• DocumentCode
    27068
  • Title

    Characteristics of wrong-way driving on motorways in Japan

  • Author

    Jian Xing

  • Author_Institution
    Nippon Expressway Res. Inst. Co., Ltd., Machida, Japan
  • Volume
    9
  • Issue
    1
  • fYear
    2015
  • fDate
    2 2015
  • Firstpage
    3
  • Lastpage
    11
  • Abstract
    Characteristics of wrong-way incidents and crashes that occurred on the entire motorway network in Japan are analysed in this study with an emphasis on wrong-way crashes. Nearly 40% of vehicles in wrong-way crashes took U-turns on the main carriageway, followed by 20% entering the wrong way at interchanges after passing the tollgate, 18% before passing the tollgate and 12% at rest areas. Wrong entries and suspected dementia were the two main contributing factors for wrong-way crashes, each accounting for nearly 30% of the total number of wrong-way crashes, followed by each 8-10% for confusion with ordinary road, taking U-turns on the main carriageway and driving under the influence of alcohol. Most wrong-way crashes because of wrong entries were caused by older drivers over the age of 60 (61%) and young drivers (22%) and most of those because of confusion with ordinary road were also caused by older drivers (86%). All the wrong-way crashes caused by suspected dementia were by older drivers over the age of 65 and occurred between 4-10 p.m. Finally some applications of recent ITS technologies to prevent wrong-way driving that have been implemented recently on motorways in Japan are briefly introduced.
  • Keywords
    behavioural sciences computing; intelligent transportation systems; road accidents; road traffic; traffic engineering computing; ITS technologies; Japan; motorway network; suspected dementia; wrong entries; wrong-way crashes; wrong-way driving characteristics; wrong-way incident characteristics;
  • fLanguage
    English
  • Journal_Title
    Intelligent Transport Systems, IET
  • Publisher
    iet
  • ISSN
    1751-956X
  • Type

    jour

  • DOI
    10.1049/iet-its.2014.0017
  • Filename
    7014431