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Activity-Based Approaches to Travel Demand Analysis & Forecasting

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  1. Activity-Based Approaches to Travel Demand Analysis& Forecasting GEOGRAPHY 111 & 211A

  2. Outline • Background • Building Blocks • Model Components, Data, and Functions • Examples

  3. Background

  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 • Health management (active living & transportation)

  5. Rapidly Emerging Movement • Smart Growth (EPA): • 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 SEE:

  6. More Web Resources • • • • • We will discuss more of these aspects in Land Use and Transportation

  7. 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, NEPA & related studies

  8. New 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)

  9. General Approach (valid for all models here) • 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, fertility rates) • 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”

  10. Motivation for Activity Social Spheres and the Four Fundamental Forces Underlying Human Activity

  11. In Essence we Model Interactions • Human – Nature -> Environmental impacts (emissions, land use, etc) • Human - Built Environment -> Transportation system impacts (crowdedness, congestion, accidents) • Human – Machine -> Driver behavior, Use of information via internet, newspapers, word of mouth, at bus stops, on the road • Human – Human -> Schedule coordination in time and space

  12. Implied Assumptions • Even when we do not explicitly define the background model, we implicitly follow some sort of conceptual model of society • Any type of hierarchy assumes predetermined entities or some kind of causality – example from demography • The unit of analysis and level of aggregation also imply we assume the most important relations are at the level we use – this will become clearer later in this class

  13. Aggregation levels • Micro = individuals and households (in traffic a vehicle) • Meso = a group of individuals (segments or geographic area – in traffic analysis it is a traffic stream or a platoon) • Macro = an entire city, a region, country, and so forth • Appropriate level depends on the specific policy application, conceptual model of society we use, the process we simulate but also data availability and time/budget (usually higher aggregation lower the cost)

  14. 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 (500-600) 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) 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

  15. Complexity Example by Cambridge Systematics for PSRC

  16. 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

  17. 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

  18. 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

  19. 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

  20. Building Blocks

  21. 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 • What happens if I go for breakfast at a restaurant by the “ride share parking lot” ?

  22. Home Work Grocery store UCEN Basic Definitions 2 Tour or Trip Chain Tour or Trip Chain • Five trips • Two tours (two trip chains) • First tour = 3-trips, home-based, 2 stops • Second tour = 2 trips, work-based, 1 stop Note: Some applications identify main tour and secondary tours

  23. University of Toronto Example ILUTE

  24. Taxonomy from Another Viewpoint • Trip based • 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 generation accounting for trip chains • Tour formation models • Many choices linked through conditional probabilities (using some sort of Nested Logit model - later) • Synthetic schedules • Agents building schedules • Regression models of schedules • Cellular automata models (TRANSIMS) – kind of stochastic simulation • Production systems – an integrated system of rules

  25. Simple 4-step model(Trip Based)

  26. 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

  27. Improved 4-step From Rossi Seminar

  28. 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

  29. 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 • Let’s talk about the ways you follow to schedule activities • Most approaches imply thinking in terms of temporal hierarchies • Let’s talk about what causes what is in your schedules

  30. The June Ma Model

  31. Activity Patterns (Schedule) • A sequence of activities, or a schedule, defines a path in space and time • What defines an 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?

  32. 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

  33. 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

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

  35. Elements in Models • Activity Frequency Analysis • Activity Duration and Time Allocation • Departure Time Decision • Trip chaining and stop pattern formation • All these models used together produce a synthetic schedule

  36. 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

  37. 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, …

  38. Effect of constraints in a time-spatial projection Capability constraints Time y Authority constraints W H L S x

  39. Interaction within a family (example of coupling constraints) • The coding of activities: • 1 – Work (W) • 2 – Work-related business (WRB) • 3 – Education (Educ) • 4 – Shopping (S) • 5 – Personal business (P) • 6 – Escort (E) • 7 – Leisure (L) • 8 – Home (H)

  40. Interaction within a family

  41. Example 1 from CentreSIM

  42. Example 2 from CentreSIM

  43. Needs Constraints Trips Set of possible schedules Set of activities Duration Travel time Constraint-Based Models –Computational Approach Combinatorial algorithms

  44. The participation in particular activities

  45. Ingredients for Activity-Based Models

  46. Ingredients of Activity-Based Models • Data on time use-allocation (Demand for Service): Information collected from persons on their current use of their time to participate in out-of-home and at-home activities and for travel from one activity location to another (called time allocation).

  47. Ingredients (continued) • Data on activity opportunities and locations (Supply of Service): Information collected from places where people can actually pursue activities, including home. It also includes other attributes of activity participation such as availability, access, cost, etc.

  48. Ingredients (continued) • Person and household time use (activity and travel) profiles: These are the models of time allocation that function the same way as the typical UTPS-like models for travel albeit in a much more complex form and providing more detailed information for analysts and planners.

  49. Ingredients (continued) • An evolutionary engine (from t to t+x): Clearly the “snapshot” approach, a single time point in the distant future, to forecasting is surpassed. Alternate future scenarios are much more useful to decision makers because of the general trends they show rather than for their exact values of the forecast parameters. Some sort of mechanism that makes a region to evolve over time through the different stages of sociodemographic, and demand-supply changes is needed to depict the paths of, for example, traffic changes and reveals the instances at which policy intervention is needed. One such engine is called microsimulation.