Optimization of the Revenue of the New York City Taxi Service using Markov Decision Processes

Sandjai Bhulai, P Li, Theresia van Essen

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

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Abstract

Taxis are an essential component of the transportation system in most urban centers. The ability to optimize the efficiency of routing represents an opportunity to increase revenues for taxi drivers. The vacant taxis cruising on the roads are not only wasting fuel consumption, the time of a taxi driver, and create unnecessary carbon emissions but also generate additional traffic in the city. In this paper, we use Markov Decision Processes to optimize the revenues of taxi drivers by better routing. We present a case study with New York City Taxi data with several experimental evaluations of our model. We achieve approximately 10\% improvement in efficiency using data from the month of January. The results also provide a better understanding of the several different time shifts. These data may have important implications in the field of self-driving vehicles.
Original languageEnglish
Title of host publication6th International Conference on Data Analytics
EditorsSandjai Bhulai, Dimitris Kardaras
PublisherIARIA
Pages47-52
Number of pages6
ISBN (Print)978-1-61208-603-3
Publication statusPublished - 12 Nov 2017
EventDATA ANALYTICS 2017 : The 6th International Conference on Data Analytics - Barcelona, Spain
Duration: 12 Nov 201716 Nov 2017
Conference number: 6

Conference

ConferenceDATA ANALYTICS 2017 : The 6th International Conference on Data Analytics
CountrySpain
CityBarcelona
Period12/11/1716/11/17

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