Contextualized Space-Time Accessibility Modeling and Measurements with NeuroFuzzy Proximity Relations

The paper proposes an analytical framework for contextualized space-time accessibility modeling using a neurofuzzy proximity relations approach that recognizes that information available to travelers is typically imperfect, incomplete, and in any case filtered through their cognitive and sensorial processes due to individual contextualized factors. As the concept of "accessibility" varies largely in different contexts, it should not only be defined a priori in crisp terms but also by means of linguistic qualitative descriptors more in tune with human cognition. These qualitative measures can be converted from and to transportation network-based metric distances, and consequently space-time prisms, using a neurofuzzy proximity relations approach. The reverse translation enables to feed crisp geoprocessing results back to information users in a form that is consistent with their own information processing capabilities. Along the same line, contextualized space-time benefit measurement is proposed via the neurofuzzy-based contextualization of the attraction of an activity location and of travel time to reach activity locations. This leads to a more individual-centric accessibility analysis that may turn out to be a core component of many applications to come, particularly location-based services.