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 nation’s 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.

Relationships between gifted selection criteria and performance in sixth -grade gifted science

This study examined the relationships between gifted selection criteria used in the Dade County Public Schools of Miami, Florida and performance in sixth grade gifted science classes.^ The goal of the study was to identify significant predictors of performance in sixth grade gifted science classes. Group comparisons of performance were also made. Performance in sixth grade gifted science was defined as the numeric average of nine weeks' grades earned in sixth grade gifted science classes.^ The sample consisted of 100 subjects who were formerly enrolled in sixth grade gifted science classes over two years at a large, multiethnic public middle school in Dade County.^ The predictors analyzed were I.Q. score (all scales combined), full scale I.Q. score, verbal scale I.Q. score, performance scale I.Q. score, combined Stanford Achievement Test (SAT) score (Reading Comprehension plus Math Applications), SAT Reading Comprehension score, and SAT Math Applications score. Combined SAT score and SAT Math Applications score were significantly positively correlated to performance in sixth grade gifted science. Performance scale I.Q. score was significantly negatively correlated to performance in sixth grade gifted science. The other predictors examined were not significantly correlated to performance.^ Group comparison results showed the mean average of nine weeks grades for the full scale I.Q. group was greater than the verbal and performance scale I.Q. groups. Females outperformed males to a highly significant level. Mean g.p.a. for ethnic groups was greatest for Asian students, followed by white non-Hispanic, Hispanic, and black. Students not receiving a lunch subsidy outperformed those receiving subsidies.^ Comparisons of performance based on gifted qualification plan showed the mean g.p.a. for traditional plan and Plan B groups were not different. Mean g.p.a. for students who qualified for gifted using automatic Math Applications criteria was highest, followed by automatic Reading Comprehension criteria and Plan B Matrix score. Both automatic qualification groups outperformed the traditional group. The traditional group outperformed the Plan B Matrix group. No significant differences in mean g.p.a. between the Plan B subgroups and the traditional plan group were found. ^

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.