Since transportation needs meet limited space and budgets and further social and environmental considerations, effective understanding of transportation demand is required to better manage the urban mobility services while meeting travel needs at different spatial and temporal granularities. Transportation demand modeling depends on collecting and analyzing geospatial data, georeferenced sociodemographic data, economic data, and environmental data, all of which are becoming accessible in ever greater variety, veracity and volume with technologies, such as positioning sensors installed on smartphones, high-performance mobile computing platforms, smart cards, or car navigation systems. This chapter reviews old and state-of-the-art methods of travel data collection and analysis, and explains the application and crucial roles of these data in improving transportation demand modeling and simulation techniques.
This chapter reviews old and state-of-the-art methods of travel data collection and analysis, and explains the application and crucial roles of these data in improving transportation demand modeling and simulation techniques. It focuses on the key role of geospatial data in improving smart mobility. Firstly, the categories of transport-related geospatial data and surveys are introduced and explained. Secondly, demand modeling —from the traditional four-step model to activity-based modeling —and the required datasets are reviewed. Thirdly, the challenges and issues of synthesizing a population and creating a population-wide demand model based on surveys and geospatial data are reviewed. Finally, by explaining and illustrating the key role of demand models in agent-based transport simulation and mobility planning, the importance of geospatial data and their application in the transport domain is elaborated. Travel surveys are the main method of data collection in the transportation field. In terms of participation, a travel survey can be categorized into two main types: participatory and non-participatory.