Analysis of Different Parking Space and its Comparison
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The document analyzes different parking spaces at the National Institute of Technology, Rourkela, collecting data on the distribution and usage of parking facilities. Using statistical methods such as the t-test, it compares the patterns and demands across various parking sites to identify similarities and differences. The study concludes that the parking patterns of the central workshop and student activity center are similar, while other comparisons show significant differences.
![IJSRD - International Journal for Scientific Research & Development| Vol. 1, Issue 8, 2013 | ISSN (online): 2321-0613
All rights reserved by www.ijsrd.com 1565
Analysis of Different Parking Space and its Comparison
Manoj Kumar Biswal1
Tarapada Mandal2
Arumuga Subhashini N3
R. V. Kumar4
Ujjal Chattaraj5
1, 2, 3, 4, 5
Department of Civil Engineering
1, 2, 3, 4, 5
National Institute of Technology Rourkela, Rourkela, India
Abstract— In the “Analysis of different parking space and
its comparison” we collected data from different parking
space of our institute N.I.T Rourkela. Initially we figured
out what is the variation of PCU with a certain time and then
we compared all these data with the help of “t- test“ to find
out whether these parking pattern and demand are same or
different. In another part we find out the “spatial and
temporal distribution” of main road traffic vehicle, here
“spatial distribution” is the variation of PCU (passenger car
unit) with distance and in “temporal distribution” variation
of PCU with time.
Key words: PCU, t- test, spatial distribution
I. INTRODUCTION
In the increasing phase of urbanization, in most cities,
designing a parking space has always been a challenging
part of a traffic system. But in many cases the ignorance of
on-street parking has lead to roadway congestion and in turn
affected the flow of the roadway and in some cases the off-
street parking space has been wasted as the system was
inadequate. As a remedy, expanding the parking space
utilizing more land area without proper parking practice has
not proved productive. In this study, the existing system of
campus of National Institute of Technology, Rourkela
(NITR). The study area possesses decorum of parking with
specific parking lots and the later area portrays academician
sense of parking.
The survey has been done to collect data of the
two-wheelers (motorised and non-motorised) from distinct
spots of NITR off-street parking lots adopting similar
methodologies at the same time. The data obtained for
different class of vehicles like 4 wheeler, 3 wheeler,
motorised and non-motorised 2 wheeler was converted into
standard passenger car unit (PCU) for unifying results. The
data from NITR has been compared statistically with
hypothesis testing method ‘t-test’ in various cross
comparisons of all off-street parking sites and on-street
parking data. The compared data of different lots of NITR
will explore the difference and similarities of the different
parking patterns and the liable pattern for off-street parking
system can be obtained. These inferences can be combined
to manage the parking system of the institution which is
academically and economically beneficial.
II. LITERATURE REVIEW
Parking facility is one of the important transport facility in
urban area specially the central districts having high retail
activity & employment opportunities. Parking policies &
pricing impacts the entire city transportation & land use.
The spatial distribution of parking of Melbourne City is
studied [3]. Generalized parking rates are assumed for
estimating the parking demand & other parameters are
ignored. The various behavioral characteristics of parking
demand for various trips, location & with various urban
areas [4]. Various factors influencing the parking demand &
also their influence on each other was tried to find out.
Relationship between parking demand & transport
accessibility was analyzed. Parking demand decreases with
good & efficient transport facility. The parking demand of
shopping centre & markets from the data obtained by
conducting parking demand survey at various locations of
Bejing [5]. Parking demand rate with different public
transport accessibility was determined & a parking demand
model with different accessibility was provided. Attitude
and behavioral responses of drivers is an important aspect to
planned parking measures. The attitude & behavioral
responses of car drivers to planned parking measures at
campus of the Eindhoven University of Technology, in
Netherland [2]. In an on-street questionnaire, car drivers
were asked their opinion about restricting access to the
campus area for cars of non-university car drivers. Parking
spaces are strategic commodities of modern day transport
facility. Few dataset allows precisely measuring the use of
spaces in terms of population, segments, activity types &
duration. The empirical measures & methods regarding the
use of parking space in a strategic urban area [6]. GIS, fuzzy
logic and weighting criteria to determine the selection of
proper parking sites. An ideal method for parking site
selection by the use of GIS, fuzzy logic and weighting
criteria to determine proper parking sites of Esfahan city in
Iran [9].
III. DATA COLLECTION SITE
We collected data from different site in N.I.T Rourkela. The
sites of are Ceramic department parking space, Central
workshop parking space, SAC front side parking space,
Main building parking space, Library parking space,
Rourkela main road.
IV. DATA COLLECTION METHODOLOGY
We collected all these data from different site in N.I.T
Rourkela from 10 am to 12 pm. We surveyed all the above
given parking place, from this survey we got how many two
wheeler motorized and nonmotorized vehicle is being
parked at a specified parking place.
V. DATA ANALYSIS AND METHODOLOGY
N.I.T Rourkela data: From these entire five sites we have
collected the data. After collection of data we convert all
these vehicles in terms of PCU (Passenger car unit) by
multiplying it with its corresponding PCU factor which is
described in IRC-6.](https://image.slidesharecdn.com/ijsrdv1i8010-140806074528-phpapp01/85/Analysis-of-Different-Parking-Space-and-its-Comparison-1-320.jpg)
![Analysis of Different Parking Space and its Comparison
(IJSRD/Vol. 1/Issue 8/2013/0010)
All rights reserved by www.ijsrd.com 1568
21.9
23.4
130.8
92.7
Table. 11: Comparison between Main Building and Library
Parking Space
Central workshop parking space and SAC front sideG.
parking space
PCU(CENTRAL
WORKSHOP)
PCU(SAC)
40.4
39.6
32.1
30.4
27.3
23.3
22.4
20.5
17.4
22.9
26.1
24.5
28.2
27.8
28
26.5
24
15.4
t critical for two-
tail =2.306004
t stat =1.256768
t stat < t critical,
so data are not
different
Table. 12: Comparison between Central Workshop and SAC
Front Side Parking Space
Central workshop parking space and library parkingH.
space
PCU (CENTRAL
WORKSHOP)
PCU(LIBRARY)
40.4
39.6
32.1
30.4
27.3
23.3
22.4
20.5
17.4
101.8
110.4
113.7
117.5
118.3
132.2
131.5
130.8
92.7
t critical for two-
tail =2.306004
t stat =14.7275
t stat > t critical,
so data are
different
Table. 13: Comparison between Central Workshop and
Library Parking Space
Central workshop parking space and main buildingI.
parking space
PCU(CENTRAL
WORKSHOP)
PCU(MB)
40.4
39.6
32.1
30.4
27.3
23.3
22.4
20.5
17.4
14.6
15.9
16.4
16.5
17.4
18.2
18.6
21.9
23
t critical for
two-tail
=2.306004
t stat =2.792758
t stat > t critical,
so data are
different
Table. 14: Comparison between Central Workshop and
Main Building Parking Space
Central workshop parking space and ceramicJ.
department parking space
PCU(CENTRAL
WORKSHOP)
PCU
(CERAMIC
DEPT.)
40.4
39.6
32.1
30.4
38.9
35.9
29.4
31.9
t critical for two-
tail =2.306004
t stat =2.54384
t stat > t critical,
27.3
23.3
22.4
20.5
17.4
37.9
57.4
69.9
72.4
67.9
so data are
different
Table. 15: Comparison between Central Workshop and
Ceramic Department Parking Space
Data of central workshop parking space and SAC front side
parking space are not different. We applied t distribution
because sample size is less than 20. From t distribution we
get two values one is” t critical” and other is” t stat” value.
If “t stat” will be greater than the “t critical” then data are
different. If “t stat” is less than “t critical” then data is not
different. So here we get only one data is not different and
remaining are different.
VII. CONCLUSION
We compared all the parking pattern of N.I.T Rourkela with
each other with the help of ‘t-test’ and we got that in case of
comparison for mean the parking pattern of Central
Workshop and Student Activity Centre is not different while
others are different. In case of comparison for parameters
we got different result here we got that parking pattern of
Student Activity Centre and Library is different and same is
the case with Central Workshop and Student Activity
Centre.
REFERENCES
[1] Guidelines for capacityof urban roads in plain areas
IRC-106 (1990).
[2] P. Waerden, A. Borgers, H. Timmermans, Attitudes and
behavioral responses to parking measures, EJTIR
(2006) 301-312.
[3] W. Young, D. Beaton, S. Satgunarajah, An analysis of
the spatial distribution of parking supply policy and
demand, ATRF (2010) 81-87.
[4] S. Chakrabarti, T. Mazumder, Behavioral
characteristics of car parking demand : A case study of
Kalkata, ITP (2010) 1-11.
[5] H. Qin, Q. Xiao, H. Guan, X. Pan, Analysis on the
parking demand of the commercial building considering
the public transport accessibility, Nature and Science
(2010) 63-68.
[6] C. Morency, M. Trepanier, Characteristics parking
spaces using travel survey data, CIRRELT (2008)1-22.
[7] S. Wong, C. Tong, C. Lam, Y. Fung, Development of
parking demand models in Hong Kong, ASCE (2000)
55-74.
[8] J. Sivasubramanian, G. Malarvizhi, A system dynamics
methodology for assessing parking demand for
commercial shopping area, CRRI (1976) 223-229.
[9] R. Farzanmanesh, A. Naeeni, A. Abdullah, Parking site
selection management using fuzzy logic and multi
criteria decision making, Environment Asia (2010) 109-
116.](https://image.slidesharecdn.com/ijsrdv1i8010-140806074528-phpapp01/85/Analysis-of-Different-Parking-Space-and-its-Comparison-4-320.jpg)
Analysis of Different Parking Space and its Comparison
- 1. IJSRD - International Journal for Scientific Research & Development| Vol. 1, Issue 8, 2013 | ISSN (online): 2321-0613 All rights reserved by www.ijsrd.com 1565 Analysis of Different Parking Space and its Comparison Manoj Kumar Biswal1 Tarapada Mandal2 Arumuga Subhashini N3 R. V. Kumar4 Ujjal Chattaraj5 1, 2, 3, 4, 5 Department of Civil Engineering 1, 2, 3, 4, 5 National Institute of Technology Rourkela, Rourkela, India Abstract— In the “Analysis of different parking space and its comparison” we collected data from different parking space of our institute N.I.T Rourkela. Initially we figured out what is the variation of PCU with a certain time and then we compared all these data with the help of “t- test“ to find out whether these parking pattern and demand are same or different. In another part we find out the “spatial and temporal distribution” of main road traffic vehicle, here “spatial distribution” is the variation of PCU (passenger car unit) with distance and in “temporal distribution” variation of PCU with time. Key words: PCU, t- test, spatial distribution I. INTRODUCTION In the increasing phase of urbanization, in most cities, designing a parking space has always been a challenging part of a traffic system. But in many cases the ignorance of on-street parking has lead to roadway congestion and in turn affected the flow of the roadway and in some cases the off- street parking space has been wasted as the system was inadequate. As a remedy, expanding the parking space utilizing more land area without proper parking practice has not proved productive. In this study, the existing system of campus of National Institute of Technology, Rourkela (NITR). The study area possesses decorum of parking with specific parking lots and the later area portrays academician sense of parking. The survey has been done to collect data of the two-wheelers (motorised and non-motorised) from distinct spots of NITR off-street parking lots adopting similar methodologies at the same time. The data obtained for different class of vehicles like 4 wheeler, 3 wheeler, motorised and non-motorised 2 wheeler was converted into standard passenger car unit (PCU) for unifying results. The data from NITR has been compared statistically with hypothesis testing method ‘t-test’ in various cross comparisons of all off-street parking sites and on-street parking data. The compared data of different lots of NITR will explore the difference and similarities of the different parking patterns and the liable pattern for off-street parking system can be obtained. These inferences can be combined to manage the parking system of the institution which is academically and economically beneficial. II. LITERATURE REVIEW Parking facility is one of the important transport facility in urban area specially the central districts having high retail activity & employment opportunities. Parking policies & pricing impacts the entire city transportation & land use. The spatial distribution of parking of Melbourne City is studied [3]. Generalized parking rates are assumed for estimating the parking demand & other parameters are ignored. The various behavioral characteristics of parking demand for various trips, location & with various urban areas [4]. Various factors influencing the parking demand & also their influence on each other was tried to find out. Relationship between parking demand & transport accessibility was analyzed. Parking demand decreases with good & efficient transport facility. The parking demand of shopping centre & markets from the data obtained by conducting parking demand survey at various locations of Bejing [5]. Parking demand rate with different public transport accessibility was determined & a parking demand model with different accessibility was provided. Attitude and behavioral responses of drivers is an important aspect to planned parking measures. The attitude & behavioral responses of car drivers to planned parking measures at campus of the Eindhoven University of Technology, in Netherland [2]. In an on-street questionnaire, car drivers were asked their opinion about restricting access to the campus area for cars of non-university car drivers. Parking spaces are strategic commodities of modern day transport facility. Few dataset allows precisely measuring the use of spaces in terms of population, segments, activity types & duration. The empirical measures & methods regarding the use of parking space in a strategic urban area [6]. GIS, fuzzy logic and weighting criteria to determine the selection of proper parking sites. An ideal method for parking site selection by the use of GIS, fuzzy logic and weighting criteria to determine proper parking sites of Esfahan city in Iran [9]. III. DATA COLLECTION SITE We collected data from different site in N.I.T Rourkela. The sites of are Ceramic department parking space, Central workshop parking space, SAC front side parking space, Main building parking space, Library parking space, Rourkela main road. IV. DATA COLLECTION METHODOLOGY We collected all these data from different site in N.I.T Rourkela from 10 am to 12 pm. We surveyed all the above given parking place, from this survey we got how many two wheeler motorized and nonmotorized vehicle is being parked at a specified parking place. V. DATA ANALYSIS AND METHODOLOGY N.I.T Rourkela data: From these entire five sites we have collected the data. After collection of data we convert all these vehicles in terms of PCU (Passenger car unit) by multiplying it with its corresponding PCU factor which is described in IRC-6.
- 2. Analysis of Different Parking Space and its Comparison (IJSRD/Vol. 1/Issue 8/2013/0010) All rights reserved by www.ijsrd.com 1566 Main building parking spaceA. We did survey at this site on 16th January 2012 from 10.00 am to 12.00 pm. Time Cycle Two-wheeler Four-wheeler PCU 10:00 16 8 3 14.6 10:15 18 9 3 15.9 10:30 18 10 3 16.4 10:45 17 11 3 16.5 11:00 18 12 3 17.4 11:15 20 12 3 18.2 11:30 20 10 4 18.6 11:45 20 11 6 21.9 12:00 18 10 8 23.4 Table. 1: PCU for Main Building Parking Space Fig. 1: Variation of PCU with time for main building parking place. Library parking space:B. Here we did survey on 17th January 2012 at same time 10.00 am to 12.00pm. Data are: Time Cycle Two-wheeler PCU 10:00 222 26 101.8 10:15 241 28 110.4 10:30 248 29 113.7 10:45 255 31 117.5 11:00 257 31 118.3 11:15 293 30 132.2 11:30 290 31 131.5 11:45 287 32 130.8 12:00 198 27 92.7 Table. 2: PCU for library parking space Fig. 2: Variation of PCU with time for library parking space. Ceramic department parking spaceC. On 18th January 2012 we did survey on ceramic department parking place. Time Cycle Two-wheeler PCU 10:00 29 19 38.9 10:15 26 19 35.9 10:30 19 20 29.4 10:45 21 21 31.9 11:00 27 21 37.9 11:15 45 24 57.4 11:30 55 29 69.9 11:45 61 22 72.4 12:00 57 21 67.9 Table. 3: PCU for Ceramic Department Parking Space Fig. 3: Variation of PCU with time for ceramic department parking space. Central workshop parking spaceD. Here we did survey on 19th January 2012 Time Cycle Two-wheeler PCU 10:00 76 20 40.4 10:15 74 20 39.6 10:30 54 21 32.1 10:45 51 20 30.4 11:00 47 17 27.3 11:15 37 17 23.3 11:30 36 16 22.4 11:45 35 13 20.5 12:00 31 10 17.4 Table. 4: PCU for Central Workshop Parking Space Fig. 4: Variation of PCU with time for central workshop parking space SAC front side parking spaceE. Time Cycle Two-wheeler PCU 10:00 26 25 22.9
- 3. Analysis of Different Parking Space and its Comparison (IJSRD/Vol. 1/Issue 8/2013/0010) All rights reserved by www.ijsrd.com 1567 10:15 34 25 26.1 10:30 30 25 24.5 10:45 38 26 28.2 11:00 37 26 27.8 11:15 40 24 28 11:30 40 21 26.5 11:45 35 20 24 12:00 21 14 15.4 Table. 5: PCU For SAC Front side Parking Space Fig. 5: Variation of PCU with time for SAC front parking space. VI. COMPARISON OF ALL DATA We compared all these data with each other to find out that either these parking pattern are same or different. Now question is which distribution we should apply. Here we are applying “t-distribution”. If t-stat value is less than the t- critical value then parking pattern will be same otherwise it will be different. The lists of the comparison between all these sites. SAC front side parking space and library parking spaceA. PCU(SAC) PCU(LIBRARY) 22.9 26.1 24.5 28.2 27.8 28 26.5 24 15.4 101.8 110.4 113.7 117.5 118.3 132.2 131.5 130.8 92.7 t critical for two- tail = 2.306004 t stat = 24.6048 t stat > t critical, so data are different. Table. 6: Comparison between SAC Front Side and Library Parking Space SAC front side parking space and library parking spaceB. and main building parking space PCU(SAC) PCU(MB) 22.9 26.1 24.5 28.2 27.8 28 26.5 24 14.6 15.9 16.4 16.5 17.4 18.2 18.6 21.9 t critical for two-tail =2.306004 t stat = 3.272781 t stat > t critical, so data are different. 15.4 23.4 Table. 7: Comparison between SAC front side and Main Building Parking Space SAC front side parking space and ceramic departmentC. parking space PCU(SAC) PCU (CERAMIC DEPT.) 22.9 26.1 24.5 28.2 27.8 28 26.5 24 15.4 38.9 35.9 29.4 31.9 37.9 57.4 69.9 72.4 67.9 t critical for two-tail =2.306004 t stat = 3.72603 t stat > t critical, so data are different Table. 8: Comparison between SAC front side and Ceramic Department Parking Space Ceramic department parking space and main buildingD. parking space PCU (CERAMIC DEPT.) PCU(MB) 38.9 35.9 29.4 31.9 37.9 57.4 69.9 72.4 67.9 14.6 15.9 16.4 16.5 17.4 18.2 18.6 21.9 23.4 t critical for two-tail =2.306004 t stat = 3.72603 t stat > t critical, so data are different Table. 9: Comparison between Ceramic Department and Main Building Parking Space Ceramic department parking space and library parkingE. space PCU (CERAMIC DEPT.) PCU (LIBRARY) 38.9 35.9 29.4 31.9 37.9 57.4 69.9 72.4 67.9 101.8 110.4 113.7 117.5 118.3 132.2 131.5 130.8 92.7 t critical for two-tail =2.306004 t stat =10.7429 t stat > t critical, so data are different Table. 10: Comparison between Ceramic Department and Library Parking Space Main building parking space and library parking spaceF. PCU(MB) PCU(LIBRARY) 14.6 15.9 16.4 16.5 17.4 18.2 18.6 101.8 110.4 113.7 117.5 118.3 132.2 131.5 t critical for two-tail =2.306004 t stat =21.1119 t stat > t critical, so data are different
- 4. Analysis of Different Parking Space and its Comparison (IJSRD/Vol. 1/Issue 8/2013/0010) All rights reserved by www.ijsrd.com 1568 21.9 23.4 130.8 92.7 Table. 11: Comparison between Main Building and Library Parking Space Central workshop parking space and SAC front sideG. parking space PCU(CENTRAL WORKSHOP) PCU(SAC) 40.4 39.6 32.1 30.4 27.3 23.3 22.4 20.5 17.4 22.9 26.1 24.5 28.2 27.8 28 26.5 24 15.4 t critical for two- tail =2.306004 t stat =1.256768 t stat < t critical, so data are not different Table. 12: Comparison between Central Workshop and SAC Front Side Parking Space Central workshop parking space and library parkingH. space PCU (CENTRAL WORKSHOP) PCU(LIBRARY) 40.4 39.6 32.1 30.4 27.3 23.3 22.4 20.5 17.4 101.8 110.4 113.7 117.5 118.3 132.2 131.5 130.8 92.7 t critical for two- tail =2.306004 t stat =14.7275 t stat > t critical, so data are different Table. 13: Comparison between Central Workshop and Library Parking Space Central workshop parking space and main buildingI. parking space PCU(CENTRAL WORKSHOP) PCU(MB) 40.4 39.6 32.1 30.4 27.3 23.3 22.4 20.5 17.4 14.6 15.9 16.4 16.5 17.4 18.2 18.6 21.9 23 t critical for two-tail =2.306004 t stat =2.792758 t stat > t critical, so data are different Table. 14: Comparison between Central Workshop and Main Building Parking Space Central workshop parking space and ceramicJ. department parking space PCU(CENTRAL WORKSHOP) PCU (CERAMIC DEPT.) 40.4 39.6 32.1 30.4 38.9 35.9 29.4 31.9 t critical for two- tail =2.306004 t stat =2.54384 t stat > t critical, 27.3 23.3 22.4 20.5 17.4 37.9 57.4 69.9 72.4 67.9 so data are different Table. 15: Comparison between Central Workshop and Ceramic Department Parking Space Data of central workshop parking space and SAC front side parking space are not different. We applied t distribution because sample size is less than 20. From t distribution we get two values one is” t critical” and other is” t stat” value. If “t stat” will be greater than the “t critical” then data are different. If “t stat” is less than “t critical” then data is not different. So here we get only one data is not different and remaining are different. VII. CONCLUSION We compared all the parking pattern of N.I.T Rourkela with each other with the help of ‘t-test’ and we got that in case of comparison for mean the parking pattern of Central Workshop and Student Activity Centre is not different while others are different. In case of comparison for parameters we got different result here we got that parking pattern of Student Activity Centre and Library is different and same is the case with Central Workshop and Student Activity Centre. REFERENCES [1] Guidelines for capacityof urban roads in plain areas IRC-106 (1990). [2] P. Waerden, A. Borgers, H. Timmermans, Attitudes and behavioral responses to parking measures, EJTIR (2006) 301-312. [3] W. Young, D. Beaton, S. Satgunarajah, An analysis of the spatial distribution of parking supply policy and demand, ATRF (2010) 81-87. [4] S. Chakrabarti, T. Mazumder, Behavioral characteristics of car parking demand : A case study of Kalkata, ITP (2010) 1-11. [5] H. Qin, Q. Xiao, H. Guan, X. Pan, Analysis on the parking demand of the commercial building considering the public transport accessibility, Nature and Science (2010) 63-68. [6] C. Morency, M. Trepanier, Characteristics parking spaces using travel survey data, CIRRELT (2008)1-22. [7] S. Wong, C. Tong, C. Lam, Y. Fung, Development of parking demand models in Hong Kong, ASCE (2000) 55-74. [8] J. Sivasubramanian, G. Malarvizhi, A system dynamics methodology for assessing parking demand for commercial shopping area, CRRI (1976) 223-229. [9] R. Farzanmanesh, A. Naeeni, A. Abdullah, Parking site selection management using fuzzy logic and multi criteria decision making, Environment Asia (2010) 109- 116.