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User Participatory Construction of Open Hazard Data for Preventing Bicycle Accidents

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Semantic Technology (JIST 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10675))

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Abstract

Recently, bicycle-related accidents, e.g., collision accidents at intersection increase and account for approximately 20% of all traffic accidents in Japan; thus, it is regarded as one of the serious social problems. However, the Traffic Accident Occurrence Map released by the Japanese Metropolitan Police Department is currently based on accident information records, and thus there are a number of near-miss events, which are overlooked in the map but will be useful for preventing the possible accidents. Therefore, we detect locations with high possibility of bicycle accidents using user participatory sensing and offer them drivers and government officials as Open Hazard Data (OHD) to prevent future bicycle accident. This paper uses smartphone sensors to obtain data for acceleration, location, and handle rotation information. Then, by classifying those data with convolutional neural networks, it was confirmed that the locations, where sudden braking occurred can be detected with an accuracy of 80%. In addition, we defined an RDF model for OHD that is currently publicly available. In future, we plan to develop applications using OHD, e.g., notifying alerts when users are approaching locations where near-miss events have occurred.

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Notes

  1. 1.

    http://www.keishicho.metro.tokyo.jp/about_mpd/jokyo_tokei/tokei_jokyo/bicycle.files/002_28.pdf (in Japanese).

  2. 2.

    http://www3.wagamachi-guide.com/jikomap/ (in Japanese).

  3. 3.

    https://www.mlit.go.jp/common/001191847.pdf (in Japanese).

  4. 4.

    http://www.kenkounippon21.gr.jp/ (in Japanese).

  5. 5.

    http://fixmystreet.org/.

  6. 6.

    http://www.image-net.org/challenges/LSVRC/.

  7. 7.

    http://motools.sourceforge.net/event/event.n3.

  8. 8.

    http://www.ohsuga.lab.uec.ac.jp/openhazarddata.

References

  1. Herbert, W.H.: Industrial Accident Prevention: A Scientific Approach. McGraw-Hill, New York (1931)

    Google Scholar 

  2. Administrative evaluation office of Ministry of Internal Affairs and Communications Administrative evaluation and monitoring of bicycle traffic safety measures. http://www.soumu.go.jp/menu_news/s-news/94984.html (in Japanese)

  3. Eisenman, S., Miluzzo, E., Lane, N.: BikeNet: a mobile sensing system for cyclist experience mapping. ACM Trans. Sens. Networks 6(1), 1–39 (2009)

    Article  Google Scholar 

  4. Saito, H., Yamada, K.: Collecting, sharing and inferring user behavior for mobile recommendation with location history and its context. In: Proceedings of European Conference on Data Mining 2013 Prague Czech Republic, July 2013

    Google Scholar 

  5. Takeuchi, S., Tamura, S.: Human action recognition using acceleration information based on hidden markov model. In: Asia-Pacific Signal and Information Processing Association, No. 1, pp. 829-832, Sapporo, October 2009

    Google Scholar 

  6. Kiyohara, T., Orihara, R., Sei, Y., Tahara, Y., Ohsuga, A.: Activity recognition for dogs based on time-series data analysis. In: Duval, B., Herik, J., Loiseau, S., Filipe, J. (eds.) ICAART 2015. LNCS (LNAI), vol. 9494, pp. 163–184. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-27947-3_9

    Chapter  Google Scholar 

  7. Kawamura, T., Umezu, K., Ohsuga, A.: Mobile navigation system for the elderly - preliminary experiment and evaluation. In: Proceedings of the 5th International Conference on Ubiquitous Intelligence and Computing, Oslo University College, pp. 578–590. Oslo, Norway (2008)

    Google Scholar 

  8. Egami, S., Kawamura, T., Ohsuga, A.: Building urban LOD for solving illegally parked bicycles in Tokyo. In: Groth, P., Simperl, E., Gray, A., Sabou, M., Krötzsch, M., Lecue, F., Flöck, F., Gil, Y. (eds.) ISWC 2016. LNCS, vol. 9982, pp. 291–307. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46547-0_28

    Chapter  Google Scholar 

  9. Arakawa, Y., Matsuda, Y.: Gamification mechanism for enhancing a participatory urban sensing: survey and practical results. J. Inf. Process. 57(1), 1–15 (2016)

    Google Scholar 

  10. Matsuda, Y., Arai, I.: An experiment of a streetlamp classifying and a vertical illuminance assessing utilizing smartphones light sensors. In: 3rd International Symposium on Technology for Sustainability (ISTS2013), No.264, Hong Kong, China, November 2013

    Google Scholar 

  11. Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet Classification with Deep Convolutional Neural Networks. In: Neural Information Processing Systems (NIPS 2012), p. 25 (2012)

    Google Scholar 

  12. Compton, M., Barnaghi, P., Bermudez, L.: The SSN ontology of the W3C semantic sensor network incubator group. Web Semant. Sci. Serv. Agents World Wide Web 17, 25–32 (2012)

    Article  Google Scholar 

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Acknowledgments

This work was supported by JSPS KAKENHI Grant Numbers 16K12411, 17H04705. I would like to thank the students cooperated with the experiment.

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Authors and Affiliations

  1. Graduate School of Informatics and Engineering, The University of Electro-Communications, Tokyo, Japan

    Ryohei Kozu, Takahiro Kawamura, Shusaku Egami, Yuichi Sei, Yasuyuki Tahara & Akihiko Ohsuga

Authors
  1. Ryohei Kozu
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  2. Takahiro Kawamura
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  3. Shusaku Egami
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  4. Yuichi Sei
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  5. Yasuyuki Tahara
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  6. Akihiko Ohsuga
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Corresponding author

Correspondence to Ryohei Kozu .

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Editors and Affiliations

  1. Griffith University, Brisbane, Queensland, Australia

    Zhe Wang

  2. Dresden University of Technology, Dresden, Germany

    Anni-Yasmin Turhan

  3. Griffith University, Brisbane, Queensland, Australia

    Kewen Wang

  4. Tianjin University, Tianjin, China

    Xiaowang Zhang

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Kozu, R., Kawamura, T., Egami, S., Sei, Y., Tahara, Y., Ohsuga, A. (2017). User Participatory Construction of Open Hazard Data for Preventing Bicycle Accidents. In: Wang, Z., Turhan, AY., Wang, K., Zhang, X. (eds) Semantic Technology. JIST 2017. Lecture Notes in Computer Science(), vol 10675. Springer, Cham. https://doi.org/10.1007/978-3-319-70682-5_20

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  • DOI: https://doi.org/10.1007/978-3-319-70682-5_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70681-8

  • Online ISBN: 978-3-319-70682-5

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