To support the implementation of real-time traffic information systems in the Stockholm city area using automatic vehicle identification (AVI) cameras, a travel time analysis platform has been developed. The system is able of managing and analyzing travel time data stored in a distributed database server where information of camera stations, traffic and weather are integrated. Using Google Map API, users are able to analyze and visualize both online and historical travel time information in an intuitive way. Several existing travel time estimation algorithms are implemented in the system, and are evaluated using four months of AVI data collected in the urban streets and arterials of and near the Stockholm downtown area. The advantages and disadvantages of those algorithms are also analyzed using the highly noisy travel time measurements collected under the urban context. In general, all these algorithms have the potential to be applied for daily travel time estimation. Finally, we point out an essential research question for real-time travel time estimation and suggest a direction that may have the potential to improve the online traffic information quality.