SimMoblity is the simulation platform of the Future Urban Mobility Research Group at the Singapore-MIT Alliance for Research and Technology (SMART) that aims to serve as the nexus of Future Mobility research evaluations. It integrate various mobility-sensitive behavioral models with state-of-the-art scalable simulators to predict the impact of mobility demands on transportation networks, intelligent transportation services and vehicular emissions. The platform enables the simulation of the effects of a portfolio of technology, policy and investment options under alternative future scenarios. Specifically, SimMobility encompasses the modeling of millions of agents, from pedestrians to drivers, from phones and traffic lights to GPS, from cars to buses and trains, from second-by-second to year-by-year simulations, across entire countries.
SimMobility is being designed to be: activity-based; multi-modal; multi-scale; fully modular; and consistent across levels. Below we further explain each of these concepts.
Activity-based - In SimMobility, representation of individuals as agents in the model is necessary for simulating how people will react in the uncertain future. The decision process of the agents is modeled by an activity-based approach. Activity-based modeling improves on single-trip modeling by combining multiple trips and activities into the schedule that drives the demand for transportation networks.
Multi-modal - One of the promising approaches to current mobility problems such as congestion, high accessibility demand at certain places, environmental impacts and energy consumption is considering the transport system from a multi-modal viewpoint. To support multi-modality SimMobility explicitly simulates private traffic, public transit, pedestrian traffic as well as freight transportation, and allows agents to switch between these modes over the course of a given day.
Multi-scale - The high-level design of SimMobility is shown in Figure 1. SimMobility comprises three primary modules differentiated by the timeframe in which we consider the behaviour of an urban system. The short-term model functions at the operational level; it simulates movement of agents at a microscopic granularity (within day). It synthesizes driving and travel behavior in detail and also interacts with a communication simulator that models the impact of device to device communication on these behaviours. The mid-term (day-to-day) simulator handles transportation demand for passengers and goods; it simulates agents’ behavior which includes their activity and travel patterns. The mid term represents moving vehicles in aggregate, and routes are generated by behavior-based demand models. The long-term (year-to-year) model captures land use and economic activity, with special emphasis on accessibility. It predicts the evolution of land use and property development and use, determines the associated life cycle decisions of agents, and accounts for interactions among individuals and firms.
Fully modular - Although we have been presenting our project as simultaneously having the three levels, of short-term, mid-term, and long-term, it would not be practical or even necessary to tightly couple them all the time. In fact, we apply the opposite concept: each level should be able to work independently and only needs to access others when necessary (e.g. if an update on accessibilities is needed for the long term for specific area or time frame, it calls the mid-term).
Consistent across all levels - The key to multi-scale integration in SimMobility is a single database model that is shared across all levels. Every agent exists and is recognized at all levels simultaneously, and information is used according to each level’s needs (e.g. the long-term model doesn’t need to know reaction times from the short-term model). In this way, the behaviours will remain consistent and, even if run separately, the impacts from one level’s model will be propagated to the others gracefully.