Inferences of Home Locations Using Smartcard Data

EasyChair Preprint 9488

Abstract

In order to understand and forecast multimodal travel demand, we need to understand habits and patterns in public transit use. Key locations such as homes or workplaces are valuable for analysing these habits. This research uses transit smartcard (MyWay) data from Canberra, Australia to infer home locations. We present three methods for inferring a home location catchment: an 800-m radius around the most frequently used bus stop for an individual, the Voronoi polygon around the centroid of the strongest cluster of frequently used stops found using K-means clustering, and the convex hull of the strongest cluster of frequently used stops from DBSCAN clustering. We are able to infer a plausible but not validated home location catchment for the majority of smartcard users. 97% of the most frequently used stops fall within 800m of the cluster-predicted home location which have the added benefit of a smaller catchment area. This pilot is a foundation for further work to support transport planning related to home-based trip patterns and home relocation behaviours.

Keyphrases: Clustering, home location, public transport, smartcard data

@booklet{EasyChair:9488,
  author    = {Durba Kundu and Somwrita Sarkar and Emily Moylan},
  title     = {Inferences of Home Locations Using Smartcard Data},
  howpublished = {EasyChair Preprint 9488},
  year      = {EasyChair, 2022}}