SERPM 7/DTA Application FSUTMS Users Group Meeting

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1 SERPM 7/DTA Application FSUTMS Users Group Meeting Ana Elias, PhD, PE, PTOE Leigh Ann White, PE, PTOE June, worldwide

2 Agenda 1. Mesoscopic Modelling and Cube Avenue Principles 2. Background on Previous DTA Applications and Results (SERPM 6.5) 3. SERPM 7 DTA Application Development 4. SERPM 7 DTA Application Results 5. Ongoing Work and Future Challenges

3 Mesoscopic Modelling A Brief Review

4 Mesoscopic Level Modeling (i.e., Avenue & DTA) Microsimulation Level Modeling (i.e., VISSIM) Macroscopic Level Modeling (i.e., SERPM) Multi-Resolution Modeling (MRM)

5 Microscopic Models Destination Lane to predict time-step driver responses, these models simulate individual vehicle trajectories on a detailed network using behavioral models: car following gap acceptance lane-change route choice Gap Lead Lag Travel Lane

6 Mesoscopic Models Simulate movements of trips with some level of resolution (packets or vehicles)

7 Mesoscopic Models Simulate movements of trips with some level of resolution (packets or vehicles) Discretely model queues in the network

8 Mesoscopic Models Simulate movements of trips with some level of resolution (packets or vehicles) Discretely model queues in the network Traffic flow performance is still evaluated using aggregate macroscopic (flow theory) relationships (e.g., speed-flow) -> Greenshield

9 GREENSHIELD Flow Speed Speed Density Flow Density Oversaturated Conditions

10 Mesoscopic Models Simulate movements of trips with some level of resolution (packets or vehicles) Discretely model queues in the network Traffic flow performance is still evaluated using aggregate macroscopic (flow theory) relationships (e.g., speed-flow) -> Greenshield Aggregation/disaggregation processes used alternatively to combine queue and flow performance measures.

11 Information Cycle Demand Routes Flows Queues Costs

12 Information Cycle Disaggregate Demand Routes Flows Queues Costs

13 Information Cycle Aggregate Demand Routes Flows Queues Costs

14 Information Cycle Disaggregate Demand Routes Flows Queues Costs

15 Information Cycle Aggregate Demand Routes Flows Queues Costs

16 CUBE AVENUE General Principles 17

17 Cube Avenue Dynamic Assignment Mesoscopic Simulation Routes and flow rates change during model period based on congested costs. Vehicles are grouped into homogeneous packets and simulated as they move through the network.

18 Static versus Dynamic Highway Load (Cube) Voyager A vehicle exists everywhere along its route during period Variables do not change over the model period Capacity not enforced (V/C > 1) Link storage unconstrained Link volumes and costs are separable and independent Time = link traversal time + junction delay Avenue Simulated packets can only be in one place at a time Flow rates change by time segments (within period) Capacity enforced (V/C 1) Link storage enforced Link volumes and queues affect upstream links and costs Time = link traversal time + junction delay + queue time

19 SERPM 6.5 Initial DTA Calibration and Validation

20 Congested Time in Static Assignment (BPR) TTTT ii = TTT 1 + αα VV/CC ββ Congested Time in DTA (HCM 2000) TTTT ii = TTT + DDD tt 0.5 VV CC 1 + VV CC JJJJJJJJJJJJ VV CC DDDDDDDDDDDDDDee2 tt

21 SERPM 6.5 Calibration 40 Miles along I-95 in Palm Beach County 1 Mile across Freeways (1) J = High Speed Arterials (4) J = Low Speed Arterials (6) J = Ramps (7) J = Toll/HOT J =

22 Model Final Calibration All Facilities Results by Facility Type

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24 Model Final Calibration (by FT)

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31 SERPM 7.0 February-April 2015 PM Peak Period Tested Only

32 Three Main Initial Questions 1. Can it work with SERPM 7? 2. Does it improve highway assignment? 3. Can SERPM 7 sliced matrices be fed directly to DTA, or do they need to be run through a matrix estimator (DODME) first?

33 34 Adapting the Model

34 Update DTA Application for SERPM 7 1. Counts and network revisions 2. Matrices adjustments 3. Additional adjustments: Turn penalty file Zero-Flow Control Delay for signalized nodes HEVAL script Modified Miami-Dade Land Use lookup files for Monte Carlo Spatial & Temporal Splits SERPM 6.5 PM Period = 3:30-6:30 PM SERPM 7 PM Period = 3:00-7 :00 PM 4. Sensitivity Analyses

35 Counts and Network 1. Initial counts provided by FIU from previous work with SERPM 6.x and DTA 2. Counts on I-95 in Miami-Dade: mainline, ramps and express lanes 3. Counts for 3-hour period by 30-minute slices (3:30-6:30 PM) 4. Model extremely sensitive to count inconsistency (i.e., illogical count location) 36

36 Counts Consistency RMSE 110% 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% DODME Convergence Iterations Assignment Avenue Assignment Count 3:30 PM 4:00 PM 4:30 PM 5:00 PM 5:30 PM 6:00 PM 45 Degree Line Time Slice RMSE% V/C Used 185 counts originating from STEWARD and FTI 1 77% % % % % % 0.05 Overall 92% 0.49

37 Counts Consistency Revision LEGEND MAIN LANE MANAGED LANES ON RAMP OFF RAMP 601 MAIN LANE VOLUMES 941 MANAGED LANES VOLUMES 461 ON RAMP VOLUMES OFF RAMP VOLUMES BALANCED VOLUMES COSITE NEW 2676 CURRENT VOLUMES ADDED VALUES ON MAIN LANE NB I HOT % % Main Lane % % 3273 NB NEW N SR SR 112 NW 3rd Ave Counts were corrected/relocated. More count locations were included from other sources and 2010 FTI (simultaneously). Worksheets for quality control were created for each time slice: 3:30 PM, 4:00 PM, 4:30 PM, 5:00 PM, 5:30 PM, and 6:00 PM.

38 Counts Consistency Assurance Initial Count Locations Final Count Locations 185 counts locations with some internal inconsistencies 455 counts locations with verified internal consistency

39 Subarea Network Revisions Path Errors Corrected: 4 links 2 links Number of lanes Posted speeds TURN Penalty file: removed penalties, Updated Prohibitions

40 Matrices Model adapted to handle additional modes: 1. SERPM 6.5 = 4 modes (DA, SR2, SR2P, TRK) 2. SERPM 7 = 10 modes 1. Nine (9) ABM: SOV GP, SOV PAY, SR2 GP, SR2 HOV, SR2 PAY, SR3 GP, SR3 HOV, SR3 PAY, SCHLBUS 2. One (1) Non-ABM (HTRK)

41 Sensitivity Analyses 1. SERPM 7 Capacities vs Calculated Capacities DODME loop Final Avenue assignment 2. Convergence ITERLOADINC & MAXITERS DODME loop (Avenue & DODME iterations) Final Avenue assignment 3. Count coverage Two different count coverages (high and low)

42 Capacity Sensitivity Analysis Storage values remained from previous analysis 330 veh/ln/mi for all facilities STORAGE = LI.1.DISTANCE*LI.1.NUM_LANES*330 Higher than recommended HCM2000 values Capacity tested C=LI.CAPACITY*3 (SERPM 7 hourly capacity scaled to model period) Calculated capacity C=LI.NUM_LANES*2500*3 (Freeway) C=LI.NUM_LANES*1800*3 (Arterials & Ramps)

43 DODME Convergence & Capacity Comparison 100% 90% 80% 70% DODME RMSE 60% 50% 40% 30% 20% 10% 0% DODME Loops Iterations SERPM 7 Capacities Calculated Capacities Final Avenue Assignment Count Range 1-5,000 Calculated Time Slice SERPM 7 Capacities Capacities Allowable 1 44% 32% 2 43% 38% 3 41% 25% 4 50% 36% 45-55% 5 46% 31% 6 75% 59% PM Period 46% 28% 32-39% AVENUE uses STORAGE in addition to CAPACITY. Therefore, typical static CAPACITY values constrain DTA too much

44 ITERLOADINC Convergence Sensitivity Analysis - I Number of iterations between packet loadings for subsequent time segments Allows for partial convergence of network for each time slice Recommended for congested networks

45 Convergence Sensitivity Analysis - II MAXITERS When ITERLOADINC >0, MAXITERS specifies max no. of iterations for final time segment Iters = (No. of time segments 1) * ITERLOADINC + MAXITERS Min Iters = (No. of time segments 1) * ITERLOADINC + 1 For subsequent iterations path builds are not performed Reduces total simulation time Example (previous slide): ITERLOADINC=10 MAXITERS=5 o Iters = (7-1)*10+5=65 o Min Iters = (7-1)*10+1=61

46 Final Results With DODME Gap 8 Warm-up 7 6 Begin Time Slice #1 Begin Time Slice #2 Begin Time Slice #3 Begin Time Slice #4 Begin Time Slice #5 Begin Time Slice #6 Gap (Parameter: Gap=0.005) Iteration

47 Final Results With DODME Departure/Arrivals 400, ,000 Vehicles 300, , , , ,000 50, Time Slice (including warm-up) Time Slice RMSE % V/C 1 28% % % % % % 0.82 Overall 29% 0.91 Vehicles Departed Vehicles Arrived

48 All Facility Types Static Assignment Dynamic Assignment 35,000 30,000 y = 1.09x R² = 0.90 V/C = 1.18 RMSE = 36% 35,000 30,000 y = 0.83x R² = 0.93 V/C = 0.88 RMSE = 34% 25,000 25,000 Output (PM) 20,000 15,000 Output (PM) 20,000 15,000 10,000 10,000 5,000 5, ,000 10,000 15,000 20,000 25,000 30,000 35, ,000 10,000 15,000 20,000 25,000 30,000 35,000 Count (PM) Count (PM) 49

49 Facility Type 1, Freeways Static Assignment Dynamic Assignment 35,000 35,000 30,000 y = 1.08x R² = 0.71 V/C = 1.09 RMSE = 23% 30,000 y = 0.81x R² = 0.67 V/C = 0.81 RMSE = 24% 25,000 25,000 Output (PM) 20,000 15,000 Output (PM) 20,000 15,000 10,000 10,000 5,000 5, ,000 10,000 15,000 20,000 25,000 30,000 35, ,000 10,000 15,000 20,000 25,000 30,000 35,000 Count (PM) Count (PM) 50

50 Facility Type 4, Higher Speed Interrupted Facility Static Assignment Dynamic Assignment 12,000 10,000 y = 1.32x R² = 0.55 V/C = 1.34 RMSE = 56% 12,000 10,000 y = 0.99x R² = 0.75 V/C = 0.98 RMSE = 24% 8,000 8,000 Output (PM) 6,000 Output (PM) 6,000 4,000 4,000 2,000 2, ,000 4,000 6,000 8,000 10,000 12, ,000 4,000 6,000 8,000 10,000 12,000 Count (PM) Count (PM) 51

51 Facility Type 6, Lower Speed Facility & Collector Static Assignment Dynamic Assignment 12,000 10,000 y = 1.38x R² = 0.16 V/C = 1.50 RMSE = 59% 12,000 10,000 y = 0.96x R² = 0.71 V/C = 0.99 RMSE = 23% 8,000 8,000 Output (PM) 6,000 Output (PM) 6,000 4,000 4,000 2,000 2, ,000 4,000 6,000 8,000 10,000 12, ,000 4,000 6,000 8,000 10,000 12,000 Count (PM) Count (PM) 52

52 Facility Type 7, Ramps Static Assignment Dynamic Assignment 12,000 10,000 y = 1.54x R² = 0.35 V/C = 1.82 RMSE = 101% 12,000 10,000 y = 0.97x R² = 0.74 V/C = 1.05 RMSE = 36% 8,000 8,000 Output (PM) 6,000 Output (PM) 6,000 4,000 4,000 2,000 2, ,000 4,000 6,000 8,000 10,000 12, ,000 4,000 6,000 8,000 10,000 12,000 Count (PM) Count (PM) 53

53 Facility Type HOT, Managed Lanes Static Assignment Dynamic Assignment 12,000 10,000 y = 0.95x R² = 0.39 V/C = 1.01 RMSE = 38% 12,000 10,000 y = 0.92x R² = 0.70 V/C = 0.90 RMSE = 22% 8,000 8,000 Output (PM) 6,000 Output (PM) 6,000 4,000 4,000 2,000 2, ,000 4,000 6,000 8,000 10,000 12, ,000 4,000 6,000 8,000 10,000 12,000 Count (PM) Count (PM) 54

54 Overall Results - All Facility Types Static Assignment Period Matrices (With DODME) 30-Minute Matrices (No DODME) 30-Minute Matrices (With DODME) Volume-to-Count Regression Slope R2 56

55 FTC 1 - Freeways Static Assignment Period Matrices (With DODME) 30-Minute Matrices (No DODME) 30-Minute Matrices (With DODME) Volume-to-Count Regression Slope R2 57

56 FTC 4 - High Speed Arterials Static Assignment Period Matrices (With DODME) 30-Minute Matrices (No DODME) 30-Minute Matrices (With DODME) Volume-to-Count Regression Slope R2 58

57 FTC 6 - Low Speed Arterials Static Assignment Period Matrices (With DODME) 30-Minute Matrices (No DODME) 30-Minute Matrices (With DODME) Volume-to-Count Regression Slope R2 59

58 FTC 7 - Ramps Static Assignment Period Matrices (With DODME) 30-Minute Matrices (No DODME) 30-Minute Matrices (With DODME) Volume-to-Count Regression Slope R2 60

59 HOT Lanes Static Assignment Period Matrices (With DODME) 30-Minute Matrices (No DODME) 30-Minute Matrices (With DODME) Volume-to-Count Regression Slope R2 61

60 Three Main Initial Questions 1. Can it work with SERPM 7? Yes! Definitely. Actually and pending more research DTA would seem to be a better match to SERPM 7 than to previous versions.

61 Three Main Initial Questions 2. Does it improve highway assignment? Yes. Substantially. Although given the limited time of this exercise, more work should be done to improve calibration. Note that current J values come from the previous SERPM version and might merit additional adjustment.

62 Three Main Initial Questions 3. Can SERPM 7 sliced matrices be fed directly to DTA, or do they need to be run through a matrix estimator (DODME) first? Yes, to certain extent. There is evident benefit in using the DODME estimator particularly for large areas. In smaller areas the differences do not seem very significant.

63 Beyond the Initial Questions Additional Lessons Learned

64 Critical Variables in Dynamic Assignment Volumes: CAPACITY versus STORAGE. The model is highly sensitive to the correct calculation/combination of both parameters. Travel time in arterials: Intersection delay simulated through Zero-Flow Control Delay deserves more testing as travel times seem to become sensitive to it only beyond certain thresholds. Note Not enough time was available during this exercise to further investigate the previous statements.

65 Sensitivity of Input Data Quality Network Coding Speeds Number of Lanes Intersections/Signals Count Consistency Location Balance Garbage #2 Garbage #1 Semi- Garbage #3 Convergence Parameters F(network congestion) HUGE GARBAGE

66 Sensitivity of Input Data Quality Network Coding Speeds Number of Lanes Intersections/Signals Count Consistency Location Balance Meso (DTA) Micro (VISSIM, CORSIM) Convergence Parameters F(network congestion) Macro (TDM) 68

67 Detail of Output Data Volumes By period By sub-period By time-slices Travel Times / Speeds By link By time segment Meso (DTA) Micro (VISSIM, CORSIM) Congestion Densities Bottlenecks Macro (TDM)

68 Various Output Capabilities Adaptable to Specific Needs 70

69 I-95 Miami-Dade Application Results -GPL I-95 Northbound GP Lanes - PM SR 836 SR 112 NW 62 ST NW 103 ST NW 135 ST NW 151 ST NW 167 ST MIAMI GARDENS DR Volume Distance (Miles) Time slice 1 Time slice 2 Time slice 3 Time slice 4 Time slice 5 Time slice 6

70 I-95 Miami-Dade Application Results - GPL I-95 Southbound GP Lanes - PM MIAMI GARDENS DR FL TURNPIKE NW 135 ST NW 103 ST NW 79 ST SR 112 SR 836 Volume Distance (Miles) Time slice 1 Time slice 2 Time slice 3 Time slice 4 Time slice 5 Time slice 6

71 I-95 Miami-Dade Application Results - ML

72 I-95 Miami-Dade Application Results - ML

73

74 SE 17th ST., Fort Lauderdale 8.00 Travel Times From Federal Hwy. to Andrews Ave. (PM-WB) Travel Times, (min) Field Model Output R1 Model Output R2 Model Output R3 Model Output R4 Model Output R5 Model Output R6 Model Output AVG Time Slice

75 SE 17th ST., Fort Lauderdale 1200 SE 17th St. East of Federal Hwy. (PM-WB) minute Volumes Field Counts Model Output R1 Model Output R2 Model Output R3 Model Output R4 Model Output R5 Model Output R6 Model Output AVG Time Slice

76 HEVAL for DTA Application Overall By Facility Type By Time Slice Assignment by Facility Type Assignment by Time Slice VMT VHT Congested Speed AM Peak Period No-Build Build1 No Toll Difference Miami EL Tolls Difference SERPM EL Tolls Difference Freeway (GP Only) 3,363,135 1,809,391 (1,553,744) -46% 2,225,646 (1,137,489) -34% 2,221,219 (1,141,916) -34% HOV / HOT 144, , , % 120,059 (24,120) -17% 120,288 (23,891) -17% Ramps 397, ,251 8,097 2% 404,525 7,371 2% 405,742 8,588 2% Arterials 8,335,647 8,473, ,683 2% 8,525, ,171 2% 8,530, ,382 2% Collectors 1,030,463 1,036,615 6,152 1% 1,038,491 8,028 1% 1,038,095 7,632 1% Overall 13,270,578 12,161,412 (1,109,166) -8% 12,314,539 (956,039) -7% 12,315,373 (955,205) -7% Time Slice 1 1,586,685 1,412,579 (174,106) -11% 1,430,513 (156,172) -10% 1,431,613 (155,072) -10% Time Slice 2 2,252,865 2,064,914 (187,951) -8% 2,087,606 (165,259) -7% 2,089,673 (163,192) -7% Time Slice 3 2,748,022 2,534,687 (213,335) -8% 2,565,796 (182,226) -7% 2,563,461 (184,561) -7% Time Slice 4 2,539,361 2,353,825 (185,536) -7% 2,380,032 (159,329) -6% 2,382,777 (156,584) -6% Time Slice 5 2,419,610 2,219,717 (199,893) -8% 2,248,625 (170,985) -7% 2,249,074 (170,536) -7% Time Slice 6 1,959,189 1,815,619 (143,570) -7% 1,841,487 (117,702) -6% 1,842,314 (116,875) -6% Overall 13,505,732 12,401,341 (1,104,391) -8% 12,554,059 (951,673) -7% 12,558,912 (946,820) -7% Time Slice 1 501, ,483 11,827 2% 513,536 11,880 2% 513,340 11,684 2% Time Slice 2 698, ,397 23,620 3% 722,273 23,496 3% 722,583 23,806 3% Time Slice 3 842, ,705 32,476 4% 874,082 31,853 4% 872,170 29,941 4% Time Slice 4 781, ,010 37,600 5% 816,579 35,169 5% 817,883 36,473 5% Time Slice 5 742, ,959 28,794 4% 768,973 26,808 4% 769,681 27,516 4% Time Slice 6 595, ,602 28,190 5% 623,258 27,846 5% 624,490 29,078 5% Overall 4,161,649 4,324, ,507 4% 4,318, ,052 4% 4,320, ,498 4% Time Slice 1 10,892 11, % 11, % 11, % Time Slice 2 16,171 16, % 16, % 16, % Time Slice 3 20,382 20, % 20, % 21, % Time Slice 4 18,663 19, % 19, % 19, % Time Slice 5 17,397 17, % 18, % 17, % Time Slice 6 13,702 14, % 14, % 14, % Overall 97, ,048 2,841 3% 100,568 3,361 3% 100,506 3,299 3% Time Slice (0.07) 0% (0.05) 0% (0.05) 0% Time Slice (0.16) 0% (0.18) 0% (0.15) 0% Time Slice (0.06) 0% (0.08) 0% (0.08) 0% Time Slice % (0.06) 0% (0.09) 0% Time Slice (0.07) 0% (0.04) 0% (0.03) 0% Time Slice (0.01) 0% (0.06) 0% (0.06) 0% Overall Average (0.06) 0% (0.07) 0% (0.07) 0%

77 Animation for Analysis (Google Earth Compatible)

78 Next Steps Finish the sensitivity analysis of Capacity versus Storage. Complete sensitivity analysis of zero flow control delay for interrupted flow facilities and verify travel time on uninterrupted facilities. Confirm whether a HOT tolling algorithm for the application is actually needed in DTA using SERPM 7 matrices. Implement programming to mimic calibration calculations to apply to future conditions for project-specific application.

79 Thank you! Copyright June 2015 Jacobs worldwide

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