Design Strategies for an Artificial Neural Network Based Algorithm for Automatic Incident Detection on Major Arterial Streets

Traffic incidents are non-recurring events that can cause a temporary reduction in roadway capacity. They have been recognized as a major contributor to traffic congestion on our national highway systems. To alleviate their impacts on capacity, automatic incident detection (AID) has been applied as an incident management strategy to reduce the total incident duration. AID relies on an algorithm to identify the occurrence of incidents by analyzing real-time traffic data collected from surveillance detectors. Significant research has been performed to develop AID algorithms for incident detection on freeways; however, similar research on major arterial streets remains largely at the initial stage of development and testing. This dissertation research aims to identify design strategies for the deployment of an Artificial Neural Network (ANN) based AID algorithm for major arterial streets. A section of the US-1 corridor in Miami-Dade County, Florida was coded in the CORSIM microscopic simulation model to generate data for both model calibration and validation. To better capture the relationship between the traffic data and the corresponding incident status, Discrete Wavelet Transform (DWT) and data normalization were applied to the simulated data. Multiple ANN models were then developed for different detector configurations, historical data usage, and the selection of traffic flow parameters. To assess the performance of different design alternatives, the model outputs were compared based on both detection rate (DR) and false alarm rate (FAR). The results show that the best models were able to achieve a high DR of between 90% and 95%, a mean time to detect (MTTD) of 55-85 seconds, and a FAR below 4%. The results also show that a detector configuration including only the mid-block and upstream detectors performs almost as well as one that also includes a downstream detector. In addition, DWT was found to be able to improve model performance, and the use of historical data from previous time cycles improved the detection rate. Speed was found to have the most significant impact on the detection rate, while volume was found to contribute the least. The results from this research provide useful insights on the design of AID for arterial street applications.

Acoustic Telemetry Studies of Bonefish (Albula vulpes) Movement Around Andros Island, Bahamas: Implications for Species Management

Bonefish (Albula spp.) support an economically important catch-and-release recreational fishery, as well as artisanal harvesting, in The Bahamas. Little is known about the large-scale movement patterns of bonefish, yet such information is essential for proper species conservation and management. I used acoustic telemetry to determine large-scale movement patterns of bonefish around Andros, Bahamas, in conjunction with presumed spawning migrations. I conclude that bonefish travel long distances from shallow flats to pre-spawning aggregation sites in proximity to off-shore reef locations. Off-shore movement to deeper reef locations occurs around both new and full moons. This study has also confirmed anecdotal reports that the North Bight is an important spawning migration corridor for bonefish. This information is critical for the protection of bonefish and identifies important habitats (e.g. migration corridors and pre-spawning aggregations) on Andros that warrant protection from coastal degradation or fishing pressures.