Transcript Document

Practical Application of
Activity-Based Models
Aggregate
4-step
Tour-based
Activity-based
Micro-simulation
4-Step is a “top down” approach
Divide population by zone / income / hh
size
(maybe also number of workers, car
ownership, age group)
More segments would be better, but there
is a practical problem…
4-Step is a “top down” approach
Divide population by zone / income / hh size
TRIP GENERATION
Adds trip purpose dimension
For example,
Home-based work,
Home-based school,
Home based shopping
Home-based other
Work-based
Other Non-home-based (NHB)
4-Step is a “top down” approach
Divide population by zone / income / hh size
TRIP GENERATION
Adds purpose dimension
TRIP DISTRIBUTION
Adds origin-destination dimension
Output is many trip matrices
4-Step is a “top down” approach
Divide population by zone / income / hh size
TRIP GENERATION
Adds purpose dimension
TRIP DISTRIBUTION
Adds origin-destination dimension
MODE CHOICE
Adds mode dimension
Output is even more trip matrices
4-Step is a “top down” approach
Divide population by zone / income / hh size
TRIP GENERATION
Adds purpose dimension
TRIP DISTRIBUTION
Adds origin-destination dimension
MODE CHOICE
Adds mode dimension
NETWORK ASSIGNMENT
Adds time of day and route dimensions
DIMENSIONALITY CRISIS!
ACTIVITY SCHEDULE
550 min
90 min
10 min
HOME
5
1 Eat, 6 Eat, 8 Sleep
25 min
5 SHOP
30 min
START 7:00
AM
20 min
25 min
7
6
4
15 min
1
15 min
240 min
7 MOVIE
2&4
SCHOOL
240 min
3
2
120 min
10 min
10 min
3 LUNCH
40 min
Major problems with
aggregate trip-based
approaches
Non-home-based trips!
Mode choice not consistent with adjacent trips
Destination choice not consistent with next trip
Time of day not constrained by adjacent trips
No substitution between tours
No interactions between household members
Aggregation errors/biases
Trip-Based to Tour-Based
Trip generation
Tour generation
(fixed rates?)
Trip time period
Tour time periods
(fixed factors?)
Trip distribution
choice
Tour destination
(gravity model?)
Trip mode choice
Tour mode choice
Intermediate stops
Tour-based to person-day-based
Tour generation
Tour time periods
Tour destinations
Tour mode choice
Intermediate stops
Day-pattern choice
Activity generation
Trip chaining
Tour sequencing
and time periods
Tour destinations
Tour mode choice
Intermediate stops
Person-day to Household-day
Day pattern choice
Activity generation
All tours individual
Day patterns linked
across HH members
Joint HH activities
Linked HH activities
(escorting)
Allocated HH activities
(maintenance tasks)
Individual activities
Some tours joint/linked
Geography of New Generation
Developed & Used
Portland (METRO)
San Francisco County (SFCTA)
New York (NYMTC)
Columbus (MORPC)
Started:
Atlanta (ARC)
Denver (DRCOG)
Dallas (NCTCOG)
Tampa Bay (FDOT)
Considering:
Houston (HGCOG)
Raleigh-Durham (CAMPO)
Sacramento (SACOG)
Kansas City (MARC)
Seattle (PSRC)
San Diego (SANDAG)
Geography of New Generation
Seattle
Portland
NY
Sacramento
Denver
Kansas
Columbus
SF
Raleigh
Dallas
Atlanta
San Diego
Houston
Tampa
Main Features
• Already in earlier designs (Portland, San Francisco, New
York):
– Tour as unit of modeling
– Consistent generation of all tours made during a person-day
– Stochastic micro-simulation application framework
• Added in later designs (Columbus, Atlanta, Denver):
– Explicit modeling of intra-household interactions
– Greater temporal detail (1 hour or less) and consistency in time use
and activity / travel scheduling
– Greater spatial detail (10,000-20,000 grid cells) for LU and walk /
bike / transit accessibility
Microsimulation is a bottom-up approach
POPULATION SYNTHESIZER
Create a synthetic population by sampling from
actual households to matches control
statistics or forecasts by zone
Output is a full list of households/persons
(like census data)
Microsimulation is a bottom-up approach
ACTIVITY AND TRAVEL SIMULATOR
Uses similar models to 4-step (activity
generation, destination choice, mode choice)
but uses the Monte Carlo method to simulate
discrete choices from probabilities
Also considers trip-chaining (tours)
and scheduling (time-of-day)
Output is a list of trips and activities
(like household travel survey data)
POPULATION SYNTHESIZER
Microsimulation is a bottom-up approach
AGGREGATOR
Compile trip matrices for network assignment or
simulation. Can also produce reports to look
at travel by specific population segments.
ACTIVITY AND TRAVEL SIMULATOR
POPULATION SYNTHESIZER
Microsimulation is a bottom-up approach
NETWORK ASSIGNMENT/SIMULATION
AGGREGATOR
ACTIVITY AND TRAVEL SIMULATOR
POPULATION SYNTHESIZER
“Continuous” space
Use very small units – GIS parcels or grid
cells (e.g. 200 meter squares)
Very good for modeling transit
accessibility and activity attractions.
Density variables used to capture
surrounding land uses.
Matrix-based measures such as in-vehicle
times remain at zonal level.
Benefits of using grid cell data
Walk access time to transit based on grid
cell GIS measures – much better results
Intra-zonal walk times based on distance
between O and D grid cells - intrazonal
dummy variable becomes insignificant
Grid cell-based measure of percent of
streets with sidewalks gives better
explanation of walk/bike share than CBD
dummy or other zone-based measures.
“Continuous” time
Use small time periods- 1 hour or half-hour
Model activity or tour start and end times
simultaneously, conditional on time remaining after
higher priority activities.
Better to capture interactions between tours and
activities.
Better for modeling peak-spreading
More accurate input to traffic simulation
Important Policy Areas
Congestion pricing / time-of-day
incentives
Policies affecting work or business
hours
Parking policies
Ridesharing policies
Demographic shifts (aging, household
composition)
How should models be judged?
Ability to predict future changes
Sensitivity to a wide range of policies
Ability to match current data
How are models typically judged?
Ability to match data on current situation
Simplicity of models, data, and forecasts
Predictability of forecasts
Replicability of forecasts
Issues in simulation error
Stochastic models do not necessarily
converge
Need to separate real variability from
simulation error.
Simulation error decreases with square
root of iterations.
Stability of results depends on level of
resolution (TAZ, county, etc.)
Simulation errors do not multiply –
compensation is more likely.
Tests of Random Simulation Error
Ran the model system (except for assignment) 100 times
Changed the random seed for each model for each run.
Analyzed the variability in results obtained from each
model in the system.
Main questions:
What is the range of results obtained?
How fast do the results converge toward the mean?
How is the variability related to the level of
aggregation?
Trips per Person
% Difference from Final Mean
3.0%
COUNTYMEAN
NEIGHMEAN
2.0%
TAZMEAN
1.0%
0.0%
-1.0%
-2.0%
-3.0%
1
7
13
19
25
31
37
43
49
55
61
67
73
79
85
91
97
Tours by Mode from a Single Origin TAZ
% Difference from Final Mean
8.0%
%DIFF-AUTO
6.0%
%DIFF-TRANSIT
%DIFF-NONMOTOR
4.0%
2.0%
0.0%
-2.0%
-4.0%
-6.0%
-8.0%
1
6
11
16
21
26
31
36
41
46
51
56
61
66
71
76
81
86
91
96
Conclusions Regarding Simulation Error
For region-wide results, a single run is adequate.
For corridor-level or neighborhood-level results, 5 to 10
runs should be adequate.
Looking at very small areas (TAZ’s), rare sub-populations
(e.g. single parents) or rare behavior (e.g. transit use in
some regions) requires more runs to reach stable results.
We have not yet looked at results with full equilibration
with assignment. The feedback from level-of-service
should dampen the variation even further.
Further Conceptual Evolution
Intra-person integrity
Activity & travel pattern configuration
Time use & activity generation
Time-space constraints on activity location
Feedback through individual time budgets
Inter-person intra-household integrity
Coordinated daily patterns
Episodic joint activity & travel
Maintenance task allocation
Car allocation
Simultaneous vs. sequential choices
At the tour or trip level – sequence of
Mode choices
Destination choices
Scheduling/sequencing choices
Trip chaining decisions
Empirical question, may vary by purpose.
More data on constraints and flexibility
would be useful
Use different sequences for different
types of situations or individuals? Need a
more flexible modeling framework.
Need dynamic models to deal with …
Advance vs. real-time planning
Simultaneous vs. sequential processes
Learning and information acquisition
Feedback processes over time
Direction of causality
Location vs. travel (induced demand)
Supply vs. demand (peak spreading)
Dynamic models will require …
Different types of data
Panels (?)
Before and after surveys
Retrospective surveys
Hypothetical choice contexts
Different types of models (?)
Strict adherence to econometric
choice theory has prevented the use
of non-static models