and make it easier to identify the

influential variables in the models. This screening of variables would result in selection

of the most appropriate variables for the model calibration. The following sections will

explain the different types of analyses carried out.

43

•

5.3.1 Percentage Distribution of Trip Generations by Trip Purpose

Figure 5.1 gives the observed home based trip generation percentages for bus

mode by trip purpose. It can be seen that the most number of bus trips are made for work

and other trips while educational trips also constitute a significant proportion of bus

travel. But one of the most noticeable observations of the household sample interview

survey is the relatively insignificant non-home based travel by bus mode. The possible

reasons for the low non-home based trips made by bus are discussed under section 5.3.2.4

of this report.

Non-home Based Trips

3 %

V Home Based Home Based Other Trips

36%

Home Based Educational

Trips 2 5 %

Figure 5.1: Percentage Distribution of Bus Trips by Purpose of Travel

5.3.2 Analysis of the Variation of a Single Variable

The observed values of the dependent variable, i.e. trip rates, are analyzed at DSD

level. Any of the significant variations observed are discussed and the possible reasons

are also mentioned. Here, the attention is drawn towards the socio-economic

characteristics of the DSD's , availability of alternative modes of travel and possible

errors in the data collection procedure in explaining the variations of the trip rates.

44

Similarly, the vehicle ownership rates and rail station density variations are also

analyzed on DSD level. The vehicle ownership is further classified as motor cycle

ownership and car ownership, which consist of cars, vans and jeeps. This is in the view

that these two vehicle ownership categories clearly represent two strata of income groups.

5.3.2.1 Home Based Work Trips per Household

The observed trip generation rates for home based work trips are graphically

displayed by means of a bar chart given in Figure 5.2. The variations of the trips

generation rates for different DSD's can be noted and the possible reasons can be

identified in terms of socio-economic parameters. This also helps to identify the errors in

the interview data, as some of the extraordinary variations that cannot be explained in

terms of socio-economic variables may be attributed to errors in the interviews.

It can be seen that Kaduwela and Mahara have the highest home based work trip

generation rates. The most convincing explanation for this is the lack of a rail station in

either of the DSD. As a result, all people who are captive to public transport service have

to use buses. Maharagama, Negombo and Panadura have the lowest work trip generation

rates. All three DSD's have a lower rate than Colombo. This observation is questionable

as Colombo has the highest vehicle ownership and the best rail facilities. Therefore the

errors in the survey sample are suspected to have contributed to the low work trips by

bus. Colombo DSD has reported a home based work trip generation of 0.42 trips by bus

per household. This figure is acceptable since higher income and resulting private vehicle

ownership as well as availability of a distributed rail network catering for employees who

prefer to avoid congested road usage may have comparatively reduced the home based

work trips by bus.

In general the following observations are conspicuous. The income level and

availability of rail transport has a negative impact on the amount of home based work

trips by bus. When there is no rail option, majority of the employees is confined to the

bus transport for home based work trips. Another observation is the DSD's located far

45

from the administrative capitals of the districts have reported a higher bus trip generation

rate. The average home based work trip generation rate by bus per household for CMR is

about 0.5 trips.

5.3.2.2 Home Based Educational Trips per Household

Home based educational trip generation rates are shown graphically in Figure 5.3.

The following observations are made regarding the household trip generation rates for

different DSD's.

There is a significant variation in home based educational trip generation rate

between DSD (e.g. Moratuwa 0.17 trips per hh, Kolonnawa 0.64 trips per hh). One of the

common observations for DSD's with a trip generation rate of 0.5 per hh or above is that

these DSD's do not have popular schools. As a result, many students have to travel to

main town centres where better schools are located. Therefore a comparatively higher

educational trip rate can be seen for these DSD's. Ja-Ela, Katana and Kelaniya DSD's

show a low home based educational trip generation rate despite not having popular

schools in them. One of the possible explanations is that there is a significant travel by

rail for school trips. The observed rail trip rates are given in Appendix 2. Similar to work

trips, there is comparatively lesser number of trips from DSD's Moratuwa, Panadura,

Maharagama and Nugegoda. The presence of popular schools as well as availability of

rail service and reasonably higher private vehicle ownership may have contributed

towards lowering the home based educational trips by bus.

A* V> V v° >* *̂ <** i>*

/////// s < Figure 5.2: Home Based Work Trip Generation Rates per Household (Bus

0.60

0.50

X 0.40 X i. v a

£ 0.30

0.20

0.10

0.00

* P J> J>* J>* . 1 * ! ^ V* J * J * V> ^ V > V « » <S? -J 1 ' 0> ^ < ^ V* <P

y / s y y / y y *• X * ^ p. ^ j , ^ j> ^> » ^ ^ j

.°" &> i<?

4r

Figure 5.3: Home Based Educational Trip Generation Rates per Household (Bus)

48

5.3.2.3 Home Based Other Trips per Household

The household trip generation rates for home based other trips by bus are given in

Figure 5.4. One of the noticeable observations is the very high trip generation rate of

Kolonnawa DSD. It is clear that some error in the household data has caused this result.

Therefore, this particular data is not included in the model calibration.

Previous research on home based other trips such as leisure trips reveal that when

the household income increases, these trips also increase (Kanafani, 1983). This

phenomenon is evident to a certain degree in the CMR data as well. For example,

Colombo DSD has a higher "other" trip rate than "work" or "educational" trips.

Homagama, Maharagama, Katana and Bandaragama DSD's have the lowest

"other" trip generation rates. Of these, Bandaragama has predominantly a rural setting

and the average income levels are low. As such the actual "other" trip generations are

less. On the other hand, Homagama, Maharagama and Katana, which may be, treated as

sub-urban areas. A significant share of the home based "other" trips in these DSD's may

be made using rail or private transport modes.

Figure 5.4: Home Based Other Trip Generation Rates per Household (Bus)

50

5.3.2.4 Non-Home Based Trips per Household

According to the household interview, the proportion of the non-home based trips

is very small, accounting only for 3% of the total trips as shown in Figure 5.1 of section

5.3.1. Similar to the home based other trips, non-home based trip generation is also

largely dependent on the income level of the households (Kanafani,1983). Generally

when the private vehicle ownership increases the non-home based trips also increase.

Conversely, most of the "average" people who are captive to public transport modes, are

engaged in professions that do not require additional travelling other than between place

of work and residence. As the variations of the non-home based trip generation rates are

very negligible, it is not possible to calibrate a model using regression method. Therefore,

a non-home based trip generation model for bus travel is not calibrated under this study.

5.3.2.5 Vehicle Ownership per Household

The vehicle ownership data is obtained from two different sources. One is the

Transport Database of the University of Moratuwa, which has been developed using the

annual vehicle licensing data. Other is the vehicle ownership data collected in the

household interviews of the CUTS - 1. The observed vehicle ownership rates from two

sources are given in Appendix 4. There is one noticeable difference between the two

databases. It is the inclusion of cars and motor cycles belonging to government and

private organizations in the Transport Database. But the CUTS - 1 based database solely

gives vehicles owned by households. Even though it is the actual vehicles owned by the

households that should be considered when modelling home based trip generation,

because of the apparent deficiencies in the interview sampling method, it was decided to

take the average value of the two databases for calibrations. However, when it comes to

the application of the models for other geographic regions, obtaining of average vehicle

ownership rates discussed above may become difficult. In such situations, it is suggested

to use the operational vehicle ownership rates suitably adjusted by a sample survey to

account for the institutions - owned vehicles.

51

The vehicle ownership is used as a proxy variable for income in transport demand

models. One of the major reasons for this is the difficulty in obtaining reliable data on

income at household level. Generally, as the income increases, the demand for travel also

increases (Kanafani, 1983). But as the income levels further increases, it is seen that

people shift from public and non-motorized forms of transport to motorized private

modes of transport. Therefore, vehicle ownership variable in a demand model explains

two scenarios. One is the increase in mobility with income and second is the change in

mode of transport. As a result, the coefficient of this variable in a demand model

ultimately shows the net effect of the two scenarios.

Figure 5.5 gives the private vehicle ownership distribution in CMR identified

separately for motor cycles and cars. As expected, Colombo DSD has the highest car

ownership rate. Nugegoda and Dehiwela DSD's also have more cars than motor cycles.

The car ownership level of Moratuwa DSD at 0.06 per household is very low. It can be

seen that car ownership level is a reasonable indicator of the average income level of the

DSD's. When the average car ownership is considered on district basis, Colombo has the

highest ownership, followed by Gampaha and Kalutara respectively.

As the car ownership increases, the proportion of the bus trips decreases. But car

ownership is not a very good variable when the trips are forecast at DSD level where the

income distribution is very large among the households. The actual proportion of the

population who owns cars (i.e. the higher income households') is a smaller percentage.

Therefore, taking average value of the car ownership for entire population does not

represent the accurate trip generation and modal share pattern for bus models.

The average motor cycle ownership is highest in the Gampaha District. This

suggests that motor cycle ownership represent the proportion of the medium income

population in the DSD. Due to relatively lesser capital and operating costs, most of the

' This study does not look into income classes in detail. If the average monthly income of the head of the household is more than Rs.20 000, it is considered as high income. This can be judged using the employment data given in interview survey.

52

middle income families can afford to have a motor cycle. As a result, motor cycle is a

definite competitor with public transport modes for home based "work" and "other" trips.

Therefore, the motor cycle ownership level here also explains any reduction in bus trip

rates.

At the same time it can be argued that majority of the people who use public

transport belongs to low and medium income groups. Since the need for mobility

increases as the income level changes from low to medium, as shown by the category

analysis on trip generation rates (Kanafani, 1983), high motor cycle ownership would

mean high trip generation rate for bus trips. This is further discussed in Section 6.4.1.2.

Even though motor cycle and car ownership explicitly describe the trip

distribution patterns of different income groups adequately, in the regression method of

model calibration where the entire population is considered, the attempts to include the

two as separate variables were failed. There is a high degree of correlation between these

variables as well. As such, an aggregate private vehicle ownership variable, formed by

adding the two variables, is used in the model calibration.

2 Medium income range is taken as Rs.5 000 - 20 000 for the head of the household.

v \ ° j& j& i ' \& jfr ~& i& ^ Vfr .tf > > ^ .£> .«> JST jr jr ^ J> j? J> J ^ J ^

Figure 5.5: Private Vehicle Ownership per Household (Average of CUTS-1 & License Data)

l i m b Hear

54

5.3.2.6 Rail Station Density Distribution

The station density is calculated as shown in Table 5.6. Figure 5.6 graphically

shows these values. It can be seen that except for Colombo DSD, other DSD's are not

adequately served by the rail stations. Even though the service frequency of rail is not

considered here, the accessibility to rail is represented by the station density. Since every

DSD does not have a rail station, in the model calibration, this variable enters the model

in exponential form. This is due to the fact that when there are no rail stations, the model

prediction should not be equal to zero. Typical format of such a regression model is given

in section 6.2.

5.3.3 Analysis of Variation of Two variables

In this section, the effect of variation of vehicle ownership and rail density on the

home based work trips taken as a percentage of total work trips is analyzed. Figure 5.7

gives the variation of percentage of home based work trips by bus with private vehicle

ownership. Definite pattern of variation cannot be identified here. This is mainly due to

the fact that work trip percentage is dependent not only on vehicle ownership but also on

the availability rail transport. In general, there is a decreasing trend in the percentage of

bus trips with the increase in vehicle ownership. This means as the vehicle ownership

increases, there is a shift of mode preference from public (bus) transport to private

transport. However, the observed bus modal share of Kalutara, Maharagama, Negombo,

Minuwangoda and Ja-Ela appears to be low.

Figure 5.8 shows the relationship between rail station density and percentage of

hbwt. It can be seen that when there are no rail stations, the bus modal share is very high.

The low home based work trip generation percentage may be attributed to sampling error.

As the rail station density increases, the bus modal share gradually decreases. Again,

Maharagama and Negombo data points seem to under predict the bus trips.

0.35

0.30

Figure 5.6: Observed Rail Station Densities in CMR

95.0

90.0 4> Biyagama

56

85.0

80.0

£. 75.0 H 2

es 5? 70.0

Bandaragama 4> Kaduwela

. • Kelaniya Kolonnawa Mahara

e Horana 4> • Kesbewa f

• Panadura # Homagama Katana

Nugegoda

65.0

60.0

55.0

4> Moratuwa

• Kalutara • Ja-Ela

Wattala

Dehiwela

Gampaha •

«> Minuwangoda

* Negombo Maharagama

Colombo

50.0 0.15 0.20 0.25 0.30 0.35

PV per HH

0.40 0.45 0.50 0.55

Figure 5.7: Modal Share of HBWT by Bus Vs Average Private Vehicle Ownership per HH

- 9 5 * -

90.0

Kesbewa

4> Biyagama

85.0

4> Kaduwela

80 0 + Nandaragama

Mahara Katana

75-0 • Horana

Kolonnawa u „ ™ o „ o m , ... + Panadura Homagama •

70.0 Nugegoda

; Gampaha 65.0 -) * # +

; Wattala Minuwangoda • Kalutara

60.0

55.0

Kelaniya

• Ja - Ela

• Dehiwela Negombo

Moratuwa

4> Colombo

Maharagama

o.i -0.05 0.05 0.1 0.15

Stations/km2

0.2 0.25 0.3 0.35

Figure 5.8: Percentage of H B W T by Bus Vs Rail Station Density