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Techniques for efficient detection of rapid weather changes and analysis of their impacts on a highway network

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Abstract

Adverse weather conditions have a significant impact on the safety, mobility, and efficiency of highway networks. Weather contributed to 23 percent of all non-reoccurring delay and approximately 544 million vehicle hours of delay each year (2014). Nearly 2.3 billion dollars each year are spent by transportation agencies for winter maintenance that contribute to close to 20 percent of most DOT’s yearly budgets (2014). These safety and mobility factors make it important to develop new and more effective methods to address road conditions during adverse weather conditions. Given weather and traffic sensors installed along side of the highway networks, how can we automatically detect weather and traffic change events and prevent from the traffic delay or harsh weather accidents? To this end, we propose a novel framework to address this problem. This paper develops techniques for efficiently detecting rapid weather change events and analyzing their impacts on the traffic flow characteristics of a highway network. It is composed of three components, including 1) detection of rapid weather change events in a highway network using the streaming weather information from a sensor network of weather stations; 2) detection of rapid traffic change events on the traffic flow characteristics (e.g., travel time) of the highway network; and 3) analysis of correlations between the detected weather and traffic change events in space and time. The proposed approach was applied to a weather dataset provided by New York State Mesonet and a traffic flow dataset the National Performance Management Research Data Set (NPMRDS) provided by NYSDOT. The empirical results provide potential evidence about the significant impacts of rapid weather change events on traffic flow characteristics of the Interstate 90 (I-90) Highway in the state of New York. We show the quantitative performance evaluation of our change event detection algorithm and three baseline methods on manually labeled the weather dataset and our method outperforms baselines in terms of precision, recall and F-score. We present the analysis of Top K detected change events as case studies and also provide the spatio-temporal correlation statistics of top k weather and traffic change events. The limitations of the proposed approach and the empirical study are also discussed.

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References

  1. 1.

    Dataset and code. https://github.com/newera912/WeatherTransportationProject

  2. 2.

    Mesonet website. http://www.nysmesonet.org

  3. 3.

    U.S. department of transportation, federal highway administration. https://ops.fhwa.dot.gov/weather/q1_roadimpact.htm

  4. 4.

    How do weather events impact roads? retrieved from 25 usdot fhwa road weather management program (2014)

  5. 5.

    Barua S, Sander J (2014) Mining statistically sound co-location patterns at multiple distances. In: In SSDBM. ACM, pp 7

  6. 6.

    Chen F, Zhou B A generalized matching pursuit approach for graph structured sparsity. In IJCAI’16 (2016)

  7. 7.

    Datla S, Sharma S (2008) Impact of cold and snow on temporal and spatial variations of highway traffic volumes. JTRG

  8. 8.

    Elhenawy M, Chen H, Rakha HA (2015) Traffic congestion identification considering weather and visibility conditions using mixture linear regression. In: TRB 94Th annual meeting, pp 15–3323

  9. 9.

    Ghahramani Z, Hinton GE (2000) Variational learning for switching state-space models. Neural Comput 12:831–864

  10. 10.

    Haynes K.F.p., Eckley I (2017) A computationally efficient nonparametric approach for changepoint detection. In: Statistics and computing 27(5):pp 1293–1305

  11. 11.

    James N, Matteson D (2013) ecp: an r package for nonparametric multiple change point analysis of multivariate data. In: arxiv:1309.3295

  12. 12.

    Jiang R, Fei H, Huan J A family of joint sparse pca algorithms for anomaly localization in network data streams. In: TKDE (2013)

  13. 13.

    Kwon T, Fu L, Jiang C (2013) Effect of winter weather and road surface conditions on macroscopic traffic parameters. TRB’s TRR Journal

  14. 14.

    Lamm R, Choueiri EM, Mailaender T (1990) Comparison of operating speeds on dry 10 and wet pavements of two-lane rural highways. In: TRR 1280

  15. 15.

    Liu S, Yamada M, Collier N, Sugiyama M (2013) Change-point detection in time-series data by relative density-ratio estimation. Neural Networks

  16. 16.

    Martchouk M, Mannering FL, NEXTRANS C (2009) Analysis of travel time reliability on indiana interstates. Technical report. NEXTRANS

  17. 17.

    Neill DB, McFowland E, Zheng H (2013) Fast subset scan for multivariate event detection Statistics in medicine 32(13):pp 2185–2208

  18. 18.

    Smith BL, Demetsky MJ (1997) Traffic flow forecasting: comparison of modeling approaches. TRANSP ENG-ASCE:123(4):pp 261–266

  19. 19.

    Smith BL, Williams BM, Oswald RK (2002) Comparison of parametric and nonparametric models for traffic flow forecasting. TRC: Emerging Technologies: 10(4):pp 303–321

  20. 20.

    Stathopoulos A, Karlaftis MG (2003) A multivariate state space approach for urban traffic flow modeling and prediction TRC

  21. 21.

    van Stralen W (2013) The influence of adverse weather conditions on the probability of congestion on dutch highways. Ph.D. thesis, TU Delft, Delft University of Technology

  22. 22.

    Tessier MA, Morency C, Saunier N (2015) Impact of weather conditions on traffic: Case study of montreal’s. In: TRB

  23. 23.

    Wu YJ, Chen F, Lu CT, Yang S (2015) Urban traffic flow prediction using a spatio-temporal random effects model. ITS Journal

  24. 24.

    Zhao L, Chen SIJ (2012) Analysis of weather impact on travel speed and travel time reliability. In: CICTP. ASCE, pp 1145–1155

  25. 25.

    Zhou B, Chen F Graph-structured sparse optimization for connected subgraph detection. In: ICDM (2016)

Download references

Acknowledgments

This research is made possible by the New York State (NYS) Mesonet. Original funding for the NYS Mesonet was provided by Federal Emergency Management Agency grant FEMA4085DRNY, with the continued support of the NYS Division of Homeland Security and Emergency Services; the state of New York; the Research Foundation for the State University of New York (SUNY); the University at Albany, SUNY; the Atmospheric Sciences Research Center (ASRC) at SUNY Albany; and the Department of Atmospheric and Environmental Sciences (DAES) at SUNY Albany. Special thanks to the New York State Department of Transportation (NYSDOT) for use of the NPMRDS data, and the New York State Thruway, for their support of this research. This research is supported by the Research and Innovative Technology Administration of the U.S. Department of Transportation through the Region 2 – University Transportation Research Centers Program.

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Correspondence to Adil Alim.

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Alim, A., Joshi, A., Chen, F. et al. Techniques for efficient detection of rapid weather changes and analysis of their impacts on a highway network. Geoinformatica (2020). https://doi.org/10.1007/s10707-020-00395-x

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Keywords

  • Change event detection
  • Correlation study
  • Weather
  • Highway network