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Trans. Planning Journal
| Title | AN AI-BASED IMAGE-DETECTION MODEL AND FIELD TEST FOR DETECTING TRAFFIC INCIDENTS |
|---|---|
| Author | Pei-Ju Wu、Chi-Hwa Chen、Jau-Ming Su、Tung-Ling Wu、Chi-Chang Huang、Jiun-Kuei Jung、Yu-Fen Ho |
| Summary | The detection and reporting of traffic incidents is of major importance to all those involved in traffic management, as it results in increased labor and prolonged periods of complex communication. However, few studies have explored how these pressures might be relieved through the use of artificial intelligence (AI). Accordingly, this study aims to develop an AI-based image-detection model, the Single Shot MultiBox Detector (SSD) with a deep neural network, which will detect and report traffic incidents automatically, and thus enhance the efficiency of traffic management. This study used the field case of a real intersection in Kaohsiung City, Taiwan, to test the effectiveness of the proposed approach, and the results indicated that the proposed traffic-incident SSD model was successful not only in identifying traffic incidents, but also in monitoring key background traffic parameters such as the numbers and speeds of vehicles on the road. This pioneering research also demonstrates how AI-based image-detection technology for traffic incidents could be installed in the physical environment, and provides clear and valuable guidance to traffic managers interested in utilizing AI technology in their field. |
| Vol. | 48 |
| No. | 3 |
| Page | 159 |
| Year | 2019 |
| Month | 9 |
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