Short-distance commuters in the smart city

TL;DR

This paper analyzes short-distance commuter preferences in Singapore using real-time data from smart transportation systems, proposing novel algorithms to assess walkability and public transport attributes, revealing that commuters do not differentiate between bus and metro choices.

Contribution

It introduces a data-driven approach using raw GPS data instead of surveys to model commuter preferences and proposes new algorithms for assessing transportation mode attributes.

Findings

Commuters do not differentiate between bus and metro choices.

The approach leverages real-time data from smart transportation systems.

Nested structures for public transport modes are rejected.

Abstract

This study models and examines commuter's preferences for short-distance transportation modes, namely: walking, taking a bus or riding a metro. It is used a unique dataset from a large-scale field experiment in Singapore that provides rich information about tens of thousands of commuters' behavior. In contrast to the standard approach, this work does not relay on survey data. Conversely, the chosen transportation modes are identified by processing raw data (latitude, longitude, timestamp). The approach of this work exploits the information generated by the smart transportation system in the city that make suitable the task of obtaining granular and nearly real-time data. Novel algorithms are proposed with the intention to generate proxies for walkability and public transport attributes. The empirical results of the case study suggest that commuters do no differentiate between public transport choices (bus and metro), therefore possible nested structures for the public transport modes are rejected.