Urban link travel time estimation using traffic states-based data fusion

Abstract

Estimated travel time is a key input for many intelligent transport systems (ITS) applications and traffic management functions. There are numerous studies that show that fusing data from different sources such as global positioning system (GPS), Bluetooth, mobile phone network (MPN), and inductive loop detector (ILD) can result in more accurate travel time estimation. However, to date, there has been little research investigating the contribution of individual data sources to the quality of the final estimate or how this varies according to source-specific data quality under different traffic states. Here, three different data sources, namely bus-based GPS (bGPS) data, ILD data, and MPN data, of varying quality are combined using three different data fusion techniques of varying complexity. In order to quantify the accuracy of travel time estimation, travel time calculated using automatic number plate recognition (ANPR) data are used as the ‘ground truth’. The final results indicate that fusing multiple data together does not necessarily enhance the accuracy of travel time estimation. The results also show that even in dense urban areas, bGPS data, when combined with ILD data, can provide reasonable travel time estimates of general traffic stream under different traffic states.