Abstract
Deep learning is recently regarded as the closest artificial intelligence model to human brain. It is about learning multiple levels of representation and abstraction that help to make sense of data such as images, sound, and text. Based on MapReduce framework and Hadoop distributed file system, this paper proposes a distributed approach for detect offending drivers and training the Deep Neural Network models such as Convolutional Neural Network (CNN) and Long Short Term Memory network (LSTM). Its implementation and performance are evaluated on Big Data platform Hadoop. The intelligence growing process of human brain requires learning from Big Data. The main contribution of this paper is that it is implemented to analyze traffic big data and to detect offending drivers in Hadoop by CNN with Support Vector Machine (SVM) and LSTM. The efficiency of the proposed method is computed by using experimental and theoretical analysis.
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References
Aghdam HH, Heravi EJ (2017) Guide to convolutional neural networks. Springer 10:973–978
Aoyama K (1997) Next Generation Universal Traffic Management System (UTMS'21) in Japan. Intelligent Transportation System, 1997. ITSC'97, IEEE Conference on IEEE.
Arcos-García Á, Soilán M, Álvarez-García JA, Riveiro B (2017) Exploiting synergies of mobile mapping sensors and deep learning for traffic sign recognition systems. Expert Syst Appl 89:286–295
Arcos-García Á, Álvarez-García JA, Soria-Morillo LM (2018a) Deep neural network for traffic sign recognition systems: An analysis of spatial transformers and stochastic optimisation methods. Neural Netw 99:158–165
Arcos-García Á, Álvarez-García JA, Soria-Morillo LM (2018b) Evaluation of Deep Neural Networks for traffic sign detection systems. Neurocomputing 316:332–344
Asadianfam S, Shamsi M, Kenari AR (2020a) TVD-MRDL: traffic violation detection system using MapReduce-based deep learning for large-scale data. Multimed Tools Appl 80:1–28
Asadianfam S, Shamsi M, Rasouli Kenari A (2020b) Big data platform of traffic violation detection system: identifying the risky behaviors of vehicle drivers. Multimed Tools Appl 29:24645–24684
Atlas LE, Homma T, Marks RJ (1988) An artificial neural network for spatio-temporal bipolar patterns: application to phoneme classification. In: Anderson DZ (ed) Neural Information Processing Systems, pp 31–40
Cantabella M, Martínez-España R, Ayuso B, Yáñez JA, Muñoz A (2019) Analysis of student behavior in learning management systems through a Big Data framework. Future Gener Comput Syst 90:262–272
Chen M, Mao S, Liu Y (2014a) Big data: a survey. Mobile Netw Appl 19(2):171–209
Chen M, Mao S, Zhang Y, Leung VC (2014b) Big data: related technologies, challenges and future prospects. Springer, Berlin
De Mauro A, Greco M, Grimaldi M, Ritala P (2018) Human resources for Big Data professions: a systematic classification of job roles and required skill sets. Inf Process Manag 54(5):807–817
Dean J, Ghemawat S (2008) MapReduce: simplified data processing on large clusters. Commun ACM 51(1):107–113
Dvornek NC, Ventola P, Pelphrey KA, Duncan JS (2017) Identifying autism from resting-state fMRI using long short-term memory networks. International workshop on machine learning in medical imaging. Springer, Berlin
Fukushima K, Miyake S (1982) Neocognitron: A self-organizing neural network model for a mechanism of visual pattern recognition. Competition and cooperation in neural nets. Springer, Berlin, pp 267–285
Furey TS, Cristianini N, Duffy N, Bednarski DW, Schummer M, Haussler D (2000) Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 16(10):906–914
Gantz J, Reinsel D (2011) Extracting value from chaos. IDC Iview 1142(2011):1–12
Goh YM, Chua D (2009) Case-based reasoning for construction hazard identification: case representation and retrieval. J Constr Eng Manag 135(11):1181–1189
Goodfellow I, Bengio Y, Courville A (2016) Deep learning (adaptive computation and machine learning series). Adaptive Computation and Machine Learning series, MIT Press
Guo S, Ding L, Luo H, Jiang X (2016) A Big-Data-based platform of workers’ behavior: observations from the field. Accid Anal Prev 93:299–309
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735–1780
Hubel DH, Wiesel TN (1968) Receptive fields and functional architecture of monkey striate cortex. J Physiol 195(1):215–243
Jeon S, Hong B (2016) Monte Carlo simulation-based traffic speed forecasting using historical big data. Future Gener Comput Syst 65:182–195
Kim H (2019) Multiple vehicle tracking and classification system with a convolutional neural network. J Ambient Intell Humaniz Comput. https://doi.org/10.1007/s12652-019-01429-5
Kumar CR, Anuradha R (2020) Feature selection and classification methods for vehicle tracking and detection. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-020-01824-3
Kutlu H, Avcı E (2019) A novel method for classifying liver and brain tumors using convolutional neural networks, discrete wavelet transform and long short-term memory networks. Sensors 19(9):1992
Laney D (2001) 3D data management: controlling data volume, velocity and variety. META Group Res Note 6(70):1
LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, Jackel LD (1989) Backpropagation applied to handwritten zip code recognition. Neural Comput 1(4):541–551
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444
Lotfi E (2012) Trajectory clustering and behaviour retrieval from traffic surveillance videos. Majlesi J Multimed Process 1:1–8
Lu W (2019) Big data analytics to identify illegal construction waste dumping: A Hong Kong study. Resour Conserv Recycl 141:264–272
McLauchlan P, Beymer D, Coifman B, Mali J (1997) A real-time computer vision system for measuring traffic parameters. cvpr, IEEE.
Moghaddam AM, Ayati E (2014) Introducing a risk estimation index for drivers: a case of Iran. Saf Sci 62:90–97
Munoz-Organero M, Ruiz-Blaquez R, Sánchez-Fernández L (2018) Automatic detection of traffic lights, street crossings and urban roundabouts combining outlier detection and deep learning classification techniques based on GPS traces while driving. Comput Environ Urban Syst 68:1–8
Osman AMS (2018) A novel big data analytics framework for smart cities. Future Gener Comput Syst 91:620–633
Park SH, Jung K, Hea JK, Kim HJ (1999) Vision-based traffic surveillance system on the internet. Computational Intelligence and Multimedia Applications, 1999. ICCIMA'99. Proceedings. Third International Conference on, IEEE.
Patterson J, Gibson A (2017) Deep learning: a practitioner’s approach. O’Reilly Media Inc., Newton
Rahemi Z, Ajorpaz NM, Esfahani MS, Aghajani M (2017) Sensation-seeking and factors related to dangerous driving behaviors among Iranian drivers. Personality Individ Differ 116:314–318
Rakotonirainy A, Schroeter R, Soro A (2014) Three social car visions to improve driver behaviour. Pervasive Mobile Comput 14:147–160
Rao X, Lin F, Chen Z, Zhao J (2019) Distracted driving recognition method based on deep convolutional neural network. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-019-01597-4
Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117
Secundo G, Del Vecchio P, Dumay J, Passiante G (2017) Intellectual capital in the age of Big Data: establishing a research agenda. J Intellect Cap 18(2):242–261
Shvachko K, Kuang H, Radia S, Chansler R (2010). The hadoop distributed file system. Mass storage systems and technologies (MSST), 2010 IEEE 26th symposium on, IEEE.
Tao D, Zhang R, Qu X (2017) The role of personality traits and driving experience in self-reported risky driving behaviors and accident risk among Chinese drivers. Accid Anal Prev 99:228–235
Wallach I, Dzamba M, Heifets A (2015) AtomNet: a deep convolutional neural network for bioactivity prediction in structure-based drug discovery. arXiv preprint http://arxiv.org/abs/1510.02855
White T (2012) Hadoop: the definitive guide. O’Reilly Media Inc, Newton
Yin L, Cheng Q, Wang Z, Shao Z (2015) ‘Big data’for pedestrian volume: Exploring the use of Google Street View images for pedestrian counts. Appl Geogr 63:337–345
Yu Y, Li J, Wen C, Guan H, Luo H, Wang C (2016) Bag-of-visual-phrases and hierarchical deep models for traffic sign detection and recognition in mobile laser scanning data. ISPRS J Photogramm Remote Sens 113:106–123
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Asadianfam, S., Shamsi, M. & Rasouli Kenari, A. Hadoop Deep Neural Network for offending drivers. J Ambient Intell Human Comput 13, 659–671 (2022). https://doi.org/10.1007/s12652-021-02924-4
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DOI: https://doi.org/10.1007/s12652-021-02924-4