ADAPTS: Agent-based Dynamic Activity
Planning and Travel Scheduling
Update on Model Development and Data Collection
Joshua Auld
CTS IGERT Seminar Presentation
February 26, 2009
Overview
 Accomplishments/IGERT Requirements
 Introduction: Activity-Based Modeling
 ADAPTS Framework (mostly complete)
 Population Synthesis (complete)
 Activity Generation (in progress)
 Activity Scheduling (complete)
 GPS Travel Survey / Activity Planning (in progress)
Update on Accomplishments and
IGERT Requirements
IGERT Requirements
 Requirements completed:
–
–
–
–
All coursework
Preliminary qualifications
Proposal defense (09/08)
International internship:
 One month at University of Toronto
 Work with Eric Miller, Matt Roorda, and others
 One publication, advising on thesis proposal, future work
 Remaining
– Domestic internship
– Finish dissertation
Potential Collaboration Opportunities
 Kostas Goulias, UCSB
 Ram Pendyala, ASU
 Harry Timmerman, Eindhoven
 All working on variations of dynamic
activity based models or GPS data
collection for models
Publications

Auld, J.A., A. Mohammadian and M.J. Roorda (2009). Implementation of a
Scheduling Conflict Resolution Model in an Activity Scheduling System.
Forthcoming in Transportation Research Record: Journal of the Transportation
Research Board

Auld, J.A., A. Mohammadian and K. Wies (2009). Population Synthesis with
Region-Level Control Variable Aggregation. Forthcoming in Journal of
Transportation Engineering.

Auld, J.A., A. Mohammadian and S.T. Doherty (2009). Modeling Activity
Conflict Resolution Strategies Using Scheduling Process Data. Forthcoming in
Transportation Research Part A: Policy and Practice. (available online
December 2008)

Auld, J. A., C. Williams, A. Mohammadian and P. Nelson (2009). An
Automated GPS-Based Prompted Recall Survey With Learning Algorithms.
Journal of Transportation Letters, 1 (1), 59-79

Auld, J.A., A. Mohammadian and S.T. Doherty (2008). Analysis of Activity
Conflict Resolution Strategies. Transportation Research Record: Journal of the
Transportation Research Board. 2054, 10-19
Presentations
 AATT08 (10th International Conference on Applications of
Advanced Technology in Transportation)
– Conflict resolution
– Population Synthesis
 TRB09 (88th Annual Meeting of the TRB)
– ADAPTS framework
– Scheduling rules model
 Transport Chicago
– GPS Survey
 UPCOMING:
– TRB Planning Applications – Population Synthesis Forecasting
– IATBR (potentially) – Dynamic Activity Planning
– TRB 2010
Introduction
Need for Travel Demand Modeling
1.
Intro
2.
Framework
3.
Population
Synthesis
4.
Activity
Generation
5.
Activity
Scheduler
6.
Survey
Results
 TDMs are used in many policy and
planning analyses
–
–
–
–
Impacts of construction
Location/necessity of new construction
Congestion pricing
Impacts of other transportation demand
policies:




HOV lanes
Telecommuting, flex-time shifts
Transit oriented development
Land-use policies
Why do we need travel demand model
for ITA development?
1.
Intro
2.
Framework
3.
Population
Synthesis
4.
Activity
Generation
5.
Survey
Results
ITA Implementation and usage
Changes in: travel planning, travel behavior
(encourage rideshare, efficient trip planning, schedule optimization, the list goes on….)
Changes in: travel demand, transportation network utilization
Costs and benefits to society
-Need to be evaluated (initially and on a continuing basis)
- Essential in order for public/private implementation to succeed)
How do we evaluate behavioral changes, travel demand changes
and hence costs/benefits?
ACTIVITY-BASED TRAVEL DEMAND MODEL!
Activity based modeling
1.
Intro
2.
Framework
3.
Population
Synthesis
4.
Activity
Generation
5.
Survey
Results

Use of activity-based modeling
– Microsimulation models, which develop an activity schedule for
modeled individuals
– Usually at the household or individual level
– Pattern of activities and travel explicitly developed for entire population

Can represent time very accurately
– Time of day choice often a core model component

Have a behavioral basis
– Can represent response to policy changes very well
– Location choice, time of day choice, mode choice utility based
– Explicitly captures trip chaining response

Currently lacking:
– Representation of planning dynamics
– Realistic activity planning
– Integration with traffic simulation – usually done through feedback
Issues in Activity-Based Modeling
1.
Intro
2.
Framework
3.
Population
Synthesis
4.
Activity
Generation
5.
Survey
Results
 Fixed order of priority of activities:
– Activities added to schedule and attributes picked in fixed order
– In other models: activities added in order of assumed priority
– Does not match observations from data (Roorda et al. 2005)
 Fixed order of attribute scheduling:
– In ALBATROSS: Party > Duration > Time > Mode > Location
– In other models: nesting structure fixed, calling order fixed
– Again, does not match actual scheduling process
 Scheduling planning dynamics
– Order of decisions can impact subsequent decisions
– Impulsive/unexpected events in simulation or scenarios
– Currently, entire schedule generated then executed
 May lead to erroneous results, especially with behavioralbased demand management strategies
ADAPTS Framework
Framework - Introduction
1.
Intro
2.
Framework
3.
Population
Synthesis
4.
Activity
Generation
5.
Survey
Results
 ADAPTS scheduling process model:
– Simulation of how activities are planned and scheduled
– Extends concept of “planning horizon” to activity attributes
– Time-of-day, location, mode, party composition
 Fits within overall framework of activity-based
microsimulation model
– Constraints from long-term simulation (land-use model)
– Combined with route choice and traffic simulation
 Models being generated for Chicago region
– Datasources: CHASE planning data, CMAP household travel
survey, CMAP land-use database, Census 2000

Auld, J.A. and A. Mohammadian. ADAPTS: Agent-based Dynamic Activity
Planning and Travel Scheduling Model – A Framework. Proceedings of the
88th Annual Meeting of the Transportation Research Board (DVD), January
11-15, 2009, Washington, D.C.
Overall Integrated Land-Use
Transportation Model Framework
1.
Intro
2.
Framework
3.
Population
Synthesis
4.
Activity
Generation
5.
Survey
Results
Land Use Patterns
Home/Work
Location choice
Work/Home Change
and Choice Model
Population
Synthesis
Household
Composition
Household
Long-Term Context
Transportation
System
Vehicle Ownership
Vehicle Transaction
Model
Long-term Decision Making
Short-term Simulation
Activity/Travel
Model
Traffic Simulation
Framework: ADAPTS model
1.
Intro
2.
Framework
3.
Population
Synthesis
4.
Activity
Generation
5.
Survey
Results
Current
Focus
Waiting on
GPS data
Mostly
complete
Decision Example:
1.
Intro
2.
Framework
3.
Population
Synthesis
4.
Activity
Generation
5.
Survey
Results
Unplanned (Queued) Activities
Activity Being Scheduled
T1
Executed Activities
Current Simulation Time
Plan new activity
-Ttime
Twho = Tmode
-Tloc
Ttime
Tloc
PlanTmode
and
Ttime
-Twho-with
loc
mode/who
Plan time-of-day
Plan Tlocation
who-with
-Tmode
Scheduled Activities with Trips
Texec
Execute
Activity
Texecute
Simulation Time
T
T
T
T
T
Shop
Time: ?
Loc: ?
Mode: ?
Schedule
At Home
Time: 12:00 AM – 8:00 AM
Loc: Home
Mode: None
Work
Time: 8:00 AM – 4:00 PM
Loc: HOME
?
Mode: None
?
Shop
Time: 4:00 – 5:00
Loc: Mall
?
Mode: Auto
?
Framework: Simulation Objects
1.
Intro
2.
Framework
3.
Population
Synthesis
4.
5.
Activity
Generation
Survey
Results
World
Attributes:
Zonelist[1,2,…,Z]
Time
Methods:
Run Simulation()
Zone
Attributes:
ZoneData
HHList[1,2,…,H]
Long term Memory
Act 1
TAZ
…
Act 2
U
TAZ
U
Potential Loc. Memory
Act M
TAZ
TAZ
U
U
Loc 1
Loc 1
Loc 2
Loc 2
…
…
Loc N
Loc M
Social Connections
ID
Household Attributes
HHID
HHSize
NumWorkers
NumChildren
FamIncome
Vehicle List[1,2,…M]
HHMemList [1,2,…N]
Friend1
Friend2
Individual - ID
…
Friend P
Entity Methods
GenerateActivity()
AddActivity()
RemoveActivity()
SetPlanTimes()
PlanStart()
…
PlanMode()
ScheduleActivity()
ResolveConflicts()
isOccupied?()
isTraveling?()
Individual Attributes
ID
HHID
Age
Gender
Income
JobStatus
Educ. Status
Family Type
HOMETAZ
WORKTAZ
Activity Schedule
ID
Act 1
Act 2
…
Act Q
Activity Attributes
ID
StartTime
Duration
PlanHorizon
TravelMode
Location
WhoWith
Type
TAZ
Remaining work
1.
Intro
2.
Framework
3.
Population
Synthesis
4.
Activity
Generation
5.
Survey
Results
 Attribute planning order model
– When to run each attribute sub-model
– Need to collect planning data
– GPS activity planning survey – starting soon
 Time-of-day, mode choice, party composition, etc.
– Model from CMAP travel data, GPS survey and other
sources
– Combination of model types, logit, decision tree, etc.
 Incorporate traffic simulation – work with VISTA
 Fit all models into overall framework
Population Synthesis
Population Synthesis
1.
Intro
2.
Framework
3.
Population
Synthesis
4.
Activity
Generation
5.
Survey
Results
 Presented at AATT09
 Upcoming presentation at TRB Planning
Applications Conference in Houston
 Published:
– Auld, J.A., A. Mohammadian and K. Wies
(2009). Population Synthesis with Region-Level
Control Variable Aggregation. Forthcoming in
Journal of Transportation Engineering.
Population Synthesis - overview
1.
Intro
2.
Framework
3.
Population
Synthesis
4.
Activity
Generation
5.
Survey
Results

Generating Synthetic individuals for the simulated region
– Using Census or HH survey data
– Generate all individuals in region

GOAL: transfer joint distribution and sample household to small
geographies (usually PUMS to Census Tract/BG
– Detailed samples (joint-distributions) given at large geographies (PUMS)
– Marginal distributions found at small geographies (CT/BG)
– Want to transfer joint-distribution to small area then draw from samples

Two stages:
– IPF: generate joint distribution across several control variables from sample
– Selection: selecting households from sample data to build population

New features:
– Marginal constraints in household selection
– Customizable – no fixed geography/variables
– Subregional control variable aggregation – combine infrequent marginal
categories at subregion level
– Built-in scenario evaluater/forecast tool
Population Synthesis - IPF
1.
Intro
2.
Framework
3.
Population
Synthesis
4.
Activity
Generation
5.
Survey
Results
 IPF algorithm:
– Iteratively update seed matrix to match one each control variables
– Continue until convergence (or iteration limit) is reached
– Assumption: Correlation structure (odds-ratios)
remains the same for each zone in the region
Male
Female
Marginal
Male
Female
Marginal
Transit
2.0
3.0
12
Transit
5.0
7.0
12
Auto
6.0
5.0
23
Auto
12.9
10.1
23
Marginal Cur Total
18
8
17
8
Marginal Cur Total
18
17.836
17
17.164
F
2.25
2.13
F
1.01
0.99
Male
Female
Marginal
Transit
4.5
6.4
12
Auto
13.5
10.6
23
Cur Total
F
10.9
1.10
24.1
0.95
Male
Female
Marginal
Transit
5.0
7.0
12
Auto
13.0
10.0
23
Cur Total
F
12.0
1.00
23.0
1.00
Updating Factors at each stage
Continue until (F-1) < e (convergence threshold)
Marginal
18
17
Marginal
18
17
Population Synthesis – HH selection


After fitting distribution for zone:
For each household in sample data
1.
Intro
2.
Framework
3.
Population
Synthesis
– Calculate selection probability Ph
4.
Activity
Generation
Ph 
5.
Survey
Results
Wh
NR
W 
ih
Where,
Ph
NR
W
F
iC
F
i iC
= Selection probability for household h
= Total number of household in region list
= Household weight
= correction factor for the required household
this value is 1 unless attempting to add a fraction
portion of a household, then it is the remainder
= 1 if HHi is of type C, 0 otherwise
– Determine if household to be added – marginal constraint
– If added
 Update number of households required for zone Nz
 Update number of households of type (Mc) for zone
– Continue until no more households needed
(1)
Population Synthesis – Base
Procedure Results
2.
Framework
3.
Population
Synthesis
4.
Activity
Generation
5.
Survey
Results

22.00%
Base Procedure
Validation:
21.00%
20.00%
WAAPD (%)
Intro
19.00%
18.00%
17.00%
16.00%
15.00%
14.00%
10
100
1000
10000
100000
1000000
Distribution Matrix Size (cells)
Baseline

Validation of
category
aggregation routine:
Mean WAAPD (%)
1.
23.00%
22.50%
22.00%
21.50%
21.00%
20.50%
20.00%
19.50%
19.00%
New - Mean
22.9% 23.0%
21.0%
20.5%
19.9%
IW
IWT*
20.0%
IWTA*
Control Variables
Automated
Manual
19.6%
19.9%
IWTAV**
Population Synthesis – SURE
forecasting model
 SURE marginal changes forecasting model:
–
–
–
–
–
System of linear regression equations
Related only through correlated error terms
Accounts for cross equation correlations
d(hh,pop,emp) -› dhhsize=1, dhhsize=2, etc.
Estimate change in hhsize and num workers categories
Household Size Model Results
 HH1
 HH2
Param.
t-stat
t-stat


Constant
37.02 15.44
12.96
3.83
D_HH
0.17 33.57
0.28 81.94
D_EMP
0.02
8.44
0.00
-D_HSIZE -253.18 19.86
-166.56 18.71
D_JPH
-23.03 -9.82
0.00
-COOK
0.00
--20.40 -5.63
DHHS^2
-79.52 11.95
0.00
-DHHS^3
116.98
5.87
39.75
2.89
R
2
0.643
0.854
 HH3-4
t-stat

-49.99 -13.23
0.40
86.56
-0.01
-7.77
 HH5+
t-stat

0.00
-0.15
52.68
0.00
-3.67
174.76
17.12
20.37
15.25
8.55
5.62
244.98
5.92
0.00
33.65
5.14
--
0.00
-117.48
--6.63
79.51
-39.24
11.95
-3.36
0.853
0.781
Population Synthesis - Forecasting
1.
Intro
2.
Framework
3.
Population
Synthesis
4.
Activity
Generation
5.
Survey
Results
 Use SURE model to estimate marginal changes:
 Update marginals
 Run popsyn with new marginals and base sample
– Generates forecast population
– Closest distribution to base sample that satisfies forecast
marginals
– Other categories (Race, Age, Income), can be adjusted through
scenario analyzer
Base Yr
HHSIZE:
1990
1980
1990
NWORK:
1990
1980
1990
Model Results
RMSE
R2
Prop. Updating
RMSE
R2
Forecast
Cat. Avg
1980
2000
2000
386
452
452
61
75
58
0.728
0.801
0.865
78
122
89
0.552
0.466
0.682
1980
2000
2000
501
516
516
77
98
76
0.747
0.815
0.804
98
143
106
0.5946
0.6013
0.6164
Activity Generation
Activity Generation:
Overview
1.
Intro
2.
Framework
3.
Population
Synthesis
4.
Activity
Generation
5.
Survey
Results
 First step in activity-travel simulation
 Current focus of work – very preliminary
 Generate activities randomly
– Monte carlo simulation at each timestep
– Drawn from probability distribution for each activity type
Node 3 (Age > 36.5)
Node 1
N = 367
Social Avg. = 0.3244
Social Std. dev. = 0.3505
 Example:
AGE <= 36.5
Alpha:
Beta:
Min:
Max:
1.0474
33.2423
0
9.66
AGE > 36.5
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Node 4 (Age < 36.5, HSIZE < 1.5)
Node 2
N = 125
Social Avg. = 0.4388
Social Std. dev. = 0.4341
Alpha:
Beta:
Min:
Max:
Node 3
N = 242
Social Avg. = 0.2652
Social Std. dev. = 0.2806
0
HHSIZE <= 1.5
0.2
0.4
HHSIZE > 1.5
0.6
0.8
1
1.2
2.1515
2.5625
0
1.67
1.4
Node 5 (Age < 36.5, HSIZE > 1.5)
Alpha:
Beta:
Min:
Max:
Node 4
N = 25
Social Avg. = 0.6836
Social Std. dev. = 0.3767
Node 5
N = 100
Social Avg. = 0.3776
Social Std. dev. = 0.4258
1.6
0
0.2
0.4
0.6
0.8
1
1.2
0.5522
3.3648
0
2.71
1.4
1.6
Activity Generation:
Correction Factors
1.
Intro
2.
Framework
3.
Population
Synthesis
4.
Activity
Generation
5.
Survey
Results
 Using observed generation rates gives incorrect results
– Due to collisions (i.e. activity conflicts)
– Activities split, postponed, deleted, etc.
 Unobserved planned activity generation
 Try to correct generation distributions through simulation:
– fi* = S(ifi), minimize (fi* - fi)  i  activity types
– ifi approximates unobserved planned activity generation
– Must be solved simultaneously
 Example: mean-fitting technique, t = t-1 (*t-1 / ); 1 = 1.0
1.2
1.2
RUN 1
1
0.8
0.8
0.6
0.6
0.4
0.4
1
RUN 2
1
0.8
0.2
0.2
0
0
0
0.005
0.01
EST
I = 1
0.015
ACT
0.02
1
RUN 3
0.6
0.6
0.4
0.4
0.2
0.2
0
0
0.005
0.01
EST
0.015
ACT
I = 2.86
0.02
RUN 4
0.8
0
0
0.005
0.01
EST
0.015
ACT
I = 4.03
0.02
0
0.005
0.01
EST
0.015
ACT
I = 3.88
0.02
Activity Scheduling
Activity Scheduler
 Rules for adding activities to the planned schedule
 Conflicts arise due to random generation of
activities/unexpected acts
 Scheduler resolves conflicts to create feasible schedule
 Activity Scheduling Combines:
– Conflict Resolution Model
– Scheduling Rules
 Related publications:
– Auld, J.A., A. Mohammadian and M.J. Roorda (2009). Implementation of a
Scheduling Conflict Resolution Model in an Activity Scheduling System.
Forthcoming in Transportation Research Record: Journal of the
Transportation Research Board
– Auld, J.A., A. Mohammadian and S.T. Doherty (2009). Modeling Activity
Conflict Resolution Strategies Using Scheduling Process Data.
Forthcoming in Transportation Research Part A: Policy and Practice.
(available online December 2008)
– Auld, J.A., A. Mohammadian and S.T. Doherty (2008). Analysis of Activity
Conflict Resolution Strategies. Transportation Research Record: Journal of
the Transportation Research Board. 2054, 10-19
Conflict Resolution - Introduction
 Conflict resolution in previous models
–
–
–
–
Assumed priority rules
Simple heuristics
Not very realistic
Usually based on travel survey, NOT Process data
 Conflict resolution in scheduling process data
– Look at how conflicts actually resolved during scheduling
– Empirical observations (Roorda et al. 2005, Ruiz et al.
2005)
– Conflict resolution models (Ruiz and Timmermans 2006)
 Based on actual scheduling data
 Modify preplanned activities surrounding conflicting activity
Conflict Resolution Model
 Due to dynamic nature of scheduling, conflicts
naturally arise
– Timing, location, resource
 Conflict resolution model chooses strategy for
resolving conflict
– Currently only for timing
– Uses decision trees
– Strategies based on demographics, constraints, schedule
characteristics, etc.
Type 1
Conflicting
Original
Type 2
Conflicting
Original
Type 3
Type 4
Conflicting
Conflicting
Original
Original
Time
Time
Conflict Resolution Model
 Decision Tree model
– Represent rule-based conflict solving
– Evaluated using Exhaustive CHAID (Biggs et al. 1991)
decision tree
 Need to be Manually optimized
 Discrete choice models
– Utility-based conflict resolution solving
– Multinomial logit
– Nested logit (potential correlation for modify choices)
 Dependent variable
– Four resolution strategies: RS1-RS4
– Out-of-home and in-home modeled separate for all
Model Performance Comparison
 Similar performance for all models
– Approximately 73% correct predictions
– Less accurate prediction of type 3 (modify both activities)
resolutions
 Typical problem in any classifier model due to low
observation
– Predict type 4 resolutions (delete original) well
– 26% improvement over null model
TABLE 6 Predictive Ability of Conflict Resolution Models
% Correctly Predicted
RS-1
RS-2
RS-3
RS-4
Overall
DT-Training
90.7%
76.6%
16.5%
58.6%
72.4%
DT-Test
85.5%
69.2%
23.3%
52.0%
68.1%
MNL
90.1%
70.9%
13.8%
62.5%
73.4%
NL
91.2%
69.8%
16.0%
56.5%
72.5%
100.0%
0.0%
0.0%
0.0%
46.9%
Null Model
Note: Resolution Strategy Types 1, 2, 3 and 4 are as defined in Table 1.
Conflict Model Discussion

In-home conflict resolution
–
–
–
–
–

Similar for decision tree and logit models
Travel requirements most highly significant
Duration, personal fixity, overlap in both
In DT model: conflict type significant
In logit models: time fixity, original duration
Out-of-home conflict resolution
– Again, similar for both models
– Plan horizon is most significant – preplanned more likely to be deleted
– Other significant variables: conflict type, overlap, duration

In conclusion:
–
–
–
–
Activity, conflict and fixity attributes most important
Sociodemographic do not matter much
Similar to observations in other studies – Ruiz, Timmermans
Choice of model does not have much impact on outcome
Scheduling Rules - Overview
 Set of rules for scheduling randomly generated activities
 Attempts to resolve conflicts by modifying each activity – series
of rules determine how modifications are made
 System based on the scheduling rules found in TASHA model
 Includes results of conflict resolution model:
– TASHA – conflict resolution based on ad hoc logical rules
– New rules – ad hoc logical rules determine how conflict resolution
strategy is implemented
– Possible resolutions for two activities in conflict: delete original
activity, modify original, modify conflicting, modify both
 New rules allow for the consideration of more complicated
conflict types and deletion operations
 When activities can be truncated, each activity assumed to be
truncated proportionally to duration
Scheduling Rules –
Comparison to TASHA
TASHA Conflict Cases
Case 1: Inserted Original
Case 2: Overlap End
Case 3: Overlap Start
Case 4: Overlap Start & End
Work/Home/Null
Updated Conflict Cases
Conflicting Activity
Case 1: Inserted Original
Case 2: Overlapped Original
Case 3: Overlap Start
Case 4: Overlap End
Original Activity
Note: For agenda insertion, there are two additional conflict cases: overlapping only the beginning or only the end of an activity.
Activities with a type listed indicate that only a conflict with the given type will be considered.
Case 5: Overlap End & Start
Case 6: Insert & Overlap Start
Case 7: Overlap End & Insert
Case 8: Insert/Overlap Start /End
Conflicting Activity
Original Activity
Any Combination of Deleted or Home/Null Activities
Note: New conflict cases exclude all situations with more than 1 activity entirely overlapped.
‘Deleted’ activity refers to a scheduled activity whose resolution has been set to ‘Delete’ by the resolution model.
Scheduling Rules - Example


Scheduling Example:

Under the TASHA rules:
i.
Move Activity A, align end of Activity A
with start of Activity B
ii.
Move Activity B backward
iii.
Truncate Activity A and Activity B
proportionally to their durations
iv.
Insertion is not feasible.
















Under the new rules, situation handled as follows:
i.
If resolution type is ‘Delete Original’
a.
Remove Activity B from schedule, add Activity A
ii.
If resolution type is ‘Modify Original’
a.
Move Activity B, align start of Activity B with end of Activity A
b.
Truncate Activity B
c.
Insertion is not feasible
iii.
If resolution type is ‘Modify Conflicting’
a.
Move Activity A, align end of Activity A with start of Activity B
b.
Truncate Activity A
c.
Insertion is not feasible
iv.
If resolution type is ‘Modify Both’
a.
Move Activity A, align end of Activity A with start of Activity B
b.
Move Activity B backward
c.
Truncate Activity A and Activity B proportional to durations;
d.
Insertion is not feasible.


Activity A
Activity B
Home/Null
Scheduling Rules - Validation


Actual CHASE activities scheduled with TASHA and ADAPTS
Compare results v. actual schedule with sequence alignment measure
– Align schedules activity type by activity type
– Weight insertion/deletion and move operations separately
Scheduling Comparison Results for TASHA vs. Updated Model
TASHA
Updated - avg
Updated - std.
% change
WDel,Ins
1
1
–
–
Delete Cost
352
349
16
–
Insert Cost
212
371
23
–
Move Cost
3,115
2,336
230
–
Total Cost
3,680
3,055
225
-17.0%
TASHA
Updated - avg
Updated - std.
% change
2
2
–
–
684
643
31
–
371
624
35
–
3,156
2,464
234
–
4,211
3,731
225
-11.4%
TASHA
Updated - avg
Updated - std.
% change
3
3
–
–
995
931
38
–
532
902
56
–
3,199
2,563
176
–
4,726
4,396
159
-7.0%
Note: TASHA refers to the scheduling results of a newly generated implementation of the TASHA scheduling rules.
New scheduling model results averaged of 200 model runs.
For all cases TASHA result is outside of 99% C.I. of updated model mean.
Run time was 3.4s for both simulations created in C#.NET and run on a 2.0GHz dual-core processor with 2GB of RAM.
Scheduling Rules - Validation
Total Hours Spent on Activities
8000
7000
Work-Business
Primary Work
Return Home
School
Other
Joint Other
Shop
Joint Shop
6000
5000
4000
3000
% Difference v. Actual
TASHA
NEW
1.3%
-1.4%
3.6%
-1.3%
6.1%
16.4%
8.6%
11.0%
-5.1%
-3.9%
-0.9%
-1.5%
-4.4%
2.2%
-0.6%
0.2%
2000
1000
0
WorkBusiness
Primary
Work
Return Home
School
PLANNED
TASHA
Other
NEW
Joint Other
EXECUTED
Shop
Joint Shop
GPS Data Collection Update
Background:
GPS-enabled surveys
1.
Intro
2.
Framework
3.
Population
Synthesis
4.
Activity
Generation
5.
Survey
Results
 Published in:
– Auld, J. A., C. Williams, A. Mohammadian and P. Nelson (2009).
An Automated GPS-Based Prompted Recall Survey With
Learning Algorithms. Journal of Transportation Letters, 1 (1),
59-79
 Currently focusing on replacing activity diary
–
–
–
–
Lower respondent burden
Capture more accurate trip/activity attributes
Longer range/panel studies
Gain more detailed information, esp. for route selection
 Enhanced by technological progress
– Person-based, wearable GPS loggers
– Increased battery life
– Differential / Assisted GPS
New GPS Survey:
Key features
1.
Intro
2.
Framework
3.
Population
Synthesis
4.
Activity
Generation
5.
Survey
Results
 Internet enabled and entirely automated
– Participants upload data to central server
– Survey completed on same day as data acquisition
 Scans data to generate interactive PR survey
– Utilize Google Maps API
– Activity timeline
 Participants validate activity/travel episodes
 Survey activity-travel attributes
– Who with, planning horizons, location choices, route and mode
choice decisions
 Incorporate learning algorithms to reduce survey burden
– Suggest answers known with some confidence
– Remove questions when answers known with high confidence
– Proactively identify likely upcoming activities and prompt for
planning data
– Pre-populate planning items for learned recurrent activities
Design of GPS survey:
Activity location finding
1.
Intro
2.
Framework
3.
Population
Synthesis
4.
Activity
Generation
5.
Survey
Results
 Designed to overcome issues regarding personbased tracking
 Track all modes and indoor/outdoor activities
 Activity-location finding:
– Distance and time thresholds
– Heuristics to determine threshold values
– Distance threshold varies with land-use pattern, travel
mode, etc.
– Time threshold varies with travel mode
Demonstration:
Activity-travel verification
1.
Intro
2.
Framework
3.
Population
Synthesis
4.
Activity
Generation
5.
Survey
Results
GPS Survey:
Activity-travel prompted recall survey
1.
Intro
2.
Framework
3.
Population
Synthesis
4.
Activity
Generation
5.
Survey
Results
GPS Survey: Activity Patterns
1.
Intro
2.
Framework
3.
Population
Synthesis
4.
Activity
Generation
5.
Survey
Results
Daily Average Activity Rates
Work / Business
School
Pick-up / Drop-off Others
Basic Needs / Meal
Shopping
Leisure / Entertainment / Recreation
Services
Other
Social
Household Obligations
0
0.1
0.2
0.3
Survey
0.4
TravelTracker
0.5
0.6
0.7
GPS Survey – Planning Results
1.
Intro
2.
Framework
3.
Population
Synthesis
4.
Activity
Generation
5.
Survey
Results
100%
13%
24%
80%
27%
22%
60%
40%
24%
8%
24%
42%
12%
12%
10%
8%
13%
6%
9%
7%
12%
42%
20%
26%
26%
0%
Activity Plan Horizon
Impulsive
Timing Plan Horizon
Same Day
< 1 Week
Location Plan Horizon
> 1 Week
Routine
Mode Plan Horizon
Don't Know /Missing
5.
Survey
Results
0%
0%
63%
11%
22%
11%
Other
80%
0%
0%
Basic Needs / Meal
Activity
Generation
17%
0%
Social
4.
100%
0%
Recreation
100%
90%
44%
0%
Leisure / Entertainment
Population
Synthesis
67%
0%
Household Obligations
3.
53%
0%
Services
Framework
72%
25%
Shopping - Other
2.
62%
31%
Shopping - Grocery
Intro
Pick-up / Drop-off Others
1st Routine % 100%
Act Routine % 89%
1.
School
GPS Survey: Planning Order Results
70%
60%
50%
40%
30%
20%
Work / Business
10%
0%
Timing First
Location and Mode
All at once
Location First
Timing and Location
Mode First
Timing and Mode
Note: 1st Routine % indicates the percentage of activities for each type for which the indicated first planned
attribute(s) were routine.
Act Routine % indicates the percentage of activities of each type which had ‘routine’ activity plan horizon.
GPS Survey - Current Status
1.
Intro
2.
Framework
3.
Population
Synthesis
4.
Activity
Generation
5.
Survey
Results
 Pilot test results:
– 10 individuals for avg.
of 6.8 days
– 210 total out-of-home
activities (3.1 per day)
– Avg. completion time:
23.5 min/day
 Location finding
algorithm works well
– 97% recall with 87%
precision
Verifying Activity-Travel Pattern
 Total person-days
 Average completion time
 St. dev. of completion time
20
0:14:00
0:09:32
Answering Survey Questions (daily average)
 Total person-days
68
 Activities per day
3.9
 Trips per day
3.3
 Acts & Trips per day
7.2
 Activity question answer time
0:03:42
 Trip question answer time
0:02:45
 Completion time
0:23:37
•Design of survey captures dynamics of activity planning
GPS Survey – Next Steps
1.
Intro
2.
Framework
3.
Population
Synthesis
4.
Activity
Generation
5.
Survey
Results
 Survey starting next week
– Currently recruiting participants/training
survey assistants
– Targeting 50 elderly, 50 non-elderly
households
– Attempt to collect 2 weeks of data per
individual
 Potential collaborations:
– McMaster University – testing survey
– University of Toronto GPS Survey
The End
Questions?
Conflict Resolution Decision Tree
Mod. Orig.
Mod. Conf.
Mod. Both
Delete Orig.
Discrete choice results
Negative
Positive
Negative
Positive
Out-of-Home Conflicts
Mod. Orig.
O. Per Fix = Alone
O. Plan Same Day
O. Child Inv.
Contype 4
Overlap
Mod. Orig.
O. Duration
O. Per Fix = Alone
O. Plan Same Day
O. Child Inv.
Overlap
Contype 4
MNL MODEL
Mod. Conf.
Mod. Both
O. Out of Home
O. Per Fix = Alone
O. Plan Same Day
C. Plan Preplan
O. Plan Routine
O. Child Inv.
Contype 3
Overlap
Contype 3
NL MODEL
Mod. Conf.
Mod. Both
O. Duration
O. Duration
O. Out of Home
O. Per Fix = Alone
O. Plan Same Day
O. Plan Same Day
O. Plan Routine
C. Plan Preplan
O. Child Inv.
Overlap
Contype 3
Delete Orig.
O. Plan Preplan
O. Child Inv.
Overlap
Delete Orig.
O. Plan Preplan
C. Duration
Discrete choice results
Negative
Positive
Negative
Positive
In-Home Conflicts
Mod. Orig.
O. Duration
O. Plan Same Day
O. Travel Required
O. Time fixed
C. Time fixed
C. Travel Required
Overlap
Mod. Orig.
O. Duration
O. Plan Same Day
O. Travel Required
C. Time fixed
C. Travel Required
MNL MODEL
Mod. Conf.
O. Duration
O. Per fixity = With Others
O. Per fixity = Optional
C. Duration
C. Preplanned
O. Preplanned
O. Travel Required
NL MODEL
Mod. Conf.
O. Duration
O. Per fixity = With Others
O. Per fixity = Optional
C. Duration
C. Preplanned
Overlap
O. Preplanned
O. Travel Required
Mod. Both
O. Duration
Delete Orig.
O. Per fixity = Optional
Overlap
O. Preplanned
O. Routine
Overlap
Mod. Both
O. Duration
Delete Orig.
O. Per fixity = Optional
O. Time fixed
O. Preplanned
O. Routine
Discrete choice model
Fit statistics
IN-HOME CONFLICTS
MNL
NL
LL at zero
-600.1
-600.1
LL at convergence
-288.5
-279.2
0.512
0.529
Adjusted rho-square
OUT-OF-HOME CONFLICTS
MNL
NL
LL at zero
-627.2
-627.2
LL at convergence
Adjusted rho-square
-447.4
0.278
-465.4
0.250