Work

Detecting weak public transport connections from cellphone and public transport data

Year: 2014

Type: conference-paper

Abstract: Many modern and growing cities are facing declines in public transport usage, with few efficient methods to explain why. In this article, we show that urban mobility patterns and transport mode choices can be derived from cellphone call detail records coupled with public transport data recorded from smart cards. Specifically, we present new data mining approaches to determine the spatial and temporal variability of public and private transportation usage and transport mode preferences across Singapore. Our results, which were validated by Singapore's quadriennial Household Interview Travel Survey (HITS), revealed that there are 3.5 million public and 4.3 million private inter-district trips (HITS: 3.5 million and 4.4 million, respectively). Along with classifying which transportation connections are weak, the analysis shows that the mode share of public transport use increases from 38% in the morning to 44% around mid-day and 52% in the evening.

Source: International Conference on Big Data Science and Computing (BigDataScience)

DOI: 10.1145/2640087.2644164

Authors: Thomas Holleczek, Liang Yu, Joseph Kang Lee, Oliver Senn, Carlo Ratti, Patrick Jaillet

Institutions: Singapore Telecommunications Limited (Singapore), Institute for Infocomm Research, University of British Columbia, Singapore-MIT Alliance for Research and Technology, Massachusetts Institute of Technology

Language: English

Topic: Human Mobility and Location-Based Analysis

Subfield: Transportation

Field: Social Sciences

Domain: Social Sciences

SDGs: Sustainable cities and communities, Industry, innovation and infrastructure

Open Access status: green

OpenAlex ID: W2006022764

FWCI: 5.385

Cites: 16

Cited by: 24

Related to: 10