Modeling of road geometry and traffic accidents by hierarchical object-based and deep learning methods using laser scanning data
Road traffic accidents are global concerns since they affect human life, economy, and road transportation systems. Rapid information acquisition and insight discovery are key tasks in transportation management. Specifically, extraction of geometric road features such as slopes and superelevati...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2018
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/71397/1/FK%202018%2091%20IR.pdf http://psasir.upm.edu.my/id/eprint/71397/ |
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| Summary: | Road traffic accidents are global concerns since they affect human life, economy, and
road transportation systems. Rapid information acquisition and insight discovery are
key tasks in transportation management. Specifically, extraction of geometric road
features such as slopes and superelevation are essential information to understand the
effects of road geometry on road traffic accidents. However, to understand these
effects clearly and accurately, proper modeling techniques should be used. This study
aims to develop methods to extract geometric road features (e.g., vertical gradients,
superelevation, width, design speed) and establish associations between those features
and road traffic accidents including frequency and accident severity. There was a need
for efficient segmentation algorithm, optimization strategy, feature extraction and
classification, and robust statistical and computational intelligence models to
accomplish the set aims. Experimental results regarding road geometry extraction
indicated that the proposed methods could achieve relatively high accuracy (~ 85% -
User’s Accuracy) of road detection from airborne laser scanning data. Our method
improved the overall accuracy of classification by 7% outperforming the supervised ƙ nearest neighbor method. In addition, the results also showed that the proposed
hierarchical classification method could extract geometric road elements with an
average error rate of 6.25% for slope parameter and 6.65% for superelevation
parameter, and it is transferable to other regions of similar environments. On the other
hand, the geometric regression model predicted the number of accidents in the North-
South Expressway with a reasonable accuracy (R2 = 0.64). This model also could
identify the most influential factors contributing to the number of accidents.
Experiments on deep learning models showed that the recurrent neural network
performs better than the feed forward neural networks, statistical bayesian logistic
regression, and convolutional neural networks. This study also suggests that transfer
learning could improve the forecasting accuracy of the injury severity by nearly 10%. |
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