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Article

A Predictive Vehicle Ride Sharing Recommendation System for Smart Cities Commuting

1
DigiT.DSS.Lab, Department of Business Administration, University of West Attica, 250 Thivon & P. Ralli Str, Egaleo, 12241 Athens, Greece
2
Department of Infocommunication Technologies, ITMO University, Kronverksiy Prospect, 49, 197101 St. Petersburg, Russia
Received: 28 December 2020 / Revised: 16 January 2021 / Accepted: 22 January 2021 / Published: 27 January 2021
(This article belongs to the Special Issue Intelligent Edge Computing for Smart Cities)
Smart Cities (or Cities 2.0) are an evolution in citizen habitation. In such cities, transport commuting is changing rapidly with the proliferation of contemporary vehicular technology. New models of vehicle ride sharing systems are changing the way citizens commute in their daily movement schedule. The use of a private vehicle per single passenger transportation is no longer viable in sustainable Smart Cities (SC) because of the vehicles’ resource allocation and urban pollution. The current research on car ride sharing systems is widely expanding in a range of contemporary technologies, however, without covering a multidisciplinary approach. In this paper, the focus is on performing a multidisciplinary research on car riding systems taking into consideration personalized user mobility behavior by providing next destination prediction as well as a recommender system based on riders’ personalized information. Specifically, it proposes a predictive vehicle ride sharing system for commuting, which has impact on the SC green ecosystem. The adopted system also provides a recommendation to citizens to select the persons they would like to commute with. An Artificial Intelligence (AI)-enabled weighted pattern matching model is used to assess user movement behavior in SC and provide the best predicted recommendation list of commuting users. Citizens are then able to engage a current trip to next destination with the more suitable user provided by the list. An experimented is conducted with real data from the municipality of New Philadelphia, in SC of Athens, Greece, to implement the proposed system and observe certain user movement behavior. The results are promising for the incorporation of the adopted system to other SCs. View Full-Text
Keywords: smart cities; vehicle ride sharing; user commuting; prediction; recommendation; artificial intelligence smart cities; vehicle ride sharing; user commuting; prediction; recommendation; artificial intelligence
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MDPI and ACS Style

Anagnostopoulos, T. A Predictive Vehicle Ride Sharing Recommendation System for Smart Cities Commuting. Smart Cities 2021, 4, 177-191. https://0-doi-org.brum.beds.ac.uk/10.3390/smartcities4010010

AMA Style

Anagnostopoulos T. A Predictive Vehicle Ride Sharing Recommendation System for Smart Cities Commuting. Smart Cities. 2021; 4(1):177-191. https://0-doi-org.brum.beds.ac.uk/10.3390/smartcities4010010

Chicago/Turabian Style

Anagnostopoulos, Theodoros. 2021. "A Predictive Vehicle Ride Sharing Recommendation System for Smart Cities Commuting" Smart Cities 4, no. 1: 177-191. https://0-doi-org.brum.beds.ac.uk/10.3390/smartcities4010010

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