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 of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp9503
    as

    Download full text from publisher

    File URL: https://docs.iza.org/dp9503.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Nikolaos Askitas, 2015. "Google search activity data and breaking trends," IZA World of Labor, Institute of Labor Economics (IZA), pages 206-206, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Stefan Bauernschuster & Timo Hener & Helmut Rainer, 2017. "When Labor Disputes Bring Cities to a Standstill: The Impact of Public Transit Strikes on Traffic, Accidents, Air Pollution, and Health," American Economic Journal: Economic Policy, American Economic Association, vol. 9(1), pages 1-37, February.
    2. Li, Shanjun & Liu, Yanyan & Purevjav, Avralt-Od & Yang, Lin, 2019. "Does subway expansion improve air quality?," Journal of Environmental Economics and Management, Elsevier, vol. 96(C), pages 213-235.
    3. Rafael Lalive & Simon Luechinger & Armin Schmutzler, 2013. "Does Supporting Passenger Railways Reduce Road Traffic Externalities?," ECON - Working Papers 110, Department of Economics - University of Zurich.
    4. Brent, Daniel & Beland, Louis-Philippe, 2020. "Traffic congestion, transportation policies, and the performance of first responders," Journal of Environmental Economics and Management, Elsevier, vol. 103(C).
    5. Kacker, Kanishka & Gupta, Ridhima & Ali , Saif, 2023. "Does Traffic Congestion pose Health Hazards? Evidence from a Highly Congested and Polluted City," EfD Discussion Paper 23-10, Environment for Development, University of Gothenburg.
    6. Tang, Cheng Keat, 2021. "The Cost of Traffic: Evidence from the London Congestion Charge," Journal of Urban Economics, Elsevier, vol. 121(C).
    7. Léa Bou Sleiman, 2021. "Are car-free centers detrimental to the periphery? Evidence from the pedestrianization of the Parisian riverbank," Working Papers 2021-03, Center for Research in Economics and Statistics.
    8. Barnes, Stephen R. & Beland, Louis-Philippe & Huh, Jason & Kim, Dongwoo, 2020. "The Effect of COVID-19 Lockdown on Mobility and Traffic Accidents: Evidence from Louisiana," GLO Discussion Paper Series 616, Global Labor Organization (GLO).
    9. Mihaela, Simionescu, 2020. "Improving unemployment rate forecasts at regional level in Romania using Google Trends," Technological Forecasting and Social Change, Elsevier, vol. 155(C).
    10. Clifford Winston, 2013. "On the Performance of the U.S. Transportation System: Caution Ahead," Journal of Economic Literature, American Economic Association, vol. 51(3), pages 773-824, September.
    11. Viard, V. Brian & Fu, Shihe, 2015. "The effect of Beijing's driving restrictions on pollution and economic activity," Journal of Public Economics, Elsevier, vol. 125(C), pages 98-115.
    12. Bou Sleiman, Lea, 2023. "Displacing Congestion: Evidence from Paris," CEPREMAP Working Papers (Docweb) 2302, CEPREMAP.
    13. Daniel A. Brent & Austin Gross, 2018. "Dynamic road pricing and the value of time and reliability," Journal of Regional Science, Wiley Blackwell, vol. 58(2), pages 330-349, March.
    14. Lalive, Rafael & Luechinger, Simon & Schmutzler, Armin, 2018. "Does expanding regional train service reduce air pollution?," Journal of Environmental Economics and Management, Elsevier, vol. 92(C), pages 744-764.
    15. Deepti Goel & Sonam Gupta, 2017. "The Effect of Metro Expansions on Air Pollution in Delhi," The World Bank Economic Review, World Bank, vol. 31(1), pages 271-294.
    16. Daniel Albalate & Xavier Fageda, 2019. "Congestion, Road Safety, and the Effectiveness of Public Policies in Urban Areas," Sustainability, MDPI, vol. 11(18), pages 1-21, September.
    17. Foreman, Kate, 2016. "Crossing the bridge: The effects of time-varying tolls on curbing congestion," Transportation Research Part A: Policy and Practice, Elsevier, vol. 92(C), pages 76-94.
    18. Tassinari, Filippo, 2024. "Low emission zones and traffic congestion: Evidence from Madrid Central," Transportation Research Part A: Policy and Practice, Elsevier, vol. 185(C).
    19. Beaudoin, Justin & Farzin, Y. Hossein & Lin Lawell, C.-Y. Cynthia, 2015. "Public transit investment and sustainable transportation: A review of studies of transit's impact on traffic congestion and air quality," Research in Transportation Economics, Elsevier, vol. 52(C), pages 15-22.
    20. Hamilton, Timothy L. & Wichman, Casey J., 2018. "Bicycle infrastructure and traffic congestion: Evidence from DC's Capital Bikeshare," Journal of Environmental Economics and Management, Elsevier, vol. 87(C), pages 72-93.

    More about this item

    Keywords

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

    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.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Holger Hinte (email available below). General contact details of provider: https://edirc.repec.org/data/izaaade.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.