3.66 - Optimization Approach to Land-Use/Transportation Policy-Making

Project Description

This thesis explores the potentials of optimization for land-use/transportation policymaking purposes. Fundamentally, the research aimed to design an approach that generates efficient maps (solutions) to respond to specific land-use/transportation policy objectives. In this context, unlike simulation based land-use/transportation models which vastly employ trial and error, the purpose was to design an optimization approach which directly guarantees the efficiency of solutions.

The mixed-integer optimization model upon which the approach is based has multiple objectives and is aimed at determining land use allocations and transportation infrastructure developments taking into account current form and future demographic changes at municipality level. The objectives of the optimization model are defined to address issues such as accessibility of population to jobs and services, suitability of land units to particular land-use types, compatibility of adjacent land-use types and utilization of existing infrastructure. The model makes special emphasis to the interactions between transportation and land-use.

In addition to the development of the model, this thesis explores potential solution methods. Initially, the optimization model is solved using a branch and bound method. In general, the computational effort requirement for this method is high. For that reason, a heuristic method, genetic algorithm, is developed. The quality of algorithm parameters and that of solutions are assessed. The heuristic method provides optimum and near optimum solutions with much smaller computational efforts.

The proposed approach was tested for hypothetical cities as well as for the municipality of Coimbra (Portugal). Results suggest that the approach can be of great practical utility as planning support tool in land-use/transportation policy-making processes, in the search for efficient solutions that also care for equity concerns in spatial development.

Research Team


  • Ashenafi Aregawi
  • António Pais Antunes (supervisor)


  • P. Christopher Zegras
Financial Support
  • FCT
Stage of Progress
  • Finished in 2015