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Mode Choice Modeling Using Personalized Travel Time and Cost Data

 Microsimulation based frameworks have become very popular in many research areas including travel demand modeling where activity-based models have been in the center of attention for the past decade. Advanced activity-based models synthesize the entire population of the study region and simulate their activities in a way that they can keep track of agents’ resources as well as their spatial location. However, the models that are built for these frameworks do not take into account this information sufficiently. This paper describes the importance of accurate information by analyzing a travel survey and proposes a methodology to generate the actual alternatives that individuals had when making their trips using Google Maps API and RTA’s Goroo TripPlanner. The study reveals how some alternative modes’ travel information are limited\unavailable in the traditional travel time skim matrix which must be taken in to account in our mode choice models. Also, statistics regarding available alternatives and the constraints people encounter when making a choice are presented and considered accordingly. In order to examine the importance of accurate alternative information for mode choice modeling, two mode choice models are then developed for comparison. One of the models is estimated from the accurate alternative information described in this paper and the other is estimated from the traditional travel time skim matrix obtained from the regional travel demand model. Once the superiority of the proposed method is approved through the comparison, a nested mode choice model encompassing all modes is estimated based on the developed choice set and model significance and goodness-of-fit measures are presented accordingly.

For more information, contact Mahmoud Javanmardi.  Download final report.

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