Stated preferences surveys are most commonly used to provide behavioral insights on hypothetical travel scenarios such as new transportation services or attribute ranges beyond those observed in existing conditions. When designing SP surveys, considerable care is needed to balance the statistical objectives with the realism of the experiment. This paper presents an innovative method for smartphone-based stated preferences (SP) surveys leveraging state-of-theart smartphone-based survey platforms and their revealed preferences sensing capabilities. A random experimental design generates context-aware SP profiles using user specific socioeconomic characteristics and past travel data along with relevant web data for scenario generation. The generated choice tasks are automatically validated to reduce the number of dominant or inferior alternatives in real-time, then validated using Monte-Carlo simulations offline. In this paper we focus our attention on mode choice and design an experiment that considers a wide range of possible existing mode alternatives along with a new alternative ondemand mobility service that does not exist in real life. This experiment is then used to collect SP data or a sample of 224 respondents in the Greater Boston Area. A discrete mode choice model is estimated to illustrate the benefit of the proposed method in capturing current contextspecific preferences in response to the new scenario.
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.
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