Investigating User Ridership Sentiments for Bike Sharing Programs

Subasish Das¹, Xiaoduan Sun¹, Anandi Dutta²

¹Civil Engineering Department, University of Louisiana at Lafayette, Louisiana, USA.
²Center for Advanced Computer Studies, University of Louisiana at Lafayette, Louisiana, USA.

DOI: 10.4236/jtts.2015.52007                

Abstract

Bike sharing is considered a state-of-the-art transportation program. It is ideal for short or medium trips providing riders the ability to pick up a bike at any self-serve bike station and return it to any bike station located within the system’s coverage area. The bike sharing programs in the United States are still very young compared to those in European countries. Washington DC was the first jurisdiction to devise a third generation bike sharing system in the US in 2008. To evaluate the popularity of a bike sharing program, a sentiment analysis of the riders’ feedback can be performed. Twitter is a great platform to understand people’s views instantly. Social media mining is, thus, gaining popularity in many research areas including transportation. Social media mining has two major advantages over conventional attitudinal survey methods—it can easily reach a large audience and it can reflect the true behavior of participants because of the anonymity social media provides. It is known that self-imposed censor is common in responding to conversational attitudinal surveys. This study performed text mining on the tweets related to a case study (Capital Bike share of Washington DC) to perform sentiment analysis or opinion mining. The results of the text mining mostly revealed higher positive sentiments towards the current system.

Keywords

Bike Sharing, Social Media, Twitter Mining, Text Analytic, Sentiment Analysis, Opinion Mining

Conflicts of Interest

The authors declare no conflicts of interest.

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