SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY San Francisco DTA Model: Working Model Calibration Part 1: Process Greg Erhardt Dan Tischler Neema Nassir DTA Peer Review Panel Meeting July 25 th, 2012

SAN FRANCISCO COUNTY TRANSPORTATION AUTHORITY Agenda 9:00Background 9:30Technical Overview – Part 1 Development Process and Code Base/Network Development 10:15Break 10:30Technical Overview – Part 2 Calibration and Integration Strategies 12:00Working Lunch / Discussion 2:00Panel Caucus (closed) 3:30 Panel report 5:30Adjourn

Outline Model Overview Calibration Approach Speed Flow Parameters Presented by Dan Tischler & Neema Nassir Model Calibration Runs Current Model Parameters Key Findings 3

Model Overview

Natural breakpoint at San Bruno Mountain Park 976 TAZs 22 external stations 1,115 signals 3,726 stop controlled intersections 5

Model Overview PM Peak Model from 4:30-6:30 pm 1 hour warm-up time 3 hour network clearing time 270,000 internal trips 180,000 IX, XI or XX trips 6 Dynameq Software Platform

Calibration Approach

1.Ensure quality inputs 2.Measure anything that can be measured 3.Evaluate the results qualitatively 4.Evaluate the results quantitatively 5.Make defensible adjustments 8

Ensure Quality Inputs Identify and investigate failed signal imports Spot check stop- control—some issues with direction of 2-way stops Automate as much as possible 9

Measure Anything that can be Measured Measure speed flow parameters Change perceived cost instead of measured speed and capacity Avoid arbitrary demand changes 10

Evaluate Qualitatively 11 Example of extreme congestion

Evaluate Quantitatively Relative gap, RMSE, GEH, R- Squared Scatter plots, maps Tables by: area type, facility type, speed, turn type, time period, etc. Corridor plots Speeds 12

Make Defensible Adjustments Evaluate results and investigate worse offenders Hypothesize problems and propose changes 13

Speed Flow Parameters

Model Calibration Runs

16 Base Case – July 6 Test Change(s): Results: RMSE: Links = 133 (58%), Movements = 64 (80%) GEH: Links = 7.17, Movements = 4.59 Overall Vol/Count Ratio: Links = , Movements = This test includes intrazonal trips (assigned to the nearest centroid) and ambiguous two-way stop signs re-assigned as all-way stops At this stage, there were still network and signal issues that have since been dealt with

17 Test 1 – Speed-Flow Curve Changes Change(s): Free-flow speed, response time factor, effective length factor Results: RMSE: Links = 132 (57%), Movements = 64 (80%) GEH: Links = 7.04, Movements = 4.56 Overall Vol/Count Ratio: Links = , Movements = Increasing RTF and decreasing speeds caused gridlock in the CBD Without bus-only lanes, these changes have more impact With bus-only lanes included, capacities are too low and CBD is full of gridlock

Test 2 – Removing Bus-only Lanes: Stockton Street Example 18

19 Test 2 – Removing Bus-only Lanes Change(s): Bus-only lanes no longer specified as bus-only Results: RMSE: Links = 135 (59%), Movements = 64 (80%) GEH: Links = 7.32, Movements = 4.59 Overall Vol/Count Ratio: Links = , Movements = Got rid of gridlock in CBD People are allowed to use these lanes for right turns – how can we model that? Need to add them back in some way while still allowing for limited use – next test.

20 Test 3 – Increasing Demand Change(s): Increasing internal demand by 30% Results: RMSE: Links = 155 (68%), Movements = 72 (90%) GEH: Links = 8.18, Movements = 4.86 Overall Vol/Count Ratio: Links = , Movements = Significant gridlock all over the network Previously about 30% low on counts, but more demand overloads the network Need to fix flow patterns and speeds, not demand

Test 4 – Penalizing Locals & Collectors DTA Volumes 21 Static Volumes

Test 4 – Penalizing Locals & Collectors DTA Volumes 22 Static Volumes

23 Test 4 – Penalizing Locals & Collectors Change(s): Local and collector links had penalty of 1*FFTime added to generalized cost Results: RMSE: Links = 122 (53%), Movements = 61 (76%) GEH: Links = 6.85, Movements = 4.47 Overall Vol/Count Ratio: Links = , Movements = Arterial Plus flows are still much lower than expected – looking at speed-flow curves Important to test this again with transit-only lanes added back in some way

Current Model Parameters

Free Flow Speeds Free Flow Speed (mph) Regional CoreCBDUrban BizUrban Local25 30 Collector25 30 Minor Arterial30 35 Major Arterial30 35 Super Arterial30 35 Fwy-Fwy Connect Expressway6065 Freeway

Response Time Factors Response Time Factor* Regional CoreCBDUrban BizUrban Local1.2 Collector1.2 Minor Arterial1.2 Major Arterial1.2 Super Arterial1.2 Fwy-Fwy Connect1.2 Expressway1.2 Freeway * Response times corresponding to RTF equal to 1.1 and 1.2 are respectively and 1.5 seconds

Saturation Flow Rates Saturation Flow (vphpl) Regional CoreCBDUrban BizUrban Local Collector Minor Arterial Major Arterial Super Arterial Fwy-Fwy Connect Expressway Freeway

Other Traffic Flow Parameters 28 Effective Length (Ft.)24 Effective Length Factor1.17 Jam density (vpmpl)220

Assignment Specification 29 These values define the period of the simulation: Start of demand: 15:30 End of demand: 18:30 End of simulation period: 21:30 Transit lines simulation: Yes Re-optimization: No Re-optimization iteration(s): 0

Demand Specification 30 Demand and generalized cost for cars: class: Car_NoToll matrix: car_notoll paths: 20 intervals: 12 types (%): Car=100, generalized cost: movement expression + link expression movement expression: ptime+(left_turn_pc*left_turn)+ (right_turn_pc*right_turn) link expression: fac_type_pen*(3600*length/fspeed) Demand and generalized cost for trucks: class: Truck_NoToll matrix: truck_notoll paths: 20 intervals: 12 types (%): Truck=100, generalized cost: movement expression + link expression movement expression: ptime+(left_turn_pc*left_turn)+(right_turn_pc*right_turn) link expression: fac_type_pen*(3600*length/fspeed)

Control Plans and Results Specifications 31 Signals are applied during this period: excelSignalsToDynameq: 15: :30 These settings specify the time steps used by Dynameq. The purpose of these settings is just for analysis of the DTA results and doesn’t have any bearing on the results themselves. Simulation results: 15:30: :30: :05:00 Lane queue animation: 15:30: :30: :05:00 Transit results: 15:30: :30: :05:00

Advanced Specifications 32 These values are settings for the DTA method used by Dynameq. Traffic generator: Conditional Random seed: 1 Travel times averaged over: 450 s Path pruning: MSA reset: 3 Dynamic path search: No MSA method: Flow Balancing Effective length factor: 1.00 Response time factor: 1.00

Key Findings

Model is sensitive to changes, and can easily regress into gridlock. Bus-only lanes matter. Penalizing locals and collectors helps. Increasing internal demand 10% helps. Increasing demand 30% causes gridlock. Most runs show less congestion than we would anticipate. 34

Questions?