Person:
Guin, Angshuman

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    Using Machine Learning to Identify High Impact Incidents in Automatic Incident Detection (AID) System Generated Alarms
    (Georgia Institute of Technology, 2020-01) Kim, Han Gyol ; Guin, Angshuman ; Hunter, Michael D.
    Video-based Automatic Incident Detection (AID) technologies have been used in the past by Traffic Management Agencies to aid Incident Management. While significant improvements in video quality and computing resources have substantially improved the potential accuracy and efficiency of video-based AID, AID technologies typically still struggle to separate recurrent congestion-related stoppage of vehicles from incident related stoppages. Consequently, the number of false alarms (or non-critical alarms) remains unmanageably high. This study develops a machine learning framework for developing consolidation strategies to minimize false and non-critical alarms and associates confidence values with the alarms, thereby allowing operators to focus on higher confidence alarms during busy periods. The study first investigates the clustering and evolution patterns of the appearance of alarms over time and space. Then it uses this information in the development of a cluster identification algorithm with both spatial and temporal datasets. Finally, the study develops a method for selection of optimal parameters of the machine learning algorithm to separate the alerts for potential high-impact incidents from the alerts related to congestion and other non-critical stops or slowdowns. The results indicate a massive reduction in non-critical alerts without a significant reduction in detection rate or time-to-detect the incidents. While the framework has been used with a video-based AID system, the framework has been developed with interoperability in mind and has the potential to be applicable across all types of AID systems.
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    Atlanta I-85 HOV-to-HOT Conversion: Analysis of Vehicle and Person Throughput
    (Georgia Institute of Technology, 2013-10) Guensler, Randall L. ; Elango, Vetri ; Guin, Angshuman ; Hunter, Michael D. ; Laval, Jorge A. ; Araque, Santiago ; Colberg, Kate ; Castrillon, Felipe ; D’Ambrosio, Kate ; Duarte, David ; Khoeini, Sara ; Peesapati, Lakshmi ; Sheikh, Adnan ; Smith, Katie ; Toth, Christopher ; Zinner, Stephanie
    This report summarizes the vehicle and person throughput analysis for the High Occupancy Vehicle to High Occupancy Toll Lane conversion in Atlanta, GA, undertaken by the Georgia Institute of Technology research team. The team tracked changes in observed vehicle throughput on the corridor and collected average vehicle occupancy (persons/vehicle) data to assess changes in person throughput. Traffic volumes were collected by VDS systems on the Georgia NaviGAtor system and the team implemented a large scale quarterly data collection effort for vehicle occupancy across all travel lanes.
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    An Incident Detection Algorithm Based On a Discrete State Propagation Model of Traffic Flow
    (Georgia Institute of Technology, 2004-07-09) Guin, Angshuman
    Automatic Incident Detection Algorithms (AIDA) have been part of freeway management system software from the beginnings of ITS deployment. These algorithms introduce the capability of detecting incidents on freeways using traffic operations data. Over the years, several approaches to incident detection have been studied and tested. However, the size and scope of the urban transportation networks under direct monitoring by transportation management centers are growing at a faster rate than are staffing levels and center resources. This has entailed a renewed emphasis on the need for reliability and accuracy of AIDA functionality. This study investigates a new approach to incident detection that promises a significant improvement in operational performance. This algorithm is formulated on the premise that the current conditions facilitate the prediction of future traffic conditions, and deviations of observations from the predictions beyond a calibrated level of tolerance indicate the occurrence of incidents. This algorithm is specifically designed for easy implementation and calibration at any site. Offline tests with data from the Georgia-Navigator system indicate that this algorithm realizes a substantial improvement over the conventional incident detection algorithms. This algorithm not only achieves a low rate of false alarms but also ensures a high detection rate.