Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
Laser Scanning Technologies in Road Geometry Modeling Kapitel lesen Erstes Kapitel lesen

2020 | OriginalPaper | Buchkapitel

11. Applications of Deep Learning in Severity Prediction of Traffic Accidents

verfasst von : Biswajeet Pradhan, Maher Ibrahim Sameen

Erschienen in: Laser Scanning Systems in Highway and Safety Assessment

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Future prediction is a fascinating topic for human endeavor and is identified as a critical tool in transportation management. Understanding an entire transportation network is more difficult than transportation on a single road. The main purpose of this effort is to provide a superior route with high safety level and support traffic managers in efficiently managing road network.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Vorheriges Kapitel Predicting Injury Severity of Road Traffic Accidents Using a Hybrid Extreme Gradient Boosting and Deep Neural Network Approach
Nächstes Kapitel Forecasting Severity of Motorcycle Crashes Using Transfer Learning
Literatur
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., & Ghemawat, S. (2016). Tensorflow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:​1603.​04467.‏
Al-Ghamdi, A. S. (2002). Using logistic regression to estimate the influence of accident factors on accident severity. Accident Analysis and Prevention, 34(6), 729–741.CrossRef
Baczyński, D., & Parol, M. (2004). Influence of artificial neural network structure on quality of short-term electric energy consumption forecast. IEE Proceedings-Generation, Transmission and Distribution, 151(2), 241–245.CrossRef
Delen, D., Sharda, R., & Bessonov, M. (2006). Identifying significant predictors of injury severity in traffic accidents using a series of artificial neural networks. Accident Analysis and Prevention, 38(3), 434–444.CrossRef
Donahue, J., Anne Hendricks, L., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko, K., & Darrell, T. (2015). Long-term recurrent convolutional networks for visual recognition and description. In Proceedings of the IEEE Conference on Computer Vision and Pattern recognition (pp. 2625–2634).‏
Gardner, M. W., & Dorling, S. R. (1998). Artificial neural networks (the multilayer perceptron)—A review of applications in the atmospheric sciences. Atmospheric Environment, 32(14), 2627–2636.CrossRef
Graves, A., Mohamed, A. R., & Hinton, G. (2013, May). Speech recognition with deep recurrent neural networks. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 6645–6649).
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780.CrossRef
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097–1105).‏
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.CrossRef
Li, X., Lord, D., Zhang, Y., & Xie, Y. (2008). Predicting motor vehicle crashes using support vector machine models. Accident Analysis and Prevention, 40(4), 1611–1618.CrossRef
Li, Z., Liu, P., Wang, W., & Xu, C. (2012). Using support vector machine models for crash injury severity analysis. Accident Analysis and Prevention, 45, 478–486.CrossRef
Lv, Y., Tang, S., & Zhao, H. (2009, April). Real-time highway traffic accident prediction based on the k-nearest neighbor method. In ICMTMA’09. IEEE International Conference on Measuring Technology and Mechatronics Automation, 2009 (Vol. 3, pp. 547–550).
Ma, X., Yu, H., Wang, Y., & Wang, Y. (2015). Large-scale transportation network congestion evolution prediction using deep learning theory. PLoS ONE, 10(3), e0119044.CrossRef
Mia, M. M. A., Biswas, S. K., Urmi, M. C., & Siddique, A. (2015). An algorithm for training multilayer perceptron (MLP) for Image reconstruction using neural network without overfitting. International Journal of Scientific & Technology Research, 4(2), 271–275.
Moghaddam, F. R., Afandizadeh, S., & Ziyadi, M. (2011). Prediction of accident severity using artificial neural networks. International Journal of Civil Engineering, 9(1), 41.
Mokhtarzade, M., & Zoej, M. V. (2007). Road detection from high-resolution satellite images using artificial neural networks. International Journal of Applied Earth Observation and Geoinformation, 9(1), 32–40.CrossRef
Sameen, M. I., & Pradhan, B. (2017a). A two-stage optimization strategy for fuzzy object-based analysis using airborne LiDAR and high-resolution orthophotos for urban road extraction. Journal of Sensors.‏
Sarikaya, R., Hinton, G. E., & Deoras, A. (2014). Application of deep belief networks for natural language understanding. IEEE/ACM Transactions on Audio, Speech and Language Processing (TASLP), 22(4), 778–784.‏
Schindler, K., & Van Gool, L. (2008, June). Action snippets: How many frames does human action recognition require?. In IEEE Conference on Computer Vision and Pattern Recognition, 2008. CVPR 2008 (pp. 1–8).
Yang, G. Y. C. (1995). Geological mapping from multi-source data using neural networks. Calgary: Geomatics Engineering, University of Calgary.
Metadaten
Titel
Applications of Deep Learning in Severity Prediction of Traffic Accidents
verfasst von
Biswajeet Pradhan
Maher Ibrahim Sameen
Copyright-Jahr
2020
Buch
Laser Scanning Systems in Highway and Safety Assessment

Print ISBN: 978-3-030-10373-6

Electronic ISBN: 978-3-030-10374-3

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-10374-3_11

    Premium Partner

    Bildnachweise