Synthetic Household Attributes IPF Procedure Draw matching synthetic household from PUMS Uncontrolled attributes Worker status Auto ownership Number of block group households with controlled attributes Household size Income Age of head These are the attributes used in the Portland Study. Race is not used in the Portland study but may be of interest elsewhere. 1/22/06 DRAFT DRAFT DRAFT

Households by Number of Workers We are showing distribution of households by workers, because it is an important variable that is not controlled by the IPF procedure. The working status of individuals is simply drawn from the PUMS records, so it is important to see whether the resulting distribution is reasonably accurate. The resulting TRANSIMS distribution slightly underestimates 0 and 1 worker households and over-estimates 2+ worker households. 1/22/06 DRAFT DRAFT DRAFT

Distribution of Auto Ownership Like worker status, the number of autos is one of the households attributes that is drawn from the PUMS data. If this distribution turns out to be unsatisfactory, an Auto Ownership model can be applied and the number of autos replaced. The current thinking is that a separate auto ownership model is not necessary based on these results. We avoid using the terms “auto availability” in TRANSIMS, even though that is in fact what the Census asks in the PUMS data. This is because in TRANSIMS there is one auto available to every person of driving age, parked near their household activity location. The number of household autos that is one of the PUMS attributes is used in the Mode Preference Model and could be used in the Location Choice model if using log sums that are stratified by auto ownership. 1/22/06 DRAFT DRAFT DRAFT

Activity Pattern Generation Activity patterns generated from surveys are a good first step to developing a full activity model Easier and quicker to implement Captures Household interactions Characteristics of the household and the geographic location Ignores Disconnect between activity schedules, locations, and modes No ability for patterns to adjust over time to congestion 1/22/06 DRAFT DRAFT DRAFT

Synthetic-to-Survey Household Matching Classification and regression tree Household attributes Household size Number of workers Age of head of household Income Children less than age 5 Children ages 5 – 17 Adults ages 26 – 45 Urban area type Very urban Urban Suburban The tree used is essentially the same as that developed using the Classification and Regression Tree (CART) method by the National Institute of Statistical Sciences (NISS). To this, the Portland study added an eighth variable to each node (actually a trinary choice) corresponding to area type. The intent being to reflect different activity patterns based on density and access to transit. (We have yet to verify statistically what difference this has made) 1/22/06 DRAFT DRAFT DRAFT

Selecting Activity Patterns Survey Household H S O W Student, age 12 Worker, age 38 Low Income, Urban Synthetic Household Student, age 5-17 Worker, age 26-45 1 Car, Location Address Activity Location Choices Multiple survey cases This illustrates how a surveyed household with a particular set of attributes and diary-derived activity patterns is classified using the binary tree. Surveyed households that match the attributes corresponding to a node in the tree are chosen at random and their activity patterns matched to one of the synthetic households generated in the Population synthesizer. The activity location choice model is a sub-model within the Activity Generator and assigns locations to each activity stop in each daily pattern. 1/22/06 DRAFT DRAFT DRAFT

Location Choice Tour activity location choice model derived from existing trip-based destination choice model Two stage process Choose zone of activity based on mode choice logsums Choose activity location within the zone based on Activity location weight Airline distance to origin Work location choice balanced to employment 1/22/06 DRAFT DRAFT DRAFT

Approach Metro trip-based destination choice model TRANSIMS tour-based location choice model O H W 1/22/06 DRAFT DRAFT DRAFT

Location Choices on Complex Tours LogSum for Work for Shop for Recreate Work Attractor (1) Recreate Attractor (4) Shop Attractor (3) Home Other for Other Attractor (2) 1. Choose location of primary stop on anchor tour: 2. Choose other stops on anchor tour: 1/22/06 DRAFT DRAFT DRAFT

Location Choice Calibration Factors 1/22/06 DRAFT DRAFT DRAFT

Total Tour Distance by Tour Type In analyzing tour patterns, we look at activity strings as shown here. H = home O = other activity type W = work activity type This illustrates the cumulative distances for entire tours. In general, the longer the tour, the greater the cumulative error. These look pretty good though. 1/22/06 DRAFT DRAFT DRAFT

Mean Trip Length by Tour Leg Type Here’s what happens when you break it down by trip legs on the tour. The number of observations appears in parentheses. The WW segment is off, but it is a relatively infrequent pattern. We could have calibrated separate Betas for this and other trip types, but chose to focus on the most frequent patterns. We made no effort to calibrate “school” activity type trip lengths, because it was not subject to the location choice model—we used nearest school rules.. 1/22/06 DRAFT DRAFT DRAFT

HWH Total Tour Distance 1/22/06 DRAFT DRAFT DRAFT

HWOH Total Tour Distance 1/22/06 DRAFT DRAFT DRAFT

HWOWH Total Tour Distance 1/22/06 DRAFT DRAFT DRAFT

HOH Total Tour Distance 1/22/06 DRAFT DRAFT DRAFT

HOOH Total Tour Distance 1/22/06 DRAFT DRAFT DRAFT

HWOWH Total Tour Distance 1/22/06 DRAFT DRAFT DRAFT

HOH Total Tour Distance 1/22/06 DRAFT DRAFT DRAFT

HOOH Total Tour Distance 1/22/06 DRAFT DRAFT DRAFT

HOOOH Total Tour Distance 1/22/06 DRAFT DRAFT DRAFT

Trip Length Comparison with Trip-Based Destination Choice Model Converting to trip definitions, this looks pretty good except for schools. 1/22/06 DRAFT DRAFT DRAFT

Mode Preference Work in progress Started with the existing mode choice model for tour-mode choice at the zone level Market segmentation – constrained choice sets Activity location and time-of-day based routing to refine the tour leg (trip) mode choice Transit tour feasibility – location adjustments 1/22/06 DRAFT DRAFT DRAFT

Tour-Trip Mode Relationships 1/22/06 DRAFT DRAFT DRAFT

Purpose and Age Segmentation Work Tours Non-work Tours Age < 16 Age 16+ School Tours 1/22/06 DRAFT DRAFT DRAFT

Market Segmentation by Distance 1/22/06 DRAFT DRAFT DRAFT

Tour Mode Calibration by Trip Modes Iterative adjustment of mode-specific constants using observed and estimated shares: Drive Trips = Drive Trips on Drive Tours + Drive Trips on Park-n-Ride Tours Transit Trips = Transit Trips on Transit Tours + Transit Trips on Park-n-Ride Tours + Transit Trips on Passenger Tours Walk Trips = Walk Trips on Walk Tours + Walk Trips on Transit Tours + Walk Trips on Park-n-Ride Tours + Walk Trips on Passenger Tours + Walk Trips on Drive Tours Park-n-Ride Trips = 2 * Park-n-Ride Tours Bike Trips = Bike Trips on Bike Tours Passenger Trips = Pass. Trips on Passenger Tours 1/22/06 DRAFT DRAFT DRAFT

Initial Approach – Red Flags Mode specific constants too high Routing issues Procedures didn’t map to a logical decision process Made feedback extremely complex 1/22/06 DRAFT DRAFT DRAFT

Mode Choice Challenges in Microsimulation Environment Tracking people, vehicles and constraints Tour path needs to be physically feasible Time dependent path information Travel time is function of the exact departure time Transit schedule and service period limitations Router reveals illogical and infeasible paths Person-vehicle inconsistency Schedule infeasibility Improbable activity patterns 1/22/06 DRAFT DRAFT DRAFT

Feedback to Activity Models John Gliebe

Existing to Advanced Practice Transition Pathway Activity Generation through models Microsimulation feedback to activity-based models Existing to Advanced Practice Activity Generation using surveys to define activity patterns Microsimulation feedback to traditional models by time of day Regional traffic and transit Microsimulation Route traditional trips by time of day / DTA Assemble the data to support advanced models 1/22/06 DRAFT DRAFT DRAFT

Microsimulation Complications When decisions are applied to individual tours Feedback from microsimulation and point to point routing result in infeasible activity patterns Probability clashes with reality! When Time of Day (TOD) is modeled discretely (needed) We need to replace infeasible patterns with feasible patterns of similar probability OR -- leave probability theory out, and concentrate on process and rules 1/22/06 DRAFT DRAFT DRAFT

Recap Intended Modeling Process Choose an activity pattern Includes timed itinerary from survey Choose activity locations Choose tour modes Route the tour Feedback corrects inconsistencies with original itinerary Simulated arrival and departure times exceed maximum deviation from itinerary Some paths infeasible 1/22/06 DRAFT DRAFT DRAFT

Planned Feedback Design Hierarchy of solutions: Choose new starting times Choose new travel modes Choose new activity locations Choose whole new activity pattern Requires successive iterations and complex mode swapping routines to maintain calibration 1/22/06 DRAFT DRAFT DRAFT

Some things worked… Work tour starting time regeneration Reselected intermediate stop locations for transit tours 1/22/06 DRAFT DRAFT DRAFT

Regenerating Location Choices for Secondary Stops for Transit Routing Rationale Stage 1 Tour Mode choice based on utility of trip from home to primary activity Log-sum impedance values are dominated by auto modes Difficult to Route Solution: regenerate location choices of secondary stops using Transit-Walk log-sums 1/22/06 DRAFT DRAFT DRAFT

Regenerating Location Choices on Transit Tours W O If Tour Mode is Auto… O If Tour Mode is Transit… 1/22/06 DRAFT DRAFT DRAFT

But we had some problems… Generic description of path options from zone-to-zone skims led to infeasible choices Activity pattern complexity infeasible for many transit tours Response: Provide more specific information up front? or Use feedback to resolve the problems? 1/22/06 DRAFT DRAFT DRAFT

Information from time-dependent transit paths reveals… Two key problems: Individual transit run information and arrival time information inconsistent with the assumptions from the skims Don’t use zone aggregate skims Use specific tour/trip skims Path routing isn’t very realistic Requires fundamental reconsideration of transit path-building weights 1/22/06 DRAFT DRAFT DRAFT

Example graphic Proximate transit stops that don’t service destination… or get you home… at least not at the right time of day… without many transfers… and long walks and waits. Reliance on zonal log-sums masks real service direction of travel and temporal availability 1/22/06 DRAFT DRAFT DRAFT

Example graphic Need to consider both ends of the tour for transit availability 1/22/06 DRAFT DRAFT DRAFT

Example graphic Corridor-oriented vs. Looping Graphic example Current solution is reselection of intermediate stops on transit tours Suffers from problems noted above Mode-specific location choice? Shapes of Transit Tours vs. Auto Tours 1/22/06 DRAFT DRAFT DRAFT

Are there better ways to intra-tour consistency? Begin with skims generated by router rather than generic zone-to-zone skims Time of day choice model? Consider activity schedule in location choice May need to develop more analytical methods of feedback rather than relying on iterating out of problems e.g., variant on Evans algorithm (combined model) 1/22/06 DRAFT DRAFT DRAFT

Assessment of Initial Feedback Results Feed forward created many infeasible travel plans With generic skims, feedback had no information to improve choices Feedback won’t solve mode choice problems until mode choice is better informed about travel options 1/22/06 DRAFT DRAFT DRAFT

Assessment of Results Keith Lawton

Key Lessons Learned Initial principles (use current models) let us down in mode choice Retrospective should have… Why this model is different from trip models Reengineer mode choice to be consistent with what we discovered Auto availability-specific location choice? Time-period-specific location choice? Priority: skim methods development 1/22/06 DRAFT DRAFT DRAFT

Theme – a constructive critique Is this a good approach for transitioning from traditional to advanced practice models? Is this worth doing? Do we get worthwhile benefits? How would this help MPOs support decision makers? Does it address the technical and policy issues? Next steps…, current areas of research, lessons learned, recommendations for further study/research/evaluation 1/22/06 DRAFT DRAFT DRAFT