Framework for Generating Activity-Based Travel Demand Using Aggregated Mobility and Land-Use Big Data

Abstract

Activity-based models have emerged over the past four decades and have demonstrated a stronger capacity than traditional four-step models to capture the sequence of an individual’s daily activities, associated trips, and travel behavior. However, the state of the art shows that demand generation in activity-based models still relies heavily on detailed travel diary data, which require extensive data-collection efforts. This paper introduces an activity-based modeling framework developed for the hybridPy/SUMO simulation environment. The aim is to generate individual plans based on generally available data such as aggregated population data, vehicle ownership and other mobility-related data, as well as building-scale land-use data from open sources. The model enables to generate a detailed daily travel plan for each agent, in which the land-use type of the visited locations must match the respective activity type. In addition, the plans must meet multiple constraints, such as building capacity, individual daily travel time budgets, and opening hours, where available. The overall framework comprises three key sub-modules: (i) household synthesis, which reconstructs a synthetic household population; (ii) individual disaggregation, which disaggregates households into individual-level attributes; and (iii) daily diary and schedule generation, which produces complete activity-travel diaries with primary activities and optional secondary activities. For each sub-module, the key input data, parameters, and outputs are presented to enable users to construct daily travel-demand scenarios. This framework offers several advantages in terms of reliable, behavior-based travel demand modeling and practical applicability across different urban contexts. By presenting a step-by-step model development workflow from the user’s perspective, it enables scientists and transport planners to quickly and easily build large-scale microscopic travel-demand models without relying on disaggregated travel diary surveys or other location-specific statistics.