CSS434 Presentation Guide # slides should be around 15 for a 20-minute talk. Show a table of contents, (i.e., what you will be talking about). Get started with the background of the project you surveyed. Digest the essense of the project rather than cut and paste the contes from the papers you read. Include examples, illustrations, and performance results. Clarify pros and cons of the research/development project you surveyed. Add your opinion to improve the project. Conclude your presentation.

CSS434 Demo Talk Agent-Based Traffic Simulation Munehiro Fukuda University of Washington Bothell

Table of Contents Conventional Mathematical Models CSS434 Demo Talk: Agent-Based Traffic Simulation Table of Contents Conventional Mathematical Models Micro-Simulation: Agent-Based Models MATSim Challenges in Agent-Based Transport Simulation Summary

Backgound Macroscopic Simulation CSS434 Demo Talk: Agent-Based Traffic Simulation Backgound Macroscopic Simulation Merits Demerits Mathematical models General parameter assumptions Construction, fires, etc. considered as bias to the model Ease of real data retrieval such as highway traffic WSDOT annual traffic report Mathematical verification No micro events or interactions considered Traffic signals and lanes Parking Freight traffic Public transport No dynamic events considered Weather Dynamic trip plans

Background Agent-Based Modeling CSS434 Demo Talk: Agent-Based Traffic Simulation Background Agent-Based Modeling Micro-simulation Views interaction among a large number of simulation entities, (a.k.a. agents). Simulates an emergent collective group behavior of agents Agent-based transport simulation Model each traveler as an agent. Consider as many traffic events as possible. Simulates traffic as an interaction among travelers and events. System examples (open source) TRANSIMS: based on cellular automata MATSim: based on a queuing network

MATSim Global City Teams Challenge Super Action Cluster Summit Feb 2, 2017 MATSim Variable lenth Event-based queuing simulation XML input files Network configuration Log File Score Statistics Leg Travel Distance Statistics Events Trip Durations Optimization is performed in terms of agents’ plans. 10% agents: reroute their plans dynamically. 90% agents: choose their best score. Agent plans From Andreas Horni, Kai Nagel, Kay W. Axhausen, “The Multi-Agent Transport Simulation MATSim”

MATSim Example Global City Teams Challenge Super Action Cluster Summit Feb 2, 2017 MATSim Example https://vimeo.com/138598871 From http://www.matsim.org/scenarios

Challenges in Agent-Based Models Global City Teams Challenge Super Action Cluster Summit Feb 2, 2017 Challenges in Agent-Based Models Modeling A huge manpower would be required to model signals, lanes, parking, etc. in details rather than to give global models and parameters. Calibration Non-mathematical verifications are difficult to trust. How much detailed data can be sampled from the real world? Computation Millions of agents drive through several thousands of cells in TRANSIMS and links in MATSim.

Modeling in Agent-Based Models Global City Teams Challenge Super Action Cluster Summit Feb 2, 2017 Modeling in Agent-Based Models Links: speed, signals, and lanes Parking Public transport Freight traffic Dynamic events (e.g., accidents and weather changes) Pro: Agents and micro-simulation can describe almost whatever we want to model.

Public Transport in MATSim Global City Teams Challenge Super Action Cluster Summit Feb 2, 2017 Public Transport in MATSim Teleportion An agent is removed from one location and place at a later point of time. TransitVehicles.xml Vehicle type Passenger capasity Actual vehicles TransitSchedule.xml Transit stops with names Transit lines Routes (links) used by the transit Schedules Teleportion Con: Labor/time/data-intensive work From Andreas Horni, Kai Nagel, Kay W. Axhausen, “The Multi-Agent Transport Simulation MATSim”

Calibrations in Agent-Based Models Global City Teams Challenge Super Action Cluster Summit Feb 2, 2017 Calibrations in Agent-Based Models Realism tests * Hourly traffic flows: can be compared with automatic traffic recorders’ data Travel times and speeds: can be compared with public transports’ data Traffic patterns (queuing patterns at intersections, congested roads, freeway lane choice, merging, etc.): traffic cameras?? Available traffic data * Automatic traffic recorder(s)’ samples are sparse and imperfect. Drivers’ mentality (e.g., aggressiveness) varies in metropolitan and suburban areas. It is impossible to prepare millions of all agent itineraries and perturbations, thus we need to sample householders’ data. Comparing simulation results with real data Data-intensive and labor-intensive work * * From Wisconsin DOT Micro-Simulation Guideline: http://wisdot.info/microsimulation/

Computation in Agent-Based Models CSS434 Demo Talk: Agent-Based Traffic Simulation Computation in Agent-Based Models Large # road links A TRANSIMS simulation of 200,000 links in Portland: 0.23 sec per simulation step (1 sec) From Kai Nagel, Marcus Ricket, “Parallel implementation of the TRANSIMS micro-simulation”, Parallel Computing Vol 27(N.12), 2001 A day traffic simulation would take 5.5 hours. Large # agents A MATSim simulation of 10,000-car circular movement over 10,000 links: 51 sec From John Piger, MASS library traffic simulation application development and performance evaluation. Css497 final report, University of Washington, Bothell, WA, August 2011 A movement of 200,000 cars driving through I-405 in Bellevue would take 17 minutes, then a day traffic simulation? Solution: Parallel and distributed simulation

Future Distributed Computing in MATSim CSS434 Demo Talk: Agent-Based Traffic Simulation Future Distributed Computing in MATSim Master-slave mode Qsim on master Runs selected plans in a full queue simulation. Uses multithreading for parallelization. Psim on slave nodes Produce and evaluate plans for all agents. Pro Could distribute agents over a cluster and reduce memory usage per node. Con Would still suffer from CPU-intensive micro-simulation. From Andreas Horni, Kai Nagel, Kay W. Axhausen, “The Multi-Agent Transport Simulation MATSim”

Our Approach to MATSim Parallelization Global City Teams Challenge Super Action Cluster Summit Feb 2, 2017 Our Approach to MATSim Parallelization Decentralized model Qsim on all computing node Links and nodes are mapped to a distributed array Agents migrated over a distributed space. Performance Pro: Better than the original multithreaded MATSim Con: Load balancing needed From Zach Ma and Munehiro Fukuda, “A Multi-Agent Spatial Simulation Library for Parallelizing Transport Simulations”, WSC 2015

Final Remarks Two major agent-based transport simulators: Challenges CSS434 Demo Talk: Agent-Based Traffic Simulation Final Remarks Two major agent-based transport simulators: TRANSIMS and MATSim (The talk focused on MATSim.) Challenges Detailed modeling Agents and micro-simulation can describe almost whatever we want to model. Calibrations Limitation of real data Labor/data-intensive work Computational needs Some parallel/distributed computing efforts have been made. On-the-fly simulation linked to IoT sensors is not yet addressed because of long-time execution

CSS434 Demo Talk: Agent-Based Traffic Simulation Questions?