Calibration and Validation of the Simulation Model. Xin Zhang
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1 Calibration and Validation of the Simulation Model Xin Zhang
2 Comparison with existing tools Calibration and Validation
3 Comparisons Comparison of vehicle simulation with VISSIM Comparison of simulation w/o pedestrians impact
4 Intersection layout
5 Pre-timed Signal Plan 10s 40s 10s 40s
6 Light demand (vph) From/To Medium demand (vph) From/To Heavy demand (vph) From/To
7 Light Demand Light demand (vph) From/To
8 Movement Delays Delay (Low Demand) EWT EWL NST NSL Time Trans Mix Trans Mix Trans Mix Trans Mix
9 Medium Demand Medium demand (vph) From/To
10 Movement Delays Medium Demand EWT EWL NST NSL Time Trans Mix Trans Mix Trans Mix Trans Mix
11 Heavy Demand Heavy demand (vph) From/To
12 Movement Delays High Demand EWT EWL NST NSL Time Trans Mix Trans Mix Trans Mix Trans Mix
13 Paired t-test Demand Movement p-value 95% threshold Significance EWT Not significant Low EWL Not significant Demand NST Not significant NSL Not significant EWT Not significant Medium EWL Not significant Demand NST Not significant NSL Not significant High Demand EWT Not significant EWL Not significant NST Not significant NSL Not significant
14 Comparisons Comparison of vehicle simulation with VISSIM Comparison of simulation w/o pedestrians impact
15 Campus Drive PO4 PD1 VO1 VD1 PD2 PO1 PO2 PO3 VO2 VO3
16 Pedestrian and Vehicle Demand Pedestrians Vehicles Origin Destination Demand PO1 PD1 200 ped/hour PD2 200 ped/hour PO2 PD1 200 ped/hour PD2 200 ped/hour PO3 PD1 200 ped/hour PD2 200 ped/hour PO4 PD1 200 ped/hour PD2 200 ped/hour VO1 VD1 300 veh/hour VO2 VD1 300 veh/hour VO3 VD1 300 veh/hour
17 Simulation Result Mixed-Flow Simulation Trans-modeler Pedestrians Vehicles Pedestrians Vehicles Total Throughput Average Delay (s)
18 Comparison with existing tools Calibration and Validation
19 Calibration Procedure
20 Sensitivity Analysis Purpose: isolate parameters to understand their influence Experiment design (full-factorial or sampling) Histograms, plots and statistical test
21 Optimization problem Minimize RMSPE D = p P obs sim Dp Dp ( ) obs D N p p 2 Subject to: sim Dp = F(...) There are potentially several optimal solutions; The quality of these local solutions varies substantially; Existing optimization techniques can not guarantee finding the global optimal solution.
22 Genetic Algorithm Component: a genetic representation of the solution domain, a fitness function to evaluate the solution domain. Major steps: Initialization Selection Genetic Operators (Crossover and mutation) Termination
23 Laboratory test Set the parameters in the simulation model with the initial values (can be any values within reasonable range); Use the same demand pattern collected from the field (can use any other reasonable demand patterns) and run the simulation; Compute the output and treat them as the virtual observed data; Change the parameters to random initial values and perform the calibration based on the virtual observed data; Compare the calibrated parameter values with the initial set; and Compare the calibrated simulation results with the virtual observed data.
24 Data Collection Site Jiangnan West Jiangnan Middle Avenue Guangzhou City Guangdong Province China
25 20-minutes Video Calibration data: first 15-minutes Validation data: remaining 5-minutes
26 Simulation Input Network Demand by turning movements Free-flow moving speed Signal timings
27 Turning parameters Way-finding parameters Destination location Moving momentum Signal controller Conflict resolve parameters Aggression Patience Hesitation
28 Simulation Output Individual trajectories Individual delays Time-dependent throughput by movements
29 Layout Vehicle Network Pedestrian Network
30 Signal phasing and timing plan
31 Free-moving speed Walking speed Driving speed
32 Cumulative Counts (Pedestrian)
33 Cumulative Counts (Vehicle)
34 Delay (Pedestrian)
35 Delay (Vehicle)
36 Tunable parameter definitions Parameter name Denotation Parameter name Denotation Weight of the vehicle distance floor field veh w dist Vehicle initial aggressiveness k veh Weight of the pedestrian distance floor field ped w dist Pedestrian initial aggressiveness k ped Weight of the vehicle direction floor field veh w dir Hesitation factor k hes Weight of the pedestrian direction floor field ped w dir Vehicle patience factor veh α Weight of the vehicle signal floor field veh w sig Pedestrian patience factor ped α Weight of the pedestrian signal floor field ped w sig Vehicle cost factor veh β Pedestrian cost factor ped β
37 Sensitivity analysis Three levels per parameter (low, median and high) Latin Hypercube sampling (1000 combinations) Scattered plots and ANOVA statistical test
38 Scattered Plot
39 ANOVA-test results
40 Laboratory test Parameter Target value Initial value Calibrated value veh w dist ped w dist veh w dir ped w dir veh w sig w sig veh k k k α α β β ped ped hes veh ped veh ped (fix) (fix) (fix) (fix)
41 Laboratory comparison From To RMSE RMSPE Theil s Inequality Coefficient Pedestrians Vehicles a c a d c a c d d a d c
42
43 Convergence of GA on Actual Data
44
45 Where is the problem? ped w sig w ped sig _ obey w ped sig _ violate
46
47 Validation result From To RMSE RMSPE Theil s Inequality Coefficient Pedestrians Vehicles a c a d c a c d d a d c
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