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Automatic Traffic Enforcement Camera Operation, Based on a Business Intelligence System

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Decision Support Systems VII. Data, Information and Knowledge Visualization in Decision Support Systems (ICDSST 2017)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 282))

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Abstract

Since 2012, a new automatic traffic enforcement camera project has been in operation in Israel. Several databases are included in this project, i.e. sensor data, traffic reports, and road accident records. In 2014 a business intelligence system was developed to obtain all the data from the sensors of the new project and to merge them with the existing data to run the project effectively and efficiently. The aim of this paper is to present the process and the configuration of the business intelligence system, and to present the improvements in all measurements. In this paper we demonstrate the importance of a business intelligence system for operating, engineering, researching and managing aspects of a project.

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References

  1. WHO (World Health Organization): Road traffic injuries. Fact sheet N°358, May 2016

    Google Scholar 

  2. Elvik, R.: Speed limits, enforcement, and health consequences. Ann. Rev. Health 33, 225–238 (2012)

    Article  Google Scholar 

  3. Elvik, R.: A re-parameterisation of the Power Model of the relationship between the speed of traffic and the number of accidents and accident victims. Accid. Anal. Prev. 50, 854–860 (2013)

    Article  Google Scholar 

  4. Organization for Economic Co-operation and Development (OECD): Speed management (No. 55921). Organization for Economic Co-operation and Development, Paris (2006)

    Google Scholar 

  5. Elvik, R.: Speed enforcement in Norway: testing a game-theoretic model of the interaction between drivers and the police. Accid. Anal. Prev. 84, 128–133 (2015)

    Article  Google Scholar 

  6. De Pauw, E., Daniels, S., Brijs, T., Hermans, E., Wets, G.: An evaluation of the traffic safety effect of fixed speed cameras. Saf. Sci. 62, 168–174 (2014)

    Article  Google Scholar 

  7. Aarts, L., van Schagen, I.: Driving speed and the risk of road crashes: a review. Accid. Anal. Prev. 38, 215–224 (2006)

    Article  Google Scholar 

  8. Wijers, P.J.: Automated enforcement, get it right, make it safe. In: 16th Road Safety on Four Continents Conference Beijing, China, 15–17 May 2013

    Google Scholar 

  9. Gatso. http://www.gatso.com/en/about-gatso/history/. Accessed 23 May 2016

  10. Işık, O., Jones, M.C., Sidorova, A.: Business intelligence success: the roles of BI capabilities and decision environments. Inf. Manag. 50(1), 13–23 (2013)

    Article  Google Scholar 

  11. Bahrami, M., Arabzad, S.M., Ghorbani, M.: Innovation in market management by utilizing business intelligence: introducing proposed framework. Procedia - Soc. Behav. Sci. 41, 160–167 (2012)

    Article  Google Scholar 

  12. Negash, S.: Business intelligence. Commun. Assoc. Inf. Syst. 13, 177–195 (2004)

    Google Scholar 

  13. Chen, H., Chiang, R.H.L., Storey, V.C.: Business intelligence and analytics: from big data to big impact. MIS Q. 36(4), 1165–1188 (2012)

    Google Scholar 

  14. Elbashir, M.Z., Collier, P.A., Davern, M.J.: Measuring the effects of business intelligence system: the relationship between business process and organizational performance. Int. J. Acc. Inf. Syst. 9, 135–153 (2008)

    Article  Google Scholar 

  15. Cody, W.F., Kreulem, J.T., Krishna, V., Spangler, W.S.: The integration of business intelligence and knowledge management. IBM Syst. J. 41(4), 697–713 (2002)

    Article  Google Scholar 

  16. Nenortaite, J., Butleris, R.: Improving business rules management through the application of adaptive business intelligence technique. Inf. Technol. Control 38(1), 21–28 (2009)

    Google Scholar 

  17. Aalst, Wil M.P.: Process-Aware Information systems: lessons to be learned from process mining. In: Jensen, K., Aalst, Wil M.P. (eds.) Transactions on Petri Nets and Other Models of Concurrency II. LNCS, vol. 5460, pp. 1–26. Springer, Heidelberg (2009). doi:10.1007/978-3-642-00899-3_1

    Chapter  Google Scholar 

  18. Weber, B., Reichert, M., Rinderle-Ma, S.: Change patterns and change support features – enhancing flexibility in process-aware information systems. Data Knowl. Eng. 66, 438–466 (2008)

    Article  Google Scholar 

  19. Reichert, M., Weber, B.: Enabling Flexibility in Process-Aware Information Systems, Challenges, Methods. Technologies. Springer, Heidelberg (2012)

    Book  Google Scholar 

  20. Dumas, M., Van der Aalst, W.M., Ter Hofstede, A.H.: Process-Aware Information Systems: Bridging People and Software Through Process Technology (2005). ISBN: 978–0-471-66306-5

    Google Scholar 

  21. Watson, H.J., Wixom, B.H., Hoffer, J.A., Anderson-Lehman R., Reynolds, A.M.: Real-time Business Intelligence: Best Practices at Continental Airlines Information Systems Management (2006). Accessed 21 Dec 2006

    Google Scholar 

  22. Trieu, V.H.: Getting value from business intelligence systems: a review and research agenda. Decis. Support Syst. 93, 111–124 (2017)

    Article  Google Scholar 

  23. CBS (Central Bureau of Statistics): Press release 085/2016 (2016). http://www.cbs.gov.il/reader/newhodaot/hodaa_template.html?hodaa=201627085

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Author information

Authors and Affiliations

  1. Israel Traffic Police, R&D Unit, Beit Dagan, Israel

    Mali Sher

  2. Israel Police Technology Department, BI Unit, Jerusalem, Israel

    Guy Shifrin

Authors
  1. Mali Sher
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  2. Guy Shifrin
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Corresponding author

Correspondence to Mali Sher .

Editor information

Editors and Affiliations

  1. Department of Business Administration, University of Namur, Namur, Belgium

    Isabelle Linden

  2. Graduate School of Management, University of Plymouth, Plymouth, United Kingdom

    Shaofeng Liu

  3. Department of Business Administration, University of Namur, Namur, Belgium

    Christian Colot

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Sher, M., Shifrin, G. (2017). Automatic Traffic Enforcement Camera Operation, Based on a Business Intelligence System. In: Linden, I., Liu, S., Colot, C. (eds) Decision Support Systems VII. Data, Information and Knowledge Visualization in Decision Support Systems. ICDSST 2017. Lecture Notes in Business Information Processing, vol 282. Springer, Cham. https://doi.org/10.1007/978-3-319-57487-5_2

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