Data Fusion for Travel Demand Management: State of the Practice and Prospects



This paper provides a state of the practice review of data fusion for travel demand management (TDM). Data fusion involves the seamless detection and combination of data, from multiple sources, with the goal of extracting new knowledge from the data. For understanding the challenges and possibilities for applying data fusion for TDM, we first present system architecture requirements and several data fusion models. We then provide a brief review of major relevant industry players, finding many companies now spanning across related areas such as data provision, data aggregation, and delivery to end users, with a primary focus on automobile users and roadway conditions. Examining eleven metropolitan areas in the USA, we find several characteristics apparently associated with more advanced data fusion adoption, including degree of automobile dependence and presence of 'high tech� industry. We conclude by identifying some prospects for data fusion for TDM, as revealed through the analyses.


Travel Demand Management, Data Fusion, ITS


Intelligent Transport Systems


Travel Demand Management Symposium (TDM\'08), July 2008

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