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Severity prediction of traffic accidents with recurrent neural networks


Sameen, Maher Ibrahim and Pradhan, Biswajeet (2017) Severity prediction of traffic accidents with recurrent neural networks. Applied Sciences, 7 (6). art. no. 476. pp. 1-17. ISSN 2076-3417


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.

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Official URL or Download Paper: http://www.mdpi.com/2076-3417/7/6/476

Additional Metadata

Item Type: Article
Divisions: Faculty of Engineering
DOI Number: https://doi.org/10.3390/app7060476
Publisher: MDPI
Keywords: Severity prediction; GIS; Traffic accidents; Deep learning; Recurrent neural networks
Depositing User: Nabilah Mustapa
Date Deposited: 13 Aug 2018 03:16
Last Modified: 13 Aug 2018 03:16
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.3390/app7060476
URI: http://psasir.upm.edu.my/id/eprint/64638
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