Next Article in Journal
Shake Table Test for the Collapse Investigation of a Typical Multi-Story Reinforced Concrete Frame Structure in the Meizoseismal Area
Next Article in Special Issue
Data Mining Approaches for Landslide Susceptibility Mapping in Umyeonsan, Seoul, South Korea
Previous Article in Journal
Computational Vibroacoustics in Low- and Medium- Frequency Bands: Damping, ROM, and UQ Modeling
Article Menu
Issue 6 (June) cover image

Export Article

Open AccessArticle
Appl. Sci. 2017, 7(6), 476; doi:10.3390/app7060476

Severity Prediction of Traffic Accidents with Recurrent Neural Networks

Department of Civil Engineering, Geospatial Information Science Research Center (GISRC), Faculty of Engineering, University Putra Malaysia, UPM, Serdang 43400, Malaysia
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Saro Lee
Received: 14 March 2017 / Revised: 27 April 2017 / Accepted: 28 April 2017 / Published: 8 June 2017
(This article belongs to the Special Issue Application of Artificial Neural Networks in Geoinformatics)
View Full-Text   |   Download PDF [2652 KB, uploaded 8 June 2017]   |  

Abstract

In this paper, a deep learning model using a Recurrent Neural Network (RNN) was developed and employed to predict the injury severity of traffic accidents based on 1130 accident records that have occurred on the North-South Expressway (NSE), Malaysia over a six-year period from 2009 to 2015. Compared to traditional Neural Networks (NNs), the RNN method is more effective for sequential data, and is expected to capture temporal correlations among the traffic accident records. Several network architectures and configurations were tested through a systematic grid search to determine an optimal network for predicting the injury severity of traffic accidents. The selected network architecture comprised of a Long-Short Term Memory (LSTM) layer, two fully-connected (dense) layers and a Softmax layer. Next, to avoid over-fitting, the dropout technique with a probability of 0.3 was applied. Further, the network was trained with a Stochastic Gradient Descent (SGD) algorithm (learning rate = 0.01) in the Tensorflow framework. A sensitivity analysis of the RNN model was further conducted to determine these factors’ impact on injury severity outcomes. Also, the proposed RNN model was compared with Multilayer Perceptron (MLP) and Bayesian Logistic Regression (BLR) models to understand its advantages and limitations. The results of the comparative analyses showed that the RNN model outperformed the MLP and BLR models. The validation accuracy of the RNN model was 71.77%, whereas the MLP and BLR models achieved 65.48% and 58.30% respectively. The findings of this study indicate that the RNN model, in deep learning frameworks, can be a promising tool for predicting the injury severity of traffic accidents. View Full-Text
Keywords: severity prediction; GIS; traffic accidents; deep learning; recurrent neural networks severity prediction; GIS; traffic accidents; deep learning; recurrent neural networks
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Sameen, M.I.; Pradhan, B. Severity Prediction of Traffic Accidents with Recurrent Neural Networks. Appl. Sci. 2017, 7, 476.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Appl. Sci. EISSN 2076-3417 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top