- FineTravel: Fine-Grained Travel Time Estimation for Multiple Transportation Networks
- Project Year:
2017
- REU Student (s):
Aaron Zhang | Brown University
- Student 1 Institution:
Brown University
- Project Mentor:
Desheng Zhang
- Project Mentor Area:
Computer Science
- Project Abstract:
Estimating passenger travel time in public transportation
systems provides useful information to commuters
and improves their travel experience. Most existing work on
travel time estimation has focused on estimating riding time
for a single mode of transportation. However, passengers often
use different modes of transportation, e.g. subways and buses,
and a significant portion of travel time is spent walking and
waiting. Therefore, estimating riding time for a single mode
of transportation underestimates actual travel time. Using vehicle
GPS and passenger fare transaction records from existing
transportation infrastructures, we propose a novel unified model
that integrates multiple transportation networks in a city. In the
model, travel time is divided into components by passenger status
and transportation modality. Estimators for each component are
constructed from historical data. We evaluate the performance of
our travel time estimation model on large-scale real-world data
from multiple sources in four transportation networks: subway,
taxi, bus, and private vehicle. We show that our model is more
accurate than baseline estimates in all modalities by at least **%.
In addition, our model is elastic to support new transportation
modalities. Finally, we discuss potential applications based on the
travel time estimates from our model.
