Modeling on Scale of Public Parking Lot based on Parking Choice Behavior

YUN Meiping Department of Traffic

Engineering, Tongji University, Shanghai,

Cao'an Road No.4800, 201804; Tel (Fax):

(8621)6958- 4674-803; Email:

yunmp@mail.tongji.edu.cn

YU Ruisong Department of Traffic

Engineering, Tongji University, Shanghai,

Cao'an Road No.4800, 201804; Tel (Fax): (8621)6958-8994;

Email: yurs79@163.com

YANG Xiaoguang Department of Traffic

Engineering, Tongji University, Shanghai,

Cao'an Road No.4800, 201804; Tel (Fax): (8621)6958- 9475;

Email: yangxg@mail.tongji.edu.cn

Abstract

Most past researches on determining parking scale did not consider individual driver’s different parking choice behavior. This paper focuses on how to optimize parking scale while taking driver’s parking choice behavior into account. Firstly, the main factors of individual driver are analyzed, including parking service radius, length of parking time, trip purpose. With analysis of data from stated preference and revealed preference parking survey, the authors conclude that service radius is a key factor influencing parking scale. Then, optimization model on parking scale is put forward in which parking choice probability is quantified by adopting logit model. In the model, parking choice probability is a main measurement of parking choice behavior. Finally, unknown parameters of the model are calibrated based on data survey and analysis. The result of the model shows parking choice probability is much higher when service radius is shorter. And parking choice probability is higher when length of parking time is longer.

1. Introduction

Public parking lots provide parking space with or without pay, most of which lay in the centre part of a city or local district. They can make up for the shortage of planned parking lots. The standards of planed parking lots in most cities in China are much lower than what is required by the increasing private cars. There are two means to mitigate this problem. One is to rebuild parking lots according to the new standards.

While most of these problems appear in the centre part of a city, where land-use has been fixed already and building or rebuilding parking lots are almost impossible [1]. The other way is to optimize public parking lots to accommodate the parking needs, which is relatively more feasible. To a certain public parking lot, if there is too many parking seats it will decrease the utilization rate; if the parking seats are much less than the demand it will cause parking congestion or even impact dynamic traffic flow. This paper focuses on optimization of parking seats for a certain parking lot, which aims to improve parking service level as a traffic management measure. 2. Factors impacting parking choice probability

Parking behavior is what an individual driver do while looking for parking lots or deciding whether choose the parking lots or not. The main result of parking behavior can be formulated as parking choice probability which is influenced by service radius (namely, walking distance after parking), parking security, parking purpose, parking charge. Figure.1 is the result of factors impacting parking choice from parking survey data in city of Zhuhai in south China’s Guangdong province. We can see that parking security is the most important factor accounting for 36%, and secondly is walking distance accounting for 20%. 2.1 Influence of service radius to parking demand

Service radius is distance from parking lot to trip destination, generally is walking distance, which is

2008 International Conference on Intelligent Computation Technology and Automation

978-0-7695-3357-5/08 $25.00 © 2008 IEEE

DOI 10.1109/ICICTA.2008.273

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among the most important factors accounting for 20% shown in Figure.1. From the distribution of walking distance we can see that 29% drivers walk less than five minutes and 43% drivers walk at the range of five to ten minutes. There are only 6% drivers walking more than twenty minutes, as can be seen in Figure.2. Some relevant data survey also shows drivers are willing to change for walk distance by parking charge, that is to say they are likely to choose cheaper and farther parking lot or choose nearer and more expensive parking lot.

Parking lotsituation 19%

Charge 18%

Walking distance20%

Parking security36%

Parkinginformation

7%

Figure 1. Main factors influencing parking

choice

1514%

208%

306%

05%

524%

1043%

Figure 2. Distribution of walking distance after

parking (unit: min) 2.2 Characteristic of parking lot

Characteristics of parking lot, such as utilization rate, parking charge and parking security, can influence

parking choice probability. This paper only considers utilization rate in the model formulation. 2.3 Other possible factors

Besides above factors influencing parking demand, characteristics of travel trip also works, including trip purpose, length of parking time [2]. For example, parking choice probability for commute trip is relatively higher than that of shopping trips. If length of parking time is longer, drivers are more likely to choose a certain parking lot. These factors are quantified in the model formulation in the paper. 3. Modeling scale of public parking lot 3.1 Relevant hypothesis

(1) The model is to determine the optimum parking seats of a certain parking lot in a study area. And there is only one public parking lot need to determine the optimum seats in the study area.

(2) Land use is known in the study area. Roadside parking and other parking seats of public buildings are known.

(3) Among the parking demand factors, only factors relating parking behavior are taken into account, including service radius, length of parking time, trip purpose. 3.2 Dividing of parking demand generation subarea

Given the location of a certain parking lot, service area should be determined firstly which generally is surrounded by maximum walking distance after parking. Then, in this service area, subarea dividing can be performed according to different land use, during which following principles should be followed [3].

(1) Parking demand generation subarea should be controlled to a reasonable size. If the subarea is too large, the precision of parking seats would be deteriorated. If it is too small, the data survey and processing work strength would be increased. Generally, subarea’s radius is recommended to be less than fifty meters.

(2) Subarea districting should be based on land use, for parking generation rate always changes according to land use classification.

(3) Arterial road with heavy traffic flow should not be included in a subarea. Road and river can be the edge between subareas in order to decrease error of determining parking seats.

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(4) Service radius is the walking distance between centroid of parking generation subarea and the parking lot. To be simple, the centroid of each parking generation subarea is the location where parking demand is the highest in this subarea. 3.3 Model establishment

Car parking demand models have been researched for a long time and the models are generally accepted [4][5]. Here the following parking demand model is adopted.

∑=

⋅=N

jijiji Ry

1α , (1)

iy is average parking demand of subarea i ,

ijα is parking demand of land use of type j per

square area (or employee) in subarea i . ijR is square

measure (or number of employee) of j type land in subarea i .

)/()(g λβ ⋅⋅⋅= iji rPyX X is parking demand during peak hour. g is

parking saturation grade of subarea i during peak hour. iβ is turnover rate of subarea i . λ is

utilization rate during peak hour. jr is walking

distance between centroid of j type land and parking

lot. )( jrP is parking lot choice probability of drivers

whose destination is j type land. 4. Parameter calibration of the model 4.1 Modeling parking choice probability

From above survey data we can see that whether drivers choose the parking lot or not is influenced by walking distance, length of parking time, and trip purpose. Logit model, as a disaggregate analysis tool, is adopted as an efficient model formulation to analyze travel choice utility. Here we take binary logit model to describe parking choice probability. The model is as follow.

)3(,1

)2(,

21

2

2

1

12

1

nn

n

nin

n

VV

V

nn

VV

V

n

eeePP

eeeP

+−=

+=

＝

nP1 is the probability of choosing the parking lot

by driver n . nP2 is the probability of not choosing

the parking lot by driver n . nV1 is deterministic utility function of driver n by choosing the parking lot. nV2 is deterministic utility function of driver n not choosing the parking lot.

Influencing factor of utility function is characteristic vector as ],,[ 1 ′= inkinin XXX . K is the number

of variables. ],,[ 1 ′= kθθθ are unknown

parameters of characteristic vector inX . Then if θ is determined, the parking choice probability can be defined. And inV is function of θ and inX .

),( inin XfV θ= . Suppose inV and inX is a liner function, then,

∑=

==K

kinkkinin XXV

1' θθ .

Then parking choice probability can be formulated as following.

( )

( )

( ) )5(,1

1

)4(,1

1

21

21

21

'

'

12

'1

nn

nn

nn

XX

XX

nn

XXn

eePP

eP

−−

−−

−−

+=−=

+=

θ

θ

θ

4.2 Parameter calibration for parking choice

Probability of parking choice )(rP is function of walking distance, length of parking time, and trip purpose. Based on the parking survey data in Zhuhai city, parking choice probability model considering influencing factors is listed as following table1.

If |tk|>1.96, it means that inkX evidently impact parking choice probability under 95% confidence coefficient. If |tk|>1.64, it means that inkX evidently impact parking choice probability under 90% confidence coefficient.

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Table 1. Parameter calibration of parking choice probability model

Factors

Item

Dumb variable

Length of parking time

(min)

Walking time after parking

(min)

Trip purpose

work Shopp- ing

Busi- ness

variable 1inX 3inX 2inX 4inX 5inX 6inX

utility of choosing parking lot nV1 1 T=1, 1-30 T=2, 31-60 T=3, 60-120 T=4, >120

T=1, 1-5 T=2, 6-10 T=3, 11-20 T=4, >20

Yes: 1 No: 0

Yes: 1 No: 0

Yes: 1 No: 0

utility of not choosing parking lot nV2 0 0 0 0 0

Unknown parameter 1θ 2θ 3θ 4θ 5θ 6θ

iθ calculated data 1.249 0.895 -2.080 -1.268 -1.483 -0.560 T test tk value 1.169 2.045 -2.632 -1.131 -1.682 -0.461

4.3 Model analysis

The result of the model shows, under confidence coefficient 95%, parking choice probability is influenced by walking distance and length of parking time. Parking choice probability is much higher when walking time is shorter ( 3θ =-2.080). And parking choice probability is higher when length of parking time is longer ( 3θ =0.895). If we set confidence coefficient 90%, parking choice probability is evidently impacted by trip purpose. Parking probability is higher for business trip purpose than that for work and shopping. 5. Conclusion

By analyzing parking choice behavior which is quantified by parking choice probability model, optimization model of determining parking seats of certain parking lot is presented. In the established model, walking distance after parking, length of parking time, and trip purpose are important influencing factors. The proposed model and parameter calibration are based on field survey data. Due to some difficulties in the survey implementation, some detailed data information is not accurate so we omitted that part of data. These data used in the model formulation were collected in a medium-size city in China. So in the future research, more detailed data survey would be performed and application of improved model would be carried on.

6. Acknowledgement

This research is supported by National Natural Science Foundation of China from project “Research on mechanism of route guidance utility (No.70501023)” and project “Optimization and management of urban transport network (No. 70631002)”. The authors give acknowledgement to the above support. 7. References [1] Yaron Hollander, Joseph N. Prashker, David Mahalel, “Determining the Desired Amount of Parking Using Game Theory”, Journal of urban planning and development, ASCE, 2006, pp. 53-61. [2] Guan Hongzhi, YAO Shengyong, “A Choice Model of the Length of Parking Time in CBD”, Journal of Highway and Transportation Research and Development, Peking, 2005, pp. 144-146. [3] Yun Meiping, Yang Xiaoguang, “Improvement of Optimization Model for Road Networks Subarea Districting for Incident Management in Advanced Traffic Management Systems”, Journal of Highway and Transportation Research and Development, 2004, pp. 73-76. [4] Young, W., Thompson, R. G., and Taylor, M. A. P., “A review of urban car parking models”, Transport Rev., 1991, pp. 63-84. [5] Guan Hongzhi, Liu Xiaoming, Parking planning, design and management, People’s communication publish house, Peking, 2003.

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