An integrated design of sustainable land use and transportation system with uncertainty in future population

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 riskaverse 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.

Time-dependent transportation network design that considers health cost

“…To address the land-use transportation interaction issue, some works (Smith and Liebman 1978;Los 1979;Meng, Yang, and Wong 2000;Yim et al 2011;Li, Li, and Lam 2014) were conducted, but still did not capture the changing demand over time and gradual network upgrades. On the other hand, in the last 10 years, time-dependent frameworks (Szeto andLo 2005, 2008; • proposing a framework for the bi-objective road network design problem with land-use transportation interaction over time and health cost consideration; • proposing a general health cost function, which simultaneously captures the health impacts of traffic emissions, noise, and accidents, due to network improvement projects; • considering affected population when evaluating the health cost, and demonstrating its significance; • illustrating a paradox phenomenon that with the increasing amount of emissions, the emission induced health cost is decreasing; • developing an ABC algorithm to solve the bi-level problem, the algorithm and the model can be used as a decision-making tool of transportation projects for many cities; and • pointing out the existence of the health inequity issue.…”

Combining robustness and recovery in rapid transit network design

“…Other robustness concepts have been proposed by Fischetti, Salvagin, andZanette (2011), Garcfa et al (2007) and Laporte et al (2011). Li, Li, and Lam (2014) propose a model for integrated design of a sustainable land use and transportation system with uncertainty in future population. The future population in the urban area is assumed to be a random variable with a given probability distribution.…”

A robust optimization model for green regional logistics network design with uncertainty in future logistics demand

“…where ½x + = maxf0, xg The semi-deviation, but not the variance (or standard deviation), is used as an indicator for the risk measure because variance is a symmetric statistic and gives equal weight to deviations above and below the mean without addressing the risks associated with extreme outcomes, 39,53,54 and because mean-variance does not preserve the convexity of the objective function. 51 The semi-deviation risk measure defined in equation (37) overcomes these shortcomings.…”

“…The traditional expectation models may lead to a large variation for different logistics demand scenarios. [38][39][40] In this article, a robust risk-averse objective function is thus introduced to compensate the shortcomings of the expectation models. The second stage of the proposed model, which is referred to as the ''wait-and-see'' stage, occurs after a logistics demand scenario has been determined.…”