Overall Mid Term framework
SimMobility Mid-Term (MT) simulator is an agent-based, fully econometric, and activity-based demand model integrated with a dynamic traffic assignment (DTA) model (1, 2). It is capable of simulating daily travel at both the household and individual levels. The traffic dynamics are simulated using a mesoscopic simulator. Figure 1 presents the modeling framework of the MT simulator implemented in SimMobility. Detail description of each component of the MT model can be found in wiki Mid-term page. The demand comprises two groups of behavior models: pre-day and within-day.
Figure 1: SimMobility Mid-Term Model
The pre-day models follow an enhanced version of econometric Day Activity Schedule approach to decide an initial overall daily activity schedule of the agent, particularly its activity sequence (including tours and sub-tours), with preferred modes, departure times by half-hour slots, and destinations. This is based on sequential application of hierarchical discrete choice models using a Monte-Carlo simulation approach. As the day unfolds, the agents apply the within-day models to find the routes for their trips and transform the activity schedule into effective decisions and execution plans. Through the publish/subscribe mechanism of event management, as mentioned above, agents may get involved in a multitude of decisions, not constrained to the traditional set of destination, mode, path and departure time depending upon their state in the event simulation cycle. For example, the agent could reschedule the remainder of the day, cancel an activity (or transfer it to another household member), re-route in the middle of a trip (including alighting a bus to change route), or run an opportunistic activity, like shopping while waiting.
The supply simulator follows the DTA paradigm as used previously in DynaMIT, including bus and pedestrian movements. Particularly for public transport, bus (and subway) line scheduling and headway based operations are currently being implemented. We also explicitly represent on-road bus stops and bus bays both at the mid-term and short-term, which allows for accurate estimation of impacts of the bus operations on the road traffic. Within the MT simulator, the interaction between the within-day and supply is responsible to bring the system to consistency. In addition to this, a day-to-day learning module, which feeds back network performance to the pre-day model, is introduced to update agent's knowledge.
The MT simulator takes input in the form of population (an output of the Long Term (LT) level) that contains detailed characteristics of each agent in the simulation region, and processes the day activity schedule of each agent. Furthermore, it passes the accessibility measure in the form of logsum from the top-level model of the pre-day component to the LT simulator representing maximum expected utility of activity-travel pattern at given supply conditions. The MT simulator also passes trip chains to ST simulator as a demand to further simulate smaller region traffic with microscopic details.
The steps to correctly run MidTerm can be found on this page.
References:
- Lu Y., Adnan, M., Basak, K., F. Pereira, Carrion, C., Saber, VH., Loganathan, H., Ben-Akiva, M., SimMobility Mid-Term Simulator: A State of the Art Integrated Agent Based Demand and Supply Model”. Transportation Research Board 93rd Annual Meeting. Washington DC. January 2015.
- Adnan, M., F. C. Pereira, C. M. L. Azevedo, K. Basak, M. Lovric, S. Raveau, Y. Zhu, J. Ferreira, Z. Christopher, and M. E. Ben-Akiva, Simmobility: A multi-scale integrated agent-based simulation platform. In Transportation Research Board 95th Annual Meeting, 2016, 16-2691.