Driver's dashboard – using social media data as additional information for motorway operators. Issue 9 (24th September 2018)

• Record Type: Journal Article
• Authors: Neuhold, Robert; Gursch, Heimo; Kern, Roman; Cik, Michael
• Is Part Of: IET intelligent transport systems. Volume 12:Issue 9(2018)
• Page Start: 1116
• Page End: 1122
• Publication Date: 2018-09-24
• Subjects: Internet -- social networking (online) -- traffic engineering computing -- transportation -- roads -- road traffic -- traffic information systems -- text analysis -- information filtering -- sensor fusion; social media data -- data collection -- traffic management operations -- camera monitoring systems -- Austrian motorway operator ASFINAG -- traffic messages -- traffic monitoring -- text analysis -- driver dashboard -- motorway operators -- road transportation; Intelligent transportation systems -- Periodicals; Electronics in transportation -- Periodicals; 388.31205
• DOI: 10.1049/iet-its.2018.5337
• Languages: English
• ISSNs: 1751-956X

Abstract

Abstract : Data collection on motorways for traffic management operations is traditionally based on local measurement points and camera monitoring systems. This work looks into social media as an additional data source for the Austrian motorway operator ASFINAG. A system called Driver's Dashboard was developed collecting incident descriptions from Facebook and RSS feeds, filtering relevant messages, and fusing them with traffic data. All collected texts were analysed for concepts describing road situations linking the texts from the web and social media with traffic messages and traffic data. Driver's Dashboard was designed to examine the potential of social media for traffic monitoring with respect to Austrian characteristics in social media use and road transportation with only very few messages available compared with other studies. Of 3586 messages collected within a five‐week period, only 7.1% were automatically annotated as traffic relevant. Furthermore, the traffic relevant messages for the motorway operator were analysed more in detail to identify correlations between message text and traffic data characteristics. A correlation between message text and traffic data was found in nine of 11 messages by comparing the speed profiles and traffic state data with the message text.