Multiple Path-Finding Models Using Kalman Filtering and Space Syntax Techniques

Abstract

A study was done to develop a shortest path algorithm in which the realism of way finding had been improved by incorporating the concept of road cognition. The existing shortest path algorithms consider travel time and travel distance only. However, cognition of road configurations also can be considered for providing a realistically satisfied path. Surveys have shown that people prefer a high-cognition path rather than a low-cognition path. Therefore, the study developed a shortest path algorithm to provide a realistically plausible route that is preferred by many people and that considered the travel time, travel distance, and road cognition. The profiles of travel times, which are required to estimate arrival times up to destination, are predicted by using a Kalman filtering technique to reflect time-varying travel conditions. The travel distance is calculated by using the geographic information system (GIS) attributes, and road cognition is established by applying a space syntax technique to identify road configurations. Also, space data of the space syntax model are based on GIS data. By considering both the shortest path in terms of travel time and distance and the best cognition in terms of road configurations, multiple paths are determined.