A hybrid model using logistic regression and wavelet transformation to detect traffic incidents
This research paper investigates a hybrid model using logistic regression with a wavelet-based feature extraction for detecting traffic incidents. A logistic regression model is suitable when the outcome can take only a limited number of values. For traffic incident detection, the outcome is limited...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2016-07-01
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| Series: | IATSS Research |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0386111216300139 |
| _version_ | 1811309307001569280 |
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| author | Shaurya Agarwal Pushkin Kachroo Emma Regentova |
| author_facet | Shaurya Agarwal Pushkin Kachroo Emma Regentova |
| author_sort | Shaurya Agarwal |
| collection | DOAJ |
| description | This research paper investigates a hybrid model using logistic regression with a wavelet-based feature extraction for detecting traffic incidents. A logistic regression model is suitable when the outcome can take only a limited number of values. For traffic incident detection, the outcome is limited to only two values, the presence or absence of an incident. The logistic regression model used in this study is a generalized linear model (GLM) with a binomial response and a logit link function. This paper presents a framework to use logistic regression and wavelet-based feature extraction for traffic incident detection. It investigates the effect of preprocessing data on the performance of incident detection models. Results of this study indicate that logistic regression along with wavelet based feature extraction can be used effectively for incident detection by balancing the incident detection rate and the false alarm rate according to need. Logistic regression on raw data resulted in a maximum detection rate of 95.4% at the cost of 14.5% false alarm rate. Whereas the hybrid model achieved a maximum detection rate of 98.78% at the expense of 6.5% false alarm rate. Results indicate that the proposed approach is practical and efficient; with future improvements in the proposed technique, it will make an effective tool for traffic incident detection. |
| first_indexed | 2024-04-13T09:39:59Z |
| format | Article |
| id | doaj.art-9c5ffd7b53294b07941bf50d55e4fc74 |
| institution | Directory Open Access Journal |
| issn | 0386-1112 |
| language | English |
| last_indexed | 2024-04-13T09:39:59Z |
| publishDate | 2016-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | IATSS Research |
| spelling | doaj.art-9c5ffd7b53294b07941bf50d55e4fc742022-12-22T02:51:58ZengElsevierIATSS Research0386-11122016-07-01401566310.1016/j.iatssr.2016.06.001A hybrid model using logistic regression and wavelet transformation to detect traffic incidentsShaurya Agarwal0Pushkin Kachroo1Emma Regentova2Transportation Research Center, University of Nevada Las Vegas, Las Vegas 89154, USAElectrical and Computer Engineering Department, University of Nevada Las Vegas, Las Vegas 89154, USAElectrical and Computer Engineering Department, University of Nevada Las Vegas, Las Vegas 89154, USAThis research paper investigates a hybrid model using logistic regression with a wavelet-based feature extraction for detecting traffic incidents. A logistic regression model is suitable when the outcome can take only a limited number of values. For traffic incident detection, the outcome is limited to only two values, the presence or absence of an incident. The logistic regression model used in this study is a generalized linear model (GLM) with a binomial response and a logit link function. This paper presents a framework to use logistic regression and wavelet-based feature extraction for traffic incident detection. It investigates the effect of preprocessing data on the performance of incident detection models. Results of this study indicate that logistic regression along with wavelet based feature extraction can be used effectively for incident detection by balancing the incident detection rate and the false alarm rate according to need. Logistic regression on raw data resulted in a maximum detection rate of 95.4% at the cost of 14.5% false alarm rate. Whereas the hybrid model achieved a maximum detection rate of 98.78% at the expense of 6.5% false alarm rate. Results indicate that the proposed approach is practical and efficient; with future improvements in the proposed technique, it will make an effective tool for traffic incident detection.http://www.sciencedirect.com/science/article/pii/S0386111216300139Incident detectionWavelet analysisLogistic regression |
| spellingShingle | Shaurya Agarwal Pushkin Kachroo Emma Regentova A hybrid model using logistic regression and wavelet transformation to detect traffic incidents IATSS Research Incident detection Wavelet analysis Logistic regression |
| title | A hybrid model using logistic regression and wavelet transformation to detect traffic incidents |
| title_full | A hybrid model using logistic regression and wavelet transformation to detect traffic incidents |
| title_fullStr | A hybrid model using logistic regression and wavelet transformation to detect traffic incidents |
| title_full_unstemmed | A hybrid model using logistic regression and wavelet transformation to detect traffic incidents |
| title_short | A hybrid model using logistic regression and wavelet transformation to detect traffic incidents |
| title_sort | hybrid model using logistic regression and wavelet transformation to detect traffic incidents |
| topic | Incident detection Wavelet analysis Logistic regression |
| url | http://www.sciencedirect.com/science/article/pii/S0386111216300139 |
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