This paper explores the analysis method for predicting criminal’s escape paths, which predicts the possible escape routes of the criminals or terrorists from the crime scene. Better prediction should be obtained as we explore the decision of criminals on selecting an escape path based on the path’s condition and distance from the crime scene. In addition, real-time information collected by sensors along the paths (i.e., camera sensors) can help improve the accuracy of escape path prediction. The analysis is based on the Bayesian Network, in which the path from a node to node is chosen based on the Bayes Inference theory. In particular, the criminal’s decision on the path selection is modeled by the Bayesian Network. The analysis involves finding the selection probability on each path conditional on path conditions, spotted suspected vehicles, and assumed criminal preference (i.e., distance from the crime scene). Hence, the predicted path is likely the path with the highest probability. The analysis presented in this paper would contribute to the domain of artificial intelligence, such that it can be used as the analysis tool to model and predict criminal behaviors in selecting escape paths.

1. D. P. Lyle, Forensics for Dummies. Indianapolis, IN: John Wiley & Sons, 2016.
2. S. Du, M. Ibrahim, M. Shehata and W. Badawy, “Automatic license plate recognition (ALPR): A state-of-the-art review,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 23, no. 2, pp. 311-325, 2013. https://dx.doi.org/10.1109/TCSVT.2012.2203741
3. K. Ashok and M. E. Ben-Akiva, “Dynamic origin-destination matrix estimation and prediction for real-time traffic management systems,” in International Symposium on the Theory of Traffic Flow and Transportation, Transportation and Traffic Theory, Berkeley, CA, 1993. PMCid:PMC464617
4. E. W. Dijkstra, “A note on two problems in connexion with graphs,” Numerische Mathematik, vol. 1, no. 1, pp. 269-271, 1959. https://dx.doi.org/10.1007/BF01386390
5. W. Zeng and R. L. Church, “Finding shortest paths on real road networks: The case for A*,” International Journal of Geographical Information Science, vol. 23, no. 4, pp. 531-543, 2009. https://dx.doi.org/10.1080/13658810801949850
6. J. E. Hopcroft, Data Structures and Algorithms. vol. 175, Boston, MA: Addison-Wesley, 1983.
7. J. L. Gross and J. Yellen, Graph Theory and Its Applications. Boca Raton, FL: CRC Press, 2005.
8. P. Lytrivis, G. Thomaidis, M. Tsogas and A. Amditis, “An advanced cooperative path prediction algorithm for safety applications in vehicular networks,” IEEE Transactions on Intelligent Transportation Systems, vol. 12, no. 3, pp. 669-679, 2011. https://dx.doi.org/10.1109/TITS.2011.2123096
9. J. O. Berger, Statistical Decision Theory and Bayesian Analysis. New York, NY: Springer Science & Business Media, 2013.
10. I. Salomonson and K. M. M. Rathai, “Mixed driver intention estimation and path prediction using vehicle motion and road structure information,” Master thesis, Chalmers University of Technology, Gothenburg, Sweden, 2015.
11. H. N. Nindyo, H. N. Suryana, A. S. Azirah, A. Fahmi, “Shortest path analysis for indoor navigation for disaster management,” International Journal of Technology and Engineering Studies, vol. 1, no. 2, pp. 48-52, 2015.

P. Wongsai and W. Pawgasame, “Analysis of a crime scene getaway vehicle’s escaping path,” International Journal of Technology and Engineering Studies, vol. 2, no. 5, pp. 134-139, 2016.