Profiling drivers based on driver dependent vehicle driving features


This work addresses the problem of profiling drivers based on their driving features. A purpose-built hardware integrated with a software tool is used to record data from multiple drivers. The recorded data is then profiled using clustering techniques. k-means has been used for clustering and the results are counterchecked with Fuzzy c-means (FCM) and Model Based Clustering (MBC). Based on the results of clustering, a classifier, i.e., an Artificial Neural Network (ANN) is trained to classify a driver during driving in one of the four discovered clusters (profiles). The performance of ANN is compared with that of a Support Vector Machine (SVM). Comparison of the clustering techniques shows that different subsets of the recorded dataset with a diverse combination of attributes provide approximately the same number of profiles, i.e., four. Analysis of features shows that average speed, maximum speed, number of times brakes were applied, and number of times horn was used provide the information regarding drivers’ driving behavior, which is useful for clustering. Both one versus one (SVM) and one versus rest (SVM) method for classification have been applied. Average accuracy and average mean square error achieved in the case of ANN was 84.2 % and 0.05 respectively. Whereas the average performance for SVM was 47 %, the maximum performance was 86 % using RBF kernel. The proposed system can be used in modern vehicles for early warning system, based on drivers’ driving features, to avoid accidents.

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  1. 1.

    Dagal A, Greer SE, McCunn M (2014) International disparities in trauma care. Current Opinion in Anesthesiology 27(2):233–239

    Article  Google Scholar 

  2. 2.

    Agbonkhese O, Yisa GL, Agbonkhese EG, Akanbi DO, Aka EO, Mondigha EB (2013) Road traffic accidents in nigeria: causes and preventive measures. Civil and Environmental Research 3(13):90–99

    Google Scholar 

  3. 3.

    Ran B, Jin PJ, Boyce D, Qiu TZ, Cheng Y (2012) Perspectives on future transportation research: Impact of intelligent transportation system technologies on next-generation transportation modeling. J Intell Transp Syst 16(4):226–242

    Article  Google Scholar 

  4. 4.

    Zhang J, Wang FY, Wang K, Lin WH, Xu X, Chen C (2011) Data-driven intelligent transportation systems: A survey. IEEE Trans Intell Transp Syst 12(4):1624–1639

    Article  Google Scholar 

  5. 5.

    Fazeen M, Gozick B, Dantu R, Bhukhiya M, González MC (2012) Safe driving using mobile phone. IEEE Trans Intell Transp Syst 13(3):1462–1468

    Article  Google Scholar 

  6. 6.

    Zhiwei QJ, Lan P (2004) Real-time nonintrusive monitoring and prediction of driver fatigue. IEEE Trans Veh Technol 53(4):1052–1068

    Article  Google Scholar 

  7. 7.

    D’Orazio DT, Leo M, Spagnolo P, Guaragnella C (2004) A neural system for eye detection in a driver vigilance application. In: The 7th International IEEE Conference on Intelligent Transportation Systems, pp 320–325

  8. 8.

    Carl E, Lippitt J, Forsythe C, Dixon R (2005) Supervised machine learning for modeling human recognition of vehicle-driving situations. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, pp 604-609

  9. 9.

    Saensom P, Tangamchit P, Pongpaibool P, Imkamon T (2008) Detection of hazardous driving behavior using fuzzy logic. In: Proceedings of ECTI-CON, pp 657-660

  10. 10.

    Xu C, Liu P, Wang W (2013) A genetic programming model for real-time crash prediction on freeways. IEEE Trans Intell Transp Syst 14(2):574–586

    MathSciNet  Article  Google Scholar 

  11. 11.

    Abdel-At M, Rajashekar P (2006) Calibrating a real-time traffic crash prediction model using archived weather and its traffic data. IEEE Trans Intell Transp Syst 7(2):167–174

    Article  Google Scholar 

  12. 12.

    Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    MathSciNet  Article  MATH  Google Scholar 

  13. 13.

    Abdel-Aty M, Uddin N, Pande A (2005) Split models for predicting multivehicle crashes during high-speed and low-speed operating conditions on freeways. Transportation research record 1908(1):51–58

    Article  Google Scholar 

  14. 14.

    Huazhong N, Thomas SH, Xu W, Zhou Y (2007) Detecting unsafe driving patterns using discriminative learning In: ICME07, pp 1431–1434

  15. 15.

    Huazhong N, Xu W, Zhou Y, Gong Y, Huang TS (2009) A general framework to detect unsafe system states from multisensor data stream. IEEE Trans Intell Transp Syst 11(1):4–15

    Article  Google Scholar 

  16. 16.

    Ning H, Xu W, Zhou Y, Gong Y, Huang TS (2008) Temporal difference learning to detect unsafe system states. In: Proceedings of the International Conference on Pattern Recognition, pp 1–4.

  17. 17.

    Jabon ME, Bailenson JN, Pontikakis E, Takayama L (2009) Facial expression analysis for predicting unsafe driving behavior car driving simulator. Pervasive Computing 10(4):84–95

    Article  Google Scholar 

  18. 18.

    Yuejing L, Jie L, Ming L, Xing-lin Z, Haixia Z (2010) Research on accident prediction of intersection and identification method of prominent accident form based on back propagation neural network. In: International Conference on Computer Application and System Modeling (ICCASM ), pp V1–434

  19. 19.

    Chong M (2004) Traffic accident analysis using decision trees and neural networks. In: IADIS International Conference on Applied Computing, pp 1–4

  20. 20.

    Tambouratzis T, Souliou D, Chalikias M, Gregoriades A (2010) Combining probabilistic neural networks and decision trees for maximally accurate and efficient accident prediction. In: International Joint Conference on Neural Networks (IJCNN), pp.1–8

  21. 21.

    Garima RS, Dongre S (2012) Crash prediction system for mobile device on android by using data stream mining techniques. In: Sixth Asia Modeling Symposium, pp 185–190

  22. 22.

    Lv Y, Tang S, Zhao H, Li S (2009) Real-time highway accident prediction based on support vector machines. In: Control and Decision Conference, CCDC ’09, pp 4403–4407

  23. 23.

    Na S, Xumin L, Yong G (2010) Research on k-means Clustering Algorithm. In: Third International Symposium on Intelligent Information Technology and Security Informatics, pp 63–67.

  24. 24.

    Har-Peled S, Mazumdar S (2004) On coresets for k-means and k-median clustering. In: Proceedings of the thirty-sixth annual ACM symposium on Theory of computing, New York, pp 291–300

  25. 25.

    Banfield JD, Raftery AE (1993) Model-based Gaussian and non-Gaussian clustering. Biometrics 49 (3):803–821

    MathSciNet  Article  MATH  Google Scholar 

  26. 26.

    Celeux G, Govaert G (1995) Gaussian parsimonious clustering models. Pattern Recognit 28(50):781–793

    Article  Google Scholar 

  27. 27.

    Dunn JC (1973) A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J. Cybernet 3:32–57

    MathSciNet  Article  MATH  Google Scholar 

  28. 28.

    Rezankova H, Löster T, Húsek D (2011) Evaluation of categorical data clustering. Advances in Intelligent Web Mastering 86:173–182

    Article  Google Scholar 

  29. 29.

    Loster T, Langhamrova J (2012) Disparities between regions of the Czech Republic for non-business aspects of labour market. In: International Days of Statistics and Economics, 6th ed., pp 689–702

  30. 30.

    Oliveira JV (2007) Advances in fuzzy clustering and its applications. 1 ed., Wiley, pp 4–69

  31. 31.

    Zhang GP (2000) Neural Networks for Classification: A Survey. IEEE Trans Syst Man Cybern Part C Appl Rev 30(4):451–462

    Article  Google Scholar 

  32. 32.

    Lippmann RP (1989) Pattern classification using neural networks. IEEE Communication Magazine 27 (11):47–64

    Article  Google Scholar 

  33. 33.

    Thomas JA (1991) Elements of information theory. John Wiley & Sons

  34. 34.

    Mazzei M, Palma AL (2014) Evaluating principal components analysis of particular spatial statistical models. In: Sixth International Conference on Advanced Geographic Information Systems, Applications, and Services, pp 24–30

  35. 35.

    Zaldivar J, Calafate CT, Cano JC, Manzoni P (2011) Providing accident detection in vehicular networks through OBD-II devices and Android-based smartphones. In: IEEE 36th Conference on Local Computer Networks (LCN), pp 813–819

  36. 36.

    Chong M (2004) Traffic accident analysis using decision trees and neural networks. In: IADIS International Conference on Applied Computing, Portugal, pp 1–4

  37. 37.

    Birnbaum RT, Truglia J (2000) Getting to know OBD II. Manufactured and United States

  38. 38.

    Akin D, BulentAkba (2010) A neural network (NN) model to predict intersection crashes based upon driver, vehicle and roadway surface characteristic. Sci Res Essays 5(19):2837–2847

    Google Scholar 

  39. 39.

    Moghaddam R, Afandizadeh S, Ziyadi M (2010) Prediction of accident severity using artificial neural networks. International Journal of Civil Engineering 9(1):41–48

    Google Scholar 

  40. 40.

    Wahab A, Quek C, Keong T, Takeda K (2009) Driving profile modeling and recognition based on soft computing approach. IEEE Trans Neural Netw 20(4):563–582

    Article  Google Scholar 

  41. 41.

    Lv Y, Tang S, Zhao H, Li S (2009) Real-time highway accident prediction based on support vector machines. In: Control and Decision Conference, CCDC ’09, pp 4403–4407

  42. 42.

    Qu A, Wang W, Liu P, Noyce D (2012) Real-time prediction of freeway rear-ends crash potential by support vector machine. In: Annual Meeting Transport. Res, Board: Washington

  43. 43.

    Li X, Zhang D L, Xie Y (2008) Predicting motor vehicle crashes using support vector machine models. Accid Anal Prev 40(4):1611–1618

    Article  Google Scholar 

  44. 44.

    Halim Z, Baig AR, Zafar Z (2014) Evolutionary search in the space of rules for creation of new two-player board games. Int J Artif Intell Tools 23(2):1–26

    Article  Google Scholar 

  45. 45.

    Abdel-Aty M, Rajashekar P (2006) Calibrating a real-time traffic crash prediction model using archived weather and its traffic data. IEEE Trans Intell Transp Syst 7(2):167–174

    Article  Google Scholar 

  46. 46.

    Xiao WH, Tan D (2004) Traffic accident prediction using 3-d model-based vehicle tracking. IEEE Trans Veh Technol 53(3):677–694

    Article  Google Scholar 

  47. 47.

    Ren J, Shen Y, Ma S, Guo L (2004) Applying multi-class svms into scene image classification. Innovations in Applied Artificial Intelligence 3029:924–934

    Article  Google Scholar 

  48. 48.

    Wu K, Wang S (2009) Choosing the kernel parameters for support vector machines by the inter-cluster distance in the feature space. Pattern Recognit 42(5):710–717

    Article  MATH  Google Scholar 

  49. 49.

    Young W, Sobhani A, Lenne M, Sarvi M (2014) Simulation of safety: a review of the state of the art in road safety simulation modeling. Accid Anal Prev 66(5):89–103

    Article  Google Scholar 

  50. 50.

    Faizan A, Arif U, Abbasi R, Inam H (2013) Driver profiling project report,faculty of computer science and engineering, GIK Institute, Topi, Pakistan

  51. 51.

    Kalsoom R, Halim Z (2013) Clustering the driving features based on data streams. In: 16th International Multi Topic Conference, INMIC13, Lahore, pp 89–94

  52. 52.

    Das S, Zhou S, Lee JD (2012) Differentiating alcohol-induced driving behavior using steering wheel signals. IEEE Trans Intell Transp Syst 13(3):1355–1368

    Article  Google Scholar 

  53. 53.

    Halim Z, Waqas M, Hussain SF (2015) Clustering large probabilistic graphs using multi-population evolutionary algorithm. Inf Sci 317:78–95

    Article  Google Scholar 

  54. 54.

    Guo F, Fang Y (2013) Individual driver risk assessment using naturalistic driving data. Accid Anal Prev 61:3–9

    Article  Google Scholar 

  55. 55.

    Keyes CL, Kendler KS, Myers JM, Martin CC (2015) The genetic overlap and distinctiveness of flourishing and the big five personality traits. J Happiness Stud 16(3):655–668

    Article  Google Scholar 

  56. 56.

    Shi B, Xu L, Hu J, Tang Y, Jiang H, Meng W, Liu H (2015) Evaluating driving styles by normalizing driving behavior based on personalized driver modeling. IEEE Trans Syst Man Cybern Syst. doi:10.1109/TSMC.2015.2417837

  57. 57.

    Li N, Busso C (2015) Predicting perceived visual and cognitive distractions of drivers with multimodal features. IEEE Trans Intell Transp Syst 16(1):51–65

    Article  Google Scholar 

  58. 58.

    Dovgan E, Javorski M, Tušar T, Gams M, Filipic B (2013) Comparing a multiobjective optimization algorithm for discovering driving strategies with humans. Expert Systems with Applications 40(1):2687–2695

    Article  Google Scholar 

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Correspondence to Zahid Halim.



Fig. 7

Clusters properties using minimum traffic scenario

Fig. 8

Clusters properties using average traffic scenario

Fig. 9

Clusters properties using maximum traffic scenario

Fig. 10

Clusters properties using all traffic scenarios

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Halim, Z., Kalsoom, R. & Baig, A.R. Profiling drivers based on driver dependent vehicle driving features. Appl Intell 44, 645–664 (2016).

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  • Driver behavior modeling
  • Road safety
  • Artificial neural networks
  • Clustering methods
  • Intelligent systems