Urban planners are faced with the decision of what planning policy to pursue in order to achieve the best possible future. Many cities in developed nations use comprehensive models that simulate various aspects of the urban system, capable of predicting implications of a given set of policy inputs, to assist the planning process. However, in developing countries, demographic and socioeconomic data with appropriate spatial disaggregation are difficult to obtain. This constrains the development of such comprehensive urban models to support planning decisions. In the absence of models, the plan-making process usually inclines towards a more intuitive approach.

Using simplified urban models adapted to the data constraints, this paper explores the prospects of enhancing planning in developing countries, with the aim of shifting the plan-making process from being purely intuitive towards being more scientific. The SIMPLAN (SIMplified PLANning) modelling suite has been developed for the case study city of Ahmedabad, India (the calibration per se is not discussed) to test alternative urban planning policies (combinations for land use and transport) for the year 2021. Model outputs are evaluated for key economic, environmental and social indicators. It should be noted that such a research study, in the context of developing countries, represents a first generation of studies/models, owing to the simplicity of the model structure and its accompanying limitations and data availability constraints. The modelling framework developed in this study has a visually driven user interface. This makes the model easy to understand, operate and update. Due to this attribute, it allows local planning authorities to carry out testing of several alternative planning policies themselves, without having the need to outsource modelling work to private consulting firms, usually at much higher cost.

Key model outputs indicate that dispersing cities proves to be economically beneficial to society as a whole. Compact development may prove to be better in terms of environmental and social aspects, but it may be possible to tackle the undesirable effects of dispersal by appropriate combinations of planning and management measures. The modelling outputs informed the wider debate on compact vs. dispersed urban forms. It was shown that neither of these diametrically opposite forms provide an outright ‘win–win’ solution. They are likely to perform differently in different economies and sociocultural contexts. Therefore, it would appear that each city needs to test out the pros and cons of such alterative urban planning policies before pursing a plan for the future. Learning from such modelling exercises, cities can prepare their own tailor-made policy that best satisfies their objectives, making the planning process more rigorous and transparent.