Estimating the potential shift from conventional public transport to flexible services based on smartcard transactions

TL;DR

This study analyzes smartcard data to estimate how many public transport trips could shift to flexible services, revealing that most users make both routine and occasional trips, indicating potential for significant ridership change.

Contribution

The paper introduces a clustering method to analyze smartcard data and models the potential shift from conventional public transport to flexible modes.

Findings

Less than 15% are home-work-home commuters.

At least 30% of trips are occasional and unclustered.

A regression model predicts occasional trip counts with high accuracy.

Abstract

We assume that urban travelers may prefer flexible modes of transportation over conventional public transport (PT) for making non-routine trips, and estimate the potential for such modal switch based on a database of 63 million smartcard records of PT boardings made in Israel during June 2019. The behavioral patterns of PT users are revealed by clustering their boarding records based on the location of the boarding stops and time of day, applying an extended DBSCAN algorithm. Our major findings are that (1) conventional home-work-home commuters are a minority and constitute less than 15% of the riders; (2) at least 30% of the PT trips do not belong to any cluster and can be classified as occasional; (3) The vast majority of users make both recurrent and occasional trips. A linear regression model provides a good estimate (R2 = 0.85) of the number of occasional boardings at a stop as a function of the total number of boardings, time of day, and land use composition around the location of trip origin. We conclude that the conventional PT may lose substantial urban ridership to the future flexible modes.