A social network for carpooling

Matos, Maria Luísa Ferreira; Cruz, Michael; Guimarães, Adolfo; Macedo, Hendrik

doi:10.1145/2590651.2590662

Cited by 7 publications

(6 citation statements)

References 6 publications

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“…As predicted (Baslington, 2008;Shim et al, 2005), socialization is among the most important factors for stimulating carpooling. Socialization happens because the sharing of the same means of travel allows the establishment of social relationships between unknown people, stimulating the start-up and development of processes that are linked to the framework of the sharing economy (Matos et al, 2014;Haustein et al, 2009). In this regard, as stated in the tweets, socialization seems to be fostered by the opportunity for passengers to share time with strangers randomly and, if desired, by the possibility to choose from various available alternatives, the driver who theoretically, has more features more compatible with their own personality.…”

Section: Resultsmentioning

confidence: 99%

Carpooling: travelers’ perceptions from a big data analysis

Ciasullo

Troisi

Loia

et al. 2018

TQM

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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.

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Section: Resultsmentioning

confidence: 99%

Carpooling: travelers’ perceptions from a big data analysis

Ciasullo

Troisi

Loia

et al. 2018

TQM

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show abstract

“…Traffic congestion is reduced due to the availability of more seats between two to seven passengers per vehicle (Patriksson, 2015). Sharing the same travel modes allows the creation of social relationships between strangers that lead to the development of sharing economy practices (Matos et al, 2014).…”

Section: Sustainable Transportation: Ride-sharing As Sustainable Practicementioning

confidence: 99%

Sharing rides and strides toward sustainability: an investigation of carpooling in an emerging market

Abutaleb

El‐Bassiouny

Hamed

2020

MEQ

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PurposeThe purpose of this paper is to examine consumer intentions toward carpooling as a collaborative consumption practice. The paper uses the theory of planned behavior (TPB) in studying consumer intentions toward carpooling. It extends the theory to add personal norms as a major component in examining consumer intentions. It also adds two main motivational factors for collaborative consumption, which are economic benefits and sustainability.Design/methodology/approachThis study is a conclusive descriptive study using surveys. The research sample consists of 500 respondents from millennials. The study uses structural equation modeling to analyze the survey results.FindingsResults showed that subjective norms and attitudes had the most significant impact on intentions toward carpooling, followed by perceived behavioral control (PBC) and personal norms. Economic benefits and sustainability also showed a significant impact on attitudes toward carpooling practice. Measurement and structural models showed a good model fit.Originality/valueThe paper provides original data on consumer intentions toward carpooling as a type collaborative consumption behavior using the TPB with the inclusion of personal norms. The research also contributes to the growing nuances of sustainability in the context of the sharing economy. The study is considered the first of its kind to combine the TPB, personal norms and motivators of collaborative consumption, including economic benefits and sustainability, to examine their impact on intentions toward carpooling.

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“…18 Obviously, among all the research directions, a common concern is routing planning. However, to the best of our knowledge, almost all work about carpooling mainly focus on minimizing the total driving distance of cars [10][11][12] instead of the average arriving distance of all passengers, which will probably cause bad user experiences for passengers. Shih-Chia Huang et al 19 put forward a concept called ''average travel distance of passengers,'' and one of their seven objectives is to minimize the ''average travel distance of passengers'' in carpooling, but a constraint in their study 19 is that drivers have their own routes, which means that drivers are not dedicated to serving passengers, they only provide free ride service to passengers on the basis of completing their own driving routes; this constraint makes the problem different from ours.…”

Section: Researches About Carpoolingmentioning

confidence: 99%

“…Many works explored the research of carpooling such as combining carpooling and social network. Maria Luı´sa Matos et al 12 developed a system called GO! that allows passengers to build a network of friends when carpooling with others in.…”

Section: Researches About Carpoolingmentioning

confidence: 99%

“…There have been a number of studies that concentrate on carpooling on several aspects, such as route planning, carpooling applications, and social network in carpooling. However, most studies in terms of route planning mainly focus on minimizing the total driving distance of cars on the premise of delivering passengers to their destinations, [10][11][12] but it may give some passengers a bad user experience. For example, in Figure 1, there are three people p 1 , p 2 , and p 3 and two vehicles v 1 , andv 2 .…”

Section: Introductionmentioning

confidence: 99%

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Minimizing the average arriving distance in carpooling

Zhao

Yang

Wang

2020

International Journal of Distributed Sensor Networks

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The massive use of cars in cities brings several problems such as traffic congestion and air pollution. Carpooling is an effective way to reduce the use of cars on the premise of meeting passenger transport needs. However, route planning will influence the efficiency of carpooling. By now, most researches on the route planning of carpooling mainly pay attention to minimizing the total driving distance of cars, but for passengers, the most crucial thing is to get to the destination as soon as possible. And in most cases, the minimum total driving distance of cars does not mean the minimal average arriving distance of all passengers. To address this issue, in this article, we formulate a novel carpooling route calculation problem with the objective of minimizing the average arriving distance of all passengers in carpooling. Then, we prove that this problem is NP-hard. To solve this problem, for the situation that the vehicle capacity is sufficient to deliver all passengers, we propose a heuristic algorithm named SimilarDirection with [Formula: see text] approximation ratio in delivery order calculation phase, where [Formula: see text] is the capacity of each vehicle. For the situation that the vehicle capacity is insufficient, we provide three algorithms named DelFar, Unchanged, and DelRan. Experimental results show that our SimilarDirection algorithm can produce less average arriving distance of all passengers than other three contrast algorithms in both the real-world dataset experiments and the synthetic dataset experiments, and DelFar has the best performance in producing less average arriving distance when the vehicle capacity is insufficient.

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A social network for carpooling

Cited by 7 publications

References 6 publications

Carpooling: travelers’ perceptions from a big data analysis

Carpooling: travelers’ perceptions from a big data analysis

Sharing rides and strides toward sustainability: an investigation of carpooling in an emerging market

Minimizing the average arriving distance in carpooling

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