Mathematical Problems in Engineering

Volume 2014 (2014), Article ID 815963, 7 pages

http://dx.doi.org/10.1155/2014/815963

Research Article

Properties Analysis on Travel Intensity of Land Use Patterns

Lishan Sun,¹ Liya Yao,² Shuwei Wang,¹ Jing Qiao,¹ and Jian Rong¹

¹Key Laboratory of Traffic Engineering, Beijing University of Technology, 100 Pingleyuan, Chaoyang District, Beijing 100124, China

²Department of Transport Engineering, Beijing Institute of Technology, 5 South Zhongguancun Street, Haidian District, Beijing 100081, China

Received 3 December 2013; Revised 19 January 2014; Accepted 10 February 2014; Published 13 March 2014

Academic Editor: Wuhong Wang

Copyright © 2014 Lishan Sun et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Quantization of the relationship between travel intensity and land use patterns is still a critical problem in urban transportation planning. Achieved researches on land use patterns are restricted to macrodata such as population and area, which failed to provide detail travel information for transportation planners. There is still problem on how to reflect the relationship between transport and land use accurately. This paper presents a study that is reflective of such an effort. A data extraction method is developed to get the travel origin and destination (OD) between traffic zones based on the mobile data of 100,000 residents in Beijing. Then Point of Interests (POIs) data in typical traffic zones was analyzed combined with construction area investigation. Based on the analysis of travel OD and POI data, the average travel intensity of each land use pattern is quantified. Research results could provide a quantitative basis for the optimization of urban transportation planning.

1. Introduction

Due to the accelerating of urbanization, major cities in China were gradually forced to carry out high-density land development. However, excessive land development has led to rapid growth of traffic demand and aggravates the traffic congestion, air pollution, and so on.

The correlation issues between land use and travel intensity were firstly put forward by Knight [1] in 1977. Since then, a large number of scholars have conducted researches on the interaction between land use and travel behavior [2–7]. Susan [8] and Pauline et al. [9] analyzed the impact of land use on the characteristics of the trips. They pointed out that with the increasing of land use density, the number of travel would decrease. Hanssen [10] proved that land use has a profound effect on travel behavior. Silva and Luis [11] established a model to unravel the influence of land use on travel behavior. Olle et al. [12] presented a preliminary research on travel behavior analysis through mobile data.

Application of mobile data to analyze the travel characteristics has become increasingly popular in recent years, particularly since 2007. Caceres et al. [13] derived the origin-destination data from mobile data. Bar-Gera [14] evaluated a cellular phone-based system for measurements of traffic speeds and travel times in Isral. Asakura and Iryo [15] analyzed the tourist behavior based on the mobile data. In Herring and his colleagues’ research [16] in 2009, the mobile data were used to forecast arterial traffic through statistical learning. On this basis, other scholars have done a lot of research to expand mobile data application and travel behavior [17–22]. As one of the key area within transportation planning, research on land use properties of transport unfortunately just attracted little attention in the application of mobile data. Such applications are urgently required in urban planning of China, as most of the cities will face more and more serious traffic congestion problems generated by crowded and disordered traffic in the coming challenge years.

With the analysis and brief history above as a backdrop, it is clear that there is a close relationship between the land use and travel behavior. However, the analytic accuracy of land’s traffic properties is rather low and there are difficulties in the residents’ travel data acquisition. This paper describes an information technology-based procedure for analysis the traffic properties of land use. The remainder of the paper is structured as follows. Section 2 introduces the travel OD calculation based on the mobile data. Section 3 presents the analysis of land use based on the POIs (Point of Interests). Travel intensity calculation is presented in Section 4, followed by the conclusions presented in Section 5.

2. Travel OD Calculation Based on the Mobile Data

In this section, the travel OD was calculated based on 100,000 residents’ mobile data of Beijing. The main steps include preprocessor of mobile data, extraction of residents’ trip chain, extraction of travel OD, and sampling expansion of travel OD.

2.1. Sampling Expansion of Travel OD

In this paper, nearly 100,000 mobile users’ data is extracted from Beijing Mobile. As of 2013, there are more than 31 million mobile users in Beijing, in which 72.84% belong to Beijing Mobile. The sampling expansion of travel OD coefficient should be calculated as 31000000 × 72.84%/100000 = 225.804. According to the expansion coefficient, the total travel amount should be 29,786,700, which is in line with the travel amount from Beijing Municipal Traffic Annual Report.

2.2. Preprocessor of Mobile Data

(a) Data Separation and Deduplication. In order to improve the validity and precision of the data, irrelevant data should be eliminated in the pretreatment stage. Generally, irrelevant data include the null value data as well as the duplicate data.

(b) Construction of Database. In order to handle the huge amount of mobile data, an appropriative database which can improve the management capability of a variety of data sources was built up. As the foundation for the following data analysis, the processed data was written to the database above, as shown in Figure 1.

Figure 1: Mobile data on database map.

(c) Mobile Data Reclassification and Sorting. After the data base construction, the mobile data of 7 days were classified into individuals, and then sorted in the ascending order of time.

2.3. Extracting Residents’ Trip Chain

The trip chain data means a collection of the residents travel stops on the track in the day time. Mobile data could assist in obtaining the data of residents travel through the steps as follows.

(a) Discriminating Stagnation Point. Calculate the residence time in each cell , and set the time threshold ( minutes, the longest bus waiting time, based on field survey). When the cell residence time is greater than , the stagnation point of the user is determined.

(b) Determining the Coverage of Base Station. The coordinates of the mobile phone base station in Beijing were obtained. On this basis, the coverage area of each mobile phone base station was determined through the principle of the Voronoi diagram. According to the coverage area of mobile phone base station and the coordinates of the point of interest, we are able to determine the affiliation of the various points of interest and mobile phone base stations.

(c) Discriminate the Home Location. The home location should be selected from the stagnation point of the user. The cross time , which is the stay period between home and stagnation point, was calculated. The user’s home position is determined when the crossover time is greater than the time threshold .

(d) Discrimination the Work Place Location. Similar to the method above, the workplace could also be identified from the stagnation point of the user. Regardless of the home location, the cross time , which is the stay period between work and stagnation point, was calculated. The user’s work position is determined when the crossover time is greater than the time threshold .

(e) Analysis Trip Purpose. On the basis of home and work location identification, the remaining stagnation points could be mainly classified into the user’s track sequence. And the trip purpose could be determined by the characteristic of interest points (culture and entertainment, live, shop, etc.), which is on the user’s track and show reasonable stay period.

2.4. Extraction of Travel OD

The travel OD could be obtained from residents’ day trip chain, which can be simplified to be expressed as Figure 2: A~F represent the traffic zones; a-b-c show a day trip chain of mobile phone user, in which a, b, and c are stagnation points, respectively.

Figure 2: Trip chain among traffic zones.

The adjacent stagnation points which are sorted according to the time could be considered as the start and end of one trip. The traffic zone for each trip could be determined through the coordinates match between the stagnation point and the traffic zone. For example in Figure 2, a-b and b-c could be considered as two valid trips. And abelongs to zone A, b belongs to zone C, and c belongs to zone E. Thus, we can determine one increase in the volume of the traffic count between zone A and C and another increase between zone C and E.

3. Analysis of Land Use Based on the POIs

POI is a phrase from the internet map search engine, for example, Baidu, youdao, and so forth. Travelers can get a lot of POIs information related to their travel purpose through internet, as shown in Figure 3. In general, POI data includes four fields: name, category, longitude, and latitude. It can not only help determine trip purpose and nature of land use but also reflect distribution of population and level of economic development.

Figure 3: Distribution of POIs on web map.

3.1. POI Extraction and Classification

POI data were extracted and divided according to the two types of major categories and subcategories, as shown in Table 1.

Table 1: Classification of POIs in Beijing.

Major categories
Subcategories

Guesthouse
Inns, chain inn, star hotels

Dining
Casual dining, Chinese fast food, western-style fast food, Japan and Korea-style fast food, west restaurant, Chinese testaurant

Leisure
Leisure square

Companies
Financial, transport, telecommunications company, culture media, utilities, press and publication, travel agencies, factories and mines, firm, insurance company, funeral and interment, securities companies, clothing and shoes, sports outdoor, training institution, research institutions

Shopping
Electronics, audio books, electronic digital, photographic equipment, mother and children, stationery, jewelry ornaments, clothing and shoes, sports outdoor, clock and optical instruments, shopping mall

Education
Preschool education, primary education, secondary education, higher education

Transport facilities
Coach station, train station

Tourist attractions
Heritage, scenic area, memorial hall, church, amusement parks, park, museum of art

Living services
Clock and optical instruments, gift and flowers, supermarket, auto services, alcohol and tea, convenience, beauty salons, post office, ticket office, dry-cleaning, lottery distribution, pet, bank, photographic prints, pharmacy, clinic, emergency center, epidemic

Leisure
Stadiums, casino, KTV, massage parlors, cinema, fitness center, resort

Medical
General hospital

Government organization
All level of government, organs and units, political parties and organizations, institutional welfare, public security institution

Estate plot
Cell property

Public services
Library, palace of culture, museum, science and technology museum

Combined with the “standard for classification of urban land” (GB50137-2011), the correspondence among the travel purpose, POI type, and land use type were descript as shown in Table 2.

Table 2: Urban land classification based on travel purpose.

Travel purpose
POI type
Land use type

School
Education
A3 education and research

Work
Companies
B2 commercial and business

Shopping
Shopping
B1 wholesale and commercial

Residential
Estate plot
R residential

Personal affair
Medical
A5 medical facilities

Leisure
Leisure
B3 recreation and sports

Tourist attractions
Tourist attractions
A7 heritage

Dining
Dining
B13 restaurant

3.2. Construction Area of POI Investigation

Network inquiries, telephone interviews, and field inquiry were adopted in this research to obtain the average structural area for each type of land. 100 locations were selected from each type of land and the results are shown in Table 3 (education type as an example), Figures 4, 5, and 6.

Table 3: Construction area of educational POI.

POI name
Land use type
Construction area (m²)

Dingfuzhuang Second Primary School
A3
1000.00

Wanquan Primary School
A3
1580.00

Beijing Cuigezhuang Primary school
A3
1960.00

Beijing Paifang Primary school
A3
2243.00

Seventh Primary
A3
2350.00

Daxing District, Fifth School
A3
3795.00

Xizhong Street Primary School
A3
4034.00

Sigenbai Primary School
A3
4123.00

Laogucheng Primary School
A3
4180.00

Binhe Primary School
A3
4206.00

Taoranting Primary School
A3
4776.00

Nanhuzhongyuan Primary School
A3
5000.00

Figure 4: Distribution histogram of construction area for educational POI.

Figure 5: Distribution histogram of construction area for medical POI.

Figure 6: Distribution histogram of construction area for shopping POI.

Since the selection of survey sites is random, it can be assumed that the scale of the construction area for the sample interest points and the overall points are independent and identically distributed. The average construction area for each kind of POIs is shown in Table 4.

Table 4: Average construction area for each pattern of POIs.

Land use type
Education
Medical
Tourist
Shopping

Average construction area
3715
39935
21812
68282

Land use type
Dining
Business
Leisure
Residents

Average construction area
254
17760
4478
158104

4. Travel Intensity Calculation

In order to calculate the travel intensity of each type of POI, 26 typical zones (including all kinds of POIs) were selected from within the whole city of Beijing randomly, in which 14 zones are within the second ring road, 3 within second to the third ring road, 4 within the third to the fourth ring, and 5 within the fourth to the fifth ring. Total travel times were calculated through mobile data using the method in Section 2, as shown in Table 5.

Table 5: Travel times for each traffic zone.

Traffic zone
Travel Times
Location

Donghuashi street
17706
Within ring road 2

Wangfujin
18743
Within ring road 2

Huafushangmao
2463
Within ring road 2

Kongmiao
5972
Within ring road 2

Workers Stadium
15871
Within ring road 2

Landianchang
31684
Within ring road 2

YongdingluxiliCommunity
15539
Within ring road 2

Qinfenghuajinyuan
7729
Within ring road 2

EnjiliCommunity
5978
Within ring road 2

ShuiqinmuhuayuanCommunity
5210
Within ring road 2

Houbajia
2061
Within ring road 2

JucaiBuilding
5644
Within ring road 2

Jianguoli
11596
Within ring road 2

Jiujumingyuan
13993
Within ring road 2

University of Finance and Economics
44379
Ring road 2-3

Sanlihesanqu
40489
Within ring road 2

Guomao center 1
4362
Ring road 2-3

Cuiweidongli
120347
Ring road 3-4

Songjiazhuang
64163
Ring road 3-4

Songyudongli
21176
Ring road 3-4

Dinghuidongli
13504
Ring road 3-4

Nanyaodi
22896
Ring road 4-5

Kuifang
12067
Ring road 4-5

Hanzhuangzidongli
18315
Ring road 4-5

National Stadium
32547
Ring road 4-5

Wudaokou
36895
Ring road 4-5

The numbers of POIs in each of the 26 traffic zones were obtained as shown in Table 6.

Table 6: The main interest point type of traffic zone.

Traffic zone
A3
A5
A7
B1
B13
B2
B3
R

Nanyaodi
0
1
1
0
0
5
4
1

Jialinhuayuan
0
0
0
0
0
0
7
1

Kuifang
1
2
1
0
0
6
0
1

Fanzhuangzidongli
1
3
0
0
0
4
4
1

National Stadium
2
5
2
0
0
6
5
1

Beijing University of Chemical Technology
1
1
1
0
0
1
3
0

Qingyunbeili
2
1
0
0
0
2
2
1

Cuiweidongli
0
2
0
0
0
0
0
1

Songjiazhang
2
3
0
0
0
4
3
1

Songyudongli
1
2
0
0
0
2
0
1

Dinghuihuayuan
1
1
0
0
0
5
4
0

Jindianhuayuan
0
0
0
0
0
3
3
1

University of Finance and Economics
0
1
0
0
0
4
0
1

Sanlihesanqu
0
4
0
0
0
3
2

QiaoJianCommunity
0
0
0
0
0
5
0
1

PanJiaYuanNanLiCommunity
0
0
0
0
0
0
0
1

Wangfujin
0
1
0
0
0
5
5
1

Kongmiao
1
1
3
0
0
4
3
0

Xinjiekouxili
1
2
0
0
0
5
0
0

Flats of huarong
1
0
0
0
4
6
0
0

Wudaokou
2
6
5
0
0
8
3
1

DonghuashiStreet
2
0
0
0
7
9
0
1

huafushangmaodalou
2
0
0
0
1
1
0
0

YangjiayuanCommunity
2
8
2
0
4
0
0
0

Tianshuiyuan
0
2
0
0
2
2
2
1

Center of Guomao
2
0
1
3
2
1
2
1

The construction area of different pattern land in traffic zones could be calculated according to the average construction area of each kind of POI in Table 5. Then the average travel intensity could be calculated through the formula as follow:

where is trips per unit of land use pattern A3; is trips per unit of land use pattern A5; is trips per unit of land use pattern B1; is trips per unit of land use pattern A7; : Trips per unit of land use pattern B13; is trips per unit of land use pattern B2; is trips per unit of land use pattern B3; is trips per unit of land use pattern R; ,  ,  ,  ,  ,  ,  ,  and   are construction area of corresponding land use patterns in traffic zone ; is total trips of traffic zone .

The calculated travel intensity of each land use pattern is shown in Table 7.

Table 7: Travel intensity of each land use pattern (trips per square meter).

Land use pattern
A3
A5
B1
A7
B13
B2
B3
R

Travel intensity
0.2154
0.0626
0.1375
0.1142
17.1181
0.2927
0.4019
0.0494

5. Conclusions

As big data era approaching, the usefulness of POI data in transportation research has become more and more clear. This paper presents a research method of analyzing the relationship between travel demand and land use based on information technology. The residents’ day trip chains based on mobile data were collected, and the land use characteristics were quantitative analyzed through POI data. Then the travel intensity of each type of land use patterns was obtained through the travel OD and land use characteristics, which may provide a quantitative basis for urban planner and traffic managers. Research method opens a new way of studying the relationship between urban planning and transportation, and the findings could be used as the foundation for land use decisions.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgments

The authors wish to acknowledge the financial support for this study provided by the National Natural Science Foundation of China (nos. 51308017, 51108028), the Beijing Municipal Natural Science Foundation (no. 8122009), Beijing Nova Program, and 973 Program (no. 2012CB725403).

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