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Development of Activity-Based Travel Demand Model

Development of Activity-Based Travel Demand Model

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Development of Activity-Based Travel Demand Model

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  1. Development of Activity-Based Travel Demand Model Kostas Goulias March 28, 2007 Presentation at Modeling Task Force

  2. Outline • Overview of models • Nested Logit type of Models • Synthetic Schedule Models • Common components • Possible SCAG Directions

  3. Background & basic definitions

  4. Policy Analysis Areas • Land use-development policies (smart growth, new urbanism) • Transit and pedestrian access and level of service improvement projects • Parking policies (restrictions, pricing by time of day) • Congestion pricing & time-of-day incentives (HOT lanes) • Policies affecting work hours (compressed work week, staggered work hours) • Ridesharing pricing and incentives • Telecommuting and related policies • Individualized marketing strategies (education) • Health management (active living & transportation)

  5. Rapidly Emerging Movement • Smart Growth (EPA & PILUT I & II): • Mix land uses • Take advantage of compact building design • Create housing opportunities and choices for a range of household types, family size and incomes • Create walkable neighborhoods • Foster distinctive, attractive communities with a strong sense of place • Preserve open space, farmland, natural beauty, and critical environmental areas • Reinvest in and strengthen existing communities & achieve more balanced regional development • Provide a variety of transportation choices • Make development decisions predictable, fair and cost-effective • Encourage citizen and stakeholder participation in development decisions

  6. Traditional Analysis Areas • Demographic shifts (aging, household composition, labor force shifts) • Changes in household size and composition, employment and geographic distributions • Impacts of new infrastructure (completion of the NHS, Major Investment Studies, corridor improvements, new major developments) • Travel times on OD pairs, congestion levels at specific locations, contribution to emission inventory, conformity & related studies

  7. New(er) Issues • Homeland security preparedness – time of day presence at specific locations and traveling • Condition of evacuation routes – best routes, fleet management, advisories to evacuating population • Behavior under emergencies (panic) – where do people go when a disaster strikes? • Planning models for traffic operations – interface with time of day traffic assignment, input to traffic simulation models • Special events management– International sport events (Olympics, World championships, Mundial and related large gatherings)

  8. Dynamic Planning Practice Evolving Paradigm of Modeling and Simulation New Research and Technology Sustainable and Green Visions The Three Pillars of the Shifting Paradigm Note: Modeling and Simulation includes quantitative and qualitative nature – regional models capture only the quantitative nature unless we make them more informative

  9. Requirements • Policies dictate to create and test increasingly more sophisticated policy assessment instruments • Account for direct and indirect effects of behavior, • Define procedures for behavioral change, and • Provide finer resolution in the four dimensions of geographic space, time, social space, and jurisdictions. • Dynamic planning needs: • Examination of trends, cycles, transitory and permanent impacts, • Invert the time progression and develop paths that lead to visions about the future. • Model developments are becoming increasingly urgent.

  10. Some examples of what is next ? • More experimental settings needed for performance measurement • Is there a way we could develop guidelines for experimental and quasi-experimental procedures to guide us in data collection? • Issues of scale in time and space – what is the right scale? • Coordination of model components – how? • Error tolerance and their mapping to strategy evaluations • Lack models designed to be used in scenario building exercises such as backcasting and related assessments. • Combine qualitative research methods with quantitative techniques in evaluations. • Invert time in current models and see what happens? • In new research and technology - > • Consider perceptions of time and space. • Distinguish different time dimensions -> tempo, duration, and clock time • Human interaction

  11. General Approach(valid for all models) • We divide information and data into exogenous and endogenous • Endogenous are predicted within the model system we design (e.g., number of trips a person makes in a day) • Exogenous are given to us and we are not able to influence with our policies (e.g., World and National economy, birth rates, immigration) • The distinction between exogenous and endogenous depends on the study/regional model development scope • the wider the impacts we “cause” the more comprehensive the model becomes and this increases the variables we need to “endogenize”

  12. Model Evolution • Regional simulation evolution: • In the 1950s and 1960s • Divide a large city (Detroit, Chicago) into a few Traffic Analysis Zones (20-30) and study a network of the highest level of highways (Interstates) • Most interesting movement from and to the CBD • Objective: find how many lanes a ring road needs • In the 1970s and 1980s • Divide a city into hundreds of Traffic Analysis Zones (500-600) and study a network of some collectors, arterials, and all higher levels highways as well as transit • All kinds of movements included (suburb to suburb emerged as key aspect) • Objective: divert traffic from cars driven alone to all other modes • In the 1990s • Divide a city into thousands of Traffic Analysis Zones (1000-3000) and study a network of some local roads, collectors, arterials, and all higher levels highways as well as transit • All kinds of movements included (suburb to suburb emerged as key aspect) • Objective: examine all kinds of policies from parking management to new construction • In the 2000s • Individuals, households, and parcels (residential and commercial) • More complex behavioral models (tours, time of day models, integration with other models) • Add a freight forecasting component • Integrate Transportation Planning with Land Use Planning Trends: Decreasing size of zones and increasing numbers of zones, closer examination of individual behavior, household as a decision making unit, expansion of the policy envelope to include car ownership, new vehicle technologies, information provision, and interface with traffic simulation - Land Use strategies designed to decrease the use of cars is also emerging as a demand management tool

  13. Complexity Example by Cambridge Systematics for PSRC

  14. Simplification • We try to identify blocks of decisions that have something in common • Most often we consider temporal ordering • We also distinguish between the domain within which an individual chooses from options versus the household as a decision making unit • We need some sort of sequential system to make our job tractable – this sequence can be a hierarchy

  15. Hierarchy Example Life Course Decisions – immigration, home ownership, place to live, education, job/career, family Long term – residence location, job location, schools for children Medium term – driver’s license, car ownership Yearly – public transportation pass/membership, vacation, enrolment in work related and recreational organized activities Monthly – pay mortgage and what else ???? Weekly – some kinds of shopping, visiting family/friends Daily – when to leave home, where to go, what transportation mode to use, with whom to do things

  16. Simplification of real world Allow to focus on decision within each temporal domain All lower level (shorter term) relationships are conditional on the previous level -> specific ways to create models Care to reflect relationships -> feedbacks Example: Car ownership and travel Hierarchies are convenient

  17. Car Ownership & Travel Get a better job – make more money Get a job - money Buy a car Replace the car Feedback from travel to car ownership – but also access to job opportunities Travel more often and longer distances Accumulate miles Car gets old All decisions are at different time points and they are conditional on past decisions

  18. Building Blocks

  19. Home Home Work Ride share parking lot Definitions 1 • Activities • In home stay • Work • Eat meal Trip Work Destination Origin Stage 2 Stage 1 • A trip with two stages

  20. Home Work Grocery store Lunch Basic Definitions 2 A a B Primary Tour or Trip Chain = A + B + C b C • Five trips • Two tours (two trip chains) • Primary tour = 3-trips, home-based, 2 stops • Secondary tour = 2 trips, work-based, 1 stop Secondary Tour or Subtour = a + b

  21. Taxonomy from Another Viewpoint • Trip based (4-step models) • Classify trips into a small set of categories • Explain variations based on a set of explanatory variables (age, gender, employment) • Develop procedures to convert these trips into vehicles per hour on highways • Tour based or trip chains (activity-based in US practice) • Activity generation accounting for trip chains • Tour formation models • Many choices linked through conditional probabilities (using some sort of Nested Logit model – later examples) • Synthetic schedules (academic) • Agents building schedules • Regression models of schedules • Cellular automata models (TRANSIMS) – kind of stochastic simulation • Production systems – an integrated system of rules

  22. Simple 4-step model(Trip Based)

  23. The 4-step Model Convert real world into Traffic Analysis Zones – Then convert highways and traffic analysis zones into a set of nodes and links building a graph

  24. Improved 4-step From Rossi Seminar

  25. Overview • Some limitations of 4-step and other older models • Zones are too large aggregates – ecological fallacy • Does not incorporate the reason for traveling – the activity at the end of the trip • Main motivation is the purpose as an activity location (places for leisure, work, shopping) • Trips are treated as if they were independent and ignores their spatial, temporal, and social interactions • Heavy emphasis on commuting trips and Home-based trips • Limited policy sensitivity (TAZs are hard to use in policy analysis) • Limited ability to incorporate environment and behavioral context • Was not envisioned as a dynamic framework of travel behavior

  26. Activity-Based Approach(es) • Activity-Based Approach • Think and model activities first (the motivation) • Consider interactions among activities and agents (people) • Derive travel as a result of activity participation (derived demand) • Consider linkages among activities and trips (interactions) • Demand for activities <-> time allocation • By definition a dynamic relationship with feedbacks • Most approaches imply thinking in terms of temporal hierarchies

  27. Activity Patterns (Schedule) • A sequence of activities, or a schedule, defines a path in space and time • What defines a person’s activity pattern? • Total amount of time outside home • Number of trips per day and their type • Allocation of trips to tours • Allocation of tours to particular HH members • Departure time from home • Arrival time at home in the evening • Activity duration • Activity location • Mode of transportation • Travel party • What else?

  28. y W L L S H Activities: H … Home W … Work L … Leisure S … Shopping W S H time x Real path Simplified path Time versus Space patterns Spatial pattern Temporal pattern activities

  29. y W L L S H Activities: H … Home W … Work L … Leisure S … Shopping W S H time x Real path Simplified path Time versus Space patterns Spatial pattern Temporal pattern activities Each activity = one episode A trip is an episode too

  30. Activities: H … Home W … Work L … Leisure S … Shopping Activities in Time and Space Time y W Ondrej Pribyl Visualization H L S x

  31. Elements in Models • Daily Patterns (entire skeletons or primary tours) • Activity Frequency Analysis (episodes) • Activity Duration and Time Allocation • Departure Time Decision • Trip Chaining and Stop Pattern Formation • Stops and Destinations • All these models used together produce a synthetic schedule

  32. Constraint Based models • Time-geography and Situational approaches in the 1970s • Attempt to show dependencies between particular trips • Based on Time Geography research in Lund School, Sweden, and a seminal paper by Hägerstrand (1970) “Why are people participating in activities? “ • to satisfy basic needs, such as survival and self-realization

  33. Why call it a constraints-based model? • Not all activities can be placed into a schedule at all times. • There are different types of constraints: • Capability constrains – maximum car speed, minimum required hours to sleep, … • Coupling constraints – meeting of a workgroup, … • Authority constraints –opening hours, speed limit, …

  34. Effect of constraints in spatio-temporal domain Capability constraints Time y Authority constraints W H L S x

  35. Activity Interaction(1471 persons – for whom question)

  36. Example 1 from CentreSIM

  37. Example 2 from CentreSIM

  38. A Fun Example

  39. Penn State Evacuation Model • Sajjad Alam, MS, 1996(simplified model of the PennState campus life – 40K students in a 120K area – urban island in a forest) • Application for general planning, circulation plan, emergency operations, and special events

  40. Used Activity Diary to Derive Time of Day Profiles Personal needs (includes sleep) Paid work Education Eat Meal Travel

  41. Activity Participation - Students

  42. Activity Participation - Faculty

  43. Activity Participation - Staff

  44. Assembled • Administrative records • Building characteristics • Developed attractiveness indicators (a gravity/distance model) • A survey of activity participation • A method to sequence activity participation

  45. Dynamic Presence on Campus

  46. Dynamic Presence on Campus

  47. Dynamic Presence on Campus

  48. Dynamic Presence on Campus

  49. Dynamic Presence on Campus