Modeling Destination Choice in MATSim Andreas Horni IVT ETH Zürich July 2011

Destination Choice in MATSim 2 initial demand analysesexecutionscoring replanning I.Search Method & Capacity Restraints II.Adding Unobserved Heterogeneity -> Adapted Search Method III.Variability Analyses IV.Model Estimation

3 I. Local Search in Our Coevolutionary System day plans fixed and discretionary activities travel time budget relatively small set of locations per iteration step time geography Hägerstrand

4 r = t budget /2 * v check all locations t travel ≤ t budget → choice set check ∑t travel ≤ t budget random choice

5 10 % ZH Scenario: 60K agents

6 I. Competition on the Activites Infrastructure load-dependent decrease of scorereduces number of implausibly overloaded facilities Load category 1: 0 – 33 % 2: % 3: % 4: > 100% 10 % ZH Scenario: 60K agents Realism Stability of algorithm

7 II. Adding Unobserved Heterogeneity: Scoring Function V +  implicit +  explicit

II. Adding Unobserved Heterogeneity: Search Space

costs(location(  max )) estimate by distance realized utilities preprocess once for every person  max –  t travel = 0 search space boundary d max = … distance to loc with  max d max

10 shopping leisure II. Adding Unobserved Heterogeneity: Results

 ij person i alternative j seed Random draws from DCM microsimulation results = random variables X III. Variability Analysis -> microsimulations are a sampling tool estimation of parameters for X (=statistic) with random sampling Results should be given as interval estimation Standard error of sampling distribution = sampling error # of runs

20 runs (goal: 30 for TRB) time, route, destination choice, 200 iterations Comparison OVER runs -> var = random var Scores population level average executed plans: : avg 0.166:  0.09:  in percent of avg III. Variability Analysis agent level Aggregation reduces var (applied in different fields e.g. filtering (moving average))

III. Variability Analysis daily link volumes Previous studies confirmed (...?) Castiglione TAZ level up to 6% std.dev.

III. Variability Analysis Hourly link volumes high! Var = f( spatial resolutions temporal resolution choice dimensions ) Previous studies at lower res levels or less choice dimensions! -> difficult to compare high!

III. Variability Analysis Large intra-run var 1.Large var through replanning modules (20% replanner) (iteration 201: 100% select best) 2. Not in equilibrium 3. Utility plateau -> genetic drift ideas? -> future research

16 Modeling Temporal Variability in MATSim intrapersonal (temporal) variability U MATSim = V +  MATSim cross-sectional model (average working day) Correlations! drawing from ? (best case)

17 Correlations t y General rhythm of life Avoidance behavior +

18 model estimation IV. Model Estimation +  explicit – penalty cap correlationstastes coefficients 