Smart Societies, Infrastructure, Technologies and Applications. First International Conference, SCITA 2017, Jeddah, Saudi Arabia, November 27–29, 2017, Proceedings

Research Article

Analysis of Tweets in Arabic Language for Detection of Road Traffic Conditions

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  • @INPROCEEDINGS{10.1007/978-3-319-94180-6_12,
        author={Ebtesam Alomari and Rashid Mehmood},
        title={Analysis of Tweets in Arabic Language for Detection of Road Traffic Conditions},
        proceedings={Smart Societies, Infrastructure, Technologies and Applications. First International Conference, SCITA 2017, Jeddah, Saudi Arabia, November 27--29, 2017, Proceedings},
        proceedings_a={SCITA},
        year={2018},
        month={7},
        keywords={Twitter analysis Traffic congestion Arabic language Big data analytics SAP HANA Smart cities},
        doi={10.1007/978-3-319-94180-6_12}
    }
    
  • Ebtesam Alomari
    Rashid Mehmood
    Year: 2018
    Analysis of Tweets in Arabic Language for Detection of Road Traffic Conditions
    SCITA
    Springer
    DOI: 10.1007/978-3-319-94180-6_12
Ebtesam Alomari1,*, Rashid Mehmood1,*
  • 1: King AbdulAziz University
*Contact email: EAlomari0011@stu.kau.edu.sa, RMehmood@kau.edu.sa

Abstract

Traffic congestion is a worldwide problem, resulting in massive delays, increased fuel wastage, and damages to human wealth, health, and lives. Various social media e.g. Twitter have emerged as an important source of information on various topics including real-time road traffic. Particularly, social media can provide information about certain future events, the causes behind the certain behavior, anomalies, and accidents, as well as the public feelings on a matter. In this paper, we aim to analyze tweets (in the Arabic language) related to the road traffic in Jeddah city to detect the most congested roads. Using the SAP HANA platform for Twitter data extraction, storage, and analysis, we discover that Al-Madinah, King AbdulAziz, and Alharamain are the most congested roads in the city, the tweets related to the road traffic are posted mostly in the rush hours, and the highest traffic tweeting time is 1 pm.