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A Crowd Sensing Approach to Video Classification of Traffic Accident Hotspots

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Machine Learning and Data Mining in Pattern Recognition (MLDM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10935))

Abstract

Despite various initiatives over the recent years, the number of traffic accidents has been steadily increasing and has reached over 1.2 million fatalities per year world wide. Recent research has highlighted the positive effects that come from educating drivers about accident hotspots, for example, through in-vehicle warnings of upcoming dangerous areas. Further, it has been shown that there exists a spatial correlation between to locations of heavy braking events and historical accidents. This indicates that emerging accident hotspots can be identified from a high rate of heavy braking, and countermeasures deployed in order to prevent accidents before they appear. In order to contextualize and classify historic accident hotspots and locations of current dangerous driving maneuvers, the research at hand introduces a crowd sensing system collecting vehicle and video data. This system was tested in a naturalistic driving study of 40 vehicles for two months, collecting over 140,000 km of driving data and 36,000 videos of various traffic situations. The exploratory results show that through applying data mining approaches it is possible to describe these situations and determine information regarding the involved traffic participants, main causes and location features. This enables accurate insights into the road network, and can help inform both drivers and authorities.

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Notes

  1. 1.

    Beside the normal road rescue tasks, patrollers had stand-by duty.

References

  1. Adminaite, D., Jost, G., Stipdonk, H., Ward, H.: Ranking EU progress on road safety. Technical report (2016). http://etsc.eu/10th-annual-road-safety-performance-index-pin-report

  2. Allgemeinder Deutscher Automobil Club: EUROTAP (2007). https://ec.europa.eu/transport/road_safety/sites/roadsafety/files/pdf/projects/eurotap.pdf

  3. An, P.E., Harris, C.J.: An intelligent driver warning system for vehicle collision avoidance. IEEE Trans. Syst. Man Cybern. Part A: Syst. Hum. 26(2), 254–261 (1996)

    Article  Google Scholar 

  4. Bergasa, L.M., Almeria, D., Almazan, J., Yebes, J.J., Arroyo, R.: DriveSafe: an app for alerting inattentive drivers and scoring driving behaviors. In: IEEE Intelligent Vehicles Symposium, Proceedings, pp. 240–245 (2014)

    Google Scholar 

  5. Bohnenblust, D., Pool, M.: Verkehrsunfälle in der schweiz 2016. Bundesamt für Statistik, BFS (2017). https://www.bfs.admin.ch/bfs/de/home/statistiken/mobilitaet-verkehr/unfaelle-umweltauswirkungen/verkehrsunfaelle.assetdetail.3103126.html

  6. Campbell, J.L., Richard, C.M., Brown, J.L., McCallum, M.: Crash Warning System Interfaces: Human Factors insights and lessons learned. Technical report (2007)

    Google Scholar 

  7. Chen, H.T., Lai, C.Y., Shih, C.A.: Toward community sensing of road anomalies using monocular vision. IEEE Sens. J. 16(8), 2380–2388 (2016)

    Article  Google Scholar 

  8. Clevenger, A.P., Ford, A.T., Sawaya, M.A.: Banff wildlife crossings project: Integrating science and education in restoring population connectivity across transportation corridors. Final report to Parks Canada Agency, Radium Hot Springs, British Columbia, Canada, p. 165, June 2009

    Google Scholar 

  9. Clevenger, T., Cypher, B.L., Ford, A., Huijser, M., Leeson, B.F., Walder, B., Walters, C.: Wildlife-Vehicle Collision Reduction Study. Technical report (2008)

    Google Scholar 

  10. Dahlinger, A., Wortmann, F., Tiefenbeck, V., Ryder, B., Gahr, B.: Feldexperiment zur wirksamkeit von konkretem vs. abstraktem eco-driving feedback. In: Wirtschaftsinformatik Konferenz (WI), March 2017. https://www.alexandria.unisg.ch/250432/

  11. European Commision: Ranking for European Road Safety (2008). https://ec.europa.eu/transport/road_safety/sites/roadsafety/files/pdf/projects/rankers.pdf

  12. European Commision: Road safety knowledge system (2008). http://safetyknowsys.swov.nl

  13. European Commission: A strategic approach to implementing countermeasures (2018). https://ec.europa.eu/transport/road_safety/specialist/knowledge/young/implementation_process/a_strategic_approach_to_implementing_countermeasures_en

  14. Glassco, R.A., Cohen, D.S.: Collision avoidance warnings approaching stopped or stopping vehicles. In: America, I.T.S. (ed.) 8th World Congress on Intelligent Transport Systems, Australia, Sydney (2001)

    Google Scholar 

  15. Goniewicz, K., Goniewicz, M., Pawłowski, W., Fiedor, P., Lasota, D.: Road safety in poland: magnitude, causes and injuries. Wiadomosci lekarskie (Warsaw, Poland: 1960), 70(2 pt 2), 352–356 (2017)

    Google Scholar 

  16. Goodwin, A., Thomas, L., Kirley, B., Hall, W., O’Brien, N., Hill, K.: Countermeasures That Work: A highway safety countermeasure guide for State highway safety offices. Eighth edition. (Report No. DOT HS 812 202). National Highway Traffic Safety Administration, Washington, D.C., January 2015

    Google Scholar 

  17. Hogema, J., Janssen, W.: Effects of intelligent cruise control on driving behaviour: a simulator study, January 1996

    Google Scholar 

  18. Hu, S., Su, L., Liu, H., Wang, H., Abdelzaher, T.F.: Smartroad: smartphone-based crowd sensing for traffic regulator detection and identification. ACM Trans. Sen. Netw. 11(4), 55:1–55:27 (2015)

    Article  Google Scholar 

  19. Jähi, H., Muhlrad, N., Buttler, I., Gitelman, V., Bax, C., Dupont, E., Giustiniani, G., Machata, K., Martensen, H., Papadimitriou, E., Persia, L., Talbot, R., Vallet, G., Yannis, G.: Investigating road safety management processes in Europe. Procedia - Soc. Behav. Sci. 48, 2130–2139 (2012)

    Article  Google Scholar 

  20. Lynam, D., Castle, J., Martin, J., Lawson, S.D., Hill, J., Charman, S.: EuroRAP 2005–06 technical update. Traffic Eng. Control 48(11), 477–484 (2007)

    Google Scholar 

  21. Ma, H., Zhao, D., Yuan, P.: Opportunities in mobile crowd sensing. IEEE Commun. Mag. 52(8), 29–35 (2014)

    Article  Google Scholar 

  22. Marshall, W.E., Garrick, N.W.: Street network types and road safety: a study of 24 California cities. Urban Des. Int. 15(3), 133–147 (2010)

    Article  Google Scholar 

  23. Misra, I., Shrivastava, A., Hebert, M.: Watch and learn: Semi-supervised learning of object detectors from videos. CoRR abs/1505.05769 (2015)

    Google Scholar 

  24. Moody, S., Melia, S.: Shared space research, policy and problems. In: Proceedings of the Institution of Civil Engineers - Transport, vol. 167, no. 6, pp. 384–392 (2014)

    Google Scholar 

  25. Murphey, Y.L., Milton, R., Kiliaris, L.: Driver’s style classification using jerk analysis. In: 2009 IEEE Workshop on Computational Intelligence in Vehicles and Vehicular Systems, CIVVS 2009 - Proceedings, pp. 23–28 (2009)

    Google Scholar 

  26. Naujoks, F., Neukum, A.: Specificity and timing of advisory warnings based on cooperative perception. In: Mensch und Computer Workshopband, pp. 229–238 (2014)

    Google Scholar 

  27. NHTSA, N.H.T.S.: 2016 Motor Vehicle Crashes: Overview. Traffic safety facts research, pp. 1–9 (2017). https://crashstats.nhtsa.dot.gov/Api/Public/Publication/812456

  28. Pan, B., Zheng, Y., Wilkie, D., Shahabi, C.: Crowd sensing of traffic anomalies based on human mobility and social media. In: Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL 2013, pp. 344–353. ACM, New York (2013)

    Google Scholar 

  29. Pande, A., Chand, S., Saxena, N., Dixit, V., Loy, J., Wolshon, B., Kent, J.D.: A preliminary investigation of the relationships between historical crash and naturalistic driving. Accid. Anal. Prev. 101, 107–116 (2017)

    Article  Google Scholar 

  30. Ruikar, M.: National statistics of road traffic accidents in India. J. Orthop. Traumatol. Rehabil. 6(1), 1–6 (2013)

    Article  Google Scholar 

  31. Ryder, B., Gahr, B., Dahlinger, A.: An in-vehicle information system providing accident hotspot warnings. In: ECIS 2016 Proceedings. Prototypes, AIS Electronic Library (AISeL) (2016)

    Google Scholar 

  32. Ryder, B., Gahr, B., Dahlinger, A., Zundritsch, P., Wortmann, F., Fleisch, E.: Spatial prediction of traffic accidents with critical driving events – insigths from a nationwide field study. Transp. Res.: Part A (2017, submitted)

    Google Scholar 

  33. Werneke, J., Vollrath, M.: How to present collision warnings at intersections?- A comparison of different approaches. Accid. Anal. Prev. 52, 91–99 (2013)

    Article  Google Scholar 

  34. Wirz, M., Strohrmann, C., Patscheider, R., Hilti, F., Gahr, B., Hess, F., Roggen, D., Tröster, G.: Real-time detection and recommendation of thermal spots by sensing collective behaviors in paragliding. In: Proceedings of 1st International Symposium on From Digital Footprints to Social and Community Intelligence, SCI 2011, pp. 7–12. ACM, New York (2011)

    Google Scholar 

  35. World Health Organization: Global Status Report on Road Safety 2015. Technical report (2015)

    Google Scholar 

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Author information

Authors and Affiliations

  1. University of St. Gallen, 9000, St. Gallen, Switzerland

    Bernhard Gahr, André Dahlinger & Felix Wortmann

  2. ETH Zurich, 8000, Zurich, Switzerland

    Benjamin Ryder

Authors
  1. Bernhard Gahr
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  2. Benjamin Ryder
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  3. André Dahlinger
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  4. Felix Wortmann
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Corresponding author

Correspondence to Bernhard Gahr .

Editor information

Editors and Affiliations

  1. Institute of Computer Vision and Applied Computer Sciences, Leipzig, Germany

    Petra Perner

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Gahr, B., Ryder, B., Dahlinger, A., Wortmann, F. (2018). A Crowd Sensing Approach to Video Classification of Traffic Accident Hotspots. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2018. Lecture Notes in Computer Science(), vol 10935. Springer, Cham. https://doi.org/10.1007/978-3-319-96133-0_14

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  • DOI: https://doi.org/10.1007/978-3-319-96133-0_14

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