Extracting potential Travel time information from raw GPS data and Evaluating the Performance of Public transit - a case study in Kandy, Sri Lanka

Abstract

The widespread use of location-enabled devices on public transportation vehicles produces a huge amount of geospatial data. The primary objective of this research study is to build a solution framework that can process a large amount of geospatial data obtained from GPS (Global Positioning System) receivers fixed on different buses on different routes, preprocess, clean, and transform that data for analysis. There are various challenges associated with the processing of GPS data, like discontinuities, non-uniformities, poor network coverage, and human errors. This study proposes two novel, simple algorithms to extract bus trip and bus stop sequences, from the crude raw data, incorporating those challenges. Moreover, the dwell times at the bus stops are estimated solely using this GPS data in three different possible scenarios in the data filtering process. When considering the previous related studies in this area, the proposed approaches are applied to GPS data obtained at a medium sample rate (for example, 15 seconds) for heterogeneous traffic conditions, and also with a unique dwell time estimation process. In addition, statistical methods are implemented to analyse a variety of novel public transit-system performance metrics, such as (i) excess journey time (EJT); (ii) excess dwelling time (EDT); (ii) excess running time (ERT); and (iv) segment idle time ratio (SITR), at different time horizons, where these metrics are developed in the absence of schedule data. These metrics facilitate the transport authorities in real-time bus monitoring, evaluating their performance, and identifying inappropriate driving behaviours. A detailed explanation is provided through a case study of two main routes in the Kandy district of Sri Lanka.