Highway traffic accident prediction using VDS big data analysis

Journal of Supercomputing

Volumn 72, Issue 7, 2016, Pages 2815-2831

  Park, Seong hun ^a   Kim, Sung min ^a   Ha, Young guk ^a  

^a Konkuk University   (South Korea)

Author keywords

Accident prediction; Big data inference; Classification; Imbalance data; MapReduce

Indexed keywords

CLASSIFICATION (OF INFORMATION); DATA MINING; FORECASTING; HIGHWAY ACCIDENTS; TRAFFIC CONTROL;

ACCIDENT PREDICTION; CLASSIFICATION ANALYSIS; EFFECTIVE SOLUTION; HARDWARE RESOURCES; HIGHWAY TRAFFIC ACCIDENTS; IMBALANCE DATUM; MAP-REDUCE; PREDICTING MODELS;

BIG DATA;

EID: 84954553696     PISSN: 09208542     EISSN: 15730484     Source Type: Journal    
DOI: 10.1007/s11227-016-1624-z     Document Type: Article

References (25)

1. Lv Y, Tang S, Zhao H (2009) Real-time highway traffic accident prediction based on the k -nearest neighbor method. In: International conference on measuring technology and mechatronics automation (ICMTMA), vol 3, pp 547–550
2. Yu R, Liu X (2010) Study on traffic accidents prediction model based on RBF neural network. In: 2nd international conference on information engineering and computer science (ICIECS), pp 1–4
3. Lv Y, Tang S, Zhao H (2010) Research on influence extention of two-lane highway intersections based on traffic accident database. In: International conference on optoelectronics and image processing (ICOIP), vol 2, pp 244–246
4. Kamei Y, Monden A, Matsumoto S, Kakimoto T, Matsumoto K-I (2007) The effects of over and under sampling on fault-prone module detection. In: Empirical software engineering and measurement, pp 196–204
5. Gothenberg A, Tenhunen H (1998) Performance analysis of low oversampling ratio sigma-delta noise shapers for RF applications. In: Proceedings of the 1998 IEEE international symposium on circuits and systems (ISCAS’98), vol 1, pp 401–404
6. Dean J, Ghemawat S (2008) MapReduce: simplified data processing on large clusters. Commun ACM 51:107–113
7. Lee T, Kim H, Rhee K-H, Shin S-U (2013) Implementation and performance of distributed text processing system using Hadoop for e-discovery cloud service. Innov Inf Sci Technol Res Group (ISYOU) 4:12–24
8. Zhang F, Sakr M (2013) Dataset scaling and MapReduce performance. In: 2013 IEEE 27th international on parallel and distributed processing symposium workshops and PhD forum (IPDPSW), pp 1683–1690
9. Guruzon. http://guruzon.com. Accessed 20 Nov 2013
10. Chen T-S, Hu X-Q, Li S-A, Zhou C-L (2008) Multi-class diagnosis classification on high dimension data by logistic models. In: 2008 international conference on machine learning and cybernetics, vol 6, pp 3301–3306
11. Seliya N, Xu Z, Khoshgoftaar TM (2008) Addressing class imbalance in non-binary classification problems. In: 20th IEEE international conference on tools with artificial intelligence (ICTAI’08), vol 1, pp 460–466
12. Maithani S, Tyagi R (2008) Noise characterization and classification for background estimation. In: International conference on signal processing, communications and networking (ICSCN’08), pp 208–213
13. Yan Z, Wang X, Du L (2011) Design method of highway traffic safety analysis model. In: International conference on transportation, mechanical, and electrical engineering (TMEE), pp 151–154
14. Beshah T, Ejigu D, Abraham A, Snasel V, Kromer P (2011) Pattern recognition and knowledge discovery from road traffic accident data in Ethiopia: implications for improving road safety. In: 2011 world congress on information and communication technologies (WICT), pp 1241–1246
15. Ramani RG, Shanthi S (2012) Classifier prediction evaluation in modeling road traffic accident data. In: 2012 IEEE international conference on computational intelligence and computing research (ICCIC), pp 1–4
16. Ghimire B, Bhattacharjee S, Ghosh SK (2013) Analysis of spatial autocorrelation for traffic accident data based on spatial decision tree. In: 2013 fourth international conference on computing for geospatial research and application (COM.Geo), pp 111–115
17. Apache, Apache Hadoop. https://hadoop.apache.org/. Accessed 20 Nov 2013
18. Apache, Apache Hive. https://hive.apache.org/. Accessed 20 Nov 2013
19. Apache, Apache Mahout. https://mahout.apache.org/. Accessed 13 Jan 2014
20. Chawla NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: synthetic minority over-sampling technique. J Artif Intell Res 16:321–357
21. Vapnik VN (1998) Statistical learning theory. Wiley, New York
22. Kukar M (2004) Transduction and typicalness for quality assessment of individual classifications in machine learning and data mining. In: Fourth IEEE international conference on data mining (ICDM’04), pp 146–153
23. Raghavendra PS, Chowdhury SR, Kameswari SV (2010) Comparative study of neural networks and k -means classification in web usage mining. In: International conference on internet technology and secured transactions (ICITST)
24. Rahayu SP, Purnami SW, Embong A (2008) Applying kernel logistic regression in data mining to classify credit risk. Inf Technol 2:1–6
25. Mountassir A, Benbrahim H, Berrada I (2010) An empirical study to address the problem of unbalanced data sets in sentiment classification. In: 2012 IEEE international conference on systems, man, and cybernetics (SMC), pp 3298–3303