From trip purpose to space-time flexibility: a study using floating car data and google popular times

Understanding the relationship between space-time flexibility and trip purpose is essential for efficiently planning transportation systems and to better understand travel behaviour, as it affects not only the demand for different modes of transport, but also the travellers route/service and departure time choice. The study aims to rigorously explore the temporal and spatial flexibilities inherent to various trip purposes–work, shopping, sustenance, and others–by harnessing the capabilities of Floating Car Data (FCD) and Google Popular Times (GPT). FCD provides high-resolution data on vehicular movements, offering insights into spatio-temporal characteristics such as routes, speeds, and origin-destination points. Conversely, GPT furnishes a nuanced perspective on the temporal aspects of activities by revealing visitation patterns at different venues. Through a probabilistic approach, the proposed methodology innovatively infers users' flexibility through the analysis of spatio-temporal features from both FCD and GPT. This data is subsequently employed to assemble sample Origin-Destination (OD) matrices, where each matrix represents trips from a specific origin (O) to a designated destination (D) within a defined time frame, all sharing comparable levels of flexibility. The findings offer valuable insights into the interconnection between trip purpose and flexibility, thereby paving the way for the development of an OD demand estimation model that incorporates spatio-temporal flexibility as a parameter, enhancing the precision and adaptability of transportation planning endeavours.