An activity scheduling and multi-agent micro-simulation platform (ASMMSP) based on long-term cellular data and considering multi-mode transfer

Abstract

With the growth of urban residential density, cities have developed into metropolitan, resulting in increasingly complex individual travel behaviours. These developments pose challenges to current simulation models in activity scheduling. This paper proposed an activity scheduling and multi-agent micro-simulation platform (ASMMSP). By incorporating long-term cellular data, the platform can eliminate the reliance on personal attributes in activity scheduling, which improves the simulation flexibility and accuracy. ASMMSP also focuses on transfer behaviours between different travel modes. The platform comprises three systems: agent, public transportation, and road network. At each moment, agents evaluate their current states and activity schedules, then change schedules based on the comparison results, current travel conditions, and historical travels. ASMMSP reconstructs the traffic condition within the research area by integrating the current traffic flow and activity schedules iteratively. Furthermore, ASMMSP allows for observation of real-time traffic conditions. It also enables adjustments to public transportation, road network structure, and traffic volume, which can simulate the traffic impact from emergencies, gatherings, road maintenance, and public transportation adjustments. These functions support traffic models applied in traffic planning, development, and construction. Finally, this paper demonstrates the above capabilities through two case studies in the first ring road of Chengdu.