Investigating the impacts of telecommuting on the spatial, temporal, and modal distribution of travel using an agent-based transport simulation model

Technological advancements over the past few decades have facilitated telecommuting, but its adoption surged significantly when travel restrictions forced workers to work from home during the pandemic. This shift significantly reduced peak-hour traffic flow and congestion, but the impact of this travel demand management strategy on 24-hour travel is not well understood. This study aims to evaluate the impacts of telecommuting on 24-hour traffic flow using an agent-based transport simulator. Methodologically, a nested structure is implemented to generate departure time, mode, and destination choice joint decisions and accommodate inter-dependencies. Given the behavioral differences among different population groups, separate models are implemented for these different groups: commuters, telecommuters, non-workers, students attending school in-person/online. Following the generation of 24-hour activities, activity itineraries are applied within a dynamic agent-based multimodal transport network model using the open-source MATSim platform. This modeling and simulation exercise has been implemented for the entire population of the Okanagan region of British Columbia, Canada. After thorough validation, the simulation results suggest that with the increase in telecommuting population, an increase in all types of non-mandatory travel is predicted to occur near the urban centers during the off-peak hours – resulting in the spreading of the peak over the day.