Activity and Tour Based Modeling Seminar

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1 Transportation leadership you can trust. presented by Thomas Rossi
Cambridge Systematics, Inc. April 13, 2004 Activity and Tour Based Modeling Seminar Acknowledgments Seminar developer Thomas Rossi, Cambridge Systematics Overseen by Texas Transportation Institute Gary Thomas Penelope Weinberger Federal Highway Administration Michael Culp 2 Acknowledgments Oversight committee Kostas Goulias, Penn State University Rebekah Anderson, Mid-Ohio Regional Planning Commission Bill Davidson, PB Consult Keith Lawton, Portland Metro Mark Bradley, Mark Bradley Research and Consulting Other contributors Harry Timmermans, Ryuichi Kitamura, Chandra Bhat Seminar Objectives Understand the limitations of traditional trip based models Learn about existing activity and tour based modeling
procedures Understand the concepts behind such models Identify the ways in which these models are estimated and
the data requirements Discuss how activity and tour based models can be applied 3 What Do You Know What Do You Know About Activity and About Activity and Tour Based Modeling? Tour Based Modeling? Two New Types of Models Tour based models Unit of travel is tour (beginning/ending at home) rather than
trip Characteristics (mode, destination, time of day) of trips in a
tour are modeled as related Activity based models Demand is assumed to be for trip making, rather than
activities Activity patterns with locations converted to tours All activity based models are tour based, but not all tour based models are activity based 4 The Role of Modeling in Transportation Planning Development of transportation plans Analysis of proposed transportation improvement projects Analysis of proposed transportation policies If conformity issues exist, needed for air quality analysis Land use planning The Four-Step Modeling Process An Old Friend? Trip Distribution by Purpose Trip Distribution by Trip Distribution by Purpose Purpose Assignment by Time Period/Mode Assignment by Assignment by Time Period/Mode Time Period/Mode Evaluation and Other Procedures Evaluation and Evaluation and Other Procedures Other Procedures Time of Day? Time of Day? Time of Day? Trip Generation by Purpose Trip Generation by Trip Generation by Purpose Purpose Mode Choice by Purpose Mode Choice Mode Choice by Purpose by Purpose Feedback of Congested Travel Times Feedback of Feedback of Congested Congested Travel Times Travel Times Transportation Network Supply Transportation Transportation Network Supply Network Supply 5 What Types of Models What Types of Models Do You Use Now? Do You Use Now? How comfortable are you with them? How comfortable are you with them? ? ? What are the Limitations of Your What are the Limitations of Your Trip Based Models? Trip Based Models? What Assumptions Do What Assumptions Do You Make? You Make? Analytical Analytical Data Data 6 Some Limitations of Trip Based Models Aggregation errors, many caused by the use of zones Trips are treated as independent of one another Sequential nature of four-step process Some More Limitations of Trip Based Models Behavior modeled in earlier steps unaffected by choices
modeled in later steps Effects of changes in transportation system not modeled
in all steps Lack of sensitivity of trip generation to accessibility/cost
(no induced travel) 7 Even More Limitations of Trip Based Models Demand is assumed to be for trip making, rather than
activities Limited number of segmentation variables can be
considered Limitations on types of policy analyses that can be
considered Analyses That Cannot Be Done Using Conventional Models Effects of level of service changes for one trip on other trips
in a tour Effects of level of service changes for one person on others
in household Identification of specific persons/households affected by
policy actions 8 ? ? How Old How Old How Friendly How Friendly The Four-Step Modeling Process An Old Friend? Concept of Tours Coffee Stop Coffee Stop Work Work Lunch Lunch Stop at Store Stop at Store Home Home 9 First United States Tour Based Models Boise (1994) Boise (1994) New Hampshire New Hampshire (1996) (1996) First United States Tour Based Models Boise Developed by Cambridge Systematics for Ada County New Hampshire Developed by Cambridge Systematics for New Hampshire
Department of Transportation 10 Early Tour Based Models Prior to United States Implementation Dutch national model Stockholm, Sweden Features of First Working Tour Based Models Tour Level Number of tours by type/purpose Number of intermediate stops for each tour Tour primary destination choice Tour level mode choice 11 Features of First Working Tour Based Models Trip Level Location of intermediate stops (trip destination choice) Trip level mode choice Correspondence Between Four-Step and Tour Based Models Trip Generation Number of trips by purpose could be derived from Number of tours by purpose Number of intermediate stops for each tour Home based work trips Half tours between home and work with no intermediate
stops Home based non-work trips All other initial and final legs of tours Non-home based trips Trips between primary destinations/intermediate stops 12 Correspondence Between Four-Step and Tour Based Models Trip Distribution Primary destination choice for tour Destination choice for intermediate stops (dependent on
locations of home and primary destination) Correspondence Between Four-Step and Tour Based Models Mode Choice Mode choice for tour (whether automobile is brought) Mode choice for intermediate stops (dependent on
tour level mode choice) 13 Tour Generation Models Models for each defined tour purpose Multinomial logit specification Inputs Primary destination choice utility logsum (induced travel) Socioeconomic characteristics of traveler/household Output Number of tours by purpose Tour Generation Model Example New Hampshire Model Work Tours One and Two Person Households 0 0 0 0 0 0 0.7535 0.7535 Summer Summer Dummy Dummy 0.1702 0.1702 0.1702 0.1702 0.08215 0.08215 0 0 Income Income Category Category 7.555 7.555 6.070 6.070 3.018 3.018 0 0 Workers Workers - - 12.60 12.60 - - 7.840 7.840 - - 2.345 2.345 0 0 Constant Constant Three Three Tours Tours Two Two Tours Tours One One Tour Tour Zero Zero Tours Tours Variable Variable 14 Tour Stops Models Models number of stops and work subtours Multinomial logit specification Inputs Intermediate stop destination choice utility logsum Socioeconomic characteristics of traveler/household Output Number of stops and subtours Tour Stops Model Example New Hampshire Model Work Tours - - 0.3018 0.3018 0.9116 0.9116 - - 0.2377 0.2377 - - 0.0957 0.0957 - - 4.378 4.378 Two Stops Two Stops One Sub One Sub 0 0 0.5996 0.5996 - - 0.2377 0.2377 0 0 - - 2.554 2.554 Two Stops Two Stops Zero Subs Zero Subs - - 0.3018 0.3018 0 0 - - 0.3018 0.3018 0 0 SF Dummy SF Dummy 0.5573 0.5573 0.3521 0.3521 0.5966 0.5966 0 0 In (Income) In (Income) - - 0.2377 0.2377 - - 0.2377 0.2377 - - 0.2377 0.2377 0 0 Workers Workers - - 0.0957 0.0957 0 0 - - 0.0957 0.0957 0 0 Vehicles Vehicles - - 3.738 3.738 - - 1.534 1.534 - - 3.695 3.695 0 0 Constant Constant One Stop One Stop One Sub One Sub One Stop One Stop Zero Sub Zero Sub Zero Stops Zero Stops One Sub One Sub Zero Stops Zero Stops Zero Sub Zero Sub Variable Variable 15 Destination Choice Models Combine trip attraction and trip distribution components
of four-step models Multinomial logit specification Models estimated/applied at two levels Tour level The location of the primary activity of tour Trip level The locations of intermediate stops on tour Singly constrained models (as are trip based logit
destination choice models) although artificial
constraints can be used if there is feedback Primary Destination Choice Models Separate models by tour purpose Alternatives are the destination zones Other inputs Socioeconomic characteristics of traveler/household Land use data (employment, etc.) Travel impedance captured using the mode choice
utility logsum 16 Intermediate Stop Destination Choice Models Alternatives are the zones for intermediate stops Inputs to multinomial logit Socioeconomic characteristics of traveler/household Land use data (employment, etc.) Additional time (impedance) to each sampled destination Output Zone for trip destination Nested logit mode choice models, one per tour purpose Alternatives Auto, transit, sometimes non-motorized, and park-and-ride Inputs Socioeconomic characteristics of traveler/household Land use data Number of stops on tour Level of service skims by time period
(best available transit path) Considers both Origin (O) Destination (D) and D O
level of service Output Mode for tour Tour Level Mode Choice Models 17 Trip Mode Choice Models Nested or multinomial logit models, one per tour purpose Inputs Socioeconomic characteristics of traveler/household Land use data Mode of tour Level of service skims (for O-D trip leg) by time period Output Mode for each trip on tour Trip Assignment Basically the same as for trip based models O-D trip table matrices must be created from information
on tours and stops 18 Time of Day Early United States models did not include time of day Tour level time of day Departure time from home Arrival time back at home Information on timing/duration of primary activity Trip level time of day (for each stop) Multinomial logit models May be modeled before destination or mode choice Other Tour Model Components Auto ownership model External travel model Usually treated as trip based for non-residents
(no data for tours) Can be treated as either trip or tour based for residents,
but no data on external destinations Commercial vehicle model Usually treated as trip based 19 Tour Based Modeling Data Requirements Basically the same as for trip based models Household/traveler characteristics Origin, destination, mode, etc. for all trips Which tours comprise trips (available from household
surveys) Data preparation Arrange travel into tours and trips within tour Classify households by structure/lifecycle Classify persons by age, worker status, household
structure/lifestyle Tour Based Modeling Model Estimation Same type of estimation process as four-step models
(logit estimation software) Many more models to estimate compared to four-step Data can be stretched thin be careful with specification 20 Tour Based Modeling Model Application Could use aggregate, sample enumeration, or
microsimulation approach Some modeling software beginning to incorporate
tour based approach Probably need custom software (can draw on existing
tour based models) Run times can be significantly longer, depending on
efficiency of programming Tour Based Modeling Model Validation Most validation tests of trip based models can (and
should) be performed for tour based models: Volume/VMT/screenline comparisons to counts Trip length frequencies Mode shares Tests of input data Comparisons of base and forecast years Other tests should also be performed: Trips per tour by purpose Tours per household by purpose, etc. 21 Tour Based Modeling Summary Model structure Generally known Model estimation procedures Generally same as trip based models Data requirements Generally same as trip based models Data processing Significantly greater than trip based models Run times Significantly greater than trip based models Analytical capabilities Greater than trip based models Definition of Activity Based Modeling Treatment of travel as a demand derived from the desire to
participate in other activities Focus on sequences/patterns of behavior Households as decision-making units Examination of timing and duration of activities and travel Incorporation of spatial, temporal, and interpersonal
constraints Recognition of interdependence of events Use of household/person classification schemes based on
differences in activity needs, commitments, and constraints Source: Kitamura (1996). 22 Activity Based Modeling Relation to Tour Based Modeling All activity models are tour based, but not all tour based
models are activity based Daily activity patterns have related travel patterns, which
are expressed as tours Tours, as sequences of trips, can be modeled without
modeling the underlying activity patterns (although most
modern models are activity based) Two Types of Activity Based Models Realization Realization Calculated Probabilities Calculated Probabilities or Realization or Realization Implementation Implementation Rule Based Rule Based Probabilistic Probabilistic Application Application Utility or Utility or Satisfaction Satisfaction Utility Utility Maximization Maximization Choice Stage Choice Stage Complex Search Complex Search Heuristic Heuristic Exhaustive (Feasible) Exhaustive (Feasible) or Simple Heuristic or Simple Heuristic Search Stage Search Stage Hybrid Simulation Hybrid Simulation Econometric Econometric Model Type Model Type Source: Based on Bowman and Ben Source: Based on Bowman and Ben - - Akiva (1996). Akiva (1996). 23 Activity Based Models Terminology In-home activities Activity opportunity Location in time and space where an activity can be pursued Duration The length of time an activity is performed
(excluding travel to/from the activity) Daily activity schedule A listing of activities to be pursued by an individual during
the day along with their locations in time and space Activity Based Models Early Research Oi and Shuldiner (1962) Introduced concept of travel as a derived demand Hagerstrand (early 1970s) Delineated systems of constraints on activity participation Chapin (early 1970s) Identified patterns of behavior across time and space Jones/Heggie (late 1970s/early 1980s) In depth interviews with small samples Gaming simulation 24 Activity Based Models Concepts up to the Early 1990s Bowman and Ben-Akiva Classified as econometric Introduced the concept of the daily activity pattern model Incorporated time of day decision Identified daily activity pattern, primary activity, primary tour
type, and number/purpose of secondary tours Implemented as system of nested logit models Activity Based Models Concepts up to the 1990s Satisficing approaches STARCHILD (1986 Recker, McNally, Root) MIDAS (1992 Goulias and Kitamura) SMASH (1993 Ettema, Borgers, Timmermans) AMOS (1995 Kitamura, Pendyala, Pas et al) FAMOS (Ongoing Pendyala, Kitamura et al) 25 Examples of Activity Based Models Portland Portland Columbus Columbus San Francisco San Francisco New York New York Examples of Activity Based Models Portland Developed by Portland Metro, Mark Bradley, John Bowman,
Cambridge Systematics San Francisco Developed by Cambridge Systematics, Parsons
Brinckerhoff, and Mark Bradley for San Francisco County
Transportation Authority 26 Examples of Activity Based Models New York Developed by Parsons Brinckerhoff with AECOM,
Cambridge Systematics, Urbitran, Urbanomics, Alex Anas,
NuStats, George Hoyt for New York Metropolitan
Transportation Council Columbus Developed by Parsons Brinckerhoff and Mark Bradley for
Mid-Ohio Regional Planning Commission Other Examples of Activity Based Models ALBATROSS (Netherlands) Arentze, Timmermans,
Hofman TRANSIMS Developed by Los Alamos National
Laboratories for U.S. Department of Transportation 27 Daily Activity Schedule Daily Activity Pattern Daily Activity Pattern Primary Tour Primary Tour Timing, Destination, Mode Timing, Destination, Mode Secondary Tour Secondary Tour Timing, Destination, Mode Timing, Destination, Mode Source: Bowman and Ben Source: Bowman and Ben - - Akiva (1996). Akiva (1996). Home Home Travel Travel In-Home Activities Choice between in-home and out-of-home activities may
be affected by transportation system HOWEVER, to model this choice, need survey data on
in-home activities Note that in-home includes not only technology driven
activities (telecommuting, shopping on-line, etc.) but
more traditional activities such as recreation 28 Activity Based Models Time of Day Modeling As in tour based modeling, need to jointly model start/end
times of tours and of intermediate stops Start time of activity = arrival time of trip End time of activity = departure time of trip Since activities are being modeled, activity durations are
being modeled Tours can take a long time! Cannot assign (as is done with trips) tours to individual
time periods Start/end time period combination defines alternatives Example Time of Day Model Portland Source: Bradley, Cambridge Systematics, and Portland Metro, 199 Source: Bradley, Cambridge Systematics, and Portland Metro, 199 8. 8. 4:00 P.M. 4:00 P.M. - - 6:59 P.M. 6:59 P.M. P.M. P.M. 7:00 P.M. 7:00 P.M. - - 2:59 A.M. 2:59 A.M. LA LA 9:30 A.M. 9:30 A.M. - - 3:59 P.M. 3:59 P.M. MD MD 7:00 A.M. 7:00 A.M. - - 9:29 A.M. 9:29 A.M. A.M. A.M. 3:00 A.M. 3:00 A.M. - - 6:59 A.M. 6:59 A.M. EA EA Time Periods Time Periods (15) LA (15) LA - - LA LA (14) P.M. (14) P.M. - - LA LA (13) P.M. (13) P.M. - - P.M. P.M. (12) MD (12) MD - - LA LA (11) MD (11) MD - - P.M. P.M. (10) MD (10) MD - - MD MD (9) A.M. (9) A.M. - - LA LA (8) A.M. (8) A.M. - - P.M. P.M. (7) A.M. (7) A.M. - - MD MD (6) A.M. (6) A.M. - - A.M. A.M. (5) EA (5) EA - - LA LA (4) EA (4) EA - - P.M. P.M. (3) EA (3) EA - - MD MD (2) EA (2) EA - - A.M. A.M. (1) EA (1) EA - - EA EA Definitions of Alternatives Definitions of Alternatives EA = Early MD = Midday LA = Late 29 Example Time of Day Model Portland (continued) Conditional on tour type, purpose, importance,
person/household variables Logit models with logsums from mode/destination choice Source: Bradley, Cambridge Systematics, and Portland Metro, 199 Source: Bradley, Cambridge Systematics, and Portland Metro, 199 8. 8. Example Time of Day Model Columbus Hours Hours 4.00 4.00 6.30 6.30 9.30 9.30 15.30 15.30 18.30 18.30 27.30 27.30 Early Early A.M. A.M. Midday Midday P.M. P.M. Late Late Every Person at the Beginning of Simulation has a Max Time Windo Every Person at the Beginning of Simulation has a Max Time Windo w w Scheduling the Mandatory (Work) Activity Scheduling the Mandatory (Work) Activity Residual Windows for the Next Activity Residual Windows for the Next Activity Source: Anderson, Al Source: Anderson, Al - - Akhras, Gill, and Donelly, 2003. Akhras, Gill, and Donelly, 2003. Centering 16 Centering 16 - - Hour Active Window (Currently 6.00 Hour Active Window (Currently 6.00 - - 22.00) 22.00) 30 Joint Activities/Intra-Household Interactions Source: Anderson, Al Source: Anderson, Al - - Akhras, Gill, and Donelly, 2003. Akhras, Gill, and Donelly, 2003. Household Size Household Size Household Size Household Size = One (No Joint Travel) Household Size = One (No Joint Travel) Household Size = One (No Joint Travel) Household Composition/Location/Income/Car Ownership Household Household Composition/Location/Income/Car Ownership Composition/Location/Income/Car Ownership 1. Linked Daily Activity Patterns for Household Members 1. Linked Daily Activity Patterns for Household Members 1. Linked Daily Activity Patterns for Household Members Non-Mandatory (Maintenance/Discretionary) Non Non - - Mandatory Mandatory (Maintenance/Discretionary) (Maintenance/Discretionary) At Home/Absent (No Travel) At Home/Absent At Home/Absent (No Travel) (No Travel) 3. Joint Household Tour Generation 3. Joint 3. Joint Household Household Tour G Tour G eneration eneration 4. Non-Mandatory Individual Tour Generation 4. Non 4. Non - - Mandatory Individual Tour Generation Mandatory Individual Tour Generation 5. Primary Destination and Time of Day for Non-Mandatory Joint and Individual Tours 5. Primary Destination and Time of Day for Non 5. Primary Destination and Time of Day for Non - - Mandatory Joint and Individual Tours Mandatory Joint and Individual Tours 6. Mode, Secondary Stop Frequency, and Location 6. Mode, Secondary Stop Frequency, and Location 6. Mode, Secondary Stop Frequency, and Location Mandatory (Work/University /School) Mandatory Mandatory (Work/University /School) (Work/University /School) Time window overlaps and synchronization indices Time window overlaps and Time window overlaps and synchronization indices synchronization indices 2. Primary Destination and Time of Day for Mandatory Tours 2. Primary Destination and 2. Primary Destination and Time of Day for Mandatory Tours Time of Day for Mandatory Tours Example of Joint Household Travel Modeling Columbus Fully joint tours generated by shared non-mandatory
activity Partially joint tours (pick-ups/drop-offs) generated by
synchronized mandatory activities (work/school) Fully and partially joint tours generated by altruistic
escorting 31 Dynamic Transition and Static Models Longer term decisions Dynamic models (panel data) Residential choice
Workplace choice
Car ownership
Household demographic transitions Shorter term decisions Daily activity patterns and related travel Examples MIDAS, DEMOS Activity Based Modeling Data Requirements Origin, destination, mode, etc. for all trips Activity based household surveys
(already used in many MPOs) For switching/satisficing models, may need
stated preference surveys For some types of models (e.g., MIDAS), need panel
survey data The future process data? 32 Activity Based Modeling Data Requirements Types of Surveys Activity diary Location diary Longitudinal (panel) survey Stated preference survey Activity Based Modeling Model Estimation Logit models estimated with estimation software More models to estimate compared to four-step or
tour based Data can be stretched thin be careful with specification 33 Activity Based Modeling Model Application Could use sample enumeration, but modern models use
microsimulation Modeling software does not yet accommodate
activity based approach can use for assignment
and network and matrix maintenance Need custom software (can draw on existing
activity based models) Run times can be much longer, depending on efficiency
of programming Microsimulation requires multiple runs (see next session) Activity Based Modeling Model Validation Most validation tests of trip and tour based models can
(and should) be performed for activity based models: Other tests should also be performed: Activities per person and tour Comparison of modeled joint participation to observed Comparison of modeled time at home to observed Checks of activities generated but not satisfied 34 Microsimulation of Households/Persons Conventional models are aggregate We model groups of similar households and attribute the same behavior to all of them It is possible to model the behavior of individual
households and persons Synthetic Population/Households How to define households and persons Number of persons Workers Ages Income Data sources Census PUMS
CTPP
SF1, SF3 Household survey How to derive Iterative proportional fitting Random sampling from survey or PUMS data 35 Application of Microsimulation Approach Compute probabilities for each choice Apply Monte Carlo simulation, based on the choice
probabilities, to determine behavior Run models multiple times (varying random number
seeds) to obtain reasonable average results Replicability of Results In aggregate and probabilistic models applied using
probabilities directly, results are the same every time
model is run When Monte Carlo simulation is used, results differ
(unless random number seed is kept constant) To obtain average results, need to run model several times Castiglione et al suggest that 10-20 runs are needed to
stabilize at the zone level, 5-10 runs for neighborhoods Number of runs will vary depending on level of detail Are the differences between scenario results within
the simulation error? 36 Resource Issues Run times, even without repeated runs to stabilize results,
can be long Simulation of choices of every person (possibly millions)
in region Efficiency of custom programs Hardware requirements significantly greater than for
traditional aggregate models Activity Based Modeling Summary Model structure Most working United States models are based on either the
Ben-Akiva/Bowman daily activity pattern approach or the
approach used by Vovsha et al, but other approaches have
been successfully tested Model estimation procedures Discrete choice models similar to trip based models,
rule based approaches Data requirements Need activity patterns, in some models may need
longitudinal data 37 Activity Based Modeling Summary (continued) Data processing Significantly greater than trip based models Run times Significantly greater than trip based models Analytical capabilities Significantly greater than trip based models Stockholm Tour Based Model 1994 Source: Algers et al, 1995. Activity and Travel Activity and Travel Mobility and Lifestyle Mobility and Lifestyle Car Ownership Car Ownership Work Location Work Location School School Recreation Recreation (Indoor) (Indoor) Personal Personal Business (Four) Business (Four) Business Business Shopping Shopping (Two Types) (Two Types) Social Social (Two Types) (Two Types) Work Tours Work Tours 38 New Hampshire Statewide Model Structure * Module Run Using EMME/2. * Module Run Using EMME/2. Zone Data Zone Data Networks Networks Auto Ownership Auto Ownership Tour Generation Tour Generation Primary Destination Choice Primary Destination Choice Tour Mode Choice Tour Mode Choice Tour Stops Tour Stops Stop Destination Choice Stop Destination Choice Trip Mode Choice* Trip Mode Choice* Trip Assignment* Trip Assignment* Time of Day* Time of Day* External/ External/ Truck Travel* Truck Travel* Source: Cambridge Systematics,
1998. San Francisco County Model Suite of C++ programs developed for other model
components Synthetic sample of households/persons Work location model Vehicle availability model Tour/trip generation and time of day models
(full day activity pattern) Tour destination choice/tour mode choice models Intermediate stop destination choice models Trip mode choice models, writes TP+ trip tables TP+ software used for skim building, assignment 39 San Francisco County Model Structure Population Synthesizer Vehicle Availability Model Full Day Tour Pattern Models Time of Day Models Nonwork Tour Destination Choice Models Tour Mode Choice Models Intermediate
Stop Choice Models Trip Mode Choice Models Visitor Trip and Destination Choice Model Visitor Trip Mode Choice Model Transit Assignment by Time Period (5) Regional Trip Tables for Non-SF Trips Zonal Data Workplace Location Model Highway Assignment by Time Period (5) Accessibility Measures Network Level of Service Logs um Variab les All Remaining Models Logs um V a
r
i
a
b
le
s All Models Source: Cambridge Systematics et al, 2001. Portland Model Structure Zonal Population and Land Use Data Zonal Population and Land Use Data Representative Sample of Households, Representative Sample of Households, Network Times, Costs, Differences Network Times, Costs, Differences Input O O - - D Trip Matrices by Mode, Purpose, D Trip Matrices by Mode, Purpose, Time of Day, and Income Class Time of Day, and Income Class Output Full Day Activity Pattern Full Day Activity Pattern Home Based Tour Home Based Tour Times of Day Times of Day Home Based Home Based Tour Tour Mode and Destination Mode and Destination Work Based Work Based Subtour Subtour Models Models Location of Intermediate Stops Location of Intermediate Stops for Car Driver Tours for Car Driver Tours Predicted Tours by Predicted Tours by Purpose and Type Purpose and Type Predicted Tours by Predicted Tours by Purpose, Type and Purpose, Type and Time of Day Time of Day Predicted Tours by Predicted Tours by Purpose, Type, Purpose, Type, Times of Day, Mode, Times of Day, Mode, and Primary Destination and Primary Destination Accessibility Logsum Accessibility Logsum Values by Tour Purpose Values by Tour Purpose and Tour Type and Tour Type Accessibility Logsum Accessibility Logsum Values by Tour Purpose, Values by Tour Purpose, Tour Type, Times of Day, Tour Type, Times of Day, Mode, and Destination Mode, and Destination Accessibility Logsum Accessibility Logsum Values by Tour Purpose, Values by Tour Purpose, Tour Type, and Times Tour Type, and Times Source: Lawton, 2001. Source: Lawton, 2001. 40 Columbus Model Household members simulated in priority order Choice conditional on choices of other household
members Work/school tours predicted first, then joint tours, then
other individual tours Remaining available time window influences choices at
each stage No explicit tradeoff between making stops or
additional tours Columbus Model Structure Source: Anderson, Al Source: Anderson, Al - - Akhras, Gill, and Donelly, 2003. Akhras, Gill, and Donelly, 2003. Highway Highway Network Project Coding Network Project Coding Transit Network Coding Transit Network Coding Network Preparation Network Preparation Build Highway Build Highway Paths/Skims Paths/Skims Accessibility Accessibility Indices Indices External External Model Model Truck and Truck and Commercial Commercial Vehicle Model Vehicle Model Subarea Extraction Subarea Extraction Special Special Generator Generator Model Model Post Post - - Processing/AQ Processing/AQ Reporting Reporting Transit Assignment Transit Assignment Pre Pre - - Assign Process Assign Process Highway Assignment Highway Assignment Microsimulation Microsimulation Household Synthesis Household Synthesis Auto Ownership Auto Ownership Daily Activity Agenda Daily Activity Agenda Tour Production Tour Production Individual and Joint Individual and Joint Primary Destination Primary Destination Time of Day Time of Day Entire Tour Mode Entire Tour Mode Secondary Stops Secondary Stops Trip Modes Trip Modes Prepare Socioeconomic Prepare Socioeconomic Land Use Zonal Data Land Use Zonal Data Build Transit Build Transit Paths/Skims Paths/Skims Highway and Transit Skims Highway and Transit Skims Feedback Loop Feedback Loop Trips Tables Trips Tables Daily Network Daily Network Period Networks Period Networks AM, MD, PM, NT AM, MD, PM, NT Period Networks Period Networks AM, MD AM, MD Multiclass Vehicle Trip Multiclass Vehicle Trip Tables by Period Tables by Period Networks Networks Microsimulation Reporting Microsimulation Reporting Microsimulation Records Microsimulation Records Networks with Flows Networks with Flows Trip Tables by Period Trip Tables by Period Core Tour Based Core Tour Based Choice Models Choice Models Networks with Flows and Times Networks with Flows and Times 41 Columbus Core Models H = Household Attributes PT = Person Type P = Purpose or Category A = Autos Owned O = Tour Origin (home) D = Tour Primary Destination M = Tour Mode TP = Time Period S = Number and Location of Stops m = Trip Mode Source: Anderson, Al Source: Anderson, Al - - Akhras, Gill, and Donelly, 2003. Akhras, Gill, and Donelly, 2003. Household Synthesis Auto Ownership Daily Activity Tour Production Household Synthesis Household Synthesis Auto Ownership Auto Ownership Daily Activity Daily Activity Tour Production Tour Production Tour Mode Primary Destination Time of Day Tour Mode Tour Mode Primary Destination Primary Destination Time of Day Time of Day Secondary Stops Submodes Secondary Stops Secondary Stops Submodes Submodes Two-Way Tours with H, P, A, O, M, S, m Two Two - - Way Tours with H, P, A, O, M, S, m Way Tours with H, P, A, O, M, S, m Two-Way Person Tours with H, P, A, O, D, M, TP Two Two - - Way Person Tours with H, P, A, O, D, M, TP Way Person Tours with H, P, A, O, D, M, TP Two-Way Person Tours with H, PT, P, A, O Two Two - - Way Person Tours with H, PT, P, A, O Way Person Tours with H, PT, P, A, O Microsimulation Microsimulation Microsimulation Columbus Model Hierarchy Daily Activity Daily Activity Joint Household Tour Generation Joint Household Tour Generation Primary Destination for Maintenance and Discretion Primary Destination for Maintenance and Discretion Entire Tour Mode Combination Entire Tour Mode Combination Stop Frequency and Location Stop Frequency and Location Trip Mode Trip Mode Work and School Tour Time of Day and Primary Destination Work and School Tour Time of Day and Primary Destination Maintenance and Discretionary Tour Time of Day Maintenance and Discretionary Tour Time of Day Day level with Day level with Intra Intra - - Household Household Interaction Interaction Tour Level Tour Level Trip Level Trip Level Individual Maintenance and Discretionary Tour Generation Individual Maintenance and Discretionary Tour Generation Source: Anderson, Al Source: Anderson, Al - - Akhras, Gill, and Donelly, 2003. Akhras, Gill, and Donelly, 2003.

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