This is the paper presented in 22 Nov 2005 at the National Sustainable Development Seminar, Shah Alam organized by UiTM The Dynamic Interaction between Road Density and Land Uses in West Malaysian Cities Muhammad Zaly Shah Bin Muhammad Hussein, Ph.D. 1 * Mohd Noor Bin Awang 2 Ho Chin Siong, Ph.D. 1 Othman Bin Che Puan, Ph.D. 3 Hishamuddin Bin Mohd Ali, Ph.D. 4 Mohd Nor Bin Said 4 Norizam Bin Katmon 5 1 Dept. of Urban and Regional Planning, Faculty of Built Environment 3 Faculty of Civil Engineering 4 Faculty of Engineering and Geoinformation 5 Faculty of Computer Science and Information Resources Universiti Teknologi Malaysia 81310 Skudai, Johor, Malaysia 2 MN Consult, Taman Universiti, 81300 Skudai, Johor * This is the paper presented in 22 Nov 2005 at the National Sustainable Development Seminar, Shah Alam organized by UiTM The Dynamic Interaction between Road Density and Land Uses in West Malaysian Cities Muhammad Zaly Shah Bin Muhammad Hussein, Mohd Noor Bin Awang 2 Ho Chin Siong, Othman Bin Che Puan, Hishamuddin Bin Mohd Ali, Mohd Nor Bin Said Norizam Bin Katmon 5 National Sustainable Development Seminar, Shah Alam organized by UiTM 22 Nov 2005 Abstract Urban planning theories state that changes in land uses affect the provision of road infrastructure and vice versa. Invariably, this interaction between land uses and road infrastructure, represented by road density, is a dynamic one. Many studies abroad have attempted to quantify this dynamic interaction. Using a two-steps approach, an empirical model that describes the interaction between land uses and road density has been developed for selected West Malaysian cities. The two steps require that, first, a regression model is developed that functionally linked road density and land uses, and second, simulating land use developments to predict changes in road density. In this paper, the evidence for the interaction is shown for two West Malaysian Cities – Johor Bahru and Kuantan. The calibration of the interaction models for these two cities shows promising result. The main advantage of such interaction models is that the model provides transport planners a planning (and, budgeting) tool to plan for future provision of road infrastructure. Keywords: Urban growth, land use planning, transport planning, quantitative model 1 Introduction Road density level inevitably influences many aspects of daily life of a community including the location, density and types of residence, social-cultural, economic and leisure activities. It also influences the range and quality of the provision of goods and services to the community, which eventually determine the life style and the quality of life of the community. Many literatures pointed out that the transportation-land use relationship is reciprocal and dynamic in nature. As our towns and cities have grown, both in terms of their population size and functions, the relative advantage of locations and accessibility within them has changed through time because of the development of new roads in response to the current pressure for development. Inevitably, these developments provide different level of accessibility to new locations. This in turn will ease the movements or reduce the travel costs between locations, which open up new opportunities and contribute to the economic vitality of specific economic and social activities as well as residential locations. Recent development in highway development in Malaysia especially toll highways have produced some significant effects in term of accessibility to certain locations resulting in land use changes in certain localities along these highways. The completion of the 848 km North- South Expressway (NSE) in 1994 and other urban toll highways especially in the Klang Valley area have enhanced accessibility to the previously remote areas and created great potential for new developments. Fast development that took place in Rawang (Rawang industrial park), Bukit Beruntung, Serendah (national car project and auto industrial park) in Selangor, Bukit Merah in Perak and Nilai in Negeri Sembilan, to mention a few, were a direct impact of improved accessibility resulting from the NSE development. The same has occurred to Puchong, in which Puchong has been transformed from a small rural town to a modern township accommodating a population of more than fifty thousand within a short span of time induced by the development of Damansara-Puchong Highway. This study then attempts to empirically quantify the dynamic interaction between land use and road infrastructure developments. The intended results would be a mathematical model that explains such interaction, if it exists, that would be beneficial to planners and decision makers alike. 1 2 Theoretical Foundation Existing transportation economic theories consider transportation as a derived demand of land uses. What this really means is that transportation in itself cannot exist except for providing accessibility to land uses or as a medium to move goods and services between land uses. Rarely do people travel for the sake of travelling. More often than not, people travel to obtain some perceived benefit at the destination, which is offered by the different land uses. Hence, the modelling of the interaction between land use and road transportation starts with a premise that future road transportation requirements are based on what land use changes that will be taking place. This relationship between land use and transportation activities is described graphically in Figure 1. Land development or conversion Trips to and from the Increase in land value area Accessibility Transportation problems improvements (observed/forecasted) New transportation policies Figure 1: Relationship between land use and transportation activities In this study, the relationship between land use and transportation is described using multivariate linear regression. Given that the transportation variable is represented by road density, the general form of the regression equation then is given in Eq. (1) as: YXi,, t f () i t (1) 2 where: Yi, t = a vector of road density for town i, i = 1, 2 at time t Xi, t = a matrix of land use variables for town i, i = 1, 2 at time t The subscript i in Eq. (1) denotes the two towns involved in this study, namely Johor Bahru and Kuantan. With Eq. (1), it is clear from the relationship that transportation, represented by road density, is dependent upon land use. The land use matrix Xi, t , however, is represented by several components described below: XEHPi,,,, t i t,, i t i t (2) where: Ei, t = a matrix of employment variables for town i, i = 1, 2 at time t Hi, t = a matrix of households variables for town i, i = 1, 2 at time t Pi, t = a matrix of property variables for town i, i = 1, 2 at time t Simply replacing Eq. (2) into Eq. (1) will give us: YEHPi,,,, t f (,,) i t i t i t (3) which now states that the road density vector Yi, t is functionally related to the independent variable matrices of employment Ei, t , households Hi, t and property Pi, t . Individually, each of the land use components – employment, households and property – can be further defined into more specific subcomponents. For employment matrix Ei, t , it can be defined more specific as: EEi, t 1 i , t (4) 3 where E1i , t is a vector of total workers for town i, i = 1, 2 at time t. For the household matrix Hi, t , the specific subcomponents are: HHHHHi, t 1 i , t,,, 2 i , t 3 i , t 4 i , t (5) where: H1i , t = a vector of households’ average income for town i, i = 1, 2 at time t H2i , t = a vector of total number of cars for town i, i = 1, 2 at time t H3i , t = a vector of total number of households for town i, i = 1, 2 at time t H4i , t = a vector of households’ average income for town i, i = 1, 2 at time t Finally, for property matrix Pi, t , the subcomponents are: PPPPPPPPPPPit, 1, it,,,,,,,,, 2, it 3, it 4, it 5, it 6, it 7, it 8, it 9, it 10, it (6) where, given town i, i = 1, 2: P1i , t = a vector of nonresidential land value (in RM) at time t P2i , t = a vector of government office floor space area (in sq. ft.) at time t P3i , t = a vector of commercial building floor space area (in sq. ft.) at time t P4i , t = a vector of commercial improvement value (in RM) at time t P5i , t = a vector of industrial building floor space area (in sq. ft.) at time t P6i , t = a vector of residential land value (in RM) at time t P7i , t = a vector of government building improvement value (in RM) at time t P8i , t = a vector of residential building improvement value (in RM) at time t P9i , t = a vector of industrial building improvement value (in RM) at time t P10i , t = a vector of number of residential unit at time t 4 Therefore, with Eqs. (4), (5) and (6), the functional relationship between road density and land use components given in Eq. (3) now becomes: YEHHHHPPPit, f (,,,,,,,,) 1, it 1, it 2, it 3, it 4, it 1, it 2, it 10, it (7) As each town i is divided into grids of 1 sq. km, a town i with 150 grids will then define the functional relationship in Eq. (7) to be: YEHHHPPPit, 1, it 1, it 2, it 4, it 1, it 2, it 10, it 1 1 1 1 1 1 1 1 YEHHHPPP it2, 1, it 2 1, it 2 2, it 2 4, it 2 1, it 2 2, it 2 10, it 2 f (8) YEHHHPPP it150, 1, it 150 1, it 150 2, it 150 4,1,2, ititit 150 150 150 10, it 150 where, for example, the variables Y and E are the road density and the number of total i2 , t 1i2 , t workers in grid 2 of town i at time t, respectively.