James I. Obuhuma; Henry O. Okoyo; Sylvester O. McOyowo

Driver Behaviour Profiling Using Dynamic Bayesian Network

Full Text (PDF, 1160KB), PP.50-59

Views: 0 Downloads: 0

Author(s)

James I. Obuhuma ^1,* Henry O. Okoyo ² Sylvester O. McOyowo ²

1. Department of Computer Science, Africa Nazarene University, Nairobi, Kenya

2. School of Computing and Informatics, Maseno University, Private Bag, Maseno, Kenya

* Corresponding author.

DOI: https://doi.org/10.5815/ijmecs.2018.07.05

Received: 1 May 2018 / Revised: 10 May 2018 / Accepted: 18 May 2018 / Published: 8 Jul. 2018

Index Terms

Driver Behaviour, Driver Profiling, GPS, Bayesian Network, Dynamic Bayesian Network, 2TBN

Abstract

In the recent past, there has been a rapid increase in the number of vehicles and diversification of road networks worldwide. The biggest challenge now lies on how to monitor and analyse behaviours of vehicle drivers as a catalyst to road safety. Driver behaviour depends on the state and nature of the road, the state of the driver, vehicle conditions, and actions of other road users among other factors. This paper illustrates the ability of Dynamic Bayesian Networks towards determination of driving styles with respect to acceleration, cornering and braking patterns. Bayesian Networks are probabilistic graphical models that map a set of variables and their conditional dependencies. Sample test results showed that the 2-Time-slice Bayesian Network model is suitable for generation of driver profiles using only four GPS data parameters namely speed, altitude, direction and signal strength against time. The model classifies driver profiles into two sets of observations: driver behaviour and nature of operational environment. Adoption of the model could offer a cost effective, easy to implement and use solution that could find many applications in vehicle driver recruiting firms, vehicle insurance companies and transport and road safety authorities among other sectors.

Cite This Paper

James I. Obuhuma, Henry O. Okoyo, Sylvester O. McOyowo, " Driver Behaviour Profiling Using Dynamic Bayesian Network", International Journal of Modern Education and Computer Science(IJMECS), Vol.10, No.7, pp. 50-59, 2018. DOI:10.5815/ijmecs.2018.07.05

Reference

[1]A. Ellison, S. Greaves, and R. Daniels, “Profiling Drivers’ Risky Behaviour Towards All Road Users,” Australas. Coll. Road …, 2012.
[2]A. Sathyanarayana, “Driver behavior analysis and route recognition by hidden Markov models,” … Electron. Safety, …, 2008.
[3]G. N. Bifulco, F. Galante, L. Pariota, M. Russo Spena, and P. Del Gais, “Data Collection for Traffic and Drivers’ Behaviour Studies: A Large-scale Survey,” Procedia - Soc. Behav. Sci., vol. 111, pp. 721–730, Feb. 2014.
[4]J. De Winter and D. Dodou, “The Driver Behaviour Questionnaire as a predictor of accidents: A meta-analysis,” J. Safety Res., 2010.
[5]J. Reason, A. Manstead, and S. Stradling, “Errors and violations on the roads: a real distinction?,” Ergonomics, 1990.
[6]K. Jakobsen, S. C. H. Mouritsen, and K. Torp, “Evaluating eco-driving advice using GPS/CANBus data,” in Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems - SIGSPATIAL’13, 2013, pp. 44–53.
[7]T. Toledo and T. Lotan, “In-vehicle data recorder for evaluation of driving behavior and safety,” … J. Transp. Res. Board, 2006.
[8]Z. Constantinescu, C. Marinoiu, and M. Vladoiu, “Driving style analysis using data mining techniques,” researchgate.net.
[9]L. Bergasa, D. Almería, and J. Almazán, “Drivesafe: An app for alerting inattentive drivers and scoring driving behaviors,” Intell. Veh., 2014.
[10]C. Arroyo, L. Bergasa, and E. Romera, “Adaptive fuzzy classifier to detect driving events from the inertial sensors of a smartphone,” Syst. (ITSC), 2016 IEEE …, 2016.
[11]G. Castignani, R. Frank, and T. Engel, “An evaluation study of driver profiling fuzzy algorithms using smartphones,” Netw. Protoc. (ICNP), 2013.
[12]H. Eren, S. Makinist, and E. Akin, “Estimating driving behavior by a smartphone,” Veh. Symp. (IV), …, 2012.
[13]G. Castignani, T. Derrmann, and R. Frank, “Driver behavior profiling using smartphones: A low-cost platform for driver monitoring,” IEEE Intell., 2015.
[14]J. Obuhuma, H. Okoyo, and S. McOyowo, “Real-time Driver Advisory Model: Intelligent Transportation Systems,” in Proceedings of the IST-Africa Conference, 2018.
[15]T. Yi, H. Li, and M. Gu, “Recent research and applications of GPS based technology for bridge health monitoring,” Sci. China Technol. Sci., 2010.
[16]T. Chalko and P. MSc, “High accuracy speed measurement using GPS (Global Positioning System),” NU J. Discov., 2007.
[17]J. Obuhuma and C. Moturi, “Use of GPS with road mapping for traffic analysis,” Int. J. Sci. Technol., 2012.
[18]Stopping sight distance, [Online]. Available: https://en.wikipedia.org/wiki/Stopping_sight_distance. Accessed: Feb. 8, 2018.
[19]M. Da, W. Wei, H. Hai-guang, and G. Jian-he, "The Application of Bayesian Classification Theories in Distance Education System." International Journal of Modern Education and Computer Science (IJMECS), Vol.3, No.4, 2011.DOI: 10.5815/ijmecs.2011.04.02
[20]M. A. Tadlaoui, S. Aammou, M. Khaldi, and R. N. Carvalho, "Learner Modeling in Adaptive Educational Systems: A Comparative Study", International Journal of Modern Education and Computer Science(IJMECS), Vol.8, No.3, pp.1-10, 2016.DOI: 10.5815/ijmecs.2016.03.01

International Journal of Modern Education and Computer Science (IJMECS)