Bayesian prediction of the duration of non-recurring road incidents
| Autor: | Banishree Ghosh, Muhammad Tayyab Asif, Justin Dauwels |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
050210 logistics & transportation
Engineering business.industry 05 social sciences Bayesian probability 010501 environmental sciences computer.software_genre 01 natural sciences Regression Support vector machine Traffic congestion 0502 economics and business Statistics Measurement uncertainty Sensitivity (control systems) Limit (mathematics) Data mining Duration (project management) business computer 0105 earth and related environmental sciences |
| Zdroj: | 2016 IEEE Region 10 Conference (TENCON). |
| DOI: | 10.1109/tencon.2016.7847964 |
| Popis: | Traffic incidents such as accidents or vehicle breakdowns are one of the major causes of traffic congestion in urban areas. Consequently, accurate prediction of duration of these incidents is considered as one of the most important challenges by traffic management authorities. Although data-driven regression methods can predict the duration of these incidents with reasonable precision. However, the prediction performance may vary considerably from one to another. Hence, it is important to provide some measure of confidence associated with the forecast duration of the incidents. Such measures can prove to be highly useful in planning real-time response. To address this issue, we propose Bayesian Support Vector Regression (BSVR), which gives error bars as the measurement of uncertainty along with the predicted duration of incidents. We also evaluate sensitivity and specificity for different error tolerance limit to assess the performance of BSVR. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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