2026-07-23T01:05:07Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:9353 2024-03-06T10:44:07Z ddc:004 open_access

Towards the Usefulness of User-Generated Content to Understand Traffic Events (Short Paper) Das, Rahul Deb Purves, Ross S. Urban mobility traffic UGC tweet event GIR geoparsing This paper explores the usefulness of Twitter data to detect traffic events and their geographical locations in India through machine learning and NLP. We develop a classification module that can identify tweets relevant for traffic authorities with 0.80 recall accuracy using a Naive Bayes classifier. The proposed model also handles vernacular geographical aspects while retrieving place information from unstructured texts using a multi-layered georeferencing module. This work shows Mumbai has a wide spread use of Twitter for traffic information dissemination with substantial geographical information contributed by the users. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Rahul Deb Das and Ross S. Purves 2018 Is Part Of LIPIcs, Volume 114, 10th International Conference on Geographic Information Science (GIScience 2018) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.GISCIENCE.2018.25 urn:nbn:de:0030-drops-93539 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GISCIENCE.2018.25 eng https://creativecommons.org/licenses/by/3.0/legalcode