Abstract
The aim of this study is to build the adaptive ANN-based models for accident duration prediction. Two types of accident duration models are conducted. Model A is used to forecast the duration at the accident notification and Model B provides multi-period update of incident duration after accident notification. The APE and MAPE of forecasted accident duration at each time point of forecast are mostly under 20%. With these two models, the estimated duration can be provided by plugging in relevant traffic data as soon as the incident is notified. The traveler and traffic management unit can generally realize the impact by the forecasted accident duration. From the assessment of model effects, this study shows very promising practical applicability of the proposed models in the Intelligent Transportation Systems (ITS) context.
| Original language | English |
|---|---|
| Title of host publication | Intelligent Transportation Society of America - 12th World Congress on Intelligent Transport Systems 2005 |
| Pages | 790-803 |
| Number of pages | 14 |
| Publication status | Published - 2009 Dec 1 |
| Event | 12th World Congress on Intelligent Transport Systems 2005 - San Francisco, CA, United States Duration: 2005 Nov 6 → 2005 Nov 10 |
Publication series
| Name | Intelligent Transportation Society of America - 12th World Congress on Intelligent Transport Systems 2005 |
|---|---|
| Volume | 2 |
Other
| Other | 12th World Congress on Intelligent Transport Systems 2005 |
|---|---|
| Country | United States |
| City | San Francisco, CA |
| Period | 05-11-06 → 05-11-10 |
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All Science Journal Classification (ASJC) codes
- Mechanical Engineering
- Electrical and Electronic Engineering
- Control and Systems Engineering
- Transportation
- Automotive Engineering
- Computer Networks and Communications
- Artificial Intelligence
- Computer Science Applications
Cite this
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Multi-period forecasting of accident duration based on multiple sources of real-time traffic data. / Wei, Chien Hung; Lee, Ying.
Intelligent Transportation Society of America - 12th World Congress on Intelligent Transport Systems 2005. 2009. p. 790-803 (Intelligent Transportation Society of America - 12th World Congress on Intelligent Transport Systems 2005; Vol. 2).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Multi-period forecasting of accident duration based on multiple sources of real-time traffic data
AU - Wei, Chien Hung
AU - Lee, Ying
PY - 2009/12/1
Y1 - 2009/12/1
N2 - The aim of this study is to build the adaptive ANN-based models for accident duration prediction. Two types of accident duration models are conducted. Model A is used to forecast the duration at the accident notification and Model B provides multi-period update of incident duration after accident notification. The APE and MAPE of forecasted accident duration at each time point of forecast are mostly under 20%. With these two models, the estimated duration can be provided by plugging in relevant traffic data as soon as the incident is notified. The traveler and traffic management unit can generally realize the impact by the forecasted accident duration. From the assessment of model effects, this study shows very promising practical applicability of the proposed models in the Intelligent Transportation Systems (ITS) context.
AB - The aim of this study is to build the adaptive ANN-based models for accident duration prediction. Two types of accident duration models are conducted. Model A is used to forecast the duration at the accident notification and Model B provides multi-period update of incident duration after accident notification. The APE and MAPE of forecasted accident duration at each time point of forecast are mostly under 20%. With these two models, the estimated duration can be provided by plugging in relevant traffic data as soon as the incident is notified. The traveler and traffic management unit can generally realize the impact by the forecasted accident duration. From the assessment of model effects, this study shows very promising practical applicability of the proposed models in the Intelligent Transportation Systems (ITS) context.
UR - http://www.scopus.com/inward/record.url?scp=84878912312&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84878912312&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84878912312
SN - 9781604236354
T3 - Intelligent Transportation Society of America - 12th World Congress on Intelligent Transport Systems 2005
SP - 790
EP - 803
BT - Intelligent Transportation Society of America - 12th World Congress on Intelligent Transport Systems 2005
ER -