| dc.description.abstract | Transportation mode choice models typically represent user decision making using utility-based
mode choice models. However, utility models assume that users make compensatory trade-offs between
decision variables to maximize their expected utility. The decision process literature raises alternative,
non-compensatory theories that suggest people employ simpler, cognitively frugal heuristics in their
decision making. Non-compensatory models, including decision tree classifiers, present an opportunity to
test the effects of transit accessibility variables on mode choices and improve descriptions of mode choice
behaviour. Dynamic forms of transit accessibility, which measure variations in transit service over time,
may better capture heuristic perceptions of transit service quality.
This research addresses the need to understand how dynamic transit accessibility (DTA) impacts
mode choices, without compensatory decision process assumptions. First, this research develops DTA
measures for the Region of Waterloo using General Transit Feed Specification (GTFS) transit schedule
information to calculate travel impedance matrices for departures at every 5-minute interval of the day.
Zonal mode shares are regressed against alternative DTA measures to analyze the effects of different
destination types, time periods of aggregation, and statistical parameters of transit accessibility (i.e., mean
and distribution over time). Based on the aggregate mode share predictive performance, a DTA metric is
selected for analysis within a binary (transit and not transit) disaggregate mode choice model. Second,
this research uses trip diary data to train and score a Chi-squared Automatic Interaction Detection
(CHAID) decision tree classifier to represent and predict rules-based mode choice processes. Finally, the
selected DTA metric is merged with the trip diary data and applied in another decision tree for
comparison. The comparison between the two rules-based mode choice models is based on overall model
accuracy, class recall, precision, and interpretability.
Results from the decision tree classifier reveal that users apply heuristics in their transportation
mode decision making, including lexicographic and aspiration-level based decision rules. User choices
depend primarily on transit pass ownership, and non-transit-pass users consider the trip’s distance
thereafter. Including DTA as an independent variable in the decision tree has a small but statistically
significant effect: users only seem to consider DTA, a generalized location-based measure, if they do not
own a transit pass and only after considering the trip-specific distance. Overall, the rules-based mode
choice models report accuracies of roughly 84%; however, low precision in the transit predictions (i.e.,
many false positives) result in an overestimation of regional transit shares. | en |
