A Calibration Procedure for Microscopic Traffic Simulation

1. 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

2. Outline • Introduction • Data preparation • Calibration • Evaluation of the overall model • Discussion • Conclusion

3. 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

4. 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

5. Study network

6. 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)

7. 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

8. Calibration procedure

9. Determining number of runs • μ, δ: • mean and std of MOE based on the already conducted simulation runs • ε: allowable error • 1-α: confidence interval

10. 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%

11. 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

12. 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

13. 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 • OD adjustment algorithm: proportional assignment • assuming the link volumes are proportional to the OD flows • Result: • 96% of all measurement locations < 5

14. 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

15. 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

16. Examples of OD profiles

17. 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

18. 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.

19. 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

20. Some calibrated OD profiles

21. volume-occupancy curve Loop station @ 2.99 Real world Simulation

22. Evaluation of Calibration (I) • Measure for goodness of fit: • Mean Abstract Percentage Error (MAPE) 3.1% (SB) 8.5% (NB) Comparison of observed and simulated travel time of SB / NB I-405

23. Evaluation of Calibration (II) 5-min traffic count calibration at major freeway measurement locations (Mean Abstract Percentage Error: 5.8% to 8.7%)

24. 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

25. 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