Bi-level cellular agent-based model: Simulation of potential impacts of high-speed rail on land cover change in the Lisbon Metropolitan Area, Portugal
Yu Shen
Instituto Superior Técnico
Guineng Chen
Instituto Superior Técnico
Luís Miguel Martínez
Instituto Superior Técnico
João de Abreu e Silva
Instituto Superior Técnico
DOI:
https://doi.org/10.5198/jtlu.2015.640
Keywords:
Agent-based Model, Land cover change, High-speed Rail
Abstract
This paper presents a bi-level cellular agent-based model (ABM) framework, which incorporates a land cover change sub-model at the local level and a socioeconomic activity growth sub-model at the regional level. In the local sub-model, a land cell is set as an agent, of which the land-cover change decisions are mainly influenced by spatial mixed logit models. The regional sub-model is driven by fixed effects panel models, generating estimates of socioeconomic variables, thanks to the accessibility improvement and local land development. The regional outputs are also distributed into the local sub-models as their inputs. By inputting the historical data of the Lisbon Metropolitan Area (LMA) in 1991, a back-casting simulation was executed for validation. It compares the simulated outputs in 2011 with the actual reference data, based on multiple resolution goodness-of-fit (MRG) methods. Three scenarios are then designed to study the potential im- pacts of high-speed rail (HSR) on land-cover change in the LMA according to different proposals of HSR station locations. The scenarios indicate that without HSR the un-built lands in the LMA are likely to be largely developed if the annual GDP growth rate holds at 1.5 percent. With HSR the simulation suggests that land development is accelerated. The opening of an additional HSR station in Setúbal besides Lisbon-Oriente does not act as an obstacle to the urbanization process in the LMA, although it reduces HSR speed and the resulting regional accessibility. However, the contribution of the added station to the land-development process is also limited. Only a few areas are likely to benefit.
