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Mining Twitter and Taxi Data for Predicting Taxi Pickup Hotspots

Published: 31 July 2017 Publication History

Abstract

In recent times, people regularly discuss about poor travel experience due to various road closure incidents in the social networking sites. One of the fallouts of these road blocking incidents is the dynamic shift in regular taxi pickup locations. Although traffic monitoring from social media content has lately gained widespread interest, however, none of the recent works has tried to understand this relocation of taxi pickup hotspots during any road closure activity. In this work, we have tried to predict the taxi pickup hotspots, during various road closure incidents, using their past taxi pickup trend. We have proposed a two-step methodology. First, we identify and extract road closure information from social network posts. Second, leveraging the inferred knowledge, prediction of taxi pickup hotspot is done near the activity location with an average accuracy of ~ 86.04%, where the predicted locations are within an average radius of only 0.011 mile from the original hotspots.

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  • (2022)A parallel grid-search-based SVM optimization algorithm on Spark for passenger hotspot predictionMultimedia Tools and Applications10.1007/s11042-022-12077-x81:19(27523-27549)Online publication date: 28-Mar-2022
  1. Mining Twitter and Taxi Data for Predicting Taxi Pickup Hotspots

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    cover image ACM Conferences
    ASONAM '17: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017
    July 2017
    698 pages
    ISBN:9781450349932
    DOI:10.1145/3110025
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 31 July 2017

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    Author Tags

    1. Classification
    2. Hotspot Relocation
    3. Social Network
    4. Taxi Pickup Hotspot Prediction
    5. Transportation Network

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    • (2024)A K-Shape Clustering Based Transformer-Decoder Model for Predicting Multi-Step Potentials of Urban Mobility FieldIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.335521125:8(10298-10312)Online publication date: Aug-2024
    • (2022)A parallel grid-search-based SVM optimization algorithm on Spark for passenger hotspot predictionMultimedia Tools and Applications10.1007/s11042-022-12077-x81:19(27523-27549)Online publication date: 28-Mar-2022

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