Modeling Crowd-Sourced Spatio-Temporal Flexibility Insights in Origin-Destination Matrices Estimation

Abstract

In response to the rapidly evolving urban landscape, there is a growing demand to enhance traditional Origin-Destination Matrices Estimation (ODME) models with new data sources that offer broader perspectives. Crowd-sourced data, in particular, can provide promising avenues for collecting high-resolution data on destination activities, reflecting real mobility patterns. Previous research investigates trip motivations and the varying travel flexibility associated with different activities, leveraging real-world crowd-sourced data like Floating Car Data (FCD) and Google Popular Times (GPT).

To address the need to integrate these insights into ODME models, this paper introduces the FlexGLS approach, an extension of the GLS model that accounts for multiple demand components characterized by spatio-temporal flexibility metrics derived from crowd-sourced data. It aims to offer a more precise representation of travel demand by integrating both temporal and spatial flexibility dimensions. Benchmarking against the traditional GLS model highlights the potential of Flex-GLS in enhancing ODME accuracy, providing valuable insights into urban travel dynamics.