Facilitation of visual pattern recognition by extraction of relevant features from microscopic traffic data
An experimental approach to traffic flow analysis is presented in which methodology from pattern recognition is applied to a specific dataset to examine its utility in determining traffic patterns. The selected dataset for this work, taken from a 1985 study by JHK and Associates (traffic research) f...
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| Format: | Others |
| Language: | en_US |
| Published: |
2010
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| Online Access: | http://hdl.handle.net/1969.1/ETD-TAMU-2036 http://hdl.handle.net/1969.1/ETD-TAMU-2036 |
| Summary: | An experimental approach to traffic flow analysis is presented in which methodology
from pattern recognition is applied to a specific dataset to examine its utility in
determining traffic patterns. The selected dataset for this work, taken from a 1985 study
by JHK and Associates (traffic research) for the Federal Highway Administration,
covers an hour long time period over a quarter mile section and includes nine different
identifying features for traffic at any given time. The initial step is to select the most
pertinent of these features as a target for extraction and local storage during the
experiment. The tools created for this approach, a two-level hierarchical group of
operators, are used to extract features from the dataset to create a feature space; this is
done to minimize the experimental set to a matrix of desirable attributes from the
vehicles on the roadway. The application is to identify if this data can be readily parsed
into four distinct traffic states; in this case, the state of a vehicle is defined by its velocity
and acceleration at a selected timestamp. A three-dimensional plot is used, with color as
the third dimension and seen from a top-down perspective, to initially identify vehicle
states in a section of roadway over a selected section of time. This is followed by
applying k-means clustering, in this case with k=4 to match the four distinct traffic states, to the feature space to examine its viability in determining the states of vehicles in
a time section. The method’s accuracy is viewed through silhouette plots. Finally, a
group of experiments run through a decision-tree architecture is compared to the kmeans
clustering approach. Each decision-tree format uses sets of predefined values for
velocity and acceleration to parse the data into the four states; modifications are made to
acceleration and deceleration values to examine different results.
The three-dimensional plots provide a visual example of congested traffic for use
in performing visual comparisons of the clustering results. The silhouette plot results of
the k-means experiments show inaccuracy for certain clusters; on the other hand, the
decision-tree work shows promise for future work. |
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