Carpooling: travelers’ perceptions from a big data analysis

Abstract

Purpose The purpose of this paper is to provide a better understanding of the reasons why people use or do not use carpooling. A further aim is to collect and analyze empirical evidence concerning the advantages and disadvantages of carpooling. Design/methodology/approach A large-scale text analytics study has been conducted: the collection of the peoples’ opinions have been realized on Twitter by means of a dedicated web crawler, named “Twitter4J.” After their mining, the collected data have been treated through a sentiment analysis realized by means of “SentiWordNet.” Findings The big data analysis identified the 12 most frequently used concepts about carpooling by Twitter’s users: seven advantages (economic efficiency, environmental efficiency, comfort, traffic, socialization, reliability, curiosity) and five disadvantages (lack of effectiveness, lack of flexibility, lack of privacy, danger, lack of trust). Research limitations/implications Although the sample is particularly large (10 percent of the data flow published on Twitter from all over the world in about one year), the automated collection of people’s comments has prevented a more in-depth analysis of users’ thoughts and opinions. Practical implications The research findings may direct entrepreneurs, managers and policy makers to understand the variables to be leveraged and the actions to be taken to take advantage of the potential benefits that carpooling offers. Originality/value The work has utilized skills from three different areas, i.e., business management, computing science and statistics, which have been synergistically integrated for customizing, implementing and using two IT tools capable of automatically identifying, selecting, collecting, categorizing and analyzing people’s tweets about carpooling.

Why Do They Ride with Others? Meta-Analysis of Factors Influencing Travelers to Carpool

“…Socializing (r = 0.15), environmental concerns (r = 0.11), saving time (r = 0.10), and saving money (r = 0.10) all had small effects on carpooling. Even though the results reflect the findings of Neoh et al [4], it is surprising that saving money and time do not have larger effect sizes, as would be assumed when looking at similar studies, such as the large analysis of Ciasullo et al [13].…”

“…"Slugging" and hitchhiking are not used synonymously with carpooling, even though they belong to the category of informal carpooling [13]. Both terms refer to a person waiting at the roadside hoping to be picked-up, with the first term being more common in United States (US) papers and hitchhiking being more common in European papers.…”

“…Ridesharing often implies the use of a mobile-phone application. This type of carpooling is also called dynamic carpooling in the literature [13]. Lift sharing and motor pooling are less common terms in the literature.…”

What are the determinants of the willingness to share rides in pooled on-demand services?

“…Time, cost and the number of co-riders are the aspects investigated in this study, and, therefore, are the focus of this literature review. Still, it is worth mentioning that there are other motives that can impact individuals' decision to adopt on-demand services (see Tirachini (2019) for a recent overview), and that, other than the mere utilitarian motives, adoption of shared mobility in general has been linked to environmental and social aspects (Ciasullo et al 2018;Javid et al 2017;Mattia et al 2019;Min et al 2019).…”

Research on Taxi Pricing Model and Optimization for Carpooling Detour Problem

“…Many scholars studied the problem of carpooling [7][8][9][10][11][12]. This research work focuses on the following two aspects: on the one hand, the characteristics of carpooling behavior, the influence factors of carpooling, and the effects of carpooling are studied [13][14][15][16][17][18]. Shaheen analyzed carpooling situation in the San Francisco Bay Area and studied passenger characteristics, behaviors, and motivation [19].…”