Google-based Mode Choice Modeling Approach

TL;DR

This paper emphasizes the importance of incorporating detailed transit alternative information into mode choice models, using a Google-based approach to better reflect real-world constraints and choices in travel demand modeling.

Contribution

It introduces a novel method for generating realistic transit alternatives using Google data, enhancing the accuracy of mode choice models in activity-based frameworks.

Findings

Transit options are limited or unavailable in certain areas.

Inclusion of actual available alternatives improves mode choice modeling.

The approach highlights the significance of spatial and resource constraints.

Abstract

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 mainly because they do not have them at the modeling stage. This paper tries to describe the importance of this information by analyzing a travel survey and generate the actual alternatives that individuals had when making their trips. With a focus on transit, the study reveals how transit alternatives are limited\unavailable in certain areas which must be taken in to account in our mode choice models. Some statistics regarding available alternatives and the constraints people encounter when making a choice are presented with a comprehensive choice set formation. A mode choice model is then developed based on this approach to represent the importance of such information.