  This paper develops an automatic freeway bottleneck identification and
visualization algorithm using a combination of image processing techniques and
traffic flow theory. Unlike previous studies that are based solely on loop
detector data, the proposed method can use traffic measurements from various
sensing technologies. Four steps are included in the proposed algorithm. First,
the raw spatiotemporal speed data are transformed into binary matrices using
image binarization techniques. Second, two post-processer filters are developed
to clean the binary matrices by filtering scattered noise cells and localized
congested regions. Subsequently, the roadway geometry information is used to
remove the impact of acceleration zones downstream of bottlenecks and thus
locate bottlenecks more precisely. Finally, the major characteristics of
bottlenecks including activation and deactivation points, shockwave speeds and
traffic delay caused by bottleneck are automatically extracted and visualized.
The proposed algorithm is tested using loop detector data from I-5
demonstrating that the proposed method outperforms the state-of-the-art methods
for congestion identification. The second test using INRIX data from I-66
demonstrates ability of the proposed algorithm to accurately extract and
visualize bottleneck characteristics.
