Journal of the Eastern Asia Society for Transportation Studies, Vol.11, 2015
Socioeconomic evaluation of Transit Oriented Development using by Detailed Spatial Scale CUE Model in Taiwan
Hiromichi YAMAMOTO a, Jumpei SANO b, Kiyoshi YAMASAKI c, Kazuki YANAGISAWA d, Atsushi KOIKE e, Morito TSUTSUMI f a,b Mitsubishi Heavy Industries, ltd., Kobe, 652-8585, Japan aE-mail: [email protected] b E-mail: [email protected] c,d Value Management Institute, ltd., Tokyo, 100-0004, Japan cE-mail: [email protected] dE-mail: [email protected] e Kobe University, Kobe, 657-8501, Japan; E-mail:[email protected] f University of Tsukuba, Tsukuba, 305-8573, Japan; E-mail: [email protected]
Abstract: In the Asia and ASEAN countries, there are a lot of urban development plans based on the concept of Transit Oriented Development (TOD). CUE model, which enables to evaluate urban development and transportation development simultaneously, is one of the major approaches to estimate the effects on a development plan based on the TOD. However, it has been difficult to evaluate it especially in Asia and ASEAN countries except Japan, due to lack of the public data on the geographic land use by small zone units. We have created a new methodology of estimating the land use area by using satellite images. It enables the land use classification on a scale, which fits the evaluation of the TOD. This paper describes evaluation results of the development plan of Taoyuan City in Taiwan by building the detailed spatial scale CUE model using our new methodology, using data.
Keywords: Computable Urban Economic Model, Transit Oriented Development, Public Transportation, Taoyuan Aerotropolis, Automated Railway, Asia and ASEAN
1. INTRODUCTION
Due to recent remarkable economic growth in Asia and ASEAN countries, metropolises in each country have a lot of urban development plans for land use and transportation. At the same time, heavy traffic congestion and increase of CO2 emission from transportation sectors are serious urban problems and caused by urban development plans centering on cars and motorcycles. In order to minimize these critical problems but keep their urban developments, these countries nowadays pay attention to the concept of Transit Oriented Development (TOD), which aims to achieve reduction of CO2 emission and economic growth simultaneously. The urban development based on the TOD is to build a public transportation oriented city or community by centralizing the stations. The TOD has been widely adopted by private railway companies in Japan such as Hankyu Corporation and Tokyu Corporation. To estimate the effect of a development plan based on the TOD, it is necessary to simulate the impact of land use development and that of transportation development at the same time. The Computable Urban Economic (CUE) model has been widely used in Japan to assess the effect of these development plans. Its characteristics are different from those of the traditional Land Use and Transportation Integration Model. The CUE model is characterized by a microeconomic foundation and by a spatial equilibrium on the basis of urban economics. Each economic agent demands or supplies
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2. Development Plan of Taiwan Taoyuan City 2.1 Taoyuan City
Taoyuan city is located in the approximately 50km west of Taipei City and was promoted to the government-ruled municipality on 25 December, 2014. Taoyuan city has been flourishing as a gateway of Taiwan by Taiwan Taoyuan International Airport, the largest international airport in Taiwan. It also has an industrial area and is expected to grow as one of the major cities in Taiwan. The population of Taoyuan city is about 2 million and this city is currently to be recognized as a bedroom town of Taipei city.
Source: Google Map Figure 1. Map of the Northern Taiwan 2.2 Transit Oriented Development: TOD
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Taoyuan city has a development plan for public railway transportation based on the Taoyuan Aerotropolis, one of the Taiwan’s national projects. Taoyuan city government announced that they would develop their city using the TOD. It means that it is necessary for the city to integrate land use development plan and transportation development plan. In the urban development plan using the TOD, residents and visitors are assumed to use public transportation in their movement and not necessary to rely on private transportation such as car and motorcycle. The metropolitan and suburban are connected by railway transportation and urban activities are accumulated around the railway stations. Since the TOD theoretically has a potential to control the acceleration of motorization, it has been paid attention as an approach of CO2 emissions reduction from transportation sector. This is one of the valuable approaches for the economic growing countries such as Asia and ASEAN when they plan their urban development. Challenges on the TOD are to evaluate the effect of the land use development plan and the transportation development plan simultaneously and to manage and optimize the dual development plan. Japan is expected to contribute and support their development plans, putting its experience and civil engineering technology on them.
Figure 2. Conceptual figure of TOD
2.3 Taoyuan Aerotropolis
Taoyuan Aerotropolis is a Taiwan’s national project in which Taoyuan city has been developing as an international gateway, taking advantage of the geographical location in Asia Pacific, the infrastructure of the seaport and the airport (Taoyuan Airport and Taipei Port) and the Free Trade Port Zone. The main objectives are 1) To develop Taoyuan Airport as a hub airport in East Asia, 2) To promote the urban development using the TOD, 3) To promote and develop the clustering industries, 4) To realize the sustainable and smart Aerotropolis, and 5) To boost the development of the dual ports. Taoyuan Aerotropolis is a new developed area around Taoyuan Airport of 4,177ha (including Taoyuan Airport site) and is divided into 5 zones. The development plans of the zones depend on the characteristics and transportation conditions of each zone and will complete in 2041. The development of Taoyuan Aerotropolis will activate industrial sectors and
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Journal of the Eastern Asia Society for Transportation Studies, Vol.11, 2015 realize globalization. According to the economic analysis by Taoyuan municipality, this project will create economic the effect of 2,300 billion New Taiwan dollars and employment opportunities of 300,000 people.
Source : Taoyuan Aerotropolice official HP:http://www.taoyuan-aerotropolis.com/jp_content/content/plan/plan04.aspx?PType=3 Figure 3. Planning concept : 5 major development zones of Taoyuan Aertoropolis
2.4 Public Railway System
In Taoyuan City, various transportation development are promoted together with Taoyuan Aerotropolis and other urban plans (refer to Table.1 and Figure.4). Taiwan Taoyuan International Airport Access MRT connects Taipei City and Taoyuan Airport in 35 minutes. In addition, Green Line connects downtowns of Taoyuan City and Taoyuan Airport. The urban development using the TOD is planned in the Taoyuan Aerotropolis where activities are centralized around the main station of Taiwan Taoyuan International Airport Access MRT and Green Line. This concept will expand to other railway transportations inside the city such as Brown Line in Taoyuan City in the future.
Table 1. Development plan of public railway system in Taoyuan Station Beginning year Railway Length Current status number of operation Taiwan Taoyuan International 53.7km 24 End of 2015 Under construction Airport Access MRT Red Line 17.2 km 7 2017 Under construction Green Line 27.8km 22 2021 Under Planning Brown Line Phase1 11.5km 8 2023 Under Planning Brown Line Phase2 12.0km 8 2023 or later Under Planning
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Source : Transportation Bureau of Taoyuan Government 101.11 version Figure 4.Development project map of public Railway system in Taoyuan
3. Dataset for the Computable Urban Economic Model
3.1 Zoning
Urban development including transportation and land use development in Taoyuan City will give the various the effects to its surrounding cities. Therefore, the target area in this simulation study sets up the northern area of Taiwan which consists of Taoyuan City, Taipei City, New Taipei City and Keelung City. It is important and necessary to set up a zone in which zero or only a station or a terminal of railway transportation exists to evaluate the effects of the TOD by the CUE model. In this paper, terminal means transportation node which consists of a few stations of different lines. We set only a station or a terminal within a zone, because we need to clarify traffic behavior of each railway station in order to evaluate TOD. If there are multiple stations or terminals within a zone, traffic behavior can’t divide into each station or terminal within the zone. We especially divide the area of Green Line into tiny zones in order to evaluate the effect of TOD around it. The number of zones in total is 301. The steps of setting the zones are as follows. 1) The whole of the northern area of Taiwan is divided into 55 zones to the prefecture or city level. 2) 7 administrative districts where transportation developments have been planned are divided into smaller zones. To divide these districts, we applied public information of polygon data by Taoyuan city. This polygon data is prepared in the detailed spatial scale. 7 administrative districts are Taoyuan city Dayuan district, Zhongli district, Taoyuan district, Bade district, Luzhu district, Guishan district and New Taipei city Xinzhuang district. 3) In case a zone has more than one stations of railway transportation, the zone is spatially equally divided into 2 or more zones, each of which has only a station or a terminal.
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a) Whole of the northern area in Taiwan b)7 administrative districts Figure 5.Coverage Area (301 zones)
3.2 Land use data 3.2.1 Dataset method of land use data
In Japan, land use data on the detailed spatial scale such as at a level of district or tinier unit have been prepared and published. On the other hand, in the other Asia and ASEAN countries, land use data have not been prepared even on the large administrative district scale such as at a level of prefecture or city unit. In the northern area of Taiwan, land use data is published by each administrative district. However, the data are at the level of prefecture or city unit. In this study, we have created a new methodology of estimating the land use area by using satellite images. It enables the land use classification on a detailed spatial scale. Tabale2 indicates setting sources and methods of each zone.
Table 2. Dataset sources and methods of land use data Sources and methods of 301 zone List of “land use data” Large Zone Small Zone “Prefecture & City level” “7 administrative district” 1 Population of each zone Public information Public information Estimated by the new 2 Employee Public information methodology Land-value Estimated by new 3 Public information (Residential/Business) methodology Building lot area Estimated by new 4 Public information (Residential/Business) methodology Land area available to supply Estimated by new 5 Public information (Residential/Business) methodology Estimated by new 6 GRP Public information methodology
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3.2.2 Application of a new methodology for estimating the land use area by satellite image
The new approach of estimating land use area by satellite image is as follows. 1) The small zones in 7 administrative districts are divided by 250 x 250m mesh. As a result, 10,374 meshes are made. Dividing the area by polygon is another option. In the case of the polygon method, it is necessary to make polygon shapes manually after classifying the area into land use categories using satellite images. However, it has a possibility of making an unstable decision of the classification because the polygon shapes of the area are made manually and the criteria of the decision are not clear. Therefore, we do not use
the polygon method. The reason we adopt the mesh size of 250Legend x 250 m is that a mesh is
smaller than the smallest zone and that it is enough to keepGreen Space the accuracyPrefecture or city for the decision making of the land use classification. Unused land boundary Land for 301 Zone 2) We classify all the mesh into four land use categories usingResidence satellite images of Google Land for Non- Earth. In particular, the land use classification was identifiedResidence by the type of building Land for within the mesh. Table 3 shows the land use categories.transportation Figure 6 shows decision flowchart of the land use classification. The area occupationinfrastructure rate in each mesh is the main criterion in the decision flow. 3) Figure 7 shows results of the land use classification in all the small zones. Using this result, we can calculate the area of land use of four categories in each small zone. 4) We also estimate the land use data such as employee in each small zone, comparing the area of land use at a level of large area scale (prefecture or city) from public sources with the calculated area data of land use in each small zone.(refer to Figure 8) In addition, we don’t consider difference of land use area by type of buildings in this study.
Table 3. Categories of land use Categories of land use Item Green Space, Unused land Green space, Unused land, River, Pond, etc... Land for residence Residential house, Apartment building Urban Land for Non-residence Factory, Commercial factory area Land for Transportation Railway, Roadway, Airport, Port, Large-scale ground, infrastructure etc...
Figure 6.Decision flowchart of land use classification
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Figure 7.Result of land use classification in small zones of 7 administrative districts
Large zone’s land-use data Estimation of each small zone’s By public information land-use data by new method
Residential Area : 50m2 Residential Area : 20m2 Applying of new (land use classification by figure5) (land use classification by figure5) estimation method 2 Population : 500 Population : 200 Residential Area : 100m (Estimation by area ratio) (Estimation by area ratio)
Population : 1,000 Residential Area : 20m2 Residential Area : 10m2 (land use classification by figure5) (land use classification by figure5) Population : 200 Population : 100 (Estimation by area ratio) (Estimation by area ratio)
Figure 8.Estimation example of the land use data in the small zone for population data
3.2.3 Dataset of land use data
Figure 9 and Figure 10 show examples of dataset, population density and employee density. The population density data of the small zones are obtained from the web site of Taoyuan government and shown in the Figure 9 for the purpose of comparison with the employee density. While the open sources give us land use data such as employee density by the large zone units (the original-scale), the new methodology for estimating the land use area by satellite images can show land use data by small zone units (down-scaled). This new methodology has possibility to be applied widely to evaluate the urban development using the TOD in Asia and ASEAN countries where land use data is not sufficient. On the next step, we are planning to develop an automatic decision algorism for the land use classification and to apply this methodology to other cities in Asia.
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a)Original-scale b)Down-scaled [ by open source ] Figure 9.Example of land use data - population density -
a)Original-scale b)Downscaled [ by the new methodology ] Figure 10.Example of land use data - Employee density –
3.3 Transportation data 3.3.1 Transportation network data
In this study, we build railway network and roadway network in 2009 as existing ones and in 2021 as future ones. Table 4 shows a list of the transportation network model and Figure 11 shows a dataset of transportation network in 2021.
Table 4. List of transportation network model Segment Network model 2009 2021 METRO [Taipei subway] ✓ ✓ Existing TRA[Local line :Taoyuan to Taipei] ✓ ✓ Railway Taiwan High Speed Rail ✓ ✓ transit Purple / Blue line ✓ Future Red line ✓ Green line ✓ Existing road ✓ ✓ Roadway Future road ✓
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a)Railway b)Roadway Figure 11.Dataset of transportation network [2021 year]
3.3.2 Target of transport methods
Table 5 shows a list of the transportation modes. The passenger trip consists of the usual trip and the unusual trip and we set four transportation modes for the passenger trip. On the other hand, we set only truck mode for the freight trip. The passenger Origin-Destination (OD) in the usual day is an endogenous variable, while the passenger OD of unusual day and the freight OD are exogenous variables.
Table 5. List of objectives of OD behavior and transportation modes Objectives of OD behavior Transportation modes Commuting trip, Personal trip, Usual trips Railway ,Bus, Car, Motorcycle Passenger Business trip Unusual trips Travel, Business trip Railway ,Bus, Car, Motorcycle Freight transport Truck
3.3.3 OD Data
Table 6 shows sources of the OD data. The OD data of railways, cars and motorcycles are cited from published data by the Taiwan government. On the other hand, since the OD data isn’t published in public, the OD data of bus is estimated based on the trip volume of each objective in the northern area of Taiwan, the share ratio of the transportation modes of each objective in it, and the OD data of railways, cars and motorcycles. The OD data of truck is also estimated based on the published information.
Table 6. Sources of OD data Transportation modes Source of OD data Establishment of Model of Transportation Demand forecasting in Railway, Car, Motorcycle Taipei Metropolis and Strategy of Transportation Planning and Development in Taoyuan Prefecture Bus Estimation by above OD data Estimation by following information Freight transport “Survey report on motor vehicle freight traffic Taiwan area R.O.C”
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4. Computable Urban Economic Model : CUE Model
4.1 Structure of CUE Model
The CUE model, a land use transportation interaction model based on microeconomic theory, is a statics equilibrium model proposed by Ueda(1991), Ueda(1992), Takagi, Muto, and Ueda(2000) and Ueda, Tsutsumi, Muto and Yamasaki(2013). Its basic framework is applied generally in Japan. The model in this study which is especially suitable for the analysis of a metropolitan region, is derived from the framework of the above model and the model is purposed in Yamasaki et al.(2008). The structure of the CUE model is indicated in Figure12. In the model, there are three agents, namely households, firms and absentee landowners. All the agents attempt to maximize their utility or revenues by changing their location. The consumption and investment in the trip and land will keep changing until the land market and transportation market in each zone reaches a state of equilibrium. Values such as rent and generalized transportation costs also reach a state of equilibrium, which means that locators cannot enjoy a higher level of utility or revenue in zones other than the present one. The premise conditions are as follows: 1) The three agents are households, firms and absentee landowners where each household is presumed to be a person with the same preference and each firm is presumed to be an employee without industry classification. 2) The metropolis is divided into several zones and each zone has homogeneous geographical and economic features. 3) The metropolis is a closed region which means there is no interaction between the subject metropolis and any outside region. In addition, the population and the employee are regarded as exogenous variables. 4) The overall model reaches a state of equilibrium when the land market and the transportation market reach equilibrium. The equilibrium state in the land market and transportation market are based on the equal utility principle and equal travel time principle, respectively. 5) Households and firms allocate themselves by maximizing their utilities and profits. However, any additional costs owing to relocation behavior are not considered.
Figure 12.Whole structure of CUE
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4.2 Formulation of Each Agent’s Behavior
The Land use model is an equilibrium model of the land market which corresponds perfectly to the land demand and the land supply and the calculation of equilibrium is performed by the land value. The subjects of behaviors are households, firms and landowners and the land market is in the residential and business area. Table7 shows behavior models of household and firm, Table8 shows a behavior model of landowner, and Table 9 shows equilibrium conditions.
Table 7. Behavior models for households and firms Household behavior Firm’s behavior The households earns the income by providing The firm produces the composite goods by labor and consumes the composite good and land inputting land service, business trip so as to service so as to maximize his utility budget and maximize its profit under the production time constraint. technology constraint.
퐻 퐻 퐻 퐻 퐵 푉푖 = max [훼푧 ln 푧푖 + 훼푎 ln 푎푖 + 훼푥 ln 푥푖 ] 퐵 퐵 퐵 퐵 퐵 푧퐻,푎퐻,푥퐻 ∏ max[푍푖 − 푟푖 푎푖 − 푞푖 푥푖 ] 푖 푖 푖 푖 푎퐵,푥퐵 푖 푖 퐵 퐵 훽푎 퐵 훽푏 s. t. 푧퐻 + 푟퐻푎퐻 + 푞퐻푧퐻 = 푤푇 − 푞푤푥푤 s. t. 푍푖 = 휂푖(푎푖 ) (푥푖 ) 푖 푖 푖 푖 푖 푖 푖 1 = 퐼 1−훽 푖 1 푟퐵 푏 훽 −훽푏 훽푎+훽푏−1 퐵 푖 푏 푎푖 = { ( ) ( 퐵) } i: zone 휂푖 훽푎 푞푖 퐻 1 푉푖 : utility level 1−훽 −훽 훽 +훽 −1 Goods 푧퐻: consumption level of composite good 1 푞퐵 푎 훽 푎 푎 푏 푖 푥퐵 = { ( 푖 ) ( 푎) } 푎퐻: land service 푖 퐵 consumption 푖 휂푖 훽푏 푟푖 퐻 or 푥푖 : private trip 1 1 퐵 훽푎 퐵 훽푏 훽푎+훽푏−1 훼푧, 훼푎, 훼푥: distribution parameter 푟 푞 production 퐵 1−훽 −훽 푖 푖 퐻 푍 = (휂푖) 푎 푏 {( ) ( ) } 푟푖 : residential land rent 푖 훽 훽 behavior 푤 푎 푏 푞푖 : commuting cost (business) 1 model 퐻 푞 : commuting cost (private) 퐵 훽푎 퐵 훽푏 훽푎+훽푏−1 푖 퐵 1 푟푖 푞푖 푤: wage rate ∏ (1 − 훽푎 − 훽 ) { ( ) ( ) } ｂ 휂 훽 훽 푇: total time available 푖 푖 푎 푏 푥푤: number of consumption trip (business) 푖 i: zone 퐼푖: income 퐵 푍푖 : output of the composite goods firm 퐵 푟푖 : business land rate 퐵 푞푖 : business trip rate 퐵 푎푖 : land service 퐵 푥푖 : business trip input 휂푖: parameter regarding to production efficiency 훽푎, 훽푏: distribution parameter
Location Probability for location chooses behavior Probability for location chooses behavior chooses formulate by logit model. formulate by logit model.
behavior 푒푥푝휃퐻(푉 + 휏퐻) 푒푥푝휃퐵(∏ +휏퐵) 푃퐻 = 푖 푖 푃퐵 = 푖 푖 model 푖 퐻 퐻 푖 퐵 퐵 ∑푖 푒푥푝휃 (푉푖 + 휏푖 ) ∑푖 푒푥푝휃 (∏푖 +휏푖 )
퐻 퐵 푁푖 = 푃푖 ・푁푇 퐸푖 = 푃푖 ∙ 퐸푇
i: zone i: zone 퐻 푃퐵: probability that a firm chooses 푃푖 : probability that a household chooses 푖 푁푖: population 퐸푖: number of employee 푁푇: total population of urban area 퐸푇: total employee of urban area 퐵 휃퐻: logit parameter 휃 : logit parameter ∏ 푉푖: utility level 푖 :utility level 퐻 휏퐵: other factor of utility level 휏푖 : other factor of utility level 푖
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Household behavior Firm’s behavior Demand of residential land and generation of Demand of business land and generation of private trip formulate by followings. business trip formulate by followings.
퐻 퐻 퐻 퐵 퐵 퐵 Demand of 푍퐴푖 = 푎푖 푃푖 푁푇 푍퐴푖 = 푎푖 푃푖 퐸푇 퐻 퐻 퐻 퐵 퐵 퐵 residential/business 푍푋푖 = 푥푖 푃푖 푁푇 푍푋푖 = 푥푖 푃푖 퐸푇 land i: zone i: zone & 퐻 퐵 푍퐴푖 : residential land demand 푍퐴푖 : business land demand 퐻 퐵 Generation of 푍푋푖 : number of private trip 푍푋푖 : number of business trip 퐻 퐵 Private/business 푃푖 : probability that a household chooses 푃푖 : probability that a firm chooses trip 푁푇: total population of urban area 퐸푇: total employee of urban area 퐻 퐵 푎푖 : residential land consumption area 푎푖 : business land consumption area per person 퐻 퐵 푥푖 : private trip 푥푖 : business trip per person
Table 8. Behavior model for landowner Residential market Business market The absentee landowner supplies the land for the households and firms with the land supply function by followings. 퐻 퐵 Land supply 퐻 𝜎푖 퐻푂 퐵 𝜎푖 퐵푂 푦푖 = (1 − 퐻 ) 푌푖 푦푖 = (1 − 퐵 ) 푌푖 by the 푟푖 푟푖 absentee 퐻 퐵 landowner 푦푖 : land supply for residential use 푦푖 : land supply for business use 퐻푂 퐵푂 푌푖 : land area available to supply 푌푖 : land area available to supply 퐻 퐵 𝜎푖 : parameter 𝜎푖 : parameter
Table 9. Equilibrium conditions Residential zone market Population Business zone market Employee
퐻 퐻 ∑ 푁푖 = 푁푡 퐵 퐵 ∑ 퐸푖 = 퐸푡 y푖 = 푍퐴푖 y푖 = 푍퐴푖 푖 푖
4.3 Formulation of Transportation Behavior
The transportation model consists of the trip generation model, the choice of destination model and choice of transportation mode model. Table10 shows each transportation model
Table 10. Transportation models Passenger [ usual trip ] Passenger [ unusual trip ] Trip generation of private, commute and business Trip generation of private and commute formulate by followings. formulate by followings.
푊푆 푊푆 퐻 퐻 푍푋푖 = 휇푖 푃푖 푁푇 퐻′ 푅 퐻 퐻 퐻 푍푋푖 = 퐴푅푉 ∙ 퐻 퐵 푍푋푖 = 푥푖 푃푖 푁푇 푅 + 푅 퐵 퐵 퐵 푍푋푖 = 푥푖 푃푖 퐸푇 푅퐵 푍푋퐵′ = 퐴푅푉 ∙ i: zone(origin) 푖 푅퐻 + 푅퐵 푊푆 Trip 푍푋푖 : trip generation of commute 퐻 i: zone(origin) generation 푍푋푖 : trip generation of private 퐵 푍푋퐻′: trip generation of commute at Taoyuan airport model 푍푋푖 : trip generation of business 푖 퐻 푍푋퐵′: trip generation of private at Taoyuan airport 푃푖 : probability of choosing zone for household 푖 푃퐵: probability of choosing zone for business 퐴푅푉: amount of arrival at Taoyuan airport 푖 퐻 푁 : total population of urban area 푅 : composition rate of pleasure of the visitors to 푇 Taiwan 퐸 : total employee of urban area 푇 푅퐵 : composition rate of business of the visitors to 푊푆: consumption rate of trip generation for commute 휇푖 Taiwan 퐻 푥푖 : private trip consumption 퐵 푥푖 : business trip input
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Passenger [ usual trip ] Passenger [ unusual trip ] Probability for choice of destination formulate by Probability for choice of destination formulate by logit model. logit model.
exp (푉푚) 1) Private trip 푚 푖푗 푃푖푗 = 푚 ∑푗′ exp (푉 ) 푖푗′ 푁푇푚 푚 푚 푚 푚 푚 푚 푗 푉푖푗 = 휃1 ln 푆푗 + 휆 퐴퐶퐶푖푗 푃 = 푗 ∑ 푁푇푚 푚 푚 푗′ 푗′ 퐴퐶퐶푖푗 = ln ∑ exp (푉푖푗푘) 푘 j: zone(destination) 푚 i: zone(origin) 푁푇푗 : number of tourists j: zone(destination) m: purpose of trip 2) Business trip
mϵ{푊푆(푐표푚푚푢푡푒), 퐻(푝푟푖푣푎푡푒), 퐵(푏푢푠푖푛푒푠푠)} exp (푉푚) 푚 푖푗 k: way of trip 푃 = 푖푗 ∑ exp (푉푚) kϵ{푐(푐푎푟), 푟(푟푎푖푙푤푎푦), 푏(푏푢푠), 푠(푠푐표표푡푒푟)} 푗′ 푖푗′ 푚 푚 푚 푚 푚 푚 푉푖푗 = 휃1 ln 푆푗 + 휆 퐴퐶퐶푖푗 Choice of 푃푖푗 : probability of choosing 푚 푚 푚 푉푖푗 : utility level by purpose m 퐴퐶퐶푖푗 = ln ∑ 푒푥푝(푉푖푗푘) destination 푚 푆푗 : index of draw by purpose m 푘 model 퐴퐶퐶푚: index of accessibility by purpose m 푖푗 i: zone(origin) 푚 푉푖푗푘: utility level of transportation k by purpose m j: zone(destination) 푚 휃1 : parameter of index of draw by purpose m m: purpose of trip 휆푚: parameter of index of accessibility by purpose m (log sum parameter) mϵ{푊푆(푐표푚푚푢푡푒), 퐻(푝푟푖푣푎푡푒), 퐵(푏푢푠푖푛푒푠푠)} k: way of trip kϵ{푐(푐푎푟), 푟(푟푎푖푙푤푎푦), 푏(푏푢푠), 푠(푠푐표표푡푒푟)} 푚 푃푖푗 : probability of choosing 푚 푉푖푗 : utility level by purpose m 푚 푆푗 : index of draw by purpose m 푚 퐴퐶퐶푖푗 : index of accessibility by purpose m 푚 푉푖푗푘: utility level of transportation k by purpose m 푚 휃1 : parameter of index of draw by purpose m 휆푚: parameter of index of accessibility by purpose m (log sum parameter)
Probability for choice of transportation mode formulate by logit model.
exp (푉푚 ) exp (푉푚 ) 푚 푖푗푘 푚 푖푗푘 푃푖푗푘 = 푚 푃푖푗푘 = 푚 ∑푘′ exp (푉푖푗푘′) ∑푘′ exp (푉푖푗푘′)
푚 푚 푐 푚 푚 푚 푚 푚 푐 푚 푚 푚 푉푖푗푐 = 휃2 푡푖푗 + 훽2 푛푖푗 + 훽3 푢 + 푎푐 푉푖푗푐 = 휃2 푡푖푗 + 훽2 푛푖푗 + 훽3 푢 + 푎푐
푚 푚 푟 푚 푚 푚 푚 푟 푚 푚 푉푖푗푟 = 휃2 푡푖푗 + 훽2 푛푖푗 + 푎푟 푉푖푗푟 = 휃2 푡푖푗 + 훽2 푛푖푗 + 푎푟
푚 푚 푏 푚 푚 푚 푚 푏 푚 푚 푉푖푗푏 = 휃2 푡푖푗 + 훽2 푛푖푗 + 푎푟 푉푖푗푏 = 휃2 푡푖푗 + 훽2 푛푖푗 + 푎푟
Choice of 푚 푚 푠 푚 푚 푚 푚 푠 푚 푚 푉푖푗푠 = 휃2 푡푖푗 + 훽1 푑푖푗 + 훽2 푛푖푗 푉푖푗푠 = 휃2 푡푖푗 + 훽1 푑푖푗 + 훽2 푛푖푗 transportation i: zone(origin) , j: zone(destination) mode model 푚 푃푖푗푘: share parameter of transportation k by purpose m 푚 푉푖푗푘: utility level of transportation k by purpose m 푐 푟 푡푖푗: generalized cost (time) of car , 푡푖푗: generalized cost (time) of railway 푏 푠 푡푖푗: generalized cost (time) of bus , 푡푖푗: generalized cost (time) of scooter 푑푖푗: dummy variable( if link ij<8km:0 , link ij >8km:1) 푛푖푗: incident of traffic accident rate , u: dummy variable of car preference(car:1 , other:0) 푚 푚 휃2 : parameter of generalized cost by purpose m , 훽1 : parameter of dummy variable by purpose m 푚 훽2 : parameter of traffic accident rate by purpose m 푚 훽3 : parameter of dummy variable of car preference 푚 푚 푎푐 : absolute term of car by purpose m , 푎푟 : absolute term of railway by purpose m
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5. Estimation of Parameters
5.1Land use model section
The values of spend distribution parameters and production function parameters are quoted from Chen, Tsutsumi, Yamasaki, Iwakami(2013), which are applied to the land use model. These values were estimated using the least-square method in the demand function on the utility maximization issue of households and profit maximization behavior of firms.
Table 11. Distribution parameters Consumption of Generation trips for Consumption of residential lands private purpose residential lands α푎 α푥 α푧 Parameter 0.0107 0.2693 0.7200 - t-statics 4.16 16.16 [ 1 − α푎 − α푥 ]
Table 12. Production function parameters Consumption of Generation trips for commercial lands business purpose β푎 β푏 Parameter 0.0037 0.0680 t-statics 3.63 5.50
5.2 Transportation model section
The values of the destination choice parameters and the transportation mode choice parameters are quoted from Chen, Tsutsumi, Yamasaki, Iwakami(2013) which are applied to transportation model. These values are estimated using the parson trip data in Taiwan.
Table 13. Parameters of the destination choice model [Passenger trip] Commute/School Private Business
parameter t-statics parameter t-statics parameter t-statics Accessibility index : 휆푚 -0.0088 -24.2 -0.0099 -23.5 -0.0045 -22.4 Attracting customers 푚 10.8169 9.6 10.8865 9.7 14.2815 15.4 index: 휃1 likelihood ratio∶ 𝜌2 0.36 0.36 0.34
Table 14. Parameters of the transportation mode choice model Commute/School Private Business
parameter t-statics parameter t-statics parameter t-statics 푚 Generalized cost : 휃2 -0.0058 -16.1 -0.0049 -11.6 -0.0027 -10.9 푚 Distance dummy : 훽1 -0.2926 -9.8 -0.1192 -3.9 -- -- 푚 Accident variable : 훽2 ------0.1725 -3.2 푚 Preference dummy∶ 훽3 ------0.8051 -36.8 푚 Car constant term : 푎푐 -1.2196 -45.0 -0.3398 -12.7 0.4843 19.9 푚 Train constant : 푎푟 -1.483 -73.3 -1.113 -47.9 -1.5628 -65.7 likelihood ratio : 𝜌2 0.48 0.27 0.83
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6. Results of CUE Simulation
6.1 Simulation case
The target area of the simulation is the northern area of Taiwan centering on Taoyuan city and the target year is 2021. The simulation results in 2021 are compared with that in 2009 (refer to Table 15). Table 4 shows a list of the transportation network model in 2009 and 2021. Although Taoyuan Aerotropolis is scheduled to complete in 2041, we quote to the target values of the development in 2021 from the master plan by Taiwan’s government.
Table 15. Simulation cases Railway Road way Taoyuan Year Existing Future Existing Future arerotoropolis Case1 2009 ✓ ✓ Case2 2021 ✓ ✓ ✓ ✓ ✓
6.2 Future of setting scenario
Table16 shows growth rates on economics, population and employee in 2021.We estimate each growth rate in 2021 by multiplying that in 2009.
Table 16. Growth rate of rate on economics, population and employee Growth rate Item Reference [ 2009 to 2021 ] GRP 1.40 Quoting the GDP growth ratio [%/year] in the whole of Taiwan Calculating the population growth rate using the estimated result of future population in the whole of Taiwan and the current Population 1.05 population ratio of the northern area of Taiwan (Taoyuan city, Taipei city, New Taipei city, Keelung city) to the whole of Taiwan. Calculating the employee growth rate using the average growth Employee 1.23 ratio on the labor supply and demand in northern area of Taiwan and the number of future employee in the whole of Taiwan.
6.3 Calculation method of CO2 emission
The amount of CO2 emission is calculated using the following formula. The target transportation modes are car and motorcycle. The total amount of CO2 emission is the sum of that generated from each road link in the northern area of Taiwan. The CO2 emission intensity is a function of driving speed of car and motorcycle. The shape of the function is a form of the convex downward, whose horizontal axis is the driving speed and whose vertical axis is the CO2 emission intensity. When the traffic demand of a road link has increased, traffic congestion will occur in the road link and the driving speed in the road link will be reduced. As a result, the amount of CO2 emission generated from the road link as well as the CO2 emission intensity will increase.
CO2푙 = 푇푙 ∙ 퐷푙 ∙ 푓(푣푙) CO2푙: amount of vehicle CO2 emissions in road link l 푇푙: number of vehicle trips of car and motorcycle in road link l 퐷푙: length in road link l 푣푙: driving speed in road link l
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푓(푣푙): CO2 emission intensity by driving speed 6.4 Evaluation of simulation results
Figure 13 shows the simulation results of variation of the population density and the employee density. Figure 14 shows a change of the land value. Both of figures show the differences of each zone between in 2009 and in 2021. When the planned land use development and transportation development by 2021 are achieved, the population density and the employee density will increase intensively around the stations of Red Line and Green Line. In particular, increasing rate of population around the stations is 7% and it around Taoyuan Aerotropolis is 74%. Similarly, increasing rate of employee 25% around the stations and it around Taoyuan Aerotropolis is more than 100%. In addition, the land value along the railway lines, especially at the connection point of Green Line and Red Line increases. The residential area and the business area are reallocated around the stations of railways by the land use development and transportation development. In particular, increasing rate of residential land value is 3% and business land value is 11% around the stations. As a result, this area is converted to an urban form based on TOD with centering the stations of railway transportations. The number of passengers by public transportation increases, especially the number of the railway trips and the bus trips (refer to Figure15 a). However, the main transportation modes for the traffic behaviors consist of cars and motorcycles which share 75 % in the total traffic behavior in Taoyuan City (refer to Figure15 b). Therefore, it is necessary to reduce the utilization ratio of car and motorcycle by applying new urban and transportation policies in order to realize the development plan using the TOD, in which residents and visitor are assumed to use public transportation in their movement. In addition, the reduction ratio of CO2 emission of per capita from cars and motorcycles is 0.5% in the whole of the northern area of Taiwan, when we compared the result of development with-transportation to that of without-transportation. Although 0.5% is a small amount, the transportation development plan by Taoyuan City seems to have a potential to control the amount of CO2 emission from cars and motorcycles considering the increase of the traffic demand due to the population growth in the northern area of Taiwan. This result indicates to realize the reduction of CO2 which is expected as the effect of TOD. While the ratio of passengers by public transportation increases among four transportation modes in Taoyuan City, residents and employee still choose cars and motorcycles of life behaviors. This seems to be because the existing public transportation is not enough in Taoyuan City. In addition, it is expected that the behavior range of residents becomes wider due to the traffic development and it increases of the number of trips of residents in Taoyuan City to huge business accumulation cities such as Taipei City. In this sense, one of the concerns is that it is difficult for Taoyuan City to realize a compact urban form centering the stations of railway transportations, which is the basic concept of the TOD. Besides the transportation development plan, it will be necessary to combine other policies to realize the TOD in Taoyuan City. For example, Japanese TOD by private railway companies coordinate the life styles of people along their railway lines by not only the residential area and business area developing but also developing commercial or entertainment facilities by themselves. This was one of the key to realize the urban form centering the stations of railway transportations. To realize the urban development based on the TOD in Taoyuan City, it is necessary to integrate transportation development and other detailed policies, for example, such as limiting the use of cars and motorcycles around the railway stations, and promoting of home building and industries within the walking distance from the stations.
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a)Population density b)Employee Figure 13.Simulation result of population and employee differences between in 2009 and in 2011
a)Land for residential b)Land for business Figure 14.Simulation result of land value differences between in 2009 and in 2011
a)Increasing ratio of transportation trip b)Transportation mode in 2009 & 2021 Figure 15.Simulation result of transportation modes
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7. Conclusions
In this paper, we have built a detailed spatial scale CUE using a new methodology of estimating the land use area by using satellite images. The paper has obtained the detailed scale land use information in Taoyuan City where the land use data is not sufficient, by the new methodology. In addition, the detailed spatial scale CUE model in Taoyuan City in Taiwan gives us the simulation results of the traffic behavior and the change of the land use at a level of the area of each railway station unit. It is on a scale enough to evaluate the transportation and land use development plan based on the TOD. We have confirmed that, through the results of the simulation in Taoyuan City, the detailed spatial scale CUE model has potential to be applied to any other Asia and ASEAN countries where the land use data have not been sufficient.
There remain a few of issues for the future work in order to improve this study. Firstly, it is necessary to assess the suitable mesh size in order to ensure enough accuracy of the methodology of estimating the land use area by satellite image. Secondly, the methodology of estimating the land use area is required to be efficient. An automatic decision algorism for the land use should be developed. Finally, it is necessary to improve accuracy of the methodology and CUE model. Therefore, it is necessary apply them to other cities in Asia. Moreover, it is necessary to observe the situation of the urban development in the examined city and obtain the updated data constantly, because existing actual land use data for assessing accuracy in this new methodology isn’t prepared.
8. Acknowledgments
In preparing this paper, we quoted important basic data from “ An Impact Analysis of the Taiwan Taoyuan International Airport Access MRT System – Considering the interaction between land use and transportation behavior”. We express gratitude to Hsin-Ti CHEN(Graduate student, Faculty of Engineering, Information and Systems, University of Tsukuba) and Kazuki Iwakami(Value Management Institute, Ltd.), who are the authors of the article.
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9. Reference
Ueda T (1991) A model analysis on the impact of transport improvement on household locations through increase in opportunities for life activities, Infrastructure Planning Review ,9, 237-244.(in Japanese) Ueda T.(1992) A General Equilibrium Model with re-defined Location Surplus, Infrastructure Planning Review, no.10,pp.183-190.(in Japanese) Muto S, Akiyama T, Takagi A (2000) Econometric Evaluation of an Urban Road Network Project Considering the Spatial Structure Change, Traffic Engineering, 44, 205-214.(in Japanese) Ueda T, Tsutsumi M, Muto S, Yamasaki K (2013) Unified computable urban economic model, The annals of regional science, Vol.50 issue1, pp341-362 Yamasaki K, Ueda T, Iwakami K (2008) Evaluation of the economic effects by reducing the fare on Tokyowan Aqua-Line(TWAL) with endogenous developed population and induced/developed traffic, Expressways and Automobiles, 51 no.6, 20-32(in Japanese) Yamasaki K, Ueda T, Muto S (2013) Impacts of Transport Infrastructure Policies in Population-Declining Metropolitan Area, Metropolitan Regions Advances in Spatial Science 2013, pp425-449 Chen H, Tsutsumi M, Yamasaki K, Iwakami K (2013) An Impact Analysis of the Taiwan Taoyuan International Airport Access MRT System – Considering the interaction between land use and transportation behavior, Journal of the Eastern Asia Society for Transportation Studies, Vol.10, 2013, pp315-334 Taniguchi M (2009) “Compact City” and “Transport Oriented Development” : History, Philosophy and Reality, urban planning, 58(5), 5-8. (in Japanese) Tsutsumi M, Yamasaki K, Koike A, Seya H (2012) Computable Urban Economics Models: Current Issue and Perspectives, Japan Society of Civil Engineers, Vol.68, No.4, pp344-357.(in Japanese)
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