Modeling Framework and Implementation of Activity- and Agent-Based Simulation: An Application to the Greater Boston Area

Abstract

This paper presents a utility-maximizing approach to agent-based modeling with an application to the Greater Boston Area (GBA). It leverages day activity schedules (DAS) to create a framework for representing travel demand in an individual’s day. DAS are composed of a sequence of stops that make up home-based tours with activity purposes, intermediate stops, and subtours. The framework introduced in this paper includes three levels: (1) the Day Pattern Level, which determines if an individual will travel and, if so, what types of primary activities and intermediate stops they will do; (2) the Tour Level, which models the mode, destination, and time-of-day of the different primary activities; and (3) the Intermediate Stop Level, which generates intermediate stops. The models are estimated for the GBA using the 2010 Massachusetts Travel Survey (MTS). They are then implemented in SimMobility, the agent-based, activity-based, multimodal simulator. It run in a microsimulation using a Synthetic Population. Produced results are consistent with the MTS. Compared with similar activity-based approaches, the proposed framework allows for more flexibility in modeling a wide range of activity and travel patterns.

Implementation of a Danish activity-based model

“…The activity-based modeling framework and discrete choice models that we implemented were based on the work developed by Siyu [8] for Singapore and Viegas et al [9] for Boston, both of which follow the Day Activity Schedule approach proposed by Bowman and Ben-Akiva [10] [1]. The framework consists of hierarchical discrete choice models that can be grouped into three levels: the day-pattern level, the tour level, and the intermediate stop level.…”

“…SimMobility was entirely developed in C++ to favor efficiency and performance; the embeddable and lightweight scripting language LUA is used for implementing the models' specifications [9][14], due to its relatively simpler and more intuitive language syntax.…”

Evaluating the impacts of shared automated mobility on-demand services: an activity-based accessibility approach

“…While different modeling features can be found in other ABM frameworks, SimMobility MT relies on a multi-level architecture with several feedback loops linking the different travel decisions, allowing us to showcase the benefits of our proposed ABA method for the evaluation of AV deployments. For a review of existing generic ABM frameworks, the reader is referred to Rasouli and Timmermans (2014) and Viegas de Lima et al (2018).…”

Integrated simulation of activity-based demand and multi-modal dynamic supply for energy assessment

“…Days are divided into four different sections: AM peak, PM peak, mid-day, and nighttime. Activities in MTS were categorized into six main categories, which are work, education, shopping, personal activities, recreation, and escort [27]. Individual travel behavior is validated using the data from future mobility survey in GBA.…”

“…If an individual is on schedule, their activities and travel are simulated as per the schedule generated in pre-day. Otherwise if the individual is off-schedule, their schedule is recomputed for the rest of the day based on a framework similar to preday, but adjusted for information on the current state [27]. Figure 3 shows the times at which people make their trips classified by tour types.…”