Abstract
Traffic safety plays an important role in smart transportation, and it has become a social issue worthy of attention. For detection of safe driving, we focus on the collection, processing, distribution, exchange, analysis and utilization of information, and aim at providing diverse services for drivers and passengers. By adopting crowdsourcing and crowd-sensing, we monitor the extreme driving behavior during the process of driving, trying to reduce the probability of traffic accidents. The smartphones are carried by passengers, which can sense the driving state of the vehicles with our proposed incentive mechanism. After the data is integrated, we are able to monitor the driving behavior more accurately, and finally secure the public transit. Finally, we developed a safe driving App for monitoring and evaluation.
This work is supported by National Natural Science Foundation of China (61672284, 41301407), Funding of Security Ability Construction of Civil Aviation Administration of China (AS-SA2015/21), Fundamental Research Funds for the Central Universities (NJ20160028, NT2018028, NS2018057).
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Chatzimilioudis, G., Konstantinidis, A., Laoudias, C., ZeinalipourYazti, D.: Crowdsourcing with smartphones. IEEE Internet Comput. 16, 36–44 (2012)
Guo, Y., Guo, B., Liu, Y., Wang, Z., Ouyang, Y., Yu, Z.: CrowdSafe: detecting extreme driving behaviors based on mobile crowdsensing. In: Proceedings of the 14th IEEE International Conference on Ubiquitous Intelligence and Computing (UIC 2017), 4–8 August 2017, San Francisco, California, USA (2017)
Khaisongkram, W., Raksincharoensak, P., Shimosaka, M., Mori, T., Sato, T., Nagai, M.: Automobile driving behavior recognition using boosting sequential labeling method for adaptive driver assistance systems. In: Dengel, A.R., Berns, K., Breuel, T.M., Bomarius, F., Roth-Berghofer, T.R. (eds.) KI 2008. LNCS (LNAI), vol. 5243, pp. 103–110. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-85845-4_13
Ogie, R.I.: Adopting incentive mechanisms for large-scale participation in mobile crowdsensing: from literature review to a conceptual framework. Hum.-Cent. Comput. Information Sci. 6(1), 24 (2016)
Vasconcelos, I., Oliveira Vasconcelos, R., Olivieri, B., Roriz, M., Endler, M., Colaço Junior, M.: Smartphone-based outlier detection: a complex event processing approach for driving behavior detection. J. Internet Serv. Appl. 8(1) (2017)
Herzberg, P.Y.: Beyond “accident-proneness”: Using Five-Factor Model prototypes to predict driving behavior. J. Res. Pers. 43(6), 1096–1100 (2009)
Toledo, T., Koutsopoulos, H.N., Ben-Akiva, M.: Integrated driving behavior modeling. Transp. Res. Part C: Emerg. Technol. 15(2), 96–112 (2007)
Zhu, X., Yuan, Y., Hu, X., Chiu, Y.-C., Ma, Y.-L.: A Bayesian network model for contextual versus non-contextual driving behavior assessment. Transp. Res. Part C: Emerg. Technol. 81, 172–187 (2017)
Liu, X., Ota, K., Liu, A., Chen, Z.: An incentive game based evolutionary model for crowd sensing networks. Peer-to-Peer Netw. Appl. 9(4), 692–711 (2016)
Thejaswini, M., Rajalakshmi, P., Desai, U.B.: Duration of stay based weighted scheduling framework for mobile phone sensor data collection in opportunistic crowd sensing. Peer-to-Peer Netw. Appl. 9(4), 721–730 (2016)
Pournajaf, L., Xiong, L., Sunderam, V.: Dynamic data driven crowd sensing task assignment. Procedia Comput. Sci. 29 (2014)
Li, H., Jia, K., Yang, H., Liu, D., Zhou, L.: Practical blacklist-based anonymous authentication scheme for mobile crowd sensing. Peer-to-Peer Netw. Appl. 9(4), 762–773 (2016)
Guo, B., et al.: TaskMe: toward a dynamic and quality-enhanced incentive mechanism for mobile crowd sensing. Int. J. Hum.-Comput. Stud. 102, 14–26 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Du, Y., Guo, X., Shi, C., Zhu, Y., Li, B. (2018). DSDCS: Detection of Safe Driving via Crowd Sensing. In: Gan, G., Li, B., Li, X., Wang, S. (eds) Advanced Data Mining and Applications. ADMA 2018. Lecture Notes in Computer Science(), vol 11323. Springer, Cham. https://doi.org/10.1007/978-3-030-05090-0_15
Download citation
DOI: https://doi.org/10.1007/978-3-030-05090-0_15
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-05089-4
Online ISBN: 978-3-030-05090-0
eBook Packages: Computer ScienceComputer Science (R0)