Boosting Ride Sharing With Alternative Destinations
| Autor: | Valéria Cesário Times, Salvatore Rinzivillo, Raffaele Perego, Chiara Renso, Vinicius Monteiro de Lira |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Carpooling
050210 logistics & transportation Boosting (machine learning) Computer science Mechanical Engineering Shopping mall 05 social sciences Individual mobility ComputerApplications_COMPUTERSINOTHERSYSTEMS 02 engineering and technology Destinations Environmental economics green mobility Computer Science Applications flexibility Traffic congestion 020204 information systems 0502 economics and business 11. Sustainability Automotive Engineering 0202 electrical engineering electronic engineering information engineering ride sharing |
| Zdroj: | IEEE Transactions on Intelligent Transportation Systems IEEE transactions on intelligent transportation systems 19 (2018): 2290–2300. doi:10.1109/TITS.2018.2836395 info:cnr-pdr/source/autori:de Lira V.M.; Perego R.; Renso C.; Rinzivillo S.; Times V. C./titolo:Boosting Ride Sharing With Alternative Destinations/doi:10.1109%2FTITS.2018.2836395/rivista:IEEE transactions on intelligent transportation systems (Print)/anno:2018/pagina_da:2290/pagina_a:2300/intervallo_pagine:2290–2300/volume:19 |
| ISSN: | 1524-9050 |
| DOI: | 10.1109/TITS.2018.2836395 |
| Popis: | People living in highly populated cities increasingly experience decreased quality of life due to pollution and traffic congestion. With the objective of reducing the number of circulating vehicles, we investigate a novel approach to boost ride-sharing opportunities based on the knowledge of the human activities behind individual mobility demands. We observe that in many cases the activity motivating the use of a private car (e.g., going to a shopping mall) can be performed in many different places. Therefore, when there is the possibility of sharing a ride, people having a pro-environment behavior or interested in saving money can accept to fulfill their needs at an alternative destination. We thus propose activity-based ride matching (ABRM), an algorithm aimed at matching ride requests with ride offers, possibly reaching alternative destinations where the intended activity can be performed. By analyzing two large mobility datasets extracted from a popular social network, we show that our approach could largely impact urban mobility by resulting in an increase up to 54.69% of ride-sharing opportunities with respect to a traditional destination-oriented approach. Due to the high number of ride possibilities found by ABRM, we introduce and assess a subsequent ranking step to provide the user with the top-k most relevant rides only. We discuss how ABRM parameters affect the fraction of car rides that can be saved and how the ranking function can be tuned to enforce pro-environment behaviors. This is the a pre-print version. Full version is available at the IEEE Transactions in Intelligent Transportations Systems https://ieeexplore.ieee.org/document/8370062 |
| Databáze: | OpenAIRE |
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