modeling the localization of local parking lots using ...

SAJMR Spectrum: A Journal of Multidisciplinary Research 2014, ISSN 2278-0637

MODELING THE LOCALIZATION OF LOCAL PARKING LOTS USING GIS: A CASE STUDY OF TABRIZ CITY SABA BAZDAR, DR. KHALIL VALIZADEH KAMRAN Faculty of Geography & Planning, University of Tabriz, Tabriz, Iran

ABSTRACT The City managers excessive attention to the ongoing traffic has led to the ignorance of another part of the city traffic which is the stationary traffic. Therefore localization and establishment of public class parking lots by using the capabilities of GIS system can play an important role in decreasing traffic, economizing fuel consumption, decreasing air pollution and accelerating and facilitating public transportation. In this paper, we discuss about localization and establishment of local parking lots using AHP-Fuzzy model in GIS system and with regard to effective factors in localization for a sample region of Tabriz city. Keywords: localization, local parking lots, traffic, capacity, GIS _________________________________________________________________________ INTRODUCTION

Pinnacle Research Journals http://www.prj.co.in

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Nowadays the increasing urban population and the current heavy traffic problems necessitate the attention toward establishing roads, sidewalks and public parking lots (Bhuyan & Nayak, 2013). Due to the limited capacity of the public transportation system in large cities, the increasing use of private vehicles on one hand, have caused heavy traffics in urban streets and on the other hand it reduces the efficiency of the public transportation system. The drivers after reaching their destination are always looking for a place to park their cars which leads to traffic jam itself (Patnaik, 2013). The City managers excessive attention to the ongoing traffic (vehicle in motion) has led to the ignorance of another part of the city traffic which is the stationary traffic (parked vehicles). Thus localization and establishment of public parking lots trough the GIS system capabilities can effectively reduce traffic, save fuel, reduce air pollution, accelerate and facilitate public transportation. In recent decades the application of emerging technologies such as GIS and remote sensing have greatly increased and provided unique information about land use and urban planning (Pujades, & Barbat, 2009). Due to vertical urban development policy for optimal use of the city potentials this study through studying the Tabriz Imam Street tries to solve some of the existing problems of network through localizing the local parking spaces.


Traffic Volume Traffic volume includes the number of vehicles that move across a road during a certain period of time (which is not necessarily defined) , in a particular direction or directions of one or more lines of a road. Traffic volume might be defined for a special type of vehicle (such as cars, buses or trucks) or generally for all types of vehicles that move across a certain road and in this second situation the traffic volume unit is Passenger Car Unit or (PUC). Table1: Coefficient of vehicles on the road column

Vehicle

Urban roads Two line

1

Car- taxi

2

Pickup

3

Minibus

4

Bus

5

Truck

6

Trailer

7

Motorcycles

Rural roads Four line

Resource: Shahi, 1988

2

The area under study Tabriz is the greatest North-western metropolitan city of Iran which is about 131 km2 at eastern latitude 46,11,46,23 and northern latitude 38,1,38,9 with 1340 m height in the plain of Tabriz (Asghari Zamani, 2000).


The area under study is is Emam Khomeini Street which one of the main axes of communication located in the north of district 2 and most of the area is under business or administrative application. After Janbazan square which is a traffic nod due to the extension of the business sites we face a special change into non-accumulated area. In addition to these nodes, the intersection of the Abresan and Shahryar Square are other nodes of this path (Zysta consulting engineering, detailed design of Tabriz, 2006).


Mathods Research and data analysis method The method used in this paper is objective, practical and functional-based and based on data collection, it is among analytical researches, under the category of survey, software type, field data and case studies. The required data for this study are as follows: 1. The land use, building density and the street maps that can be obtained from the municipalities of the districts under study. 2. Raw Statistically measured and limited traffic data These figures have been collected in July 2nd in 2013 from West Point (opposite of the Museum of Azerbaijan) field and manually in 14 consecutive hours between the hours of 6am and 20pm. These data include traffic information, including the number of passing vehicles from both sides of the Street divided into cars, vans and pickups, trucks, minibuses, buses, bicycles and motorcycles in the southern part of the street. The next step is to create a database for parts in Arc GIS. After preparation of the maps we can obtain the required maps. Methods to measure and calculate the traffic volume (v) Traffic volume may be measured through simple (manual calculation) or automatic counting system. In this research the manual method is used. The manual measurement is done through directly counting all the vehicles that pass a specific point. This is usually done with paper and pencil and drawing short lines clusters of five lines and … so that each line represents the passage of one vehicle.

3

Then the accumulated statistics change into traffic volume through PCU equivalent conversion coefficients.


Methods to measure and calculate the actual capacity (c) The capacity is equivalent to maximum uniform passenger vehicles that can pass a point or section of a lane or pass through the whole way in a specific time and governing conditions of traffic. Practical capacity, is the capacity that is lower than the ideal capacity in the saturation conditions. Practical capacity is achieved through the following equation:

C = 1800 × g/c × K1 × K2 × K3 × K4 × K5× K6 Where: C = capacity of the street


g = the effective green traffic lights c = lights’ cycle (total time of green, red, yellow) Table 2. K1 = coefficient of capacity adjustment of left turn moves based on the opposite volumes Number of coefficient of capacity adjustment for the opposite volumes main lines 199 - 0 599 - 200 799 - 600 1000 - 800 % 91 % 50 % 33 % 25 Line 1 % 95 % 75 % 66 % 62 Line 2 % 96 % 83 % 77 % 75 Line 3

Above 1000 % 20 % 60 % 73

Table3. the coefficient of setting the passengers down Number of coefficient of capacity adjustment for the opposite volumes main lines high medium low No parking % 61 % 70 % 80 1 Line 1 Line 2 Line 3

% 80 % 85

% 75 % 90

% 90 % 95

1 1

Table 4: K3 = coefficient of street parking Number of coefficient of capacity adjustment for the opposite volumes main lines high medium low No parking % 20 % 69 % 80 1 Line 1 Line 2 Line 3

% 50 % 50

% 70 % 75

% 85 % 90

1 1

Table 5: K4 = coefficient of adjustment within each lane 2/50

2/75

3

3/25

3/5

3/75

coefficient of adjustment

% 87

% 90

% 93

% 97

% 99

1

4

Lane width


Table 6: K5 = coefficient of adjustment to the streets without refuge

Number of One line lines coefficient of % 90 adjustment

More line % 95

than

1


Table 6: K5 = coefficient of adjustment to the streets without refuge Type of district

coefficient of adjustment

Central

% 90

Other

1

2-7 Table reference: Jiang & Yao, 2006 4-4- Fuzzy and AHP localization According to the results of Jiang and Claramunt (2004) AHP-FUZZY method is one of the best ways to locate parking lots which is used in this study. First, in order to set location, the effective parameters are determined in parking location. Then, the parameters are given weight and the data layers are prepared with respect to the calculated weights and GIS analysis functions. The prepared layers are synthesized Determining appropriate parking location Factors:


5

According to the district under study, the results of previous stages and urban planning and traffic expert’s opinion in this area 5 parameters are selected. 1. Distance from the center of trips: It is the most important parameter in parking location. The distance between the parking space and the center of trips must be in a way that the users have the lowest walkway to the center which include commercial, treatment and residential sectors in this study. Accordingly walking distance of the centers of attraction of trip to the parking lot is divides into 4 categories (140 meters from the center), (250 meters from the center), (350 meters from the center and 450 m from the center) (Karimi, 2008). 2. Usage: This parameter contains the classes of residential, commercial, medical, administrative and religious-cultural. 3. Traffic: In this class the access of the parts art classified based on the lane width. 4. The value of the land: This class contains information layer plaques per construction each plaque in divided into five classes: too expensive, expensive, moderate, inexpensive and very inexpensive. 5. Area: The area of the building is the parameter marker. The standard area of this district is 1000 square meters and square or rectangular shape. According to the established criteria the data required at this stage consists of layers of streets, building plaques and their usage, and the property value. The layers of passage and usage are prepared and the area layer is prepared by “ area calculate” order in

the table of plaque’s maps and the property value is prepared by Consultation with a number of local housing counselors. Then, according to the width and traffic flow of passages, streets and alleys they are divided into five categories, and each category was given a weight. And the weight of all construction plaques adjacent to these streets and alleys was considered as that


plaque’s weight in the parking location with regard to traffic. In order To prepare the layer of distance from the trip center, according to the results of previous stages the trip centers were determined and Walkway distances from the centers of attraction were classified and weighted. Then with the Network Analyze function in Arc GIS 10.1 software the walking distance of each plaque was calculated then according to the weights the value of each plaque designated for parking establishment was determined according to the measure of the distance from the centers of attraction. The plaques near the center of attraction have higher value. Then the usage layers are divided into five categories, and inappropriate applications such as mosques, historical and educational center are determined as inappropriate land uses and the rest of the usages are weighted. Thus 5 layers are ready to be synthesized. After the preparation of the layers in Arc GIS they became images through the analytical functions maps and in order to locate AHPFuzzy method the images were transferred to the Idrisi software and through using GIS analysis Options and the Decision Support function and Decision Wizard the location was found. In this part the required data was collected in the form of coherent and systematic information and used for questions and hypotheses. Results and findings At this stage, according to the procedures outlined in the previous sections, traffic characteristics and capacity of the street, are analyzed and proceed to examine the effects of street parking on the practical capacity of streets. Traffic Volume Table 8 represents the statistics on traffic volume of the district under study in 14 consecutive hours in a regular day. The data were collected for all the hours of the study according to the following table example (for 11 o’clock)

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(Table 8: The traffic volume at 11) History

Towards the Abresan intersection

Position (opposite Museum of Azerbaijan)


nd

June 2 2013 Time/ vehicle type 11-11:15

car

bus

Minibus

Truck

truck

bicycle

motorcycle

total

%

434

Van and pickup 19

0

0

0

3

20

25

501

25.57427259

11:15-11:30

489

23

0

0

0

0

9

20

541

27.61613068

11:30-11:45

446

10

0

0

0

0

18

22

496

25.31904033

11:45-12

375

14

0

0

0

2

14

16

421

21.49055641

Total

1744

66

0

0

0

5

61

83

1959

100

equivalent

1

1.2

2.75

2.5

3

2

0.3

0.5

13.25

0

volume

1744

79.2

0

0

0

10

18.3

41.5

1893

0

the


Table 9: Street traffic from 6 to 20 Traffic volume 455.1

hour

1130.8

7_8

1154.8

8_9

1428.8

9_10

1663.6

10_11

1893

11_12

1701.8

12_13

1755.6

13_14

1402.8

14_15

1381.8

15_16

1435.3

16_17

1754.6

17_18

1935.2

18_19

1672.95

19_20

6_7

Practical capacity Using section the method in 4-3 the practical capacity of the streets is calculated for each hour. The response to the calculations is presented in Table 11.The Method of calculation is given in the following table.


7

(Table 10. The practical capacity of the street during the hours of 8 to 14 and 16 to 19 per an hour) Calculating the capacity of the streets at all hours

n.L

Fixed adjustment coefficient

1800

Capacity adjustment coefficient of the left turn movements based on the opposite volumes

K1

73%

Street Parking coefficient

K2

50%

Lane width adjustment coefficient for each lane

K3

99%

adjustment coefficient for the streets without refuge

K4

100%

The adjustment coefficient to set out the passengers

K5

90%

adjustment coefficient of urban areas

K6

100%

g/c = 1

Green light Time

g

Traffic lights (all lights green, yellow, red)

c

1756.161

C

3


(Table 11: Passage Capacity between 6 - 20) Time

C

6_7

3414.5

7_8

3336.7

8_9

1756.2

9_10

1756.2

10_11

1756.2

11_12

1756.2

12_13

1756.2

13_14

1756.2

14_15

1756.2

15_16

2634.2

16_17

1756.2

17_18

1756.2

18_19

1756.2

19_20

1756.2

Estimating street parking effects As mentioned in the above steps for calculating the actual capacity of Main Street 6 factors are used one and of them is street parking. In the study of the street under study the effect of this coefficient is 50% due to the variety of street parking spaces. However, the practical capacity and the capacity of the street are surveyed when the varity of street parking is low, according to Table 4, the impact of this factor is 90% and the following results are obtained. (Table 12: practical capacity of the streets with few street parking spaces from 8 to 19 per an hour) n.L

Fixed coefficient

1800

3


8

Calculating the capacity of the streets at all hours

Capacity adjustment coefficient of the left turn movements K1 based on the opposite volumes

73%

Street Parking coefficient

K2

90%

Lane width adjustment coefficient for each lane

K3

99%

adjustment coefficient for the streets without refuge

K4

100%

The adjustment coefficient to set out the passengers

K5

90%

adjustment coefficient of urban areas

K6

100%


g/c = 1

Green light Time

g

Traffic lights (all lights green, yellow, red)

c

3161.0898

C

(Table 13: Traffic volume and practical capacity of the streets with few street parking space at consecutive hours) time

V

C

6_7

455.1

3414.5

7_8

1130.8

3336.7

8_9

1154.8

3161

9_10

1428.8

3161

10_11

1663.6

3161

11_12

1893

3161

12_13

1701.8

3161

13_14

1755.6

3161

14_15

1402.8

3161

15_16

1381.8

3161

16_17

1435.3

3161

17_18

1754.6

3161

18_19

1935.2

3161

19_20

1672 .95

3161

9

Findings and conclusions for local parking location First the number of required parking lots is calculated using the findings noted in the previous section and then the mentioned factors are weighted by the mentioned methods and the parking lots are located.


Estimating the required parking spaces To calculate the required parking spaces the actual practical capacity of the existing parking is reduced from the practical capacity and street parking and the number of parked cars is obtained. 3161-1756=1405 Based on the above calculation parking space for 1405 cars is needed. In order to calculate the area for local parking lots the number of require parking spaces in multiplied by 14m (Equal to the amount of standard space for a parked car): 1405*14=19670 (about 2 hectares)


Now in order to obtain the number of local parking lots this 19670 m is divided by 1000 4 story constructions. That means each parking lot is established as 4000 m Infrastructure in 4 stories with the capacity of 210 cars. 19670/4000=4.9 Based on the above equation it is concluded that 5 public parking lots is required in the area. In this study the street is divided into eastern and western parts and the main absorbing centers are determined and the following maps and analysis are obtained. 6-2- Map of indicators evaluation in the western part (maps No. 1-5): 1- Distance from absorbing center Distance from absorbing center- western part Map Guide absorbing center

m m m m

2- Rating usages

10

Map of Rating usages-Western Part


Map Guide Lowest value Highest value


3. Rating western part access Rating access- western part


4. Rating western part value

Rating Value- western part

Map Guise Lowest value


11

Highest value

5. Dividing western areas

Devideing Areas map- western part

SAJMR Spectrum: A Journal of Multidisciplinary Research Map Guide 2 2014, ISSN 2278-0637 m 2

m 2 m m2 2 m 2 m m2

6-3- 6-2- Map of indicators evaluation in the eastern part (maps No. 6-10): 6- Distance from absorbing center

Map of Distance from absorbing center-Eastern Part

Map Guide

absorbing center

12

m m m

7- Rating usages


Map of Rating usages-Eastern Part



8- Rating eastern part access

Map of Rating access-Eastern Part

Map Guide

Lowest value Highest value

9- Rating eastern part value

Map of Rating Value-Eastern Part

Map Guide Lowest value


13

Highest value


10- Dividing western areas Map of Dividing areas -Eastern Part

Map Guide

6-4- Prioritizing the criteria


14

In this part the criteria are prioritized. First the 5 main criteria are compared and their relative importance is determined and prioritized. As indicated in the following tables the distance from the absorbing center have the highest priority and then we have usage, access, land value, and area respectively.


The matrix for weighting the layers

Distance from absorbing center Land useage

Distance from absorbing center

Land useage

access

Ground value

Space

access Ground value Space

Table 14. The results of weighting through AHP method

•

0/3128

access

Land value

0/1223

0/0398

area

0/0351

15

0/49

usage Distance from absorbing centers


After prioritizing the maps are changed into rester through the fuzzy method and after the final analysis and valuating the local areas with the potential of having local parking lots the optimality of the sections is identified. (Maps 11-14) 11. Localizing local parking lots in the western side


Map of the best locations for local parking lots -Western side

Map Guide Improper 4th Priority 3rd Priority th 24stnd Priority Priority 1 Priority

12 optimal areas for the parking lots in western parts

16

Map of the Localizing local parking lots -Western Parts


Map Guide

The


13. Localizing local parking lots in the eastern side

Map of the Localizing local parking lots -Eastern side

17

Map Guide

14 The optimal areas for the parking lots in eastern parts


Map of the Localizing local parking lots -Eastern Part

Map Guide Improper 4th Priority 3rd Priority 2nd Priority 1st Priority


7- Conclusion From 1960 to 1980 the rapid growth of cities caused a failure in providing services and led to a lower quality of municipal services since 1360 the financial cut s in governmental funding to the municipalities which are the only organization providing municipal services resulted an imbalance between the residential and service usage. One of the services that suffer from the decline in the level of service and has caused a management crisis in big cities is the transport service. In order to solve the problems traffic management is suggested as a desirable tool. This measure does not demand complicated and long-term process and through management projects and far from constructional projects the urban traffic will be improved. As mentioned before one of the ways to improve the street services is to limit the street parking spaces and offering local parking lots. In this study a street was divided into eastern and western parts and through the application of models and the analysis of usages, the absorbing points of these two areas were determined and the optimal location for the parking lots was identified and based of the estimation the requirement for parking spaces for these areas includes 5, 1000 meters 4 story parking lots to park 1405 cars.

References


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Bhuyan, P., & Nayak, M. S. (2013). A Review on Level of Service Analysis of Urban Streets. Transport Reviews, 33(2), 219-238. Dahal, K. R., & Chow, T. E. (2014). A GIS toolset for automated partitioning of urban lands. Environmental Modelling & Software, 55, 222-234. Jiang, B., & Claramunt, C. (2004). Topological analysis of urban street networks. Environment and Planning B, 31(1), 151-162. Jiang, B., & Yao, X. (2006). Location-based services and GIS in perspective. Computers, Environment and Urban Systems, 30(6), 712-725. Kim, J.-J., & Kim, D.-Y. (2009). Effects of a building’s density on flow in urban areas. Advances in Atmospheric Sciences, 26(1), 45-56. Klosterman, R. E., Brail, R. K., & Bossard, E. G. (1993). Spreadsheet models for urban and regional analysis: Center for Urban Policy Research New Brunswick, NJ. Landis, J., Zhang, M., & Fotheringham, A. (1999). Using GIS to improve urban activity and forecasting models: three examples. Spatial models and GIS: New potential and new models, 63-81. Lantada, N., Pujades, L. G., & Barbat, A. H. (2009). Vulnerability index and capacity spectrum based methods for urban seismic risk evaluation. A comparison. Natural hazards, 51(3), 501-524. Li, D.-F. (2007). Compromise ratio method for fuzzy multi-attribute group decision making. Applied Soft Computing, 7(3), 807-817. Meyers, L. S., Gamst, G., & Guarino, A. J. (2006). Applied multivariate research: Design and interpretation: Sage. Mohapatra, S. S., Bhuyan, P. K., & Rao, K. (2012). Genetic algorithm fuzzy clustering using GPS data for defining level of service criteria of urban streets. Munoz, F. (2003). Lock living: urban sprawl in Mediterranean cities. Cities, 20(6), 381-385. Papinski, D., & Scott, D. M. (2011). A GIS-based toolkit for route choice analysis. Journal of Transport Geography, 19(3), 434-442. Patnaik, A. K. (2013). Level of service criteria of roads in urban indian context. Shahi, J. (1988). traffic engineering. Center for Academic Publication, Tehran.

modeling the localization of local parking lots using ...

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