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International Conference on Information Technology & Systems

ICITS 2018: Proceedings of the International Conference on Information Technology & Systems (ICITS 2018) pp 319–329Cite as

Creating Predictive Models for Forecasting the Accident Rate in Mountain Roads Using VANETs

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 721))

Abstract

Monitoring the road network status of an entire country in a visual way (as traditionally) is very hard, so different mechanisms to do it in an automatic manner have been investigated. In particular, nomadic pervasive sensing platforms based on VANETs have been recently deployed. However, the level of road damage is a relative variable, and it is necessary to predict the particular impact of the same in each case, in order to prioritize the conditioning works. Therefore, in this paper a predictive model for forecasting the accident rate in mountain roads, considering the measures previously obtained through a nomadic sensing environment (and through the weather office) is defined. The model considers the type of road under study as well as different analysis scales to perform the calculations. The model is based on Taylor’s series and multivariate functions. Real data related to Valais (Switzerland) road network is employed to construct and validate the proposed model.

B. Bordel and R. Alcarria—On leave from: Universidad Politécnica de Madrid, Madrid, Spain.

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References

  1. Federal Statistical Office (FSO): Land use in Switzerland. Results of the Swiss land use statistics. https://www.bfs.admin.ch/bfsstatic/dam/assets/348992/master. Accessed 26 Aug 2017

  2. Roads and traffic 2017 - Developments, facts, figures. Annual publication of the Federal Roads Office. https://www.astra.admin.ch/astra/en/home/documentation/facts-and-figures.html. Accessed 26 Aug 2017

  3. Dupont, A., Bocchi, Y., Rizzo, G.: NOSE: a NOmadic scalable ecosystem for pervasive sensing, computing and communication. In: International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, pp. 884–893. Springer, Cham, July 2017

    Google Scholar 

  4. NOSE dashboard. https://nose-e3fe0.firebaseapp.com/app/#/dashboard/sTemp. Accessed 26 Aug 2017

  5. Saqr, H.E., Pearl, D.K., Yates, A.J.: A review and predictive models of gang ioside uptake by biological membranes. J. Neurochem. 61(2), 395–441 (1993)

    Article  Google Scholar 

  6. Sánchez, B.B., Alcarria, R., De Rivera, D.S., Sánchez-Picot, A.: Predictive algorithms for mobility and device lifecycle management in cyber-physical systems. EURASIP J. Wirel. Commun. Network. 2016(1), 228 (2016)

    Article  Google Scholar 

  7. Quartieri, J., Mastorakis, N.E., Iannone, G., Guarnaccia, C., D’ambrosio, S., Troisi, A., Lenza, T.L.L.: A review of traffic noise predictive models. In: Recent Advances in Applied and Theoretical Mechanics, 5th WSEAS International Conference on Applied and Theoretical Mechanics (MECHANICS 2009) Puerto De La Cruz, Tenerife, Canary Islands, Spain, pp. 14–16, December 2009

    Google Scholar 

  8. Papageorgiou, M., Diakaki, C., Dinopoulou, V., Kotsialos, A., Wang, Y.: Review of road traffic control strategies. Proc. IEEE 91(12), 2043–2067 (2003)

    Article  Google Scholar 

  9. Williams, B.M., Hoel, L.A.: Modeling and forecasting vehicular traffic flow as a seasonal ARIMA process: theoretical basis and empirical results. J. Transp. Eng. 129(6), 664–672 (2003)

    Article  Google Scholar 

  10. Malo, J.E., Suarez, F., Diez, A.: Can we mitigate animal–vehicle accidents using predictive models? J. Appl. Ecol. 41(4), 701–710 (2004)

    Article  Google Scholar 

  11. Loft, S., Sanderson, P., Neal, A., Mooij, M.: Modeling and predicting mental workload in En route air traffic control: critical review and broader implications. Hum. Factors 49(3), 376–399 (2007)

    Article  Google Scholar 

  12. Lin, L., Li, J., Chen, F., Ye, J., Huai, J.P.: Road traffic speed prediction: a probabilistic model fusing multi-source data. IEEE Transactions on Knowledge and Data Engineering (2017)

    Google Scholar 

  13. Li, L., You, S., Yang, C., Yan, B., Song, J., Chen, Z.: Driving-behavior-aware stochastic model predictive control for plug-in hybrid electric buses. Appl. Energy 162, 868–879 (2016)

    Article  Google Scholar 

  14. Massera Filho, C., Terra, M.H., Wolf, D.F.: Safe optimization of highway traffic with robust model predictive control-based cooperative adaptive cruise control. IEEE Trans. Intell. Transp. Syst. 18, 3193–3203 (2017)

    Article  Google Scholar 

  15. Bordel, B., Alcarria, R., Robles, T., Martín, D.: Cyber–physical systems: extending pervasive sensing from control theory to the internet of things. Pervasive Mob. Comput. 40, 156–184 (2017)

    Article  Google Scholar 

  16. Amini, S., Gerostathopoulos, I., Prehofer, C.: Big data analytics architecture for real-time traffic control. In: 2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS), pp. 710–715. IEEE, June 2017

    Google Scholar 

  17. Götte, C., Keller, M., Haß, C., Bertram, T.: Model predictive planning and control applied to critical traffic situations. ATZ Worldwide 118(9), 64–69 (2016)

    Article  Google Scholar 

  18. Suh, J., Yi, K., Jung, J., Lee, K., Chong, H., Ko, B.: Design and evaluation of a model predictive vehicle control algorithm for automated driving using a vehicle traffic simulator. Control Eng. Pract. 51, 92–107 (2016)

    Article  Google Scholar 

  19. Carrier, M., Apparicio, P., Séguin, A.M.: Road traffic noise in Montreal and environmental equity: what is the situation for the most vulnerable population groups? J. Transp. Geogr. 51, 1–8 (2016)

    Article  Google Scholar 

  20. Wang, V.S., Lo, E.W., Liang, C.H., Chao, K.P., Bao, B.Y., Chang, T.Y.: Temporal and spatial variations in road traffic noise for different frequency components in metropolitan Taichung. Taiwan. Environ. Pollut. 219, 174–181 (2016)

    Article  Google Scholar 

  21. Pappas, G.J., Lafferriere, G., Sastry, S.: Hierarchically consistent control systems. IEEE Trans. Autom. Control 45(6), 1144–1160 (2000)

    Article  MathSciNet  Google Scholar 

  22. Dufour, L., Genoud, D., Ladevie, B., Bezian, J.J.: Heating and hot water industrial prediction system for residential district. In: 2016 30th International Conference on Advanced Information Networking and Applications Workshops (WAINA), pp. 821–826. IEEE, March 2016

    Google Scholar 

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Acknowledgments

This publication was produced in the framework of the RCSO NOSE project. Borja Bordel has received funding from the Ministry of Education through the FPU program (grant number FPU15/03977); and Ramón Alcarria thanks the Spanish Ministry of Education, Culture and Sport for the professor’s mobility program (José Castillejo’s 2017 grant).

Additionally, these results were supported by the Ministry of Economy and Competitiveness through SEMOLA project (TEC2015-68284-R) and from the Autonomous Region of Madrid through MOSI-AGIL-CM project (grant P2013/ICE-3019, co-funded by EU Structural Funds FSE and FEDER).

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Authors and Affiliations

  1. Institute of Information Systems, University of Applied Sciences Western Switzerland (HES-SO), Sierre, Valais, Switzerland

    Borja Bordel, Ramón Alcarria, Gianluca Rizzo & Antonio Jara

  2. Universidad Politécnica de Madrid, Madrid, Spain

    Borja Bordel & Ramón Alcarria

Authors
  1. Borja Bordel
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  2. Ramón Alcarria
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  3. Gianluca Rizzo
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  4. Antonio Jara
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Corresponding author

Correspondence to Borja Bordel .

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Editors and Affiliations

  1. DEI/FCT, Universidade de Coimbra, Coimbra, Portugal

    Álvaro Rocha

  2. Systems Department, Universidad Estatal Península de Santa Elena, La Libertad, Ecuador

    Teresa Guarda

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Bordel, B., Alcarria, R., Rizzo, G., Jara, A. (2018). Creating Predictive Models for Forecasting the Accident Rate in Mountain Roads Using VANETs. In: Rocha, Á., Guarda, T. (eds) Proceedings of the International Conference on Information Technology & Systems (ICITS 2018). ICITS 2018. Advances in Intelligent Systems and Computing, vol 721. Springer, Cham. https://doi.org/10.1007/978-3-319-73450-7_31

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  • DOI: https://doi.org/10.1007/978-3-319-73450-7_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73449-1

  • Online ISBN: 978-3-319-73450-7

  • eBook Packages: EngineeringEngineering (R0)

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