Sensing discomfort of standing passengers in public rail transportation systems using a smart phone

TL;DR

This study investigates how acceleration affects standing passenger discomfort in public rail systems, introducing a discomfort index and a predictive method using smartphone data to improve ride comfort analysis.

Contribution

It presents a novel discomfort index and a predictive model based on acceleration data, enhancing assessment of passenger comfort in rail transit systems.

Findings

Discomfort correlates with acceleration levels.

Discomfort index effectively compares different rail lines.

Predictive model estimates passenger discomfort from acceleration data.

Abstract

This paper aims to investigate the effect of acceleration on the discomfort of standing passengers. The acceleration levels from different public rail transport lines such as the mass rapid transits (MRTs) and light rail transits (LRTs) of Singapore, as well as the associated qualitative data indicating the discomfort of standing passengers were collected and analyzed. Based on a logistic regression model to analyze the data, a discomfort index was introduced, which can be used to compare various rail lines based on ride comfort. A method for predicting the discomfort of passengers based on the acceleration values was proposed for any given train line.