عنوان مقاله [English]
Land-use and development density decisions have always been amongst the most controversial issues in urban planning. Various approaches have been proposed to deal with these decisions. However, these approaches have been mainly theoretical rather than practical. Transit Oriented Development (TOD) is amongst the most recent approaches to urban planning and, consequently, land-use and development density decision-making. TOD has been defined as “a compact, mixed-use, community, centered around a transit station that, by design, invites residents, workers and shoppers to drive their cars less and ride mass transit more.” This paper aims at proposing a mathematical model for land-use and development density decisions based on the principles of TOD. TOD is generally considered to have three dimensions: design, density and diversity. Design needs to be prepared according to specific conditions and circumstances of each particular station area. On the other hand, planning for development density and diversity needs to be developed from a holistic viewpoint, regarding different macro-scale objectives and constraints. In this paper, the problem of development density and diversity optimization based on the principles of TOD is modeled as a mathematical programming problem with multiple objectives. The first objective is to maximize development density in station areas, and the second objective is to minimize the difference between each station’s ratio of job-housing balance and its ideal value (ratio of employed people to the number of residential units) in each TOD area. Several constraints related to the objectives of Tehran master plan have also been incorporated into the model. The resultant nonlinear model was transformed into a Multiple Objective Linear Programming (MOLP) problem using simple mathematical transformations. Then, using AUGMented Epsilon CONstraint (AUGMECON) technique, it was transformed into a single objective Mixed Integer Linear Programming (MILP) problem. Finally, the model was applied to a real case study in the 12th District of Tehran metropolitan area and the results were thoroughly analyzed.
Statistical analysis of the results shows that the elasticity of diversity to development density is -1.017. In other words, 1% improvement in diversity leads to a 1.017% decrease in the development density index. Optimal trade-off between these objectives depends on (1) their relative impact on car ownership ratio, vehicle-miles travelled and similar criteria, (2) particular micro-scale issues of each station area as well as the goals and strategies of the municipality for each station area. Previous studies show that land-use diversity has a higher impact on the aforementioned criteria than development density. However, these results depend highly on urban development, urban transportation patterns and the behaviour of citizens. Hence, proper decision-making needs a separate study on the aforementioned impacts on travel behaviour of the citizens in the context of Tehran.
Furthermore, the Pareto solutions of the proposed model provide a set of alternative development policies and enable the policy-makers to select among them based on their specific conditions and limitations. The proposed model results can be applied to future urban development plans.
BERNICK, M. & CERVERO, R. 1997. Transit Villages in the 21st Century, New York, McGraw-Hill.
Boomsazegan-Paydar. 2007. Tehran Comprehensive Plan (Main Document). Center of Studies and Research for Tehran.
BRES, A. 2014. Train stations in areas of low density and scattered urbanisation: towards a specific form of rail oriented development. Town Planning Review, 85, 261-272.
CERVERO, R. & KOCKELMAN, K. 1997. Travel demand and the 3Ds: Density, diversity, and design. Transportation Research Part D: Transport and Environment, 2, 199-219.
CHEN, M., HUANG, Z. & ZHANG, M. A GIS Based Model for Land Use and Transit-Integrated Corridor. 12th International Conference of Transportation Professionals, 2012 Beijing. ASCE, 1598-1607.
DEVELOPMENT, H. C. O. A. A. U. 2015. Urban Master Planning statute. In: DEVELOPMENT, H. C. O. A. A. U. (ed.). Tehran: Iran Ministry of Road and Urban Development.
HSIEH, S., SCHULER, N., SHI, Z., FONSECA, J. A., MERECHAL, F. & SCHLUETER, A. 2017. Defining density and land uses under energy performance targets at the early stage of urban planning processes. Energy Procedia, 301-306.
KUZMYAK, J. R., DOUGLAS, G. B. & SPIELBERG, F. 2003. Land-use and Site Design: Traveler Response to Transportation System Change. Washington D.C.: Transit Cooperative Research Program (TCRP).
LI, J., GUO, X. & HU, T. Station Planning Model of a Transit Vllage around a Subway. COTA International Conference of Transportation Professionals, 2016 Shanghai. 924-931.
LIN, J. J. & GAU, C. C. 2006. A TOD Planning Model to Review the Regulation of Allowable Development Densties around Subway Stations. Land Use Policy, 23, 353-360.
LITMAN, T. 2015. Land Use Impacts on Transport: How Land Use Patterns Affect Travel Behavior. Victoria: Victoria tTransport Policy Institute.
MA, X., CHEN, X., LI, X., DING, C. & WANG, Y. 2018. Sustainable station-level planning: An integrated transport and land use design model for transit-oriented development. Journal of Cleaner Production, 170, 1052-1063.
MANVILLE, M. & SHOUP, D. 2005. People, Parking, and Cities. Journal Of Urban Planning And Development, 131, 233-245.
MAVROTAS, G. 2009. Effective implementation of the ε-constraint method in Multi-Objective Mathematical Programming problems. Applied Mathematics and Computation, 213, 455-465.
SPEARS, S., BOARNET, M. G. & HANDY, S. 2010. Senate Bill 375 - Research on Impacts of Transportation and Land Use-Related Policies. California Air Resources Board.
WANG, X., KHATTAK, A. & ZHANG, Y. 2013. Is Smart Growth Associated with Reductions in CO2 Emissions. Transportation Research Record: Journal of the Transportation Research Board (Transportation ), 2375, 62-70.
WARD, D. P., MURRAY, A. T. & PHINN, S. R. 2003. Integrating spatial optimization and cellular automata for evaluating urban change. The Annals of Regional Science, 37, 131-148.
YU, C. S. L., H. L. 2000. A robust optimization model for stochastic logistic problems. International Journal of Production Economics, 64, 385-397.