Our Research Interest

• However, Activity-Based travel patterns from these data are not clear & obvious e.g.

• activities of callers?
• periodic variations of travel?
• distinct behavioral clusters of travelers?

• Currently, transport planners are looking into alternative datasets for transport planning e.g.

A Short Study

• An Origin-Destination matrix with:

• What data did we use?

• What was our objective?

• To capture the activities (trip purpose), in an unsupervised way?

1. Origin zone
2. Destination zone
3. Trip purpose
4. Start day of trip
5. End  day of trip
6. Hour period of trip
7. Class of traveler e.g Home worker

Related Work & Limitation

• To capture these latent patterns, the current state-of-the-art has proposed parametric models, e.g.

• Limitation: They impose a priori bounds on the model complexity (i.e. the number of latent activities are pre-specified)

• simple models may under-represent the complexity in the data

• Overly complex models may overfit to data & are expensive to compute.

Proposed Solution

• Hierarchical Segmented Infinite Hidden Markov Model (H-siHMM)

• Proposed a Bayesian nonparametric approach to develop a flexible statistical model

Empirical Analysis

• Used trip counts on weekdays in destination zone “977”

• All the trips for the same hourly period were gathered together

Preliminary Results (1/3)

• The proposed model captured 3 hidden activities (labeled “A1”, “A2”, and “A3” in figures below)

They match ground truth activities “Other”, “At home” and “Other”, respectively.

Preliminary Results (2/3)

• Graph for periods T5 upto T23

Preliminary Results (3/3)

• Statistical properties of captured activities

• mu = location/center
• sigma = standard deviation
• theta = probability of transition between activities

On-going & Future Work

•  At least four on-going improvements:
  • Use disaggregated data
  • Model several regions simultaneously
  • Add other rich domain information, e.g. land use
  • Add models for weekends

Thank You