Abstract
This article proposes a new model for integrated design of a sustainable land use and transportation system with uncertainty in future population. In the proposed model, the future population in the urban area is assumed to be a random variable with a given probability distribution. A set of chance constraints with regard to road capacity expansion, housing and employment supplies and environmental impacts is incorporated to consider the sustainability of urban land development and transportation infrastructure improvement. The proposed model is formulated as a two-stage robust optimisation problem. The first stage of the proposed model (before the future urban population is realised) is to optimise the land use and transportation system by maximising a robust risk-averse objective function subject to various chance constraints for consideration of the system sustainability. The second stage of the proposed model, after the future population has been determined, is a scenario-based stochastic location and route choice equilibrium problem. A heuristic solution algorithm, which is a combination of penalty function method, simulated annealing method and Gauss-Seidel decomposition approach is developed to solve the proposed model. An illustrative example is given to show the application of the proposed model and solution algorithm. The findings show that the integrated design of the sustainable land use and transportation system depends very much on the level of uncertainty in future population, capital budget for urban development, and confidence levels of the chance constraints.
Original language | English |
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Pages (from-to) | 160-185 |
Number of pages | 26 |
Journal | Transportmetrica A: Transport Science |
Volume | 10 |
Issue number | 2 |
DOIs | |
Publication status | Published - 1 Feb 2014 |
Keywords
- chance constraint
- integration of land use and transportation
- population uncertainty
- simulated annealing method
- sustainability
- two-stage robust design
ASJC Scopus subject areas
- Transportation
- General Engineering