traffic impact assessment of new commercial...
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TRAFFIC IMPACT ASSESSMENT OF NEW COMMERCIAL DEVELOPMENT
IN THE NEIGHBOURHOODS OF SKUDAI TOWN
NAZIR HUZAIRY BIN ZAKI
UNIVERSITI TEKNOLOGI MALAYSIA
TRAFFIC IMPACT ASSESSMENT OF NEW COMMERCIAL
DEVELOPMENT IN THE NEIGHBOURHOODS OF SKUDAI TOWN
NAZIR HUZAIRY BIN ZAKI
A project report submitted in partial fulfillment of the
requirements for the award of the degree of
Master of Engineering (Civil-Transportation and Highway)
Faculty of Civil Engineering
Universiti Teknologi Malaysia
JAN 2013
iii
Dedicated to my beloved mama, Hasnah Binti Abdul Rahim
and ayah, Zaki Bin Ahmad Ghazi
iv
ACKNOWLEDGEMENT
First and foremost I would like to thank Allah S.W.T. for His blessing I finally
completed my Master Project without much hassle and I able to it on time.
I would like to express my gratitude and special thanks to my supervisor Dr. Anil
Minhans who for the past two semesters had tremendously helped me to finish my
Master Project. Dr. Anil would not mind giving me new ideas and ways to solve my
problems even though he is busy with his own work and other duties.
Heartiest thanks to Ms. Salida and Ms. Aisyah from MPJBT that had helped me by
giving the data required. A thousand thanks to management personnel from Giant U
Mall, Jusco Taman Universiti and Carrefour Sutera Utama which had gave me the
permissions to use their sites for data collection.
A special thanks also for my friends and family who has supported me all out during
this time. Without their support and encouragement I may not be able to complete
my Master Project on time.
Thanks.
v
ABSTRACT
Urban areas in Malaysia including Johor Bahru are witnessing a rampant commercial
development. The purpose of development can easily be defeated by unanticipated
traffic congestion and other negative impacts. This paper deals with traffic impact
assessment (TIA) of proposed commercial development in the neighbourhoods of
Skudai Town. In traffic impact studies, estimation of mean trip rate is a central
component for TIA and adoption of inaccurate trip rates can result into
underestimation or overestimation of development traffic, both with undesirable
impacts. This paper analyses three regimes of Trip Rate Analysis, Cross-
Classification Analysis and Regression Analysis techniques to determine the future
development traffic for a proposed Tesco hypermarket (TH) within Skudai Town.
Furthermore, the obtained mean trip rates for critically examined for their adoption
and forecasting traffic for base year 2015 and horizon year 2025. The results
indicated significant variances in the estimated entry mean trip rates when compared
with the entry trip rates from Trip Generation Manual (TGM) Malaysia. These
estimated mean trip rates were then tested to measure the performance of critical
intersection in the immediate vicinity of proposed Tesco Hypermarket for the
opening year 2015. Critical Intersection is analysed using SIDRA software to
estimate delay, a criterion for determining the level of service (LOS) provided to
motorists. The traffic projections made for horizon year 2025 were further analysed,
depicting a LOS F with 2716.9s of average delay. Furthermore, traffic improvements
were proposed to mitigate the impact of future development traffic. In this regard, the
study provided a framework for the estimation of trip rates for local Malaysian
conditions and their adoption guidelines. These offer indispensible assistance to TIA
that can assist developers or local authorities in decision making.
vi
ABSTRAK
Kawasan Bandar di Malaysia termasuk Johor Bahru sedang menyaksikan
pembangunan komersial yang pesat. Matlamat pembangunan boleh dikalahkan
dengan kesesakkan lalu lintas dan impak negatif lain yang tidak dijangka. Kajian ini
membincangkan tentang taksiran impak trafik (TIA) oleh cadangan pembangunan
komersial di dalam kawasan kejiranan Bandar Skudai. Di dalam kajian impak trafik,
pengiraan kadar perjalanan adalah perkara asas untuk TIA dan penggunaan kadar
perjalanan yang tidak tepat boleh menghasilkan kadar yang terlalu tinggi atau rendah
yang mana kedua-duanya mempunyai impak yang tidak diingini. Kajian ini
menganalisa tiga rejim iaitu Analisa Kadar Perjalanan, Analisa Klasifikasi
Menyilang dan Analisa Regresi untuk mengetahui trafik di masa hadapan disebabkan
oleh pembangunan Pasaraya Besar Tesco (TH). Purata kadar perjalanan yang
diperoleh diuji secara kritikal untuk pengunaan dan ramalan trafik untuk tahun dasar
2015 dan tahun ufuk 2025. Hasil kajian menunjukkan terdapat perbezaan ketara di
antara kadar perjalanan masuk yang di kira dengan kadar perjalanan masuk daripada
Manual Generasi Kadar (TGM) Malaysia. Kadar perjalanan yang di kira diuji untuk
mengira prestasi simpang kritikal di dalam kawasan persekitaran cadangan TH untuk
tahun pembukaan 2015. Persimpangan kritikal itu dianalisa mengunakan perisian
SIDRA untuk mengira kelewatan iaitu kriteria untuk mengetahui Tahap Servis
(LOS) untuk pengguna jalanraya. Analisa diteruskan dengan unjuran trafik untuk
tahun ufuk 2025 yang menunjukkan LOS F dengan purata kelewatan 2716.9s.
Tambahan lagi, penambahbaikkan trafik telah dicadangkan untuk mengatasi impak
trafik akibat pembangunan di masa hadapan. Dalam hal ini, kajian ini memberi
rangka kerja dan juga garis panduan pengunaan untuk pengiraan kadar perjalanan
untuk situasi Malaysia. Ini memberi bantuan yang amat penting kepada TIA yang
boleh membantu pemaju dan pihak berkuasa tempatan di dalam membuat keputusan.
vii
TABLE OF CONTENTS
CHAPTER TITLE PAGE
DECLARATION ii
DEDICATION iii
ACKNOWLEDGEMENT iv
ABSTRACT v
ABSTRAK vi
TABLE OF CONTENTS vii
LIST OF TABLES x
LIST OF FIGURES xv
LIST OF ABREVATIONS xvii
LIST OF APPENDIX xviii
1 INTRODUCTION 1
1.1 Background of Study 1
1.2 Problem Statement 2
1.3 Objectives 5
1.4 Scope of Study and Limitations 5
1.5 Significance of Study 6
2 LITERATURE REVIEW 7
2.1 Introduction 7
2.2 Effects of Improper Trip Rate Estimation 8
2.3 Development of Trip Generation Manual 9
2.4 Type of Land Use 10
viii
2.5 Current Traffic Condition 14
2.5.1 Origin-Destination Survey 14
2.6 Forecasting Travel Demand 15
2.6.1 Sequential Steps of Travel Forecasting 16
2.6.2 Trip Generation 18
2.6.3 Types of Trips 18
2.6.4 Determination of Mean Trip Rates 21
2.7 Traffic Growth Factor 24
2.8 Transport Improvement 24
3 RESEARCH METHODOLOGY 26
3.1 Introduction 26
3.2 Collection of Data 29
3.3 Study Boundary 29
3.4 Current and Proposed Land Use Developments 31
3.5 Type, Location and Characteristics of Proposed New
Commercial Development
36
3.6 Number and Locations of Existing Commercial
Developments with Similar Characteristics
39
3.7 Questionnaire Surveys 41
3.7.1 Customers Survey 41
3.7.2 Shop Owners Survey 43
3.8 Vehicle Counts 43
3.8.1 Vehicles Count at Existing Commercial
Developments
44
3.8.2 Vehicles Count at Critical Intersection near the
Proposed Tesco
46
3.9 Mean Trip Rate Estimation Procedures 48
3.9.1 Trip Rate Analysis Procedures 48
3.9.2 Cross-Classification Analysis Procedures 50
3.9.3 Regression Analysis Procedures 51
3.10 Projected Traffic Volume 52
3.11 Appropriate Road Improvement 54
ix
4 ANALYSIS AND RESULTS 55
4.1 Introduction 55
4.2 Trip Rate Analysis 56
4.2.1 Giant U Mall 56
4.2.2 Jusco Taman Universiti 56
4.2.3 Carrefour Sutera Utama 57
4.2.4 Mean Trip Rate Using Trip Rate Analysis 57
4.3 Cross-Classification Analysis 58
4.3.1 Giant U Mall 58
4.3.2 Jusco Taman Universiti 70
4.3.3 Carrefour Sutera Utama 82
4.3.4 Mean Trip Rate Using Cross-Classification
Analysis
98
4.4 Regression Analysis 99
4.4.1 Mean Trip Rate Using Regression Analysis 105
4.5 Comparison of Entry Mean Trip Rate Using Trip Rate
Analysis, Cross-Classification Analysis, Regression
Analysis and Trip Generation Manual
106
4.6 Traffic Impact Assessment 107
4.6.1 Base Year Traffic Volume at Critical
Intersection
107
4.6.2 Projected Traffic Volume at Opening Year
2015
110
4.6.3 Projected Traffic Volume in Horizon Year
2025
112
4.7 Transport Improvement 114
5 CONCLUSIONS 115
5.1 Conclusions 115
REFERENCES 119
APPENDIX A-C 121
x
LIST OF TABLES
TABLE NO TITLE PAGE
2.1 Effects of improper estimation of trip rates (Minhans, A.
et. al., 2012)
8
2.2 Typical trip production table (Garber and Hoel, 2009) 22
2.3 Typical trip attractions table (Garber and Hoel, 2009) 23
3.1 Districts within each zone together with population size 31
3.2 Percentage of each type of land use in Skudai Town as
obtained from MPJBT
33
3.3 General characteristics of the proposed TH, GUM, JTU
and CSU
40
3.4 PCU factors as taken from Malaysian Trip Generation
Manual 2010 by HPU
49
4.1 Trip Rate Analysis results (GUM) 56
4.2 Trip Rate Analysis results (JTU) 56
4.3 Trip Rate Analysis results (CSU) 57
4.4 Weekday AM peak, weekday PM peak, weekend AM
peak and weekend PM peak mean trip rate PCU trips
per hour per 100m2 GFA and standard deviation
58
4.5 Weekday AM peak survey data (GUM) 59
4.6 No. of shops in each GFA category versus no. of
employees
59
4.7 Average number of person trips per shop per hour
versus GFA and number of employees
60
4.8 Percentage of shops in each GFA category 60
xi
4.9 Number of person trips per hour with respect to each
GFA category and number of employees
60
4.10 Weekday PM peak survey data (GUM) 62
4.11 No. of shops in each GFA category versus no. of
employees
62
4.12 Average number of person trips per shop per hour
versus GFA and number of employees
63
4.13 Percentage of shops in each GFA category 63
4.14 Number of person trips per hour with respect to each
GFA category and number of employees
63
4.15 Weekend AM peak survey data (GUM) 65
4.16 No. of shops in each GFA category versus no. of
employees
65
4.17 Average number of person trips per shop per hour
versus GFA and number of employees
66
4.18 Percentage of shops in each GFA category 66
4.19 Number of person trips per hour with respect to each
GFA category and number of employees
66
4.20 Weekend PM peak survey data (GUM) 68
4.21 No. of shops in each GFA category versus no. of
employees
68
4.22 Average number of person trips per shop per hour
versus GFA and number of employees
69
4.23 Percentage of shops in each GFA category 69
4.24 Number of person trips per hour with respect to each
GFA category and number of employees
69
4.25 Weekday AM peak survey data (JTU) 71
4.26 No. of shops in each GFA category versus employees 71
4.27 Average number of person trips per shop per hour
versus GFA and number of employees
72
4.28 Percentage of shops in each GFA category 72
4.29 Number of person trips per hour with respect to each
GFA category and number of employees
72
xii
4.30 Weekday PM peak survey data (JTU) 74
4.31 No. of shops in each GFA category versus no. of
employees
74
4.32 Average number of person trips per shop per hour
versus GFA and number of employees
75
4.33 Percentage of shops in each GFA category 75
4.34 Number of person trips per hour with respect to each
GFA category and number of employees
75
4.35 Weekend AM peak survey data (JTU) 77
4.36 No. of shops in each GFA category versus no. of
employees
77
4.37 Average number of person trips per shop per hour
versus GFA and number of employees
78
4.38 Percentage of shops in each GFA category 78
4.39 Number of person trips per hour with respect to each
GFA category and number of employees
78
4.40 Weekend PM peak survey data (JTU) 80
4.41 No. of shops in each GFA category versus no. of
employees
80
4.42 Average number of person trips per shop per hour
versus GFA and number of employees
81
4.43 Percentage of shops in each GFA category 81
4.44 Number of person trips per hour with respect to each
GFA category and number of employees
81
4.45 Weekday AM peak survey data (CSU) 83
4.46 No. of shops in each GFA category versus no. of
employees
84
4.47 Average number of person trips per shop per hour
versus GFA and number of employees
84
4.48 Percentage of shops in each GFA category 84
4.49 Number of person trips per hour with respect to each
GFA category and number of employees
85
4.50 Weekday PM peak survey data (CSU) 87
xiii
4.51 No. of shops in each GFA category versus no. of
employees
88
4.52 Average number of person trips per shop per hour
versus GFA and number of employees
88
4.53 Percentage of shops in each GFA category 88
4.54 Number of person trips per hour with respect to each
GFA category and number of employees
89
4.55 Weekend AM peak survey data (CSU) 91
4.56 No. of shops in each GFA category versus no. of
employees
92
4.57 Average number of person trips per shop per hour
versus GFA and number of employees
92
4.58 Percentage of shops in each GFA category 92
4.59 Number of person trips per hour with respect to each
GFA category and number of employees
93
4.60 Weekend PM peak survey data (CSU) 95
4.61 No. of shops in each GFA category versus no. of
employees
96
4.62 Average number of person trips per shop per hour
versus GFA and number of employees
96
4.63 Percentage of shops in each GFA category 96
4.64 Number of person trips per hour with respect to each
GFA category and number of employees
97
4.65 Weekday AM peak, weekday PM peak, weekend AM
peak and weekend PM peak mean trip rate PCU trips
per hour per 100m2 GFA and standard deviation using
Cross-Classification Analysis
98
4.66 Dependency between GFA and number of employees to
number of trips
99
4.67 Dependency of GFA to number of trips 99
4.68 Dependency of number of employees to number of trips 100
4.69 Mean trip rate from regression Analysis for GUM 102
4.70 Mean trip rate from regression Analysis for JTU 103
xiv
4.71 Mean trip rate from regression Analysis for CSU 105
4.72 Weekday AM peak, weekday PM peak, weekend AM
peak and weekend PM peak mean trip rate PCU trips
per hour per 100m2 GFA and standard deviation using
Regression Analysis
106
4.73 Comparison of entry mean trip rate per 100 m2 GFA
using trip analysis, cross-classification analysis,
regression analysis and Trip Generation Manual
107
4.74 Base year intersection performance 110
4.75 Number of trips generated by the proposed TH 111
4.76 Intersection performance at opening year 2015 112
4.77 Projected intersection performance in horizon year 2025
with the proposed TH
113
4.78 Projected intersection performance in horizon year 2025
with the proposed TH and proposed road improvement
114
xv
LIST OF FIGURES
FIGURE NO TITLE PAGE
2.1 Type and intensity of land uses in Skudai Town (source:
MPJBT Johor Map 2010)
12
2.2 Travel forecasting process 17
2.3 Typical home based (HB) trip with respect to trip
production and trip attraction
19
2.4 Typical non-home (NHB) based trip with respect to trip
production and trip attraction
19
2.5 Trips characteristics (Minhans, A., 2008) 20
2.6 Transport improvement methods (Minhans, A., Pillai,
C. M., 2012)
25
3.1 Flowchart for the activity of the study 28
3.2 Study area boundary (not to scale) 30
3.3 Boundary of each zone within Skudai Town 30
3.4 Current and proposed all type of land use in Skudai
Town as obtained from MPJBT
32
3.5 Current and proposed commercial type of land use in
Skudai Town as obtained from MPJBT
34
3.6 Current commercial type of land use in Skudai Town as
obtained from MPJBT
35
3.7 Proposed (2020) commercial type of land use in Skudai
Town as obtained from MPJBT
36
3.8(a) Location of new proposed TH at Taman Sri Pulai
Perdana (not to scale)
38
xvi
3.8(b) Location of new proposed TH at Taman Sri Pulai
Perdana (not to scale)
38
3.9 Location of proposed TH, GUM, JTU and CSU (not to
scale)
40
3.10 Surveyor locations for the manual vehicle counting for
GUM
44
3.11 Surveyor locations for the manual vehicle counting for
JTU
45
3.12 Surveyor locations for the manual vehicle counting for
CSU
45
3.13 Location of critical intersection nearby the proposed TH 47
3.14 Geometry layout of the intersection 47
4.1 Weekday AM peak (GUM) 100
4.2 Weekday PM peak (GUM) 101
4.3 Weekend AM peak (GUM) 101
4.4 Weekend PM peak (GUM) 101
4.5 Weekday AM peak (JTU) 102
4.6 Weekday PM peak (JTU) 102
4.7 Weekend AM peak (JTU) 103
4.8 Weekend PM peak (JTU) 103
4.9 Weekday AM peak (CSU) 104
4.10 Weekday PM peak (CSU) 104
4.11 Weekend AM peak (CSU) 104
4.12 Weekend PM peak (CSU) 105
4.13 Base year (2012) weekday AM peak hour traffic volume 108
4.14 Base year (2012) weekday PM peak hour traffic volume 108
4.15 Base year (2012) weekend AM peak hour traffic volume 109
4.16 Base year (2012) weekend PM peak hour traffic volume 109
4.17 Projected traffic volume at opening year 2015 without
the proposed TH in PCU/hr for all traffic movements
111
4.18 Projected traffic volume in horizon year 2025 with the
proposed TH in PCU/hr for all traffic movements
113
xvii
LIST OF ABBREVIATIONS
Symbol Descriptions
LOS Level of Service
TIA Traffic Impact Assessment
GFA Gross Floor Area
HCM Highway Capacity Manual
HPU Highway Planning Unit Malaysia
ITE Institute Transportation Engineers
O-D Origin-Destination
IDR Iskandar Development Region
TAZ Traffic Analysis Zone
HB Home Based
NHB Non-Home Based
TH Tesco Hypermarket
GUM Giant U Mall
JTU Jusco Taman Universiti
CSU Carrefour Sutera Utama
xviii
LIST OF APPENDIX
APPENDIX TITLE PAGE
Appendix A Customers Survey Form 121
Appendix B Shop Owners Survey Form 125
Appendix C Manual Vehicle Count Sheet 126
1
CHAPTER 1
INTRODUCTION
1.1 Background of Study
Policy makers in Malaysia under the current 9th Malaysian Plan intend to
develop Malaysia into a developed nation by the year 2020 based from the National
Transformation Program (NTP). This is revealed in rampant urban development in
many states of Malaysia especially southern state of Johor. Given the location value
of Skudai Town due to its close proximity to Singapore, the development is even
large both in scale and intensity. Skudai Town also contain an international
university, Universiti Teknologi Malaysia which imparts education to over 20,000
students and employs more than 4,000 academic and technical staff. In this regard,
Skudai Town may well be regarded as university town.
But, without a proper understanding of development impacts on the road
network, the intent of the developments which are to bring economic and social
purposes could be easily defeated by unanticipated traffic congestion and other
negative impacts. It is well known that new development will generate new traffic
volume on the existing road network and if the existing road network are unable to
cater for the new amount of traffic volume, traffic congestion will occur which will
2
lead to poor economic return due to delay in travel time, air pollution and high fuel
consumption during congestion and even accidents.
New development can also decrease the accessibility and mobility of
travelers if not planned properly. In other words, the extra traffic generated from the
new development will have an adverse impact on the Level of Service (LOS) of the
road network. Therefore, it is essential to conduct a traffic impact assessment (TIA)
during planning stage of the new development to determine the amount of traffic that
will be generated from the new development and to determine if the existing road
network is capable to sustain the combination of old and new development traffic. If
the existing road network is unable to accommodate the new traffic, ways of
mitigation can be planned based on the results obtained from TIA such as modifying
the road geometrics, adding of an additional lane, providing roundabout or
configuring traffic signal among others that seems appropriate.
Skudai Town is rapidly growing with many new mixed land use development
such as new malls, shop houses, residential areas, factories and many more. Only few
years ago that Skudai Town was under developed with only few residents and
commercial areas on top of already established Universiti Teknologi Malaysia
(UTM). Due to the rapid growth, TIA study for Skudai Town is necessary to be
conducted for estimating the appropriateness of the application of Malaysian trip
rates.
1.2 Problem Statement
TIA is very important during the planning stage of any new development to
avoid congestion and other negative impacts that may arise. The problem in this
country is that the standard and procedures in producing and evaluating of TIA report
3
still has not been standardized or well developed (Wee Ka Siong, 2001). This will
raised concern on validity of the estimation of traffic volume projection.
Because there are no proper standards to follow, traffic engineers or traffic
planners usually refer to guidelines of TIA and use trip generation rates from
previously established manuals such as Trip Generation Manual from Highway
Planning Unit (HPU) in Malaysia or by Institute Transportation Engineers (ITE) in
the United States. Experiences from past projects in determining trip rates are also
used as analogy for estimating trip rates for upcoming projects.
Even though the trip rates stated in the HPU trip manual was based from
study done all across Malaysia, but it was still at early stages with limited number of
survey sites especially for less common types of land uses. On the other hand, the
problem with the ITE manual is that it does not reflect well on the true conditions of
typical Malaysian lifestyle and travel pattern as ITE was published based on various
studies within the United States which are different to typical Malaysian way of life,
climate, socio-economic considerations and so forth which makes the travel pattern
different between these two countries.
Both manuals provide trip rate that are insensitive to population density,
travel patterns, economic growth, accessibility etc. For example, the number of
vehicle trips going into a shopping mall for a car dependent city will considerably
higher than a city that depends on public transport even for the same shopping mall
type and area. In other words, the number of trip generated by a certain type of land
use depends on local conditions of the area. Thus, trip rates calculating procedures
can be prone to biases or errors which can result in overestimation or
underestimation of mean trip rates. These inherent flaws in the trip estimates
provided the need of this study.
4
Another source was to calculate the trip rates by the developer themselves but
the trip rates can also prone to biases or error which means the developer usually
manipulated the procedures to estimates minimum trip rates to show that the
proposed new development will not have major impacts on the current traffic volume
just to obtain a planning certificate easily or the developer lacks sound knowledge of
how to estimates the trip rates with limited errors.
When there is underestimation of trip rates, the outcome of TIA is rather
minimal which shows that the new development will not generate much traffic in the
future. That means the road network within the area will be design base on small
traffic volume which may not be enough or adequate in the future. This can lead to
serious congestion when the road is operating at capacity. This is also beneficial to
the developer due to the low cost for the development because underestimation of
trip rates requires less number of complex road networks.
By using unsuitable procedures to estimate the trip rates can also lead to
overestimation. An overestimation of trip rates shows that the new development will
create high traffic volume in the future. This means that the road network will be
designed based on high traffic volume which will be higher in terms of investment
cost and also the operating cost to construct the road network. There will also be
using lot of land spaces. This can lead to the road network be underutilized and a
wastage of spaces of land and money just to built unessential roads.
Hence, local TIA studies in Skudai Town were conducted to understand
traffic conditions that may emerge in the future and accuracy of mean trip rates.
Furthermore, the percentage of commercial developments in Skudai Town is rapidly
increasing for the past 5 years which has resulted in the rapid growth of traffic
volume annually compare to years before that which means any trip rates study done
here in the past 10 or more years may not be valid now. Therefore, this study can
provide valuable information pertaining traffic issues in Skudai Town and identical
urban areas.
5
1.3 Objectives
In order to achieve the aim of the study, the main objectives had been
identified as:
i. To estimate the mean trip rates per 100m2 of gross floor area (GFA) under
commercial land use;
ii. To test and validate the adoption of mean trip rates per 100m2
while
comparing three test regimes; Trip Rate Analysis, Cross Classification
Analysis And Regression Analysis techniques;
iii. To quantify the impacts of additional development traffic from the proposed
commercial area for the horizon year 2025 on the neighboring road network;
iv. To propose appropriate transport improvement measure for the existing road
network within Skudai.
1.4 Scope of Study and Limitations
In order to achieve the objectives, the scope of study was confined only to
commercial areas in the neighborhoods of Skudai Town. It involved collection of
information on land uses, current and proposed commercial area establishment
characteristics, traffic growth factor and current traffic volume in Skudai Town. This
study used three test methods to determined the mean trip rate for a proposed
commercial area namely Trip Rate Analysis, Cross-classification Analysis and
Regression Analysis where only the entry volumes were counted due to limitations
of time, man power and resources to count both the entry and exit traffic volume as
the exit traffic volume was assume to be the same as entry traffic volume.
6
Another limitation, that type, scale and intensity of commercial activities in
the preselected commercial markets for analysis are considered to be identical in
nature. These commercial areas are presumed to generate trip rates which are deemed
comparable. The projected traffic volumes at a critical intersection near the proposed
commercial establishment were analyzed using SIDRA software to determine the
performance of the intersection.
1.5 Significance of Study
As what already been mentioned, this study focused on TIA due to a new
commercial area development in rapidly developing Skudai Town. This study which
was based from existing commercial developments in the study area will give an
expectation on how much trips will be generated by a new propose commercial
developments in the area.
The trip rates estimation in this study will not be biased to any party, be it a
developer or municipal authorities and will be adequate enough that it will not be too
low nor too high that can lead to the new road network at capacity which can cause
congestion or underuse which is a waste in term of cost and land spaces within the
new development. The results could be used for future TIA studies from similar
development of commercial areas in Skudai Town.
Also, this study signifies the appropriateness of the application of universal
Malaysian trip rates and its impacts.
119
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