This paper illustrates the development on travel time information system and research on real-time travel time estimation and prediction algorithms using Automatic Vehicle Identification (AVI) data in the Space Travel Time Prediction (STTP) project funded by Trafikkontoret Stockholm Stad (TSS). To support the implementation of a real-time travel time information system in Stockholm using AVI data collected on urban roads, a preliminary travel time analysis platform was developed and several filtering algorithms and their modifications were implemented and evaluated for travel time estimation purposes in previous work. In the current development, the authors have extended the previous experiment platform into a web-based system in which travel time data are stored in data warehouses based on spatial and temporal dimensions, and real-time estimation and prediction are available through the internet. In addition, the authors have developed an adaptive algorithm for on-line travel time prediction based on time-series modeling approaches. Especially, the Kalman Filter has been utilized for the implementation of a recursive optimal predictor, which can handle the noise and non-stationary of travel time measurements.