IDEAS home Printed from https://ideas.repec.org/p/iza/izadps/dp9503.html
   My bibliography  Save this paper

Predicting Road Conditions with Internet Search

Author

Listed:
  • Askitas, Nikos

    () (IZA)

Abstract

Traffic jams are an important problem both on an individual and on a societal level and much research has been done on trying to explain their emergence. The mainstream approach to road traffic monitoring is based on crowdsourcing roaming GPS devices such as cars or cell phones. These systems are expectedly able to deliver good results in reflecting the immediate present. To my knowledge there is as yet no system which offers advance notice on road conditions. Google Search intensity for the German word stau (i.e. traffic jam) peaks 2 hours ahead of the number of traffic jam reports as reported by the ADAC, a well known German automobile club and the largest of its kind in Europe. This is true both in the morning (7 am to 9 am) and in the evening (5 pm to 7 pm). I propose such searches as a way of forecasting road conditions. The main result of this paper is that after controlling for time of day and day of week effects we can still explain a significant portion of the variation of the number of traffic jam reports with Google Trends and we can thus explain well over 80% of the variation of road conditions using Google search activity. A one percent increase in Google stau searches implies a .4 percent increase of traffic jams. Our paper is a proof of concept that aggregate, timely delivered behavioural data can help fine tune modern societies.

Suggested Citation

  • Askitas, Nikos, 2015. "Predicting Road Conditions with Internet Search," IZA Discussion Papers 9503, Institute for the Study of Labor (IZA).
  • Handle: RePEc:iza:izadps:dp9503
    as

    Download full text from publisher

    File URL: http://ftp.iza.org/dp9503.pdf
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Nikolaos Askitas, 2015. "Google search activity data and breaking trends," IZA World of Labor, Institute for the Study of Labor (IZA), pages 206-206, November.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    computational social science; data science; big data; behaviour; endogeneity; complexity; forecasting; prediction; Google Trends; road conditions; highways; traffic jams; stau; complex systems;

    JEL classification:

    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:iza:izadps:dp9503. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Mark Fallak). General contact details of provider: http://www.iza.org .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.