A Calibration Procedure for Microscopic Traffic Simulation

Lianyu Chu, University of California, Irvine Henry Liu, Utah State University Jun-Seok Oh, Western Michigan University Will Recker, University of California, Irvine

Outline

• •

Introduction Data preparation

•

Calibration

• •

Evaluation of the overall model Discussion

•

Conclusion

Introduction to Microscopic simulation

• • •

Micro-simulation models / simulators

– –

AIMSUN, CORSIM, MITSIM, PARAMICS, VISSIM… model traffic system in fine details Models inside a simulator

–

physical components

–

roadway network, traffic control systems, driver-vehicle units, etc

–

associated behavioral models

–

driving behavior models, route choice models To build a micro-simulation model:

–

complex data requirements and numerous model parameters

–

based on data input guidelines and default model parameters

Objective

• • • •

Specific network, specific applications Calibration:

–

adjusting model parameters

–

until getting reasonable correspondence between model and observed data

–

trial-and-error, gradient approach and GA Current calibration efforts: incomplete process

–

driving behavior models, linear freeway network Objective:

–

a practical, systematic procedure to calibrate a network-level simulation model

Study network

Data inputs

• • •

Simulator: Paramics Basic data

–

network geometry

–

Driver Vehicle Unit (DVU)

–

driver behavior (aggressiveness and awareness factors)

–

Vehicle performance and characteristics data

–

vehicle mix by type

– – –

traffic detection / control systems transportation analysis zones (from OCTAM) travel demands, etc.

Data for model calibration

–

arterial traffic volume data

– –

travel time data freeway traffic data (mainline, on and off ramps)

Freeway traffic data reduction

• •

Why

– –

too many freeway data, showing real-world traffic variations calibrated model should reflect the typical traffic condition of the target network

–

find a typical day, use its loop data How to find a typical day

– – –

vol(i): traffic volume of peak hour (7-8 AM) ave_vol: average of volumes of peak hour investigating 35 selected loop stations

–

85% of GEH at 35 loop stations > 5

GEH

 

Vol

(

i

) (

Vol

(

i

)  

ave

_

Vol

 2

ave

_

Vol

) / 2

Calibration procedure

Basic data input / Network coding Calibration of driving behavior models Calibration of routing behavior model Reference OD from planning model Route choice adjustment Total OD estimation Reconstruction of time dependent OD demands Model Fine-tuning N Volume, Travel time match?

Y Overall model validation / evaluation

Determining number of runs

N

 (

t

 / 2      ) 2 • • •

μ, δ:

–

mean and std of MOE based on the already conducted simulation runs ε: allowable error 1 α: confidence interval

Start Original nine runs Calculating the mean and its std of each performance measure Calculating the required # of runs for each performance measure Additional one simulation run N Is current # of runs enough? Y End

Step 1/2: Calibration of driving behavior / route behavior models

• •

Calibration of driving behavior models:

–

car-following (or acceleration) , and lane-changing

–

sub-network level

–

based on previous studies

– –

mean target headway: 0.7-1.0

driver reaction time: 0.6-1.0 Calibration of route behavior model

– – –

on a network-wide level. using either aggregated data or individual data stochastic route choice model

–

perturbation: 5%, familiarity: 95%

Step 3: OD Estimation

• •

Objective: time-dependent OD Method:

– –

first, static OD estimation then, dynamic OD

•

Procedure:

– – – –

Reference OD matrix Modifying and balancing the reference OD demand Estimation of the total OD matrix Reconstruction of time-dependent OD demands

Reference OD matrix

• •

Reference OD matrix

–

from the planning model, OCTAM Modifying and balancing the reference OD demand

–

problems with the OD from planning model

– – –

limited to the nearest decennial census year sub-extracted OD matrix based on four-step model morning peak hours from 6 to 9; congestion is not cleared at 9 AM

–

balancing the OD table: FURNESS technique

– –

15-minute counts at cordon points (inbound and outbound) total generations as the total

Estimation of the total OD matrix

• • •

A static OD estimation problem

– –

least square tools, e.g. TransCAD, QueensOD, Estimator of Paramcis Our method:

– – – – –

simulation loading the adjusted OD matrix evenly 52 measurement locations (13 mainline, 29 ramp, 10 arterial) quality of estimation: GEH

–

GEH at 85% of measurement locations < 5 modification of route choices

GEH

 

M

(

M obs

(

n

)

obs

(

n

)  

M sim M sim

(

n

)  2 (

n

)) / 2

OD adjustment algorithm: proportional assignment

–

assuming the link volumes are proportional to the OD flows Result:

–

96% of all measurement locations < 5

Reconstruction of time-dependent OD

• •

A dynamic OD demand estimation problem

–

research level, no effective method

–

a fictitious network or a simple network

–

practical method:

– –

FREQ: freeway network QueensOD, Estimator of PARAMICS, etc. Profile-based method:

– – –

profile: temporal traffic demand pattern based on the total OD demand matrix assign total OD to a series of consecutive time slices

Finding OD profiles

• • • •

Find the profile of each OD pair General case (from local to local):

–

profile(i, j) = profile(i) , for any origin zone, j =1 to N,

–

profile(I): vehicle generation pattern from an origin zone Special cases:

–

local to freeway

–

estimated by traffic count profile at a corresponding on-ramp location

–

freeway to local

–

estimated by traffic count profile at a corresponding off-ramp location

–

freeway to freeway*

–

roughly estimated by traffic count profile at a loop station placed on upstream of freeway mainline

–

needs to be fine-tuned

volume constraint at each time slice

Examples of OD profiles

Origin 1 1 2 3 4 2 Destination 3 4 profile(i) (known)

Fine-tuning OD profiles

• •

Optimization objectives

–

Min (Generalized Least Square of traffic counts between observed and simulated counts over all points and time slices)

–

step 1:minimizing deviation of peak hour (7-8 AM)

–

criteria: more than 85% of the GEH values < 5

–

step 2: minimizing deviation of whole study period at five-minute interval

–

together with next step

–

52 measurement points Result:

–

step 1: 87.5% of all measurement locations

Step 4: overall model fine-tuning

• •

Objectives:

–

check/match local characteristics: capacity, volume occupancy curve

– –

further validate driving behavior models locally reflect network-level congestion effects Calibration can start from this step if:

– –

network has been coded and roughly calibrated.

driving behavior models have been roughly calibrated and validated based on previous studies on the same network.

–

one of the route choice models in the simulator can be accepted.

–

OD demand matrices have been given.

Model fine-tuning method

• • •

Parameters:

–

Link specific parameters

– –

signposting setting target headway of links, etc

–

Parameters for car-following and lane-changing models

– –

mean target headway driver reaction time

–

Demand profiles from freeway to freeway Objective functions:

–

min (observed travel time, simulated travel time)

–

min (Generalized Least Square of traffic counts over all points and periods) Trial-and-error method

Some calibrated OD profiles

12.0% 10.0% 8.0% 6.0% 4.0% 2.0% 0.0% 6:00 6:15 6:30 6:45 7:00 7:15 7:30 7:45 8:00 8:15 8:30 8:45 9:00 9:15 9:30 9:45

Time of day

a freeway zone to a freeway zone a freeway zone to an arterial zone an arterial zone to an industrial zone an artertial zone to a freeway zone

volume-occupancy curve

Loop station @ 2.99

Real world 120 100 80 60 40 20 0 0 20 40 60

Percent occupancy

80 Simulation 120 100 80 60 40 20 0 0 20 40 60

Percent occupancy

80

Evaluation of Calibration (I)

•

Measure for goodness of fit:

–

Mean Abstract Percentage Error (MAPE)

MAPE

 1

T t T

  1 ((

M obs

(

t

) 

M sim

(

t

)) /

M obs

(

t

)) 600 300 400 200 100 0 6:00 6:30 7:00 200

3.1% (SB) 8.5% (NB)

0 6:00 6:30 7:00 7:30 8:00 8:30 9:00 7:30 8:00 8:30 9:00 9:30 10:00 simulation observation simulation observation 9:30 10:00 Comparison of observed and simulated travel time of SB / NB I-405

Evaluation of Calibration (II)

700 600 500 400 300 200 100 0 6:05 6:30 6:55 7:20 7:45 8:10 8:35 9:00 9:25 9:50 250 200 150 100 50 0 6:05 6:30 6:55 7:20 7:45 8:10 8:35 9:00 9:25 9:50 405N0.93ml-sim 405N0.93ml-real 405N1.93ff-sim 405N1.93ff-real 1000 800 600 400 200 0 6: 05 6: 30 6: 55 7: 20 7: 45 8: 10 8: 35 9: 00 9: 25 9: 50 405S3.31ml-sim 405S3.31ml-real 1000 800 600 400 200 0 6: 05 6: 30 6: 55 7: 20 7: 45 8: 10 8: 35 9: 00 9: 25 9: 50 405N3.04ml-sim 405N3.04ml-real 1000 800 600 400 200 0 6: 05 6: 30 6: 55 7: 20 7: 45 8: 10 8: 35 9: 00 9: 25 9: 50 405N3.86ml-sim 405N3.86ml-real 200 150 100 50 0 6: 05 6: 30 6: 55 7: 20 7: 45 8: 10 8: 35 9: 00 9: 25 9: 50 133s9.37ml-sim 133s9.37ml-real 5-min traffic count calibration at major freeway measurement locations (Mean Abstract Percentage Error: 5.8% to 8.7%)

Discussion

•

Completeness and quality of the observed data

– –

Especially important for calibration result Quality of the observed data

–

Calibration errors might have been derived from problems in observed data

–

Probe vehicle data with about 15-20 minute intervals cannot provide a good variation of the travel time

–

Quantity / Availability of observed data

–

cover every part of the network

–

some parts of the network were still un-calibrated because of unavailability of data

Conclusion

• •

Conclusion

–

a calibration procedure for a network-level simulation model

–

responding to the extended use of microscopic simulation

–

the calibrated model:

–

reasonably replicates the observed traffic flow condition

–

potentially applied to other micro-simulators Future work:

– –

inter-relationship between route choice and OD estimation an automated and systematic tool for microscopic simulation model calibration/validation