Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007 AN EMPIRICAL STUDY OF LAND USE/TRANSPORT INTERACTION IN BANGKOK WITH OPERATIONAL MODEL APPLICATION Varameth VICHIENSAN Kazuaki MIYAMOTO Lecturer Professor Department of Civil Engineering Faculty of Environmental and Information Faculty of Engineering Studies (FEIS) Kasetsart University Musashi Institute of Technology Phahonyothin Rd, Ladyao, Jatujak 3-3-1 Ushikubo-nishi, Tuzuki-ku Bangkok 10900 Thailand Yokohama 224-0015 Japan Tel: +66-2-942-8555 ext 1359 Telephone: +81-45-910-2592 Fax: +66-2-579-4575 Facsimile: +81-45-910-2593 E-mail: [email protected] E-mail: [email protected] Wiroj RUJOPAKARN Professor Faculty of Engineering Kasetsart University Phahonyothin Rd, Ladyao, Jatujak, Bangkok 10900 Thailand Tel: +66-2-942-8555 ext 1102 Fax: +66-2-579-4575 E-mail: [email protected] Abstract: This paper presents a study of land use transportation interaction in the developing countries context. Several issues regarding the development of land use model in developing metropolis are discussed. A case study of Bangkok is presented. The present situation of urban and transport development is described, showing that land use interaction is not explicitly considered in the transportation analysis. Empirical evidence from the railway projects shows that interaction of land use and transportation interaction is quite strong and must not be ignored. To analyze land use and transportation interaction, an effective tool is required. Among the existing urban models, TRANUS is selected as a pilot system for Bangkok for several reasons. The design and calibration of Bangkok model are described. Scenario analyses provide insight how land use is incorporated in the comprehensive urban transportation analysis scheme. Policies such as road pricing or TOD are effective countermeasures to compact the city and relieve traffic congestion. Lessons learned from the present system gave an idea of to develop an integrated land-use transport and the environment for Bangkok. Key Words: Land Use Interaction, Urban Transportation, TRANUS 1. INTRODUCTION That transportation and land use has closed relationship, i.e., transportation affects land use and land use affects transportation, has long been recognized by planners, economists, engineers, legislators, and politicians, perhaps for centuries. The traditional transportation modeling approach represents response of transportation to land use condition; but response of land-use to change in transportation is, however, not represented. In the other words, land use or land development is assumed to have no response to change in transportation condition. In the past few years, mathematical approach has been employed such that there are several 1250 Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007 operational urban models available. However, these large-scale models are data-hungry and require extensive effort in the model development. Unfortunately, it is characteristic of developing countries that such data required by urban model, especially land use and land price data, is neither readily available nor complete. The objective of the present study is to conduct an empirical study of urban development impact of transportation development in the developing country context by focusing on the urban railway development in Bangkok as a case study. It is also to present a development of a pilot model of integrated land- use/transportation by employing TRANUS. The rest of the paper is organized as follows. The context of developing country is described next in this section will illustrate how difficult is to develop an integrated land use/transportation model in a place where data availability is mainly the issue. Section 2 presents the background of transportation and urban development in Bangkok Metropolitan. An empirical study of railway development impact to urban development is also presented. Section 3 presents the development of TRANUS model of Bangkok while data source and calibration results are described. Section 4 presents the preliminary results of the pilot model where different policy tests are demonstrated. Section 5 discusses the model limitation and weakness, which imply and call for further development in the next stage of the study. Finally, Section 6 concludes the paper. 1.1 Background Most of the developing countries are experiencing rapid growth of urban area, uncontrollable suburbanization, uncoordinated land use, inefficient infrastructure development. These generate large amount of travel demand, give rise to the number of vehicles, and eventually result in traffic congestion. Most of the time, a solution to traffic congestion problem is to build more road, but this inversely induces more traffic; so the problem is not solved. On the contrary, land use planning and control are keys to the success. Planned and coordinated land use together with planned infrastructure will be necessary. From the planning viewpoint, the economic activities generated by transportation must be accurately estimated. Land price must be forecasted by taking into account transportation condition change. This may be achieved by using a quantitative urban model. In the modeling context, the existing urban models are built upon the requirements of model developers, which do not always meet limitations in the developing countries. These include availability and reliability of the data. Input to the model including data and human resources must be carefully developed. In some cases, there are debates that land use (or socioeconomic pattern) is too abstract to be quantified and recognized in transport analysis. Such misunderstanding may be eliminated by demonstrating a feasible land use methodology. Simpler model might be more attractive and useful for developing countries. In many cases, a number of simplified models for transportation planning have been proposed and implemented. However, there might be situation that requires complicated modeling technique; in this case analyst should look for an appropriate method to the problem. Recently discrete choice models for location analysis have obtained more interest from researchers (Wang and Kockelman, 2006) and (Miyamoto et al., 2004). Similarly, efficient techniques to prepare data for urban model may also be good tool for modelers in developing countries, (Vichiensan et al., 2006). There are some attempts in developing countries. To name a few, in Metro Manila, a microsimulation model of household location is proposed to produce an artificial set of urban locators in the land use model (Tiglao et al., 2003). In Johannesburg, a simplified land use model is applied to produce a reliable set of input to transportation demand model, which previously used a constant set of land use (Venter et al., 2006). 1251 Journal of the Eastern Asia Society for Transportation Studies, Vol. 7, 2007 In Bangkok, the government has paid attention to transportation modeling over the past ten years. It is largely built upon the conventional 4-step approach (Office of the Commission for the Management of Road Traffic, 1997). Several versions of the travel demand models have been developed, ranging from macroscopic to microscopic levels. Extensive efforts are paid for model maintenance to update the database (Office of the Commission for the Management of Land Traffic, 2000) and (Office of Transportation and Traffic Policy and Planning, 2005). The latest version of Bangkok transport model is built upon Citilabs’ proprietary software CUBE. Its Bangkok application is called e-BUM, which stands for ‘extended Bangkok Urban Model’, though it only considers transport. 625 zones are used with extensive transport network. However, its land use assumption is a major drawback of the present system. In words, the future land use is considered exogenous or given in the transportation analysis. Therefore, the evaluation of transport alternatives is conducted by using the same set of future land use. It means that the effect of transportation to land use is ignored; those include the accessibility improvement, the increase of development potential, and the increase in land value, etc. Therefore, it must not be wrong to conclude that an ideal architecture of land use/transport model for developing countries must be based on simple framework, which requires low-cost data and less-effort in the model development. Feasibility of the implementation should also be kept in mind. Since the survey data in developing countries is not always very accurate. In addition, the uncertainty inherent in the dataset must be explored and controlled; otherwise error will be magnified so that the model results are reliable (Rodier and Johnston, 2002), (Johnston and Clay, 2006). Lastly, graphical user interface must be emphasized so that the analysis approach and its result can be translated and accepted by the stakeholders, e.g., by employing efficient GIS linkage. 2. TRANSPORTATION AND URBAN DEVELOPMENT IN BANGKOK 2.1 Bangkok Metropolitan Region Bangkok Metropolitan Region (BMR) area comprises of Bangkok Metropolitan Area (BMA) and its five adjacent provinces. BMR covers 7,758 sq km. The total population of BMR in 2004 was 9,636,541, or 15.5% of the total population of Thailand. The central area of BMR is called Bangkok Metropolitan Area, or shortly BMA. It covers 1,568.7 sq km and is centered to almost all urban activities: business district, high to medium density residential areas, as well as industrial area. In 2005, BMA produced a GDP of about USD 220 billion, which accounts for about 43% of the country's GDP. Since 1960 BMA has been undergoing rapid urbanization and industrialization. The increase in population is due to the development of infrastructures such as road networks as well as real estates. From 1987 to 2000, the number of population has been decreasing in the inner area, but increasing in the middle area. That is, the population density in the inner area decreased from 15.27 to 11.09 thousand/sq km (that is 3.25 to 2.36 million). The outer area has increase in the population density from 0.77 to 1.28 thousand/sq km (which is 0.67 to 1.12 million).