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A GIS-Based Performance Measurement System for Assessing Transportation Sustainability and Community Livability: Final Report
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    Final Report
  • Abstract:
    Sustainability and livability in transportation, as the concepts referring to the capability of transportation systems to maintain the well being of our society, have been widely accepted as the critical principles to improve quality of life and health of communities. The research developed a geographic information systems (GIS)-based performance measurement system for assessing the roles of transportation in achieving these goals. Using the City of Buffalo, New York as the case study, the authors collected various data and generated twenty sustainability and livability related performance measures (PMs), including the transportation attributes, land use measures, living condition indicators, and system-wide indices. The analysis on PMs derives several policy implications and suggestions. Lessons and challenges learnt from the PM development process were also summarized to help other relevant initiatives. The PMs, supporting database, case study and findings produced by the research are expected to help a wide range of audience such as policy makers, planners and transportation engineers to gain insights about the sustainability and livability oriented performance measurement. The land use information is found as an important input data for developing such a performance measurement system. However, it is often outdated and not sufficient to support sustainability and livability assessment practices. In this context, the authors also developed the methods to predict land use classes by taking advantage of frequently updated remote sensing data. The authors utilized the multinomial logistic regression, or called multinomial logit (MNL) models, for land use classification, whose great potentials have been overlooked in the field. In addition, the authors also suggest use transportation related attributes, such as the distances from a parcel of land to the nearest road or intersection, as the ancillary attributes to improve classification performance, in addition to spectral features collected by remote sensing. The MNL models were tested on the land use data collected in the City of Buffalo, New York. The best model achieves an average prediction accuracy of 83.7%. For the residential and commercial parcels, the prediction accuracy reaches up to 94.5%. In addition, the suggested transportation attributes were also found significant in discriminating land use classes.

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