Prediction of Trends in Bus Travel Time Using Spatial Patterns

Studying patterns in traffic data is a basic analysis to understand the system. In this study, a large amount of bus travel data collected using vehicle tracking devices is analyzed for patterns. Travel time, in general, follows both spatial and temporal patterns. Spatial patterns are expected because travel times in particular sections on a roadway can be following similar patterns. For example, sections with a bus stop in it may show similar patterns due to stopping at the bus stops. The present study explores the use of data-driven approaches, primarily clustering, to identify the spatial patterns in bus travel times. Discrete Wavelet Transform (DWT) is used to extract trends from the travel time measurements. Two popular clustering algorithms - k-means and hierarchical clustering algorithms are used in this study to identify the spatial patterns and group sections with similar characteristics. Once the spatial patterns are obtained, the historic database is searched to identify similar cluster patterns and travel time trends are predicted using Pattern Sequence-based Forecasting (PSF) algorithm. The performance of the proposed algorithm for the prediction of travel time trends of trips occurring during peak and off-peak hours of a day was then compared based on prediction errors.