Social networks have been recently employed as a source of information for event detection, with particular reference to road traffic congestion and car accidents. In this paper, we present a real-time monitoring system for traffic event detection from Twitter stream analysis. The system fetches tweets from Twitter according to several search criteria; processes tweets, by applying text mining techniques; and finally performs the classification of tweets. The aim is to assign the appropriate class label to each tweet, as related to a traffic event or not. The traffic detection system was employed for real-time monitoring of several areas of the Italian road network, allowing for detection of traffic events almost in real time, often before online traffic news web sites. We employed the support vector machine as a classification model, and we achieved an accuracy value of 95.75% by solving a binary classification problem (traffic versus nontraffic tweets). We were also able to discriminate if traffic is caused by an external event or not, by solving a multiclass classification problem and obtaining an accuracy value of 88.89%.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Real-Time Detection of Traffic From Twitter Stream Analysis




    Publication date :

    2015-08-01


    Size :

    915219 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Real-Time Detection of Traffic From Twitter Stream Analysis

    D'Andrea, Eleonora | Online Contents | 2015



    From Twitter to detector: Real-time traffic incident detection using social media data

    Gu, Yiming / Qian, Zhen (Sean) / Chen, Feng | Elsevier | 2016


    A traffic monitoring stream-based real-time vehicular offence detection approach

    Liu, Ying / Ou, Guoyu | Taylor & Francis Verlag | 2018


    Real-time traffic detection

    BANERJEE ROHAN / SINHA ANIRUDDHA | European Patent Office | 2015

    Free access