Abstract
The most common travel demand model type is the trip-based model, despite major shortcomings due to its aggregate nature. Activity-based models overcome many of the limitations of the trip-based model, but implementing and calibrating an activity-based model is labor-intensive and running an activity-based model often takes long runtimes. This paper proposes a hybrid called MITO (Microsimulation Transport Orchestrator) that overcomes some of the limitations of trip-based models, yet is easier to implement than an activity-based model. MITO uses microsimulation to simulate each household and person individually. After trip generation, the travel time budget in minutes is calculated for every household. This budget influences destination choice; i.e., people who spent a lot of time commuting are less likely to do much other travel, while people who telecommute might compensate by additional discretionary travel. Mode choice uses a nested logit model, and time-of-day choice schedules trips in 1-minute intervals. Three case studies demonstrate how individuals may be traced through the entire model system from trip generation to the assignment.
Highlights
The most common travel demand modeling approach is the trip-based model, sometimes called the four-step model, which is essentially based on the concept proposed by Manheim [1]
If travel demand is simulated for individuals, it would be trivial to identify travel demand of, for example, five-person households with 2 cars, no transit pass, and low income
For each trip of the household, the choice probabilities for every destination are multiplied by an adjustment factor μj. This factor is taken from a normal distribution with a mean of ttbadjusted and a standard deviation of ten minutes to allow for some deviation from the ideal travel time budget
Summary
The most common travel demand modeling approach is the trip-based model, sometimes called the four-step model, which is essentially based on the concept proposed by Manheim [1]. A microsimulation model is presented that creates travel demand for individual synthetic households and persons. If travel demand is simulated for individuals, it would be trivial to identify travel demand of, for example, five-person households with 2 cars, no transit pass, and low income. Such a detailed analysis is possible with an activitybased model but almost impossible in traditional trip-based models. The methodology described here microsimulates travel demand but simplifies the construction of activity schedules To compensate for this shortcoming, travel time budgets are modeled explicitly for every household.
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