Short-Term Prediction of Highway Travel Time Using Multiple Data Sources

The development of new traffic monitoring systems and the increasing interest of road operators and researchers in obtaining reliable travel time measurements, motivated by society’s demands, have led to the development of multiple travel time data sources and estimation algorithms. This situation provides a perfect context for the implementation of data fusion methodologies to obtain the maximum accuracy from the combination of the available data. This chapter presents a new and simple approach for the short term prediction of highway travel times, which represent an accurate estimation of the expected travel time for a driver commencing on a particular route. The algorithm is based on the fusion of different types of data that come from different sources (inductive loop detectors and toll tickets) and from different calculation algorithms. Although the data fusion algorithm presented herein is applied to these particular sources of data, it could easily be generalized to other equivalent types of data. The objective of the proposed data fusion process is to obtain a fused value more reliable and accurate than any of the individual estimations. The methodology overcomes some of the limitations of travel time estimation algorithms based on unique data sources, as the limited spatial coverage of the algorithms based on spot measurement or the information delay of direct travel time itinerary measurements when disseminating the information to the drivers in real time. The results obtained in the application of the methodology on the AP-7 highway, near Barcelona in Spain, are found to be reasonable and accurate.