An Improved Deep Learning Model for Traffic Crash Prediction
Machine-learning technology powers many aspects of modern society. Compared to the conventional machine learning techniques that were limited in processing natural data in the raw form, deep learning allows computational models to learn representations of data with multiple levels of abstraction. In...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2018/3869106 |
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| _version_ | 1832554371370450944 |
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| author | Chunjiao Dong Chunfu Shao Juan Li Zhihua Xiong |
| author_facet | Chunjiao Dong Chunfu Shao Juan Li Zhihua Xiong |
| author_sort | Chunjiao Dong |
| collection | DOAJ |
| description | Machine-learning technology powers many aspects of modern society. Compared to the conventional machine learning techniques that were limited in processing natural data in the raw form, deep learning allows computational models to learn representations of data with multiple levels of abstraction. In this study, an improved deep learning model is proposed to explore the complex interactions among roadways, traffic, environmental elements, and traffic crashes. The proposed model includes two modules, an unsupervised feature learning module to identify functional network between the explanatory variables and the feature representations and a supervised fine tuning module to perform traffic crash prediction. To address the unobserved heterogeneity issues in the traffic crash prediction, a multivariate negative binomial (MVNB) model is embedding into the supervised fine tuning module as a regression layer. The proposed model was applied to the dataset that was collected from Knox County in Tennessee to validate the performances. The results indicate that the feature learning module identifies relational information between the explanatory variables and the feature representations, which reduces the dimensionality of the input and preserves the original information. The proposed model that includes the MVNB regression layer in the supervised fine tuning module can better account for differential distribution patterns in traffic crashes across injury severities and provides superior traffic crash predictions. The findings suggest that the proposed model is a superior alternative for traffic crash predictions and the average accuracy of the prediction that was measured by RMSD can be improved by 84.58% and 158.27% compared to the deep learning model without the regression layer and the SVM model, respectively. |
| format | Article |
| id | doaj-art-86b324bc02164bfd960d7ae487f0bb93 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-86b324bc02164bfd960d7ae487f0bb932025-02-03T05:51:41ZengWileyJournal of Advanced Transportation0197-67292042-31952018-01-01201810.1155/2018/38691063869106An Improved Deep Learning Model for Traffic Crash PredictionChunjiao Dong0Chunfu Shao1Juan Li2Zhihua Xiong3MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, ChinaMOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, ChinaMOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, ChinaMOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, ChinaMachine-learning technology powers many aspects of modern society. Compared to the conventional machine learning techniques that were limited in processing natural data in the raw form, deep learning allows computational models to learn representations of data with multiple levels of abstraction. In this study, an improved deep learning model is proposed to explore the complex interactions among roadways, traffic, environmental elements, and traffic crashes. The proposed model includes two modules, an unsupervised feature learning module to identify functional network between the explanatory variables and the feature representations and a supervised fine tuning module to perform traffic crash prediction. To address the unobserved heterogeneity issues in the traffic crash prediction, a multivariate negative binomial (MVNB) model is embedding into the supervised fine tuning module as a regression layer. The proposed model was applied to the dataset that was collected from Knox County in Tennessee to validate the performances. The results indicate that the feature learning module identifies relational information between the explanatory variables and the feature representations, which reduces the dimensionality of the input and preserves the original information. The proposed model that includes the MVNB regression layer in the supervised fine tuning module can better account for differential distribution patterns in traffic crashes across injury severities and provides superior traffic crash predictions. The findings suggest that the proposed model is a superior alternative for traffic crash predictions and the average accuracy of the prediction that was measured by RMSD can be improved by 84.58% and 158.27% compared to the deep learning model without the regression layer and the SVM model, respectively.http://dx.doi.org/10.1155/2018/3869106 |
| spellingShingle | Chunjiao Dong Chunfu Shao Juan Li Zhihua Xiong An Improved Deep Learning Model for Traffic Crash Prediction Journal of Advanced Transportation |
| title | An Improved Deep Learning Model for Traffic Crash Prediction |
| title_full | An Improved Deep Learning Model for Traffic Crash Prediction |
| title_fullStr | An Improved Deep Learning Model for Traffic Crash Prediction |
| title_full_unstemmed | An Improved Deep Learning Model for Traffic Crash Prediction |
| title_short | An Improved Deep Learning Model for Traffic Crash Prediction |
| title_sort | improved deep learning model for traffic crash prediction |
| url | http://dx.doi.org/10.1155/2018/3869106 |
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