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.

COSMOS - Congestion management strategies and methods in urban sites.

The paper describes the strategies for Congestion and Incident Management (CIM) on the basis of Automatic Congestion and Incident Detection (ACID) that COSMOS will develop, implement in SCOOT, UTOPIA and MOTION, and validate and demonstrate in London, Piraeus and Torino. Four levels of operation were defined for CIM: strategies, tactics, tools and realisation. The strategies for CIM form the top level of this hierarchy. They have to reflect the strategic requirements of the system operators. The tactics are the means that can be employed by the strategies to achieve particular goals in particular situations. The tools that are used by the tactics relate to the elements of the signal plan and the ways in which they can be modified. Strategies, tactics and tools are generally common to all three systems, while the realisation of individual strategies and tactical decisions, through the use of particular common sets of tools, will generally be system specific. For the covering abstract, see IRRD 490001.