POLARIS: Planning and Operations Language for Agentbased Regional Integrated Simulation

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1 POLARIS: Planning and Operations Language for Agentbased Regional Integrated Simulation Dr. Kuilin Zhang Department of Civil and Environmental Engineering Michigan Technological University Houghton, MI

2 Motivation The existing implementation gap between activity-based modeling (ABM) of travel demand and simulation-based dynamic traffic assignment (DTA) modeling of transportation network Not truly integrated: mostly existing implementations are decoupled using files as interface between ABM and DTA The knowledge barrier: Researchers from either demand or network side may simplify the other side models The use of the emerging high-performance parallel computing technologies in the hybrid multi-core CPU and many-core GPU systems Running 24-hour and second-by-second resolution of millions of travelers multidimensional travel decisions in large-scale metropolitan areas in a workstation or powerful laptop computer Can support real-time applications to support real-time traffic management center (TMC) operational strategies for uncertainty events by feeding real-time and historical traffic observations, traffic events, and traffic estimation and predictions An open source agent-based software design framework provides a new concept for performance and re-usability

3 POLARIS (Planning and Operations Language for Agent-based Regional Integrated Simulation) Sponsored by FHWA, USDOT, Model Traffic Management Centers and other ITS Systems Enhance Interoperability among Demand, Network, and Operation Models Research Team TRACC, Argonne National Laboratory Hubert Ley (manager), Mike Hope (computing and visualization), Vadim Sokolov (data interface and operation), Josh Auld (demand), Bo Xu (computing, now at HERE, Nokia), and Kuilin Zhang (network, now at Michigan Tech) Fundamental Goals and Philosophies Develop transportation modeling implementation standards and protocols Create an open source model development environment Connect researchers from demand, network, and operation communities with a common modeling language Offer high-performance computing tools while maintaining flexibility and modularity

4 POLARIS Repository Structure Final User Application Open Source Playground Modelers and Engineers Experimental Transportation Algorithms Specialized Transportation Data Layouts Modeling Researchers Open Source Versioned Repository Reusable Transportation Interfaces Modular Transportation Algorithms Fundamental Transportation Data Layouts Computing Researchers Core Library Memory Allocator Interprocess Communication Discrete Event Engine POLARIS Meta-Structures

5 An Agent-based Approach Element Requirements Capabilities Entity Faculty Directable Agent Some Destination Route Get Route ( ) Basic Network Path Destination Get Dest. ( ) Basic Network Dest. Routable Agent Some Destination Some Route Accurate Route Get Route ( ) Basic Network Path Get Route ( ) Accurate Est. Travel Time Fast Route Get Route ( ) Basic Network Path Get Route ( ) Fast Arrival Variance Fast Destination Get Dest. ( ) Basic Network Dest. Get Dest. ( ) Fast Suitability Robust Dest. Get Dest. ( ) Basic Network Dest. Get Dest. ( ) Robust Alternative Dest.

6 POLARIS Core Library: Re-Usable Low Level Capabilities Discrete Event Engine and Automated Multi-Threading Enable code writing from an agent-based perspective Memory Allocation Library Optimized for use in transportation modeling applications Thread-Safe Structures Specialized for time-dependent nature of simulations Inter-Process Communication for use in Cluster Computing Ease connection and communication of disparate processes

7 POLARIS Integrated Dynamic Demand and Network Modeling Travel behavior: activity, duration, location, departure time, mode, route, etc. Traffic Simulation Driving behavior: car-following, lane changing, etc. Intelligent Transportation Systems Smart signals, ramp metering, variables message signs, sensors, GPS, etc. Advanced Vehicles Connected and automated vehicles, electric vehicles, etc. Modeling Millions of Persons Travel Decisions and Network Traffic Dynamics Second-by-Second in Metropolitan Areas Real-Time Events: Weather (e.g. Snow) and Accidents Connected Vehicles ITS Facilities: Variable Message Signs, Variable Speed Signs, Ramp Meters Driver-Centric On-Line Lane-Based Microscopic Traffic Simulation and Optimal Routing

8 Individual Vehicle Driving Simulation Vehicle Driving Simulation Kinematic Wave Traffic Flow Theory Mesoscopic Simulation Microscopic Simulation (potential) Car-Following Lane-Changing Advanced Vehicles (potential) Connected Vehicles Connected vehicles through smartphones Connected vehicles through DSRC Vehicle-to-vehicle communication Vehicle-to-infrastructure communication Automated Vehicles Electric Vehicles

9 Individual Traveler s 24-hour Activity Chains An Activity Chain Home Service Social Home Three Trips Three Locations Advanced Vehicles (potential) Electric Vehicles Charging location selection Connected Vehicles Location-Based Services

10 Intelligent Vehicle Path Switching in response to Real-Time Traffic Information Intelligent Vehicles Connected through Smartphones Radio VMS Travel Behavior Change in response to Real- Time Traffic Information Change route Change activity location Change activity duration Change departure time Change activity schedule Switching Point An Intelligent Vehicle Original Path Current Path Destination

11 Weather Events Snow Travel behavior Change in response to Weather Information Change route Change departing time Change activity location Change activity schedule Driving Behavior Change in response to Weather Impacts Change speed Road capacity drop Traffic Management Center (TMC) How to disseminate weather information How to dispatch plow trucks

12 Accident Events Travel Behavior Change in response to Real-Time Accident Information Change route Change activity location Driving Behavior Change in response to Accident Events Capacity reduction Accident event duration prediction Traffic Management Center (TMC) How to disseminate real-time accident information How to dispatch two truck to accident location An Accident on I-290 An Accident on Lakeshore Dr

13 Summary An open source transportation modeling development environment and language A set of core libraries provide memory efficient high-performance computing capabilities using an agent-based approach A 3D GUI for large-scale visualization and real-time interventions Provide interfaces to model and simulate the travel and drive decisions of the emerging advanced vehicle technologies such as connected, automated, and electrified vehicles

14 Thank You Kuilin Zhang

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