Flötteröd, Gunnar

Infoscience - École polytechnique fédérale de Lausanne

Recent advances in the calibration of traveldemand models from trac countsGunnar FlötterödJune 24, 2009OutlineIntroduction and motivationMicrosimulation-based trac monitoringReal world case study  the city of ZurichSummaryOutlineIntroduction and motivationMicrosimulation-based trac monitoringReal world case study  the city of ZurichSummaryAggregate demand calibration from trac counts•typical modeling approaches demand = time-dependent origin/destination matrix + routeassignment logic supply = move ows/vehicles along routes, account forcongestion•typical demand calibration techniques OD matrix calibration path ow estimationWhy not calibrate the causation of trac?•plan A1. sleep late ,2. 9:00  18:00 work3. shop afterwards4. late at home /•plan B1. get up early /2. shop beforehand3. 9:00-18:00 work4. early at home ,OutlineIntroduction and motivationMicrosimulation-based trac monitoringReal world case study  the city of ZurichSummaryMicrosimulation-based DTAdemand simulator•individual travel behavior route choice dpt. time choice act. (location) choice•chooses plan for everysingle travelersupply simulator•interactions of vehicles trac ow dynamics congestion travel times•joint plan execution yieldsnetwork conditionstravel behavior: plansnetwork conditionsMeasurements provide additional information•Bayes theorem combines prior demand model with traccounts into posterior demand model:P(plans|counts)︸ ︷︷ ︸(3)∝ P(plans)︸ ︷︷ ︸(1)·P(counts|plans)︸ ︷︷ ︸(2)1. prior: simulation system draws from this distribution2. likelihood: prob. of trac counts given simulated plans3. posterior: revised distribution given the measurements•Calibration objective is to make the the simulator draw fromthe posterior plan choice distribution.Realization of calibrated behavior•It is possible to approximately enforce the desired posteriorplan choice  only by external manipulations of the individualchoice behavior of (re)planning travelers.•Two possible methods:1. Accept the choice of a plan only with a certain probability.Otherwise, ask for another choice.2. Add a correction term to the systematic utility of every plan atraveler considers before making a choice.OutlineIntroduction and motivationMicrosimulation-based trac monitoringReal world case study  the city of ZurichSummaryReal world case study  the city of Zurich•network with 60 492 links, synthetic population of size 187 484•calibrate all-day motorist behavior from 159 inductive loopsSettings•modeling assumptions (Matsim) fully disaggregate demand representation combined choice of route, departure time, mode disaggregate supply model (queuing simulation) (some kind of) stochastic user equilibrium•estimator setting utilize 159 ow sensors adjust all choice dimensions at once inuence driver behavior by accept/reject procedure quality evaluation only at measurement locationsResults  scatterplotsplain simulation with calibrationmorningeveningResults  all dayplain simulation with calibration•measurements available from 7:00 to 20:00•red curve is mean relative ow error∣∣qestim − qtrue∣∣ /qtrue•drastic improvement of results in real-world conditionsOutlineIntroduction and motivationMicrosimulation-based trac monitoringReal world case study  the city of ZurichSummarySummary•broadly applicable disaggregate demand calibration method exible with respect to workings of DTA simulator consistent with equilibrium and non-equilibrium models•mathematically consistent adopted formal view on microscopic modeling and simulation Bayesian approach accounts for model and data uncertainties•computationally ecient is applicable to problems of practically relevant size is applicable in real-time conditionsSummary•broadly applicable disaggregate demand calibration method exible with respect to workings of DTA simulator consistent with equilibrium and non-equilibrium models•mathematically consistent adopted formal view on microscopic modeling and simulation Bayesian approach accounts for model and data uncertainties•computationally ecient is applicable to problems of practically relevant size is applicable in real-time conditionsThank you for your attention.

Recent advances in the calibration of travel demand models from traffic counts

http://infoscience.epfl.ch/record/152463/files/VSSFK9calibration.pdf

Recent advances in the calibration of travel demand models from traffic counts

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