TEHLEESIAN1

7/28/2019 TEHLEESIAN1

1/163

CHAPTER 1

INTRODUCTION

1.1 Background

At the beginning of the twentieth century, when the revolution in

transportation was only dimly foreseen, H.G. Wells, on a visit to North America,

wrote that the way people and their belongings get from one place to another is in

itself a trivial matter but that the process involves other matters that have an almost

fundamental relation to the social order (Edwards, 1992).

The truth of this observation is everywhere apparent. Transportation is only

one of many factors influencing the nature of society, but its special role derives

from the fact that without it, the effective operation of other sectors of the economy

is almost always precluded.

It is unarguable that transport is essential to the functioning of any society. It

influences the location and range of productive and leisure activities, residence,

provision of goods and services available for consumption. It inevitable influences

the quality of life.

In view of the growth of demand to come, will a transport-dependent societybe able to cope with the growing backlog of needs and to meet the still higher

1


2/163

demand for quality service that seems inevitable? Although the accomplishments of

transportation are notable, but there is growing concern over congestion, physical

deteriorating, accidents and so on. Current trends raise momentous issues for

transportation planners.

Furthermore, in Malaysia, the car ownership is growing rapidly everywhere

in recent year. The same situation even occurs in the university campuses. This

trend seems to be continuing in the future if the environment also remains growing in

trend as before. Besides, as a developing country, the continued growth of the

countries economy especially in the field of technology, engineering and other

developments consequently increased the needs of experts in the respective fields.

With these growths, university campuses have been expending in the aspect of

population, premises, facilities and infrastructures which including the traffic and

transportation networks.

However, relatively little attention has been given to parking and

transportation issues in and around college campuses in Malaysia. Such issues are

important as future policy makers in transportation are exposed to and influenced by

these systems. University campuses actually provide an excellent laboratory for

implementing various transportation management alternatives. Therefore, in this

study, the main campus of Universiti Teknologi Malaysia in Skudai, Johor Bahru, is

selected as a study area because it is one of the largest universities among 19

universities in Malaysia with approximately 28,000 students and 3,500 staffs

attending the main campus.

1.2 Need for the Study

The state of the road has been a favourite topic of conversation from earliest

times. The debate continues today. The problem of inadequate public transport, of

2


3/163

road congestion, of inadequate parking space and of the need to preserve the good

quality of the environment provide the source of news for all local newspaper.

In fact the necessity for transport planning is largely self-evident.

Throughout the day and night people are engaged in a variety of activities which

including working, going to school, shopping and so on. Hence, to take part in these

activities people frequently need to travel between their origin and destination for

some distance. It is a fact to admit that all of these activities are very depending on

the provision and level of services of traffic and transportation system available in

the zoning areas.

As happen in most of the areas, transport problems also occur in the

university campuses. For instance, Universiti Teknologi Malaysia in Skudai with the

area of 1,222 hectare and students of approximately 28,000 and 3,500 staffs could be

simply treated as a small urban area.

Wells (1975) highlighted that the small town and we can perhaps visualize

a typical town as being one of about 30,000 population has transport problems, but

they are different from those of the large town. The solutions too are different. And

because of its size, the capital resources likely to be available for transport

investment in a small town, within a typical planning period, are also small

possibly less than proportionately so.

The same idea might be applied in the campus with such a big population.

As we know, transport problems always occur with the growth of population. In

order to achieve a totally well planned and organized campus, traffic and

transportation system is an essential portion, in fact the prior part of the whole

development planning of the campus. Without a proper planning of traffic and

transportation system in the campus, a lot of unwanted transport issues such as delay

in attending lectures, lack of parking space and green zones, environment pollution

might be raised. If there is little doubt about the need for transportation planning,

there is much more doubt about its success.

3


4/163

1.3 Objectives of the Study

The main objectives of this study are to:-

i) obtain AM and PM peak hour trip generation volumes in terms of

numbers of students, numbers of staffs and total numbers of

population;

ii) obtain peak hour trip generation volumes of the trips attracted by the

home-based students going to lectures and staffs going to work

activities to Universiti Teknologi Malaysia from in-campus hostels in

terms of vehicle ownerships / modes used for traveling;

iii) obtain peak hour trip generation volumes of the trips attracted by the

home-based students going to lectures and staffs going to work

activities to Universiti Teknologi Malaysia from off-campus hostels

and private residential areas in terms of vehicle ownerships / modes

used for traveling;

iv) obtain the trip generation linear regression equations for the trip

generation above by conducting the relevant statistical analysis for

model building of Universiti Teknologi Malaysia;

v) determine and compare the correlated independent variables between

objective (ii) and (iii);

1.4 Scope of the Study

To accomplish the foregoing objectives, the scope of the study is defined to

include the following:-

i) to collect reliable information of land use and numbers of population;

4


5/163

ii) to obtain the existing traffic and transportation system provided and

policy establishment;

iii) to ascertain trip generation among the zoning areas within the

boundary of study area by way of Origin & Destination (O&D)

interview surveys;

iv) to identify and allocate variables such as population and vehicle

ownerships / modes of travel, which can be used to forecast the trip

generations in terms of the particular parameters;

1.5 Proposed Research Methodology

This proposed study intends to determine the weekday peak hour trip

generation linear regression equations to the main campus of Universiti Teknologi

Malaysia in terms of the numbers of students, staffs and total population, and also the

vehicle ownerships / modes of traveling by the population staying in in-campus

hostels or off-campus hostels and surrounding residential area as to form specific and

reliable trip generationmodels particularly for the main campus of Universiti

Teknologi Malaysia in Skudai, Johor.

In order to form a transportation model, there is a common basic approach

which can be applied to all forms of transportation planning, including planning for

local transport policy. This approach may be summarized by three distinct phases,

which consist of a survey, analysis and model building phase; a forecasting phase;

and an evaluation phase. The first phase give the result of the existing travel

demand, and the way that this demand satisfied on the existing transport facilities.

The relationship between the present demand and the existing environment is

examined. This examination enables models to be model of these relationships,

which can then be used, in the forecasting phase. The forecasting stage uses the

relationships established in the analysis and model building stage to make estimatesof the future travel demand. In this case, it requires the planner to provide a plan for

5


6/163

testing. Therefore, information is needed on the population and transport facilities

proposed within an area, so that the results that would follow from such a plan can be

forecast. Finally, the evaluation stage assesses the results of the two previous phases

to see whether they satisfy defined objectives.

This study focuses on the first phase of the process, i.e. the survey, analysis

and model building phase. The detail of the methodology of this study is discussed

in Chapter Three.

6


7/163

CHAPTER 2

LITERATURE REVIEW

2.1 Introduction

The way people and their belongings get from one place to another is in itself

a trivial matter but that the process involves other matters that have an almost

fundamental relation to the social order. Therefore, it could not be denied that

transport is essential to the functioning of any society.

The demand for transport derives from the needs of people to travel from one

place to another to carry out the activities of their daily lives. This demand for

movement is affected by (Bruton, 1985):-

i) the location of the home, workplace, shopping, educational and other

activities;

ii) the nature of the transport system available; and

iii) the demographic and socio-economic characteristics of the

population.

Bruton (1985) also highlighted that the demographic and socio-economic

characteristics which are most significant in influencing the demand for transportare:-

7


8/163

i) population size;

ii) population structure, by age and sex;

iii) household size, structure and formation rate;

iv) the size and structure of the labour force, income level, which is

usually measured by some proxy such as number of cars owned or

owner occupation;

v) the socio-economic status of the chief economic supporter of the

household.

These characteristics can interact in a complex way to affect demand for transport,

while their location or distribution and the way the interrelationships change with

time further complicate an already complex situation.

Experience tells that building more traffic capacity is not always the answer

to solve transport problem. In many cases in Malaysia especially in the big city such

as Klang Valley and Johor Bahru, new highways are jammed as soon as they are

opened to traffic. This is a phenomenon that is occurring in urban areas throughout

the world. In order to overcome the transport problems in a long run, Owen (1992)

suggested that measures could be taken to reduce the need for transportation. Some

types of transportation can be avoided. There are many examples of how

transportation has been reduced through fortuitous events outside the transportation

system. One of them is, in the foreign countries, hauling ice gave way to

refrigeration by electricity delivered by wire and home heating that once depended

on carrying coal has likewise seen energy resources shift to wires and pipes.

In developing country like Malaysia, the above idea currently only can be

considered as a long-term vision although some cases applied but it only helps to

reduce very minority traffic problems. The recent transportation issues still need to

be solved immediately. However, the feedback effect of new transport capacity

provided always end up with encourages more development and subsequently more

traffic. Therefore, proper traffic and transportation planning is essential which

initially involves trip generation studies.

8


9/163

According to Clark (1975), the rapid rise in vehicular travel in the sixties

created acute transport problems in urban areas and led to comprehensive land use /

transportation studies (LUTS) for many towns. This resulted in the collection of

relevant data for trip generation studies as part of these LUTS, either in the form of

person movement from home interview surveys or of goods vehicle movements from

survey of owners and users.

2.2 Transport Planning

As been discussed earlier in Chapter 1, based on most of the literature study

done, there is a common basic approach can be applied to all forms of transport

planning, which may be summarized by three distinct stage:-

i) a survey, analysis and model building phase;

ii) a forecasting phase;

iii) and an evaluation phase.

The sequence of the steps basically followed as part of transport planning process is

shown in Figure 2.1.

In order to plan, initially, it is essential to have information. Thus, planning

has to be dependent on surveys, which may take different forms. One of the most,

the home interview surveys has become an accepted source of information. Prior to

any survey or data collection, the purpose and area of the study must be clear

defined. There is a tendency to collect a large amount of information that it ought to

be useful if the definition of the purpose and area are not clear. Data collection is an

expensive and time-consuming process. It is always the case that considerable

quantities of data are required and great care is needed in designing the survey stage

of a study to ensure that the results are statistically significant. There is a danger ofnot collecting enough data to calibrate models with reasonable level of significant.

9


10/163

10

Definition of thesurvey area - zoning

Inventory ofExisting Travel

Patterns

Inventory ofExisting Transport

Facilities

Inventory ofExisting Planning

Parameters

Summary of existing Travel

Characteristics

Trip Generation

Trip distribution

Trip Assignment

Modal split

Future PlanningParameters

Future TransportFacilities

Future Policies

EvaluationRevise policies

Survey

Forecast

Evaluation

Analysis &ModelBuilding

Figure 2.1: General Transport Planning Process


11/163

There is also the possibility that too much data is collected. These are both waste of

resources and hindrance to an understanding of the problem.

The data collected from an adequate survey suppose contains a lot of

information regarding travel behaviour. Analyses of these data are very useful for

designing the existing facilities such as traffic system management or arrangements

of public transport system. Besides, it provides an understanding of the relationship

between travel behaviour and the environment so that the relationships can be

established and synthesized quantitatively using modeling techniques.

Lane (1971) mentioned that it has been found from past studies which the

vast majority of work trips take place in the morning and evening peak, most

journeys begin and end at home, offices and shops attract more journeys per unit of

area than industrial or commercial land use.

The model building process is the most challenging stage of transport

planning. It is important to forecast future travel demands. The transport model is

conventionally divided into four main procedures:-

i) trip generation;

ii) trip distribution;

iii) trip assignment; and

iv) modal split.

All these stages are assumed to be separated although in the real world they are all

interlinked.

Trip generation can be considered as a decision to make a journey. It

examines the relationship between the number of trips made and certain quantifiable

parameters such as trip purpose, household income, vehicle-ownership and land use.

This stage of modeling will be discussed more detail in the next portion of this

chapter. The next stage in the transport model is trip distribution, which involves

selection of a suitable destination and analysis of the trips between zones. The trippatterns between all zones and the network information are used to develop reliable

11


12/163

relationships between these trips, their geographical origins and destinations and also

the type of vehicles they will use. Whereas, trip assignment is the process by which

the route a traveler will take between two zones is determined. It is common to

decide that the route, which can be assigned to a trip between two zones, is the route

uses the minimum time or cost of travel. Another last stage is modal split that the

individual chooses a particular mode of travel. Data collected at the survey stage is

used to determine the proportions of people using public transport.

As a completed transport model has been assembled, data consistency checks

should be followed. Actual traffic counts at certain location are checked with the

volumes from the synthetic model. If there is discrepancy, the mathematical

relationship formed earlier has to be adjusted, so call calibration. Then, finally the

model can be used to forecast future travel demands.

In order to forecast the future travel demands, it is necessary to obtain as

much information affecting travel behaviour as possible such as population,

employment and income distribution. A transport model that satisfactory explains

the present use of the existing transportation system is an invaluable guide for any

future use of any other transportation system. Forecasting normally involves

prediction of the future demand for travel, the future availability of transport and use

of the available transport.

After the completion of traffic forecasting process, the results need to be

evaluated. Firstly, the computation process and output must be reconfirmed. It is

mainly numerical evaluation to ensure that the model is mathematically correct.

Following that, the apparent accuracy of the resultant forecasts should be examined.

Other than evaluation of the model itself, operational and economic evaluations also

need to be carried out. The purpose of operational evaluation is to check whether a

new proposed system or network is able to satisfy the forecast travel patterns.

Whereas, economic evaluation is to study the cost-benefit so that to choose the best

network proposal which falls within a given budget constraint, but with maximum

benefit to the community by minimizing the cost of travel.

12


13/163

2.3 Trip Generation Modeling

As discussed in the earlier subtopic, the first stage of transport modeling is

trip generation. In this study, this stage of transport modeling will be used to

produce the trip generation model. Hence, some description of trip generation by a

few authors or researchers will be presented.

Journey is a one-way movement from a point of origin to a point of

destination. Although the word trip is literally defined as an outward and return

journey, often for a specific purpose, in transport modeling both terms are used

interchangeably (Ortuzar, 1990).

According to Lane (1971), trip generation often used as a general term to

describe the trip-end forecasting models of generation and attraction. Trip

generation is an examination of the relationship between the number of trips made

and certain quantifiable parameters. Trip generation rates are used to predict the

number of vehicle trips generated by specific land use when further planning of any

developments is to be carrying out. Based on this outcome, the volume of traffic that

would be generated by the development can be forecasted and the impacts can be

analyzed.

All traffic studies involve the estimation of the trip generation of a particular

land-use. Generally, in traffic studies, trip generation can be studied based either on

person trip generation or vehicle trip generation. Person trip generation is the total of

person trips generated by one type of land use. Whereas, vehicle trip generation

refers to the total number of vehicles generated by a land use.

Normally, transport planners are interested in all vehicular trips, but walking

trips longer than a certain study-defined threshold (say 400 meters or three blocks)

are often considered. Eventually, trips made by infants of less than five years of age

will usually be ignored (Ortuzar, 1990).

13


14/163

Every trip will have two trip ends, an origin and a destination. At both ends

there always associate with a purpose, for instance, from home to work. Based on

the study of Lane (1971), trip ends in this form were used in many of the early

studies. As transportation planning technology developed later, it was found

necessary to introduce new definitions. It was observed that trips could be classified

into two types, home-based and non-home-based, the former being defined as having

one end of the trip at the home of the person making trip, and the latter having

neither end at the home of the person making the trip. It was then also found

convenient to split trips ends into two classes: ends from which trips radiated, known

as trip generations and ends to which trips were attracted known as trip

attractions. A trip generation was therefore defined as the home end of a home-

based trip, or the origin of a non-home-based trip; while a trip attraction was

defined as the non-home end of a home-based trip or the destination of a non-home-

based trip.

As defined by Ortuzar (1990), home-based trip is one where the home of the

trip maker is either the origin or the destination of the journey. Conversely, non-

home-based trip is one where neither end of the trip is the home of the traveler. In

Klang Valley of Malaysia, Wan Ahmad, et al. (1986) identified that almost 80.5 %

of all trips are home-based trips.

In the Trip Generation Study (Pilot Study) of Malaysia (Highway Planning

Unit, 1997), trip generation can be defined as the total number of inbound and

outbound vehicle trip-ends from a site over a given period of time. Generation

here does not imply a direction. Trip is a journey of a person or vehicle that begins

at one location and ends at another one. Trip-End is defined as the start or end of a

trip. Each trip has two trip-ends, an origin and a destination. When the number of

vehicles entering or exiting a site is counted, trip-ends are counted. For the purpose

of a site-specific traffic impact study, the distinction between trips and trip-ends is

not important. For area-wide traffic studies, however, the distinction between a trip

and trip-end is very important because care needs to be taken that the trip is not

counted twice.

14


15/163

According to Definition of Terms in Trip Generation, which published by

Institute of Transportation Engineers (1991), trip is a single or one-direction vehicle

movement with either the origin or the destination (exiting or entering) inside a study

site. One trip-end is equal to one trip, as defined prior to this. For trip generation

purposes, total trip-ends for a land use over a given period of time are the total of all

trips entering plus all trips exiting a site during that designated time.

Trip generation modeling is to analysis and estimates the person or vehicle

trips. The techniques used for the purpose are usually identifying the relationship

between travel characteristics and the environment. Trip generation models are

available for land uses in many developed countries. The most common are those

presented by the Trip Generation (Institute of Transportation Engineers, 1991).

Nevertheless, the models generated in the developed countries may not be

adequate for developing country like Malaysia in view of the different life-style,

cultural, weather and socio-economic background of the populations. Therefore, it is

important to develop proper models and travel patterns for different land uses or trip

purposes in developing countries.

According to Ortuzar and Willumsen (1990), a model can be defined as a

simplified representation of a part of the real world, the system of interest, which

concentrates on certain elements considered important for its analysis from a

particular point of view.

Model building is a mathematical process used to formula relationships

between two or more variables. The term model is used to describe both simple

mathematical relationships, such as a trip generation model, which might describe

trip-making behaviour and complementary linked systems of relationships, such as

the transportation planning process. A complex model such as the transport model

comprises a number of sub-models, such as the trip generation model.

Coombe (1996) reported that mathematical models of transportation systems

have played a prominent role in transportation planning throughout the world sincethe 1960s. These models are hypotheses of how people use transport system. The

15


16/163

reason for using any kind of synthetic model is that the complexities of human

behaviour are such that future travel choices cannot be forecast directly from

observation of existing behaviour, although it is inevitable that behaviour is

represented in a simplified way in a model.

He also tells that models which have been adequately calibrated and validated

provide a means of extrapolating empirical evidence, thereby enabling more

extensive conclusions to be drawn from the necessarily limited empirical evidence of

how road users respond to changes in the road system. However, it needs to be

recognized that these extrapolations rely for their validity on the realism of the

underlying theories. Transportation models can only tell us about the relationships

actually built into them. Therefore, the models cannot inform us about drivers

reactions for which mathematical relationships or procedures have not been

developed and incorporated in the models. In that respect, of course, the evidence

from modeling exercises is only as good as the behavioural basis behind the model

forms and the response coefficients.

2.4 The Experiences of Developed Countries

The trip generation experience in the United States is long and varied. There

have been numerous studies over the years conducted by state and local governments

as well as by consultants. The most commonly used source of Trip Generation data

is the Institute of Transportation Engineers (ITE) Trip Generation manual. In United

State of America (USA), the Institute of Transportation Engineers (ITE) has

produced trip generation rates for various specific land use based on more than 3,000

trip generation studies. The ITE data has been shown to be relatively stable over

time for most land uses, despite significant increase in car ownership and two-

income families during the past 30 years. This stability might be attributed to the

reducing factor on trip rates caused by smaller household size as well as an increasein the number of households whose car ownership exceeds the number of registered

16


17/163

drivers in the household. This observation may be of use in the Malaysian context as

it suggests that once a certain level of trip generation is reached, increase in car

ownership will not necessarily result in an increased in vehicle trips.

In USA, the procedure of collecting trip generation data is relatively simple

compare to the conservative one because development patterns have resulted in a

large number of stand-alone developments with isolated parking areas which are

used only for the development under consideration. Vehicle classification counts are

not conducted since private cars dominate.

The above practice in USA varies widely from community to community.

There is a high degree of reliance on the ITE Trip Generation Manual. But, some

cities do publish their own rates based on local surveys. For special case, the degree

of professional interpretation might vary widely. However, of course, the majority of

the practices focus on the ITE Trip Generation Manual.

In United Kingdom of Britain and Northern Ireland, one source of trip

generation is the TRICS system. TRICS was formed in 1987 in Southern England to

combine and swap trip generation data for development control purposes. It is a

computer database that holds traffic-count data for a series of land uses. Key

features of the system and the approach to trip generation are as below (Highway

Planning Unit, 1997b):-

i) Input parameters include: retail floor area, office space, number of

units and so on as well as descriptions relevant to each project and its

location.

ii) The system allows a user to select one site or multiple sites which will

be included in the calculation of an average trip rate.

iii) The system is designed to maximize the scope for professional

interpretation of the data.

iv) Some guidelines and cautions based on experience with the data set

have been set forth; but no fixed and firm rules on how to use the data

have been established.v) A private consultant maintains the database and program.

17


18/163

vi) Data developed comes from a variety of sources, not just the southern

countries in England where the original data set was developed.

Suppliers of data are responsible for the quality of the data; however,

an audit check is performed before the data is added to the set.

vii) It is intended that all sites be re-surveyed on a 5-year cycle to keep

current with changing travel patterns. Data older than 6-years is

automatically excluded from the summaries.

viii) This system allows users to add their own data to the database

ix) The data set is defined with twenty three (23) land use categories.

The key advantage of this approach is that a user can focus on survey sites

that most accurately portray the project being studied. This approach requires a high

degree of experience and professionalism to obtain good results. Therefore, the UK

approach relies heavily on the professionalism of its traffic planners to interpret and

apply the trip generation data. In other wards, only guidelines are suggested and

each professional must rely on their own knowledge and experience to inter prt the

data.

The above discussions are summarized from the Trip Generation Study (Pilot

Study) of Malaysia (Highway Planning Unit, 1997b) in establishing a proper trip

generation manual to be used for the local communities in Malaysia.

2.5 Vehicle Growth in Malaysia

In Malaysia, the car ownership is growing rapidly in recent year. This trend

seems to be continuing in the future if the environment also remains growing in trend

as before. Table 2.1 shows the tabulation of the vehicle registrations by state in year

1996. Although this data does not give indication on the vehicle ownership rates, it

provides an overview for the distribution of vehicle ownership in Malaysia.

18


19/163

Table 2.1: Motor Vehicle Registration As at 31st December 1996

StateTotal Number of Registered Vehicles As

at 31st Dec., 1996

Perlis 25,466

Kedah 366,037PulauPinang 837,023

Perak 845,689

Selangor 1,036,624

Wilayah Persekutuan 1,502,890

Negeri Sembilan 325,431

Melaka 269,614

Johor 1,064,110

Pahang 334,883

Terengganu 166,583

Kelantan 239,361Sabah 257,837

Sarawak 415,136

Total 7,686,684

Source: Highway Planning Unit, 1996

Basically, Table 2.1 calculated that about 20 % of all registered vehicles are

registered in Kuala Lumpur, which rank the highest total of vehicle ownership.

Whereas, Johor reached the second highest rate with 14 % of the whole. And, the

study area of this study is located in the capital of Johor state. To highlight the rapid

growth of vehicle ownership in Malaysia, the total numbers of motor vehicle

registrations from year 1987 to 1996 are tabulated in Table 2.2.

19


20/163

Table 2.2: Motor Vehicle Registration Malaysia from 1987 1996

Year Total

1987 4,600,3841988 4,782,9161989 5,071,7861990 5,462,7781991 5,887,1761992 6,295,5081993 6,712,4791994 7,210,0891995 6,802,3751996 7,686,684

Compounded Growth (%) 5.95

Source: Highway Planning Unit, 1996

2.6 Development of Trip Generation Data in Malaysia

As mentioned earlier, in Malaysia, the first documentation relevant to trip

generation data is the Trip Generation Study (Pilot Study) conducted by the Highway

Planning Unit of Public Works Department Malaysia. Prior to the Trip Generation

Pilot Study, there was no widely accepted single source of land-use specific trip

generation rates. Generally, the traffic planners will conduct their own trip

generation studies. Nevertheless, these information gathered are neither documented

nor shared among the professionals. This leads to concern about the validity of the

trip generation that was used or the procedures used to develop the trip rate. Without

standard procedures and proper documentation of results, it may create problems

such as unverified trip rates, application of trip rates based on older or inadequate trip

generation rate which may not valid for an application of trip rates that sources are

rarely disclosed in a traffic study.

The above issues brought to the establishment of the Trip Generation Pilot

Study by the Highway Planning Unit of Public Works Department Malaysia. A

slightly different approach was taken to incorporate in the study since some

weaknesses have been noted and experienced in the ITE Trip Generation. The Trip

20


21/163

Generation Pilot Study of Malaysia endeavoured to address the question of

geographical differences, differences in economic and social conditions between

regions, vehicle classification and vehicle occupancy counts as cited in the above

discussion. The peak period definition also was expended to two-three-hour periods,

in recognition of the extended peak hours in Kuala Lumpur region.

Substantial differences should be expected in trip generation rates between

countries. Hence, the main objective of the Trip Generation Pilot Study is to gather a

large trip generation database, which would be accepted by the public and even

private sectors, for use particularly in Malaysia (Highway Planning Unit, 1997b).

2.7 Transport Studies Associated with University Campus

Corporate campuses of the 21st century, once traditionally suburban settings,

are being transformed. Across America, corporate campuses are gaining favour as

companies seek to reduce expenses by downsizing their big-city offices in favour of

less-expensive real estate in the suburban (www.vtpi.org, 2002).

The Victoria Transport Policy Institute in Canada has carried out studies on

Campus Transport Management namely Trip Reduction Programs on College,

University and Research Campuses. Campus Transport Management programs are

coordinated efforts to improve transportation options and reduce trips at colleges,

universities and other campus facilities. According to their studies, Transportation

Demand Management (TDM) tends to be particularly effective and appropriate in

such settings. It is often more cost effective than other solutions to local traffic and

parking problems, and students and employees often value having improved

transportation choices.

Transportation Demand Management (TDM) is a general term for strategiesthat result in more efficient use of transportation resources. There are many different

21
http://www.vtpi.org/http://www.vtpi.org/


22/163

TDM strategies with a variety of impacts. Some improve the transportation options

available to consumers, while others provide an incentive to choose more efficient

travel patterns. Some reduce the need for physical travel through mobility substitutes

or more efficient land use. TDM strategies can change travel timing, route,

destination or mode. TDM is recently becoming common response to transport

problems.

Brown, et al. (1998), have summarized that there is an increasing in number

of colleges and universities offer free or significantly discounted transit passes to

students and sometimes staff which is called as UPASS. According to them,

students voted overwhelmingly to support many of these programs, even though it

increases their fees.

Other than UPASS programs in North America, some campuses use vehicle

restrictions and regulations to limit automobile use. For instance, some colleges do

not provide parking permits to freshmen who live in campus. This action not only

manages to reduce the automobile use, but also encourages students to become more

active in campus activities and discourages them from taking jobs to finance a car.

These campus TDM programs are often implemented by facility managers

and administrators to address a particular problem, such as a parking shortage or

traffic congestion on nearby streets. Some are initiated by student groups to improve

their travel options and achieve environmental or community goals. For example,

UPASS programs often require students to approve a special levy to fund universal

transit passes. Student and employee organizations are often involved in program

planning and management.

Summary from the studies shows that campus TDM programs often reduce

automobile trips by 10 30 %. A program at the University of Wisconsin-

Milwaukee reduced student driving by 26 % (Meyer and Beimborn, 1996). A

University of Washington program reduced total vehicle trips to campus by 16 %

during its first year of operation (Williams and Petrait, 1993). By the year 1998, the

morning vehicle trips to the University of Washington campus decreased 19 percentover year 1990 levels, despite growth in the campus population.

22


23/163

Poinsatte and Toor (1999) pointed out that the University of Colorado ski bus

program provides students and staff with access to downhill ski areas. It was

established in 1996 and is jointly funded by the ski resorts, ticket sales and student

bus pass fees. The program has proven quite popular that during its first two years

all buses were sold out. Some students reported that it allows them to live in campus

without a car.

Standard university in Palo Alto, California planned to expand capacity by 25

%, adding more than 2.3 million square feet of research and teaching buildings,

public facilities and housing without increasing the peak period vehicle traffic. By

year 2000, 1.7 million square feet of new buildings had been developed while

automobile commute trips were reduced by 500 per day! To accomplish this, the

campus transportation management plan involves a 1.5 mile transit mall, free transit

system with timed transfers to regional rail, bicycle network, cash-out staff

parking, ridesharing program and other transportation demand management

elements. By using this approach the campus was able to add $ 500 million in new

projects with minimal planning or environment review required for individual

projects. The campus also avoided significant parking and roadway costs. Planners

calculate that the University saves nearly $ 2,000 annually for every commuter

shifted out of a car and into another mode. This also reduced regional agency traffic

planning costs. It also brings public benefits which included decreased congestion

and improved safety on surrounding roadways and the regional traffic system,

reduced pollution and improved local transit options (www.stanford.edu, 2002).

All the above examples show that most of the universities see public transport

as a key factor in securing real choice in the way the community travels to and from

study, work and leisure. It recommends measures to discourage car use in favour of

alternative modes of transport. They have to work with the local authority and local

public transport providers to make sure that the service to the campus community is

as good as it can be. However, before any plan can be scheduled, traffic and

transport study such as trip generation should be carried out to ascertain the existing

traffic condition.

23
http://www.stanford.edu/http://www.stanford.edu/


24/163

2.8 Summary

In this study, the case study done in Universiti Teknologi Malaysia, Skudai is

a microscopic traffic study. In most of the macroscopic traffic studies, a university

campus is normally only considered as an independent landuse. Trip generation rate

of universities or colleges obtained from macroscopic traffic studies with university

or college as an independent landuse is generally for the purpose of forecasting the

total trips expected to be generated if a new university or college is to be built or

upgrading of any college in certain site. However, in this study, the main purpose of

study is to determine the trip generation involving the internal route network to the

center of the campus where the administration office, library, mosque and academic

area locate. Therefore, the university itself will no longer be a land use as a whole,

but will be subdivided into many zones of different landuses. Following the

analyses, with a better knowledge of existing traffic conditions in the campus,

planning of alternative traffic and transportation system or new relevant policies

would be allowed. The details of methodology of this microscopic traffic study were

described in the following chapter, Chapter 3 and the findings were presented in

Chapter 4 followed by the concluding remarks in Chapter 5.

CHAPTER 3

24


25/163

RESEARCH METHODOLOGY

3.1 Introduction

For the purpose of this study, trip is defined as a journey made by a student or

staff from their accommodations to the academic and administration centers of the

campus or vice versa. Trip generation is the total number of trips made in one

direction to or from defined location (inbound or outbound) over a given period of

time. Peak hour is the highest one and a half hour flow of traffic during a defined

period.

In the campus-traffic study, Universiti Teknologi Malaysia is selected as the

study area. One of the main reasons is an excellent environment for implementing

various transportation policies in the campus is provided. Furthermore, it is the

largest university among 19 universities in Malaysia with approximately 30,000

populations attending the main campus almost everyday. The detail descriptions of

the study area are discussed in the following section.

The process of completing this study involves a series of steps, which could

be categorized into four basic approaches namely data collection, survey, analysis

and model building. This chapter will discuss in sequence regarding the detail

methodology and procedures applied in achieving the objectives of this study as the

following:-

i) data collection;

ii) survey procedures;iii) analysis of data;

25


26/163

iv) trip generation estimation methods.

3.2 Study Area

Universiti Teknologi Malaysia, which located in Skudai, is one of the largest

universities among 19 universities in Malaysia with approximately 28,000 students

and 3,500 staffs attending the main campus.

A brief historical background of Universiti Teknologi Malaysia should be

introduced before any assessment or planning being carry out to provide a better

overview. Basically, Universiti Teknologi Malaysia comes from Institut Teknologi

Kebangsaan (ITK). Institut Teknologi Kebangsaan was established on 14th of March

1972. ITK was formed to provide, promote and develop higher education in the field

of science, engineering, technology and architecture. The establishment of ITK was

based on the upgrading of Technical College at Jalan Gurney, Kuala Lumpur, in line

with the recommendation of the Higher Education Planning Committee in 1969. The

beginning of Technical School, which was later, upgraded to college level in1946.

Institut Teknologi Kebangsaan that was of university status was later renamed

Universiti Teknologi Malaysia (UTM) on 1st April 1975. The whole idea of the

industry of Universiti Teknologi Malaysia as a technological institution began when

a philanthropist bestowed a grant of $ 30,000.00 for the establishment of a technical

school in Kuala Lumpur to meet the needs, due to the rapid construction of cart-roads

and railway lines was undertaken to replace the navigated rivers between 10 to 15

miles from the Straits of Malacca, after the opening of the Federation of Malay States

and the Straits Settlement as economic centers especially in the tin industry in the

early 1900 (http://www.utm.my/, 2002).

Currently, Universiti Teknologi Malaysia has three campuses, namely the

1,222-hectare main campus in Skudai, Johor; the 18-hectare branch campus at JalanSemarak, Kuala Lumpur and 400 hectare branch campus at Pekan, Pahang. The first

26
http://www.utm.my/http://www.utm.my/


27/163

phase of campus construction in Skudai was completed in mid 1985. The first

academic session in the new campus started with the transfer of the Faculty of Built

Environment and Faculty of Surveying. With the transfer of Mechanical

Engineering, Civil Engineering and Electrical Engineering faculties to the Skudai

Campus during the 1989/90 sessions, the Skudai Campus became main campus

while the campus at Jalan Semarak, Kuala Lumpur became the branch campus.

The continued growth of the countries economy especially in the field of

technology, engineering and other developments consequently increased the needs of

experts in the respective fields. With these growths, Universiti Teknologi Malaysia

has been expending in the aspect of population, premises, facilities and

infrastructures which including the traffic and transportation networks. Currently,

there are ten faculties specializing in various fields of study in science, technology

and management which include Faculty of Built Environment, Faculty of Civil

Engineering, Faculty of Electrical Engineering, Faculty of Mechanical Engineering,

Faculty of Chemical and Natural Resource Engineering, Faculty of Science, Faculty

of Computer Science and Information System, Faculty of Education, Faculty of

Management and Human Resource Development and Faculty of Geoinformation

Science and Engineering. Hence, the problems of transport would definitely increase

as the population is growing up rapidly.

3.2.1 Accessibility, Traffic and Transportation System of the Campus

The main campus of Universiti Teknologi Malaysia in Skudai, is located

about 18 kilometers from the city of Johor Bahru, and 14 kilometers from the SultanIsmail Airport. It is easily accessible by air, road and rail. There are regular flights

27


28/163

from Johor Airport connect the state capital with Kuala Lumpur. Transportation

services at the airport are also readily available. Taxis and air-conditioned coaches

are a popular and cheap means of transport to and from Johor Bahru to other cities in

the Peninsular Malaysia. Furthermore, Malayan Railway (KTM) also offers train

services connecting Singapore and other states in Malaysia through Johor Bahru

station.

As mentioned earlier, Universiti Teknologi Malaysia is the largest university

in Malaysia with approximately 28,000 students attending the main campus.

Nevertheless, only approximately 10,700 students live in the campus and the

remainder of students and staffs live in off-campus housing or hostel.

There is a University Transit System operates bus services from 7:00 a.m. to

11:30 p.m. throughout the academic year except during public holiday and

semester breaks providing transportation throughout the Skudai campus and

nearby residential areas. The schedules and routes of campus-bus services

are tabulated in Appendix A. Generally, more trips are operated during the

morning, afternoon and evening peak hours especially in the campus compare

to off-campus private residential area. Only few trips are provided during the

non-peak hours. All services are actually funded by a semester fee paid by

every student. Nevertheless, majority of UTM students either drive or ride

motorcycles to the campus. Many of the students park their vehicles

illegally at the periphery curbsides or parking lots designed meant for the

staffs near their faculties. This massive influx of trips into a relatively

concentrated area has created unique transportation challenges. The recent

solution to the problem of transporting students to and from the periphery lots

is the fixed-route campus bus system. This case study generally aims to

present implications for transportation alternatives or policies at university

campuses.

3.3 Data Collection

28


29/163

The process of data collection is very subjective as the results of analysis are

very depending on the data sources, survey operations and even the sampling

considerations. In the stage of data collection, there are two kinds of data required.

The first activity is to obtain all the relevant land uses, numbers of population and

existing traffic and transportation inventories and the second step is to carry out

survey (interview) to determine the existing traffic distribution, which will be

discussed in the later section.

3.3.1 Existing Inventories

This section involved the data collection from the relevant departments of the

university which including the total numbers of populations, the area of

accommodations of the populations, the capacities of the hostels in and off the

campus, the landuses and the locations in the campus, the existing traffic and

transportation networks and the established policies which related to traffic and

transportation system of the campus.

The boundary line, landuses and route networks of the study area gathered

from the Development-Planning Unit (Harta Bina) are rearranged in Figure

3.1 in Appendix B. The total numbers of student enrolled from semester

2000/2001 to 2002/2003 and the forecast enrolment of students from

semester 2003/2004 to 2009/2010 are tabulated in Table 3.1 in Appendix C.

These data are provided by the Enrolment and Record Unit ( Unit Kemasukan

dan Rekod). The existing total numbers of staffs according to the

departments are tabulated in Table 3.2 in Appendix C. The source of data is

from the Recruitment Unit of the Registrar Office (Unit Perjawatan, Pejabat

Pendaftar).

The information regarding the tabulation and the numbers of occupants of allthe hostels that prepared by the Student Affair Department (Hal Ehwal Pelajar) are

29


30/163

summarized in Table 3.3 in Appendix C. However, the tabulation of the students

staying in the off-campus private residential areas is unknown. Therefore, the

percentages of students staying in different residential areas are mainly based on the

results of survey done in Zone 1. Whereas, the tabulation of the staffs in different

residential areas are based solely on the proportionate of the information printed in

the Staffs Guideline for Faculty of Civil Engineering (Panduan Staf Fakulti

Kejuruteraan Awam) to the information gathered from the relevant department as

tabulated in Table 3.2 in Appendix C.

3.4 Survey Procedures

This initial stage is the most essential part of any transportation planning.

The first step in any analysis unavoidably involves collection of data, which also

including field surveys other than obtaining supporting data or inventory such as land

use information discussed in the formal section. The field survey incorporated in this

study is the origin and destination (O & D) interview surveying. The procedures

employed in the interview surveying process will be elaborated in this section.

3.4.1 Zoning System

A zoning system is used to aggregate the individual premises into

manageable chunks for modeling purposes. The main two dimensions of a

zoning system, which are related, are the number of zones and their size. The

greater the number of zones, the smaller they can be to cover the same study

area.

30


31/163

The first element in this section is the definition of the area to be studied in

detail. Large-scale studies such as Klang Valley Traffic Survey would need to

describe travel over a whole conurbation and concern with the traffic distribution

expected to use the more important strategic transport facilities such as motorways,

major through routes, commuter and light rail traffic. However, smaller-scale

studies, such as traffic and transport study in the campus of Universiti Teknologi

Malaysia, can describe travel in considerably more detail.

Prior to any setting up of zones, a cordon line representing the boundary of

the study area must be established. The first choice in establishing a zoning system

is to distinguish the study area itself from the rest of the world. For the purpose of

this study, the cordon line set for this study includes the whole land area within the

boundary of the UTM campus and the periphery residential area as shown

representatively in Figure 3.1 in Appendix B.

After the delineation of a survey area, the study area is divided into analysis

units that normally called traffic analysis zones, which form the basis for the analysis

of travel movements within, into and out of region. The primary purpose of selecting

zones is to permit summarizing, within reasonably small area, the origin and

destinations of traffic. Normally the zones are numbered and all trips with origins or

destinations within a zone are assumed to begin or end at the centroid of those zones.

Care must be taken to select zones so that there are not so many of them to render

analysis cumbersome. On the other hand, too few zones will give an unrealistic

grouping of trip ends and perhaps lead to erroneous conclusions. The size of a zone

will be governed by the size of the survey area, density of population and purpose of

the study. Primarily, the division of the zoning area are defined with several pre-

determined criteria in mind, which had been set as the requirement of the zoning

system:-

i) Zoning size must be such that the aggregation error caused by the

assumption that all activities are concentrated at the centroid is not too

large;

ii) Zones should be as homogeneous as possible in their land use;iii) Zone boundaries must be compatible with cordon line;

31


32/163

iv) The shape of the zones should allow an easy determination of their

centroid connectors;

v) Zones do not have to be of equal size;

vi) Minimizing the number of intrazonal trips;

vii) Generating only connected zones and avoiding zones that are

completely contained within another zone.

In this study, the study area is divided into fourteen small zones with very

clear definition of land use, according to the criteria discussed above. Based on

Figure 3.1 in Appendix B, the fourteen zones can be defined as in Table 3.4 in the

following. Generally, Zone 1 is the administrative center that could be assumed as

part of the CBD, Zone 2 to 6 are academic areas that could be also assumed as part

of the CBD and the rest of the zones are residential area.

This traffic study aims to determine the peak hour trip generated and attracted

by the students and the staffs from or to their home to or from their faculty or office.

Hence, the travel patterns to be investigated are from Zone 7 - 14 to Zone 1 - 6 or

from Zone 1 - 6 to Zone 7 - 14. As the defining of each zone is clear, Origin and

Destination (O& D) interview survey could be planned.

Table 3.4 : Description of Small Traffic Analysis Zones

32


33/163

Zone No. Description

Zone 1 This zone could be considered as the CBD of the campus. It includes the

Sultanah Zakariah Library (PSZ), Sultan Ismail Mosque, administrative

office, Sultan Iskandar Hall (DSI) and Student Union Building (SUB), which

also provides with kinds of services such as bank, cafeteria, Photostat center,

post-office and bookshop.

Zone 2 Combination of three faculties : Faculty of Built Environment, Faculty of

Civil Engineering and Faculty of Geoinformation Science and Engineering.

Zone 3 Combination of four faculties : Faculty of Science, Faculty of Mechanical,

Faculty of Education and Faculty of Management and Human Resources

Development.

Zone 4 Faculty of Computer Science and Information System.

Zone 5 Faculty of Chemical and Natural Resources Engineering.

Zone 6 Faculty of Electrical Engineering.

Zone 7 Student Hostel within campus named Kolej Tuanku Canselor.

Zone 8 Student Hostels within campus named Kolej Rahman Putra, Kolej Tun

Fatimah and Staff Quarters named Desa Bakti 1, Desa Bakti 2 and Desa

Bakti 3.

Zone 9 Student Hostels within campus named Kolej Tun Razak and Kolej Tun

Hussein Onn.

Zone 10 Student Hostel within campus named Kolej Tun Dr. Ismail.

Zone 11 Off-campus housing area : Taman Sri Pulai, Taman Teratai, Kangkar Pulai,

Pontian, Pekan Nanas.

Zone 12 Off-campus housing area : Taman Universiti, Taman Pulai Utama, Gelang

Patah.

Zone 13 Combination of off-campus housing area : Taman Desa Skudai and off-

campus student hostel: Kolej Siswa Desa Skudai.

Zone 14 Off-campus housing area : Taman Sri Putri, Taman Sri Skudai, Taman Tun

Aminah, Taman Skudai Baru, Taman Bukit Indah, Taman Mutiara Rini,

Senai, Kulai and those housing area from the direction of Johor Bahru which

connected to Jalan Skudai to access to campus of UTM.

3.4.2 Origin and Destination Interview Survey

The origin and destination (O & D) study establishes a measure of the

patterns of movement of vehicles and passenger within a particular area of interest

33


34/163

from various zones of origin to various zones of destination. This kind of study

estimates the travel characteristics observed for a typical day or a period of time.

The O & D study yields information regarding origins and destinations of trips, times

of day in which trips are made and mode of travel. In more comprehensive studies

additional data is obtained. This includes trips purposes, land use at the beginning or

end of the trip and background social and economic data on the one making the trip.

However, it is assumed to be a special case for the study in campus UTM where the

background social and economic data on the trip maker are not extremely important.

O & D data enable the study to determine:-

i) Travel demand on existing traffic and transportation facilities;

ii) The adequacy of existing traffic and transportation facilities;

iii) The information needed for planning, location and designing new or

improved either street or transportation or both system;

iv) Travel characteristics from various types of land use.

Most O & D surveys begin with the delineation of a survey area as discussed

earlier. Procedures for making origin and destination surveys are many and varied.

The method selected for collection of the O & D information will be determined by

data needs balanced against personnel, budget and time limitations.

O & D Home Interview Survey will be conducted for this study. This method

provides the most comprehensive procedure for obtaining travel characteristics

within a study area. Representative samples of the population are selected and

personal interviews are conducted to obtain travel characteristics for the person by all

modes of transportation for the previous day.

Initially, those specific data that are relevant for the purpose of the survey

such as land use inventory, population inventory, classification of population

(students or staffs), existing traffic and transportation and existing policy have to be

identified. In this case, land use of each zone is very clear as defined earlier. And,

the numbers of population for each zone are summarized in the following Table 3.5.

34


35/163

Table 3.5 : Population of Each Zone

Zone No. Student Staff Total

Zone 7 2408 50 2458

Zone 8 3223 158 3381

Zone 9 2392 75 2467

Zone 10 2702 43 2745

Zone 11 511 861 1372

Zone 12 11118 810 11928

Zone 13 5509 230 5739

Zone 14 306 1359 1665

3.4.3 Questionnaire Design

Normally the order in which the questions are asked seeks to minimize

resistance on the part of the interviewee. For this reason, whenever possible,

difficult questions are formulated at the end of the interview. In terms of its formal

aspect, the questionnaire and the interview itself, should try to satisfy the following

objectives:-

i) the questions should be simple and direct;

ii) the number of open questions should be minimized;

iii) the information about travel must be elicited with reference to the

activities which originated the trips.

In this study, O & D interview survey has two distinct sections: personal

characteristics and identification and trip data. In the first part of personal

characteristics and identification, questions designed to classify the population

according to the following aspects are included: gender, staffs or students,

department or faculty of work or study attached to, place of accommodation and

travel mode used. In order to reduce the possibility of subjective classification, it is

important to define a complete set of choices. The second part of survey, trip data

survey, aims at detecting and characterizing all trips made by the population

35


36/163

identified in the first part. A trip is usually defined as any movement from an origin

to a destination with a given purpose. Samples of the survey forms that were utilized

are attached in Appendix D of this report.

3.4.4 Sampling Procedure

Travel is an expression of an individuals behaviour and as such it has the

characteristics of being habitual. As a habit it tends to be repetitive and the repetition

occurs in a definite pattern. In addition, travel habits of different individuals are

similar for work, schooling, shopping and other types of trips. Therefore, it is not

necessary to obtain travel information from all residents of the area under study since

the travel characteristics could be exhibited by the patterns of movement. Statistical

methods can be used with confidence for the sampling of movement (Bruton, 1985).

To ensure that a sample is representative it is necessary that the persons

included in it, are distributed geographically throughout the survey area in the same

proportion as the distribution of the total population. The size of sample to be

interviewed depends upon the total population of the area under study, the degree of

accuracy required and the density of population. The development of a sampling

plan basically also involved estimating the available manpower considering the

budget constraints, availability of enumerators and the duration of the survey. Table

3.6 shows the values, which have been postulated as recommended practice for more

than 20 years (Ortuzar and Willumsen, 1990).

Table 3.6 : Sample sizes recommended in traditional surveys

Population of areaSample size

Recommended Minimum

Under 50,000 1 in 5 1 in 10

50,000 150,000 1 in 8 1 in 20150,000 300,000 1 in 10 1 in 35

36


37/163

300,000 500,000 1 in 15 1 in 50

500,000 1,000,000 1 in 20 1 in 70

Over 1,000,000 1 in 25 1 in 100

In this study, simple random sampling method is used. This method consists

in first associating an identifier (numbers) to each unit in the population and then

selecting these numbers at random to obtain the sample. Nevertheless, due to the

limitation of manpower and time constraint, only 70 % of the minimum sample size

required according to Table 3.6 above, based on the population proportion of each

different zone, will be interviewed. Hence, if n represents the sample size of each

zone,

withPop. representing the population of the entire zone. The interviews then will be

conducted randomly over each zone.

In order to ensure a good recollection of events the survey should ask for

information about the previous day. And due to the main objective is to obtain data

for a typical working or schooling day, it is often to rule out collecting data about

Mondays and Fridays. Whereas Wednesday is selected as the co-curriculum day of

UTM students, so it should be also avoided. For this reasons, the best days to carry

out the interviews are Wednesdays and Fridays. With respect to survey times, the

best results for home (hostel) surveys are obtained at the period of the day where the

probability of finding the students at their hostels is highest, which falls between

18:00 and 21:00 hours. On the other hand, for workplace (staff) surveys, the best

times are, of course, the normal working hours, 08:00 to 16:30 hours. On the other

hand, for those students who are not staying in the hostel but in private residential

area, samples are taken randomly in the CBD of the campus (Zone 1) throughout

the whole day, 08:00 to 22:00.

37

10

.*35.0 Popn =


38/163

3.5 Analysis of Data

The analysis of data in a trip generation study concentrates on the summary

of the data and the estimation of parameters. The steps in the process of analysis in

this study are as the following:-

i) Observe the data collected. Following that, the raw data are entered

into computer media storage according to certain classifications or

characteristics. Then, the data entry process are checked again and

verified to spot for any possible error occurs so that maintains the

accuracy of the entry process.

ii) The peak hour data from all the observations are added up to provide

total trips.

iii) The other information and data gathered from the interview surveys

such as the numbers of students, staffs, total population, vehicle

ownerships or the mode used are also cumulated and observed in

detail.

iv) The trips generated are analysed for any possible relationship against

independent variables.

v) Linear regression on the chosen independent variable is carried out to

estimate the linear relationship and to test the goodness of fit using R2.

The closer the R2 to the value of 1, the linear model is more

satisfactory.

vi) The same process is repeated for various independent variables in

terms of individual linear regressions.

3.6 Methodologies in Trip Generation Estimation

38


39/163

Based on the literature review from the Trip Generation (ITE, 1991),

Malaysia Trip Generation Pilot Study (Highway Planning Unit, 1997a) and the study

of Ravi Shankar (1999), there are generally three methodologies which can be

incorporated in this study in determining the average number of trip generated by a

land use:-

i) Weighted average trip generation rate or the number of

weighted trip ends per one unit of the independent variable.

ii) A plot of actual trips versus the size of the independent

variable for each study. The numbers represented on the plots

are actual trips plotted against the size of an independent

variable.

iii) Regression equation of trips related to the size of the

independent variable.

The calculations presented in the following are from the methodology

adopted in the Trip Generation Pilot Study of Malaysia (Highway Planning Unit,

1997a). The assumptions used in estimation of trip generation rates are either

homogeneous variance for the ys, (2) or homogeneous variance for the trip-

generating unit, w2. The latter assumption is intuitively more appealing. However,

if the range of value of the xs is not too large, say from one end to the other by a

factor less than 5, there is no harm in assuming the ys have homogeneous variance

2. this assumption will simplify computation, especially of2. In this study, the

assumption used is that the ys have homogeneous variance 2. for the purpose of

standardisation of notation, let the number of sites chosen for the study be n, the trips

generated at each site be yi and the corresponding independent variable be xi, for i =

1, 2, n. The methods of estimation of the trip generation rate used in this study

will be described in the following. Besides, the estimation of the variance and

standard deviation of these trip generation estimates will be also be discussed.

39


40/163

3.6.1 Simple Average of Mean

According to Trip Generation Pilot Study (Highway Planning Unit, 1997a),

for each site, i, the trip rate ri can be obtained by:

x

yr ii = for i = 1, 2, ..n.

The simple average or mean of these ris is given by:

= =

==n

i

n

i i

i

ix

y

nr

nR

1 1

11.(1)

The variance, V, of the estimate, R, depends on the variance of the y is. If we

assume that all the yis have equal variance, 2, the variance of R is given by:

===

n

i ixnRVarV 122

2

)

1

()(

..(2)

The trip generation rate, Ri, can be computed easily from the available data y and x

according to equation (1). However, for the computation of the variance and

standard deviation of R, given by equation (2), there is a need to estimate 2 first.

This can be obtained from the yis, i = 1, 2, n by computing the following:

1

})(

{1

2

2

2

=

=

n

n

yy

n

i

i

i

.(3)

The standard deviation is just the square foot of the above quantity. In the linear

regression situation where the ys is assumed distributed with constant variance 2

about the linear model, 2 is estimated by the following:

40


41/163

2 = Residual Sum of Squares

Degree of Freedom

=

2

)(1

2

=

n

cxbyn

i

ii.(4)

when the linear regression is with intercept.

3.6.2 Linear Regression with Intercept

If we assume that the relationship between the y and x variables is linear of

the form y = b x + c where b is the slope of the line and c , the intercept, the least

squares estimates ofb

and c are as follows (Highway Planning Unit, 1997a):

= =

n

xx

n

yxyx

bi

i

n

i

ii

ii

2

2

1

)( (5)

& xbyc = .(6)

The variance of b and c are as follows:

=

2

2

)()(

xxbVar

i

.(7)

where 2 is the variance of the yis, assuming homogeneity.

41


42/163

=

2

22

)()(

xxn

xcVar

i

i

.(8)

When the linear model is accepted, the interest is on predicting the trips generated

ky for any given xk.

The predicted value of yk is given by:

cxby kk += .(9)

& })(

)(1{)(2

22

+=

xx

xx

nyVar

i

kk ..(10)

An indication of the goodness of fit of the linear model is given by R-squared (R2)

where it is defined as:

R2 = Sum of Squares due to regressionTotal Sum of Squares, corrected for mean .(11)

If R2 is close to the value 1, then the linear model is satisfactory.

The above results assume that each of the ys have the same variance, 2.

3.6.3 Principles of Trip Generation Regression Model Building

In developing regression equations it is assumed that (Salter, 1976):-

i) All the independent variables are independent of each other.

ii) All the independent variables are normally distributed.

iii) The independent variables are continuous.

42


43/163

It is usual to compute the following statistical values to test the goodness of

fit of the regression equation:-

i) Simple correlation coefficient, r, which is computed for two variables

and measures the association between them. As r varies from -1 to +1

it indicates the correlation between the variables. A value

approaching 1 indicates good correlation.

ii) Multiple correlation coefficient, R, which measures the goodness of

fit between the regression estimates and the observed data. 100R2

give the percentage of variation explained by the regression.

Nevertheless, linear regression equation unavoidable also has some

limitation. It is important to recognize that the regression process contains the

likelihood of the future values of the dependent variables Y being in error when

future values of the independent variables X1, X2, etc., are substituted into the

equation. The likely sources of error may be stated to be:

i) errors in the determination of the existing values of the independent

variables owing to inaccuracy or bias in the transportation survey;

ii) the assumption that the regression of the dependent variable on the

independent variables is linear, a matter of some importance when

future values of the independent variables are outside the range of

observed values;

iii) errors in the regression obtained owing to the scatter of the individual

values and the inadequacy of the data;

iv) difficulties in the prediction of future values of the independent

variables; for the future value of the dependent variable will only be

as good as the future estimates of the independent variables;

v) future values of the independent variable will be scattered as are the

present values;

vi) the true regression equation may vary with time because factors that

exert an influence on trip-making in the future are not included in the

present-day regression equation.

43


44/163

CHAPTER 4

ANALYSIS AND FINDINGS

4.1 Introduction

In this study, the estimations of trip generation for Universiti Teknologi

Malaysia are based on two peak periods in terms of two different kinds of

independent variables. The first trip generation analysis was conducted in terms ofnumbers of students, numbers of staffs and total numbers of population as the

44


45/163

independent variables for morning peak traffic hours (AM peak) and evening peak

traffic hours (PM peak). The second trip generation analysis was again subdivided

into two separate analysis in terms of the vehicle ownerships / modes of travel. One

was from the zones with the land use of residential for the students and the staffs

located in the compound of the campus (Zone 7, 8, 9 and 10) heading to the zones of

land use for academic and administration facilities such as lecture halls in faculties,

administration offices, library and so on (Zone 1, 2, 3, 4, 5 and 6). While, the other

one was from the zones with the land use of residential for the students and the staffs

located off the campus (Zone 11, 12, 13 and 14) heading to the zones of land use for

academic and administration facilities such as lecture halls in faculties,

administration offices, library and so on (Zone 1, 2, 3, 4, 5 and 6). The significance

of the results of the analyses are then discussed and interpreted individually and also

compared within one another from statistical point of view based on single

independent variable. The diagrams showing the travel movements for the above

two analyses are presented in Figure 4.1 in Appendix E and summarized in Figure

4.2 in Appendix E.

The duration of peak hours are estimated from the interview surveys done as

discussed in Chapter 3. According to those surveys, the AM and PM peaks occur

between 7:30 a.m. to 9:00 a.m. and 4:30 p.m. to 6:00 p.m. respectively.

Microsoft Excel in Microsoft Office Package is used to carry out the

analyses. For each key traffic hour of each group of analyses as mentioned above,

two methods are provided for estimating trip generation rates. The first is a weighted

average trip generation rates and the second is a regression-derived linear equation.

The weight average trip generation rates were checked in terms of standard

deviation. The smaller the standard deviation, the more compact the curves peak.

This reflects that most of the related zones in a single analysis have trip generation

values close to the average rate.

The regression-derived linear equation provide an equation, which could be

used to forecast the volume of total trip produced or attracted by the centers of the

campus (academic and administrative zones) in terms of an independent variable.The independent variables in this study are numbers of students, numbers of staffs,

45


46/163

total numbers of population, the vehicle ownerships or modes used by the students

and staffs to travel. The regression equation of trip generation was examined by the

coefficient of determination, R2. This R2 tells about how well the trip generation

equation matches the survey data points. R2 values approaching 1 show a good fit

while values approaching zero reflect poor representative.

4.2 Selection of Independent Variables

In the macroscopic traffic study with university as an independent landuse,

the independent variables that are normally incorporated in the regression equation of

trip generation are total numbers of student enrolment, total numbers of employees

recruited and sometime the gross floor area, gross leasable area and parking space

provided. Nevertheless, in this microscopic traffic study meant for Universiti

Teknologi Malaysia, the main idea is to establish the trip generation from the in-

campus or off-campus residential areas to the center of the campus by the students

and employees. The significance influence from the numbers of students, staffs, total

numbers of population of each residential zone and the significance use of modes of

travel are observed from the results of analyses. This is importance to predict the

traffic condition if certain policy is to be introduced. For instance, if all the hostels

are to be moved into the compound of campus, will this new policy benefit or worsen

the traffic conditions? Therefore, in this study, the numbers of students, numbers of

staffs, total numbers of population and choices of mode used for travelling (bicycle,

motorcycle, car / van, UTM bus and walking) were selected to be the independent

variables.

4.3 Trip Generation In Terms of Numbers of Population

46


47/163

This part of analyses is calculated based on the trip generated by the

population staying in the eight zones of residential area located both in and off the

compound of the campus (Zone 7, 8, 9, 10, 11, 12, 13 and 14) and are subdivided

into smaller analyses as the following:

i) AM peak trip generation in terms of numbers of students;

ii) PM peak trip generation in terms of numbers of students;

iii) AM peak trip generation in terms of numbers of staffs;

iv) PM peak trip generation in terms of numbers of staffs;

v) AM peak trip generation in terms of total numbers of population;

vi) PM peak trip generation in terms of total numbers of population;

The observed trip generation collected from the relevant zones for the

morning and evening peaks (AM peak and PM peak) and total units of independent

variable are tabulated in Table 4.1.

Table 4.1: Observed Peak Hours Trip Generation Volumes by The Number of

Population

Zone

Observed Peak Hours Trip GenerationNumbers of Population

AM Peak PM Peak

Students Staffs Total StudentsStaff

sTotal Students Staffs Total

Zone 7 70 2 72 52 2 54 84 2 86

Zone 8 70 6 76 59 6 65 112 6 118

Zone 9 80 3 83 79 3 82 84 3 87

Zone 10 86 2 88 75 2 77 95 2 97

Zone 11 11 30 41 8 30 38 18 30 48Zone 12 242 27 269 134 25 159 389 28 417

Zone 13 87 8 95 77 7 84 193 8 201

Zone 14 6 47 53 3 34 37 11 48 59

In every analysis (i) to (vi) above, the first step is to carry out the examination

of correlation between the independent variables to the trip generation during the

both peak hours. Following that, if the independent variable carried high degree of

correlation, value greater than 0.5, the observed trip generation are continued with

47


48/163

analyses for the weighted average trip generation rates and linear regression

equation.

The correlation matrix between each independent variable (numbers of

students, numbers of staffs, total numbers of population) and the observed trip

generated during the AM and PM peak hours is tabulated in Table 4.2.

Table 4.2: Correlation Matrix for AM and PM Peak Hour Trip Generation and

Number of Population

Independent

Variabl

e

Correlation Matrix

AM Peak PM Peak

Numbers ofStudents

Numbers ofStaffs

48

No. ofStudents

Tripsby

Student

No. ofStudents

1 -

Trips byStudent

0.965408902 1

No. ofStudents

Tripsby

Student

No. ofStudents

1 -

Trips byStudent

0.892595845 1

No. of StaffsTrips by

Staff

No. ofStaffs

1 -

Tripsby Staff

0.999859466 1

No. of StaffsTrips by

Staff

No. ofStaffs

1 -

Tripsby Staff

0.978186751 1


49/163

TotalNumbers ofPopulation

With reference to Table 4.2, all the correlation analyses shown high degree of

correlation. Therefore, the further analyses for the weighted average trip generation

rates and linear regression equations are conducted for all the independent variables

based on the following Table 4.3 which shown the trips per independent variable

during AM and PM peak hour and data tabulated in Table 4.1 respectively.

Table 4.3: Trips per Independent Variable of Number of Population

Zone

Trips per Independent Variable

AM Peak PM Peak

Trips per

Students

Trips per

Staffs

Trips per

Total Pop.

Trips per

Students

Trips per

Staffs

Trips per

Total Pop.

Zone 7 0.83 1.00 0.84 0.62 1.00 0.63Zone 8 0.63 1.00 0.64 0.53 1.00 0.55

Zone 9 0.95 1.00 0.95 0.94 1.00 0.94

Zone 10 0.91 1.00 0.91 0.79 1.00 0.79

Zone 11 0.61 1.00 0.85 0.44 1.00 0.79

Zone 12 0.62 0.96 0.65 0.34 0.89 0.38

Zone 13 0.45 1.00 0.47 0.40 0.88 0.42

Zone 14 0.55 0.98 0.90 0.27 0.71 0.63

From Table 4.3 and Table 4.1, the summary of trip rates based on the weighted

average calculation and the linear regression analysis are tabulated in Table 4.4.

49

Total Pop.TotalTrips

TotalPop.

1 -

TotalTrips

0.999859466 1

Total Pop.TotalTrips

TotalPop.

1 -

TotalTrips

0.999859466 1


50/163

Table 4.4: Peak Hour Weighted Average Trip Generation Rates and Linear

Regression Equations in Terms of Numbers of Students, Numbers

of Staffs and Total Numbers of Population

Independent Variable Trip Generation Rates and Equations

50


51/163

TEHLEESIAN1

Documents

Transcript of TEHLEESIAN1