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SUMMARY:Management and Control of Complex Transportation Systems—a Multi
  Agent System Approach
DTSTART:20191016T121500
DTEND:20191016T130000
DTSTAMP:20260405T193007Z
UID:72afe45584e9df77304e764d63803bab70f8487d9b2f8267b8bf1ca4
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Monty Abbas\, Virginia Tech\nAbstract:\nMotivated by th
 e need to manage a complex and evolving intelligent transportation system 
 in a collaborative framework\, we describe five components of multi-agent 
 modeling paradigm with several implementation examples from the VT-SCORES 
 research lab at Virginia Tech. The research components discussed range fro
 m driver behavior\, car-following models\, adaptive control\, connected ve
 hicles\, and variable speed limit applications. We will show examples of e
 xtracting driver behavior from large datasets\, modeling evolving system b
 ehavior with intelligent agents\, integration of state estimation and comm
 unication frameworks in a connected vehicles environment\, and the ramific
 ations of neglecting learning in modeling. The presented agent-based frame
 work is intermodal\, and can incorporate performance characteristics and n
 eeds of different users (cars\, trucks\, busses\, pedestrians\, and bikes)
 . We will also address emergency vehicles current and future applications 
 and the impact of path-based priority tunnels provided for emergency vehic
 les in each application. The presentation will also touch on the latest in
 novations at the VT-SCORES lab and how it can help address evolving and co
 mplex transportation problems.\n\nBio :\nMonty Abbas is a Professor in th
 e Transportation Infrastructure and Systems Engineering at Virginia Tech. 
 He holds a Doctor of Philosophy in Civil Engineering from Purdue Universit
 y (2001).  Dr. Abbas developed Purdue Real-Time Offset Transitioning Algo
 rithm for Coordinating Traffic Signals (PRO-TRACTS) during his Ph.D. studi
 es at Purdue University\, bridging the gap between adaptive control system
 s and closed-loop systems. He has since developed and implemented several 
 algorithms and systems in his areas of interest\, including the Platoon Id
 entification and Accommodation system (PIA)\, the Pattern Identification L
 ogic for Offset Tuning (PILOT 05)\, the Supervisory Control Intelligent Ad
 aptive Module (SCIAM)\, the Cabinet-in-the-loop (CabITL) simulation platfo
 rm\, the Intelligent Multi Objective Control Algorithms (I-MOCA)\, the Tra
 ffic Responsive Iterative Urban-Control Model for Pattern-matching and Hyp
 ercube Optimal Parameters Setup (TRIUMPH OPS)\, the Multi Attribute Decisi
 on-making Optimizer for Next-generation Network-upgrade and Assessment (MA
 DONNA)\, the Safety and Mobility Agent-based Reinforcement-learning Traffi
 c Simulation Add-on Module (SMART SAM)\, and the Broad Area-wide and Dista
 nce-wise Agent-based Signal-optimization System (BADASS). He was also one 
 of the key developers of the dilemma zone protection Detection Control Sys
 tem (D-CS) that was selected as one of the seven top research innovations 
 and findings in the state of Texas for the year 2002.  He is a recipient 
 of the Dean’s Award for Excellence in Service\, ICAT Creativity and Inno
 vation Day Process Award for the Traffic SONATA project\, Best Paper Award
 \, Western Decision Sciences Institute (WDSI) 2018 Conference\, Oak Ridge 
 National Lab Associated Universities (ORAU) Ralf E. Powe Junior Faculty En
 hancement Award and the G. V. Loganathan Faculty Achievement Award for Exc
 ellence in Civil Engineering Education. He is also a recipient of the TTI/
 Trinity New Researcher Award for his significant contributions to the fiel
 d of Intelligent Transportation Systems and Traffic Operations.
LOCATION:GC B1 10 https://plan.epfl.ch/?room==GC%20B1%2010
STATUS:CONFIRMED
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