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Data-driven Bus Crowding Prediction Models Using Context-specific Features

Published:14 September 2020Publication History
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Abstract

Public transit is one of the first things that come to mind when someone talks about “smart cities.” As a result, many technologies, applications, and infrastructure have already been deployed to bring the promise of the smart city to public transportation. Most of these have focused on answering the question, “When will my bus arrive?”; little has been done to answer the question, “How full will my next bus be?” which also dramatically affects commuters’ quality of life. In this article, we consider the bus fullness problem. In particular, we propose two different formulations of the problem, develop multiple predictive models, and evaluate their accuracy using data from the Pittsburgh region. Our predictive models consistently outperform the baselines (by up to 8 times).

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      • Published in

        cover image ACM/IMS Transactions on Data Science
        ACM/IMS Transactions on Data Science  Volume 1, Issue 3
        Special Issue on Urban Computing and Smart Cities
        August 2020
        217 pages
        ISSN:2691-1922
        DOI:10.1145/3424342
        Issue’s Table of Contents

        Copyright © 2020 ACM

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 14 September 2020
        • Accepted: 1 June 2020
        • Revised: 1 May 2020
        • Received: 1 June 2019
        Published in tds Volume 1, Issue 3

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