Model City 2011: 22 nd International Emme Users Conference

Presentation Outline Project Need CT-RAMP ABM Road Pricing EMME Implementation Pricing Sensitivity Tests Next Steps Questions

CMAP Region Population: 10.5 million Modeling Region 21 counties in 3 states Neighboring MPOs SE Wisconsin NW Indiana 1,944 TAZs Road Network 15.0K nodes 44.3K links Rail Network 6.6K nodes 19.5K links

Current Model System Four-step trip based model Fortran Trip generation Trip distribution Mode choice EMME Time-of-day factoring (8 time-of-day periods) Assignments and skimming External, truck, and airport trips Fixed trip tables SAS for pre- and post-processing Auto: SOV and HOV2+ Trucks: heavy, medium, light, and b-plate (<4 tons) Transit 7 line haul modes CTA: metro, local bus, express bus Metra: commuter rail PACE: express, regional, and local buses 12 auxiliary modes

Policy Environment GO TO 2040 Regional comprehensive plan adopted in 2010 Recommendations Implement congestion pricing Implement parking pricing Increase commitment to transit Need improved tools for testing pricing policies: ABM

Project Develop pricing (demonstration) ABM Borrow ABM from other MPOs (Atlanta, San Francisco Bay Area, San Diego, etc) Develop base year synthetic population Integrate with CMAP highway and transit networks Re-estimate/calibrate key components Destination choice Mode choice Prove usefulness of ABM; develop full ABM later

CT-RAMP Family of Models Coordinated Travel Regional Activity-based Modeling Platform Key features: Explicit intra-household interactions and Coordinated Daily Activity Patterns (CDAP) Near - continuous temporal dimension (30 minutes) Java-based package for ABM construction 7 FeatureABM4-Step Main unit of travelTourTrip Travel generation mechanism Derived from participation in activities Attributed to population a priori Structural objects for modeling Individual microsimulation of persons and households Aggregate zone-to-zone flows (trip tables)

Members of CT-RAMP Family 1 st generation: Columbus, OH (MORPC) – in practice since 2004 Lake Tahoe, NV (TMPO) – in practice since nd generation: Atlanta, GA (ARC) – in practice since 2009 San-Francisco Bay Area, CA (MTC) – in practice since rd generation: San-Diego, CA (SANDAG) – started in 2008 Phoenix, AZ (MAG) – started in 2009 Jerusalem, Israel (JTMT) – started in 2009 Chicago (CMAP) – started in 2010 Every model has many unique features 8

CT-RAMP Person Types PERSON-TYPEAGEWORK STATUSSCHOOL STATUS Full-time worker18+Full-timeNone Part-time worker18+Part-timeNone Non-working adult18 – 64UnemployedNone Non-working senior65+UnemployedNone College student18+AnyCollege + Driving age student16-17AnyPre-college Non-driving student6 – 16NonePre-college Pre-school0-5None 9

CT-RAMP Activity Types 10 PURPOSEDESCRIPTIONCLASSIFICATIONELIGIBILITY WorkWorking at regular workplace or work-related activities outside the home. MandatoryWorkers and students UniversityCollege +MandatoryAge 18+ High SchoolGrades 9-12MandatoryAge Grade SchoolGrades K-8MandatoryAge 5-13 EscortingPick-up/drop-off passengers (auto trips only). MaintenanceAge 16+ ShoppingShopping away from home.Maintenance5+ (if joint travel, all persons) Other MaintenancePersonal business/services, and medical appointments. Maintenance5+ (if joint travel, all persons) Social/RecreationalRecreation, visiting friends/family. Discretionary5+ (if joint travel, all persons) Eat OutEating outside of home.Discretionary5+ (if joint travel, all persons) Other DiscretionaryVolunteer work, religious activities. Discretionary5+ (if joint travel, all persons)

CT-RAMP Model Structure Model Re-estimated for CMAP Pricing ABM Auto ownership model Destination choice models Time-of-day choice models Mode choice models

Distributed Modeling System Main Machine Manages model run system Stores in-memory households, persons, matrices Skimming and assignment for two time periods 2 Six-Core Intel Xeon 2.66 GHz, 144 GB RAM, $10K 3 Worker Machines Solves model components (for bundles of households) Skimming and assignment for two time periods 2 Six-Core Intel Xeon 2.66 GHz, 144 GB RAM, $10K Uses Java JPPF to run worker node processes and Microsoft PsExec to run EMME processes on workers

Distributed Model System

Road Pricing Essentials Variation in Value of Time: ABM operates with a continuous VOT distribution EMME requires discrete classes (High VOT, Low VOT) Vehicle occupancy: ABM and EMME operates with 3 discrete classes (SOV, HOV2, HOV3) Route type choice: ABM and EMME explicitly treat toll and non-toll users for each segment

Advanced VOT Techniques in ABM Basic VOT estimated for each travel purpose and person type Situational variation of VOT applied for each person based on lognormal distribution Car occupancy accounted by cost sharing: VOT for HOV2 is 1.6 of highest participant VOT VOT for HOV3+ is 2.3 of highest participant VOT For static assignments VOT has to be aggregated across individuals into discrete vehicle classes

Example of VOT Distribution

Initial Value-of-Time Segmentation Vehicle Type & Value-Of- Time Non- toll SOV Non-toll HOV2 Non-toll HOV3+ Toll SOV Toll HOV2 Toll HOV3+ Auto low VOT Auto high VOT Commercial1314 Light truck1516 Medium truck1718 Heavy truck1920 External low External high Airport low Airport high

Route Type Choice Currently implemented as binary choice (toll vs. non- toll); can be extended to distinguish between managed lanes (toll vs. non-toll) and general purpose lanes (toll vs. non-toll) Explicit modeling and analysis of toll users at OD level Accounts for (negative) toll bias Allows for VOT variation / segmentation beyond 12 assignable classes

Applied Segmentation Rules Assignable trip tables are segmented by 44 classes: 12 core auto components are generated by ABM 8 truck components are handled by route type choice model implemented in EMME 12 external components are handled by route type choice model implemented in EMME 12 airport travel components are handled by route type choice model implemented in EMME

Desired Multi-Class Assignment Classes Vehicle Type & Value-Of-Time Non- toll SOV Non-toll HOV2 Non-toll HOV3+ Toll SOV Toll HOV2 Toll HOV3+ Auto + external + airport low VOT Auto + external + airport high VOT Commercial1314 Light truck1516 Medium truck1718 Heavy truck1920

EMME Implementation Constraints Currently multi-class-assignment is limited to 12 classes (will be extended soon to 30) It will be beneficial to consider more than 2 VOT classes, for example (Low, Medium, High) Possible implementation scheme: Pre-assign heavy and (possibly) medium trucks since they follow planned routes (4 classes) Assign the rest of classes with heavy and medium trucks preloaded (16 classes)

Current Multi-Class Assignment Classes Vehicle Type & Value-Of- Time Non- toll SOV Non-toll HOV2 Non-toll HOV3+ Toll SOV Toll HOV2 Toll HOV3+ Auto + external + airport low & high VOT Commercial + light truck 78 Medium truck910 Heavy truck1112

Assignment and Skimming Macro

Equilibration Details The model system requires 3-4 global iterations to reach a reasonable level of convergence Assignment and skimming macro is run before each global iteration (to generate LOS for ABM) and after the last iteration (to assign the final results) Assignment and skimming macro requires 4 internal iterations to equilibrate core and non-core components in route type choice Smart schemes are applied w.r.t highway assignment accuracy at early internal iterations and ABM accuracy at early global iterations

EMME Integration Eight databanks stored in the project folder on main machine PsExec copies two banks to each remote worker machine PsExec runs EMME macros remotely PsExec copies the banks back to the main machine Java-based ABM reads skims directly from the databank ABM is run (with sampling) ABM writes demand matrices directly to the databank

Run Times EMME Skimming and Assignment 8 databanks, 4 machines (12 threads each) Module 5.21: 6 hours 1 thread / databank Module 5.22: 1 hour 20 minutes 12 threads / databank CT-RAMP ABM 20% population: 4 hours 100% population: 17 hours Total Run Time for 1 iteration 5 hours 20 minute (with 20% sample) Will be reduced with additional machines (which is planned) 5.22 saves 78% on skimming and assignment time!

Pricing Sensitivity Trips To/From the CBD Scenario: Global pricing, 5X all toll costs

Trips To/From the CBD Scenario: Congestion Pricing, 5X peak toll costs Pricing Sensitivity

Next Steps Additional scenario testing, including corridor specific tests and cordon pricing Demonstrate usefulness of pricing ABM to policymakers Full ABM implementation, including revised transit modeling procedures Improve implementation with: Three additional worker machines Potentially EMME Modeller for data I/O, overall model running, automated creation of inputs, etc

Questions? Matt Stratton, Kermit Wies, Ben Stabler, Peter Vovsha, Surabhi Gupta,