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Predicting Road Conditions with Internet Search

Author

Listed:
  • Nikos Askitas
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 cellphones. 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) peaks2 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

  • Nikos Askitas, 2016. "Predicting Road Conditions with Internet Search," RatSWD Working Papers 252, German Data Forum (RatSWD).
    • Handle: RePEc:rsw:rswwps:rswwps252
    DOI: https://doi.org/10.17620/02671.24
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    References listed on IDEAS

    as
    1. Dietrich Braess & Anna Nagurney & Tina Wakolbinger, 2005. "On a Paradox of Traffic Planning," Transportation Science, INFORMS, vol. 39(4), pages 446-450, November.
    2. Nikolaos Askitas, 2015. "Google search activity data and breaking trends," IZA World of Labor, Institute of Labor Economics (IZA), pages 206-206, November.
    3. Gilles Duranton & Matthew A. Turner, 2011. "The Fundamental Law of Road Congestion: Evidence from US Cities," American Economic Review, American Economic Association, vol. 101(6), pages 2616-2652, October.
    4. Maxime Lenormand & Antònia Tugores & Pere Colet & José J Ramasco, 2014. "Tweets on the Road," PLOS ONE, Public Library of Science, vol. 9(8), pages 1-12, August.
    5. D. Helbing, 2009. "Derivation of a fundamental diagram for urban traffic flow," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 70(2), pages 229-241, July.
    6. Janet Currie & Reed Walker, 2011. "Traffic Congestion and Infant Health: Evidence from E-ZPass," American Economic Journal: Applied Economics, American Economic Association, vol. 3(1), pages 65-90, January.
    7. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
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    Cited by:

    1. Askitas, Nikos & Martinez, Anoop Bindra & Cereda, Fabio Saia, 2024. "The IZA / Fable Swipe Consumption Index," IZA Discussion Papers 17311, Institute of Labor Economics (IZA).

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    More about this item

    Keywords

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

    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

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