Extending the Integrated Land-Use and Transport Model Framework: Spatial Econometrics and Policy Evaluation

Abstract

Στην παρούσα εργασία εξετάζεται η διεύρυνση του μεθοδολογικού πλαισίου των Όλοκληρωμένων Μοντέλων Χρήσεων-Γης και Μεταφορών σε κάθε επίπεδο. Οι προτεινόμενες βελτιώσεις αποσκοπούν στην αύξηση των ικανοτήτων πρόβλεψης, αξιοποιώντας τα οφέλη της μικροπροσομοίωσης σε τρείς διαστάσεις, άτομο, χώρο και χρόνο, με σκοπό να καταστόύν τα μοντέλα αυτά ευέλικτα εργαλεία αξιολοόγησης πολιτικών.

In this research, the methodological framework of the Integrated Land-Use and Transport (LUTI) models is extended at every level. The objective of developing and implementing LUTI models is to predict the direct and indirect impacts of –transport and land-use– policies, on the environment, the society and the economy. The proposed improvements aim to increase the predicting capabilities of the current LUTI models, exploiting the strengths of microsimulation in three dimensions, agents, space and time, in order to render them flexible platforms for policy evaluation. The effects of the current economic crisis are discussed and explored throughout the doctoral dissertation. Aiming to decrease the required budget for a LUTI model development, public on-line data are used to a large part of the analysis. Moreover, a graph-theoretic solution for associations generation in synthetic simulation is suggested. Different types of spatial econometric models are used for the development of real estate price models, which form fundamental component of every LUTI model. Urban quality indicators (i.e. accessibility, population segregation, economic viability, available open space, housing affordability, land-use and social mix, and building density) are effectively employed manifesting the benefits of trans-disciplinary collaboration in urban planning. In this research, a policy evaluation methodology based on distributions rather than single aggregate measures of quality indicators is proposed. The results indicate that spatial econometrics effectively remove the spatial autocorrelation and achieve higher accuracy than the traditional linear regression, in predicting the dwelling prices. The impact of transportation infrastructure locations on real estate purchase prices and rents differs, depending on the type of the transit system. Qualitative transit infrastructure has preserved the real estate prices at higher levels during the crisis. Synthetic populations and real, on-line, crowdsourced data can efficiently be used for the development of LUTI models. Finally, agent-based LUTI models provide an opportunity for the development of an improved, flexible policy evaluation platform.