1 / 27

Welcome to the 2 nd MATSim Tutorial

May 17th, 2010, MATSim Tutorial, Zurich Michael Balmer, balmer@ivt.baug.ethz.ch. Welcome to the 2 nd MATSim Tutorial. The helping Hands. Francesco Ciari, ETH Zurich Christoph Dobler, ETH Zurich Dominik Grether, TU Berlin Andreas Horni, ETH Zurich Konrad Meister, ETH Zurich

desma
Download Presentation

Welcome to the 2 nd MATSim Tutorial

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. May 17th, 2010, MATSim Tutorial, Zurich Michael Balmer, balmer@ivt.baug.ethz.ch Welcometo the2nd MATSim Tutorial

  2. The helping Hands • Francesco Ciari, ETH Zurich • Christoph Dobler, ETH Zurich • Dominik Grether, TU Berlin • Andreas Horni, ETH Zurich • Konrad Meister, ETH Zurich • Rashid A. Waraich, ETH Zurich • Michael Zilske, TU Berlin

  3. The Program (1) http://matsim.org/usermeeting10 • Monday: • Installation & Getting started 1 (Konrad) • Installation & Getting started 2 (Konrad & Rashid) • Introduction & Overview (Michael B.) • Generating Demand and Supply (Francesco, Christoph & Michael Z.)

  4. The Program (2) http://matsim.org/usermeeting10 • Tuesday: • Generating Demand and Supply, cont. (Francesco, Christoph & Michael Z.) • Config Parameters (Rashid & Christoph) • Validation & Visualization (Andreas) • Real-World Case Study with MATSim: Results (Michael B.)

  5. The Program (3) http://matsim.org/usermeeting10 • Wednesday: • Programming with Java - Events (Dominik) • Programming with Java - Controller (Marcel) • Follow up MATSim User Meeting

  6. The Core of MATSim An Overview

  7. ABD & DTA interfaced activity-based demand modeling matrices dynamic traffic assignment costs flows

  8. ABD & DTA integrated activity-based demand modeling (incl. routes) individual, time dependent, daily demand („schedule“, „plan“) individual, dynamic, microscopic shares of costs physical simulation

  9. Introduction: Multi-Agent Transport Simulation (MATSim)

  10. The Process Steps initial demand execution scoring analyses replanning

  11. Demand & Supply(1) initial demand execution scoring analyses replanning

  12. Demand & Supply(2) initial demand execution scoring analyses replanning

  13. Demand & Supply(3) initial demand execution scoring analyses replanning

  14. Optimization (1) • Evolutionary Algorithm • Co-Evolutionary Algorithm “Population of individuals” “Population of Plans” initial demand execution execution execution execution execution execution execution scoring scoring scoring scoring scoring scoring scoring analyses replanning replanning replanning replanning replanning replanning replanning

  15. Optimization (2) • Execution: • Simultaneous simulation of all individuals with their selected plan • Implementation: Queue model • Purpose: • Calculating traffic • Basis to calculate the “score of the selected plan” • Parameterization: • “Flow Capacity Factor” • “Storage Capacity Factor” • … initial demand execution scoring analyses replanning

  16. Optimization (3) • Scoring: • Calculating the “fitness” (utility, score, generalized cost) • Implementation: Charypar and Nagel (2006) • Purpose: • The score measures the whole plan. • Basis for the likelihood to survive in the population of plans • Parameterization: • Desired activity duration • marginal cost of traveling • Cost for being late/early/waiting • etc… initial demand execution scoring analyses replanning

  17. Optimization (4) Selection • Selection / Strategy: • Defines how new plans are created, selected and/or deleted • Purpose: • Selection process in the evolutionary algorithm • Parameterization: • Selection of the best/logit choice/etc… • Selection for mutation/adaptation/variation • Definition of the strategy splits of the agents • etc… initial demand execution scoring analyses replanning

  18. Optimization (5) • Replanning: • How to mutate/adapt/vary a plan (to create a new plan) • Purpose: • Searching in the search space of a daily plan • Parameterization: • Which parts of the search space to actually search • How to search  Which replanning modules to use initial demand execution scoring analyses replanning

  19. Optimization (6) • Routes • Different types of Dijkstra dynamic least cost path router (different in constraints and performance)  Best respond module • Times • Time Allocator Mutator: Random variation of departure times and durations within a given range  Random mutation module • Modes • Mode Mutator: Random selection of a given mode per trip/subtour/plan  random mutation module initial demand execution scoring analyses replanning

  20. Optimization (7) • Locations • Secondary Location Choice Module: • Random selection in the choice set  random mutation module • Selection via time-space prism choice set  ~ best respond module • Times and Modes: • Planomat: simultaneous choice of departure times and given modes at sub tour level by using again an EA  best respond module • More and more: planomatX, etc… initial demand execution scoring analyses replanning

  21. MATSim EA again + + - - - - + + + + + + + + + + - - - - - - - - +sports - congestion - Shop closed

  22. Co-Evolutionary Algorithm - + - + + - - + - - - + + + - + - + + + - + + + - + + + + - - + - + + - + - + - - Iteration n Iteration 2 Iteration 3 Iteration 1 Iteration 0 + + etc.  Stable state

  23. Optimization (7) • Summary: • Execution and scoring: to calculate fitness of selected plans • Execution: the only place where agents interact • Execution also produces traffic (assignment) • Scoring defines agents’ mobility behavior • Scoring is completely individualized and can use every detailed information of the execution and the agents attributes • Replanning defines the search space and the way how to search initial demand execution scoring analyses replanning

  24. Analysis (1) • Output Data: • Events • Connection of the “Who”, “When”, “Where” and “What” • Plans • The demand that produces the events (very similar but not identical to the events) • The same format as the input and the data dumps during the optimization process.  One can perform the same analyses for all these data files. • The output can be the input for case studies. initial demand execution scoring analyses replanning

  25. Analysis (2) Event details initial demand execution scoring analyses replanning (Source: Rieser, 2008, MATSim Seminar, Castasegna)

  26. Analysis (3) • Data format and size: • Events • Tab separated or as XML (gzip) • Easily > 1GB • Plans • XML • Easily > 1GB (gzip) • Cannot be analyzed in Excel et Al. • Specialized programs (rare) • Programming needed initial demand execution scoring analyses replanning

  27. Enjoy the days! Question? Remarks? initial demand execution scoring analyses replanning

More Related