Abstract
In order to be able to assess the performance of these methods it is imperative to use data on travel times. Since this data is not readily available, an estimation algorithm was selected where travel time is determined using speed and flow data from loop detectors. Five algorithms to estimate travel times were assessed using a data set with actually measured travel times through license plate recognition.
Results of the assessment of short-range travel time predictions show that the Artificial Neural Network (ANN) method significantly outperforms the Dynamic Travel Time Estimation (DTTE) method, which in turn outperforms the Static Travel Time Estimation (STTE) method.
| Original language | English |
|---|---|
| Number of pages | 21 |
| Publication status | Published - 12 Jan 2003 |
| Event | 82nd Transportation Research Board (TRB) Annual Meeting 2003 - Washington, United States Duration: 12 Jan 2003 → 16 Jan 2003 Conference number: 82 |
Conference
| Conference | 82nd Transportation Research Board (TRB) Annual Meeting 2003 |
|---|---|
| Country | United States |
| City | Washington |
| Period | 12/01/03 → 16/01/03 |
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Keywords
- METIS-209286
Cite this
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A comparative analysis of short-range travel time prediction methods. / Huisken, Giovanni; van Berkum, Eric C.
2003. Paper presented at 82nd Transportation Research Board (TRB) Annual Meeting 2003, Washington, United States.Research output: Contribution to conference › Paper
TY - CONF
T1 - A comparative analysis of short-range travel time prediction methods
AU - Huisken, Giovanni
AU - van Berkum, Eric C.
PY - 2003/1/12
Y1 - 2003/1/12
N2 - Increasing car mobility has lead to an increasing demand for traffic information. This contribution deals with information about travel times. When car drivers are provided with this type of information, the travel times should ideally be the times that they will encounter. As a result travel times must be predicted, often on a short-term basis. Available data for such a prediction are spot measurements of speed and flow from dual induction loop detectors. In this contribution a prediction method that uses a neural network is described. The performance of the neural network approach is compared with two naïve methods that are currently in operation, using data from a short-range motorway site: the A13 motorway from The Hague to Rotterdam.In order to be able to assess the performance of these methods it is imperative to use data on travel times. Since this data is not readily available, an estimation algorithm was selected where travel time is determined using speed and flow data from loop detectors. Five algorithms to estimate travel times were assessed using a data set with actually measured travel times through license plate recognition.Results of the assessment of short-range travel time predictions show that the Artificial Neural Network (ANN) method significantly outperforms the Dynamic Travel Time Estimation (DTTE) method, which in turn outperforms the Static Travel Time Estimation (STTE) method.
AB - Increasing car mobility has lead to an increasing demand for traffic information. This contribution deals with information about travel times. When car drivers are provided with this type of information, the travel times should ideally be the times that they will encounter. As a result travel times must be predicted, often on a short-term basis. Available data for such a prediction are spot measurements of speed and flow from dual induction loop detectors. In this contribution a prediction method that uses a neural network is described. The performance of the neural network approach is compared with two naïve methods that are currently in operation, using data from a short-range motorway site: the A13 motorway from The Hague to Rotterdam.In order to be able to assess the performance of these methods it is imperative to use data on travel times. Since this data is not readily available, an estimation algorithm was selected where travel time is determined using speed and flow data from loop detectors. Five algorithms to estimate travel times were assessed using a data set with actually measured travel times through license plate recognition.Results of the assessment of short-range travel time predictions show that the Artificial Neural Network (ANN) method significantly outperforms the Dynamic Travel Time Estimation (DTTE) method, which in turn outperforms the Static Travel Time Estimation (STTE) method.
KW - METIS-209286
M3 - Paper
ER -