Input Uncertainty in the Albatross Model System
Soora Rasouli & Harry Timmermans© 2012

Input uncertainty in Albatross system
Future ResearchDiscussion

Conclusion
Analysis and Results

Introduction

LOGORasouli & Timmermans

Introduction

Introduction – State of the Art
Development and application of models of travel demand:- Collect travel survey data
-Model developing : Discrete choice, rule- based…
- Estimate- trip generation- transport mode and destination choice- Derive OD-matrices- Assign to network to generate traffic flows- Formulate planning scenarios- Convert planning variables into the explanatory variables of the model- Use estimated parameters to predict set of performance indicator variables- Apply formal multi-criteria analysis or cost-benefit analysis to choose best policyUseful ClipartanddataImages
We assumeare correct, but in reality

Issuesthey likely are not.
We assume we have perfect knowledge(model is correct), but in reality it is likely not.
We assume we have data about all influential
variables, but in reality we likely have not.
We assume that the future will unfold as
described in the scenarios, but in reality it
likely will not.
WE ASSUME A DETERMINISTIC SYSTEM AND
GENERATE A DETERMINISTIC FORECAST, BUT
URBAN – TRANSPORTATION SYSTEMS ARE
STOCHASTIC AND THE BEST WE CAN DO IS TO
GENERATE PROBABILISTIC FORECASTS.

Introduction – Uncertainty Analysis- Create (a fraction of) the synthesized population- In case of data uncertainty:- Assume multivariate distribution of uncertainty- Use Monte Carlo draws to sample realizations- In case of model uncertainty- Discrete choice models: Monte Carlo draws from error distributions- Rule-based models: Monte Carlo draws from probabilistic decision
trees- In case of both data and model uncertainty: combination and study thecontribution of different sources.
- Run model multiple times (10 – 100) with these inputs

Introduction – Uncertainty Analysis
Collect relevant generated output (performance indicators, OD matrices,traffic flows, etc.)- Calculate uncertainty in these forecasts- Coefficient of variation: st.dev. / mean
- Decomposition of uncertainty by socio-demographic variables, spatial
areas, temporal episodes, OD-pairs, links, etc.

Analysis and Results

Analysis - Procedure

1

Create synthetic
population andUse fraction forfurther uncertainty
analysis.

23

Use Monte Carlosampling and run
the Albatross
model system
multiple times.

Perform analyses
on the generated
outcomes toquantify theamount and natureof uncertainty

Albatross
Multi-agent, rule-based activity based model developed for the Dutch Ministry of Transport
for scenario evaluation.
It predicts which activities are conducted where, when, for how long, with whom and thetransportation mode involved (Arentze and Timmermans, 2000, 2004, 2008).
Albatross uses different types of constraint: Institutional constraint, temporal constraint,
space constraint …
Speed of the car affect different facets of individual activity-travel patterns such as:
possible location that can be reached within individual available time window for doingthat activity or transport modes that can be used to reach the locations for doing thatactivity.

Analyses
Uncertainty analysis of the Albatross model system regarding uncertain speed in somecrucial links.
“Basisnetwork” is a national data base. Start and end coordinate of the links and average
speed of the car on that link is represented.
Study area: Rotterdam metropolitan area (2nd largest in the Netherlands)

Statistics in 2005:- population 600,000:- households 280,000:- fraction: 10%
Albatross run 20 times.

Analysis
Input uncertainty- expert elicitation and empirical measurement of uncertainty in speed in selected links
Link ID
A16
A20
Gravendijkwal
Statenweg
Schieweg
Schiekade

Average Speed in km
80-10090-100
20-40
15-40
15-30
20-30

St. dev.
8-10
9-10
4-8
3-8
3-6
4-6

Probability distribution function
With relevant mean and st.dev
speed.Analysis
What is the effect of uncertain travel times on activity participation?
number of act Mean no of people Std. deviation CV (Std.Mean)
0 activities
15224
104.560.00691 activities
8844
93.14
0.01052 activities
4516
43.68
0.0097
3 activities
2277
44.97
0.0197
4 activities
1079
32.07
0.0297
5 activities
481
22.14
0.04606 activities
204
11.57
0.0567
7 activities
85
5.14
0.06038 activities
31
4.94
0.16099 activities
10
2.4
0.246210 activities
51.39
0.2808CV increases with increasing number of activities.

Analysis
What is the effect of uncertain travel times on start and end of the activities?
.

variation in start and end times of activities is relatively large.

3 min for work
14 min for BrgGetAnalysis
What is the effect of uncertain travel time on the duration of activities?

Work

Bring/
getSocialLeisure

Average

430,5

9,8

126,7102,2

Max

433,7

10,0

128,8103,7

Min

427,1

9,6125,7100,8

CV

0,005

0,013

0,007

0,008

Work is least affected. CV=0.50%
BrgGet is most affected. CV= 1.3%

Analysis
What is the effect of uncertain travel times on transport mode choice?

Mode choiceproportions

CarDriverSlowPublic

Car Passenger

Average
StdCV

0,385
0,002
0,006

0,433
0,003
0,006

0,070
0,001
0,014

0,111
0,001
0,009

Coefficient of variation is the highest for Public transport and lowest for
Car driver and Slow mode.

Conclusion

Conclusions
uncertainty in car speed affects thestart and the time mostly in absoluteterms and activity participation in a
relative sense (CV) at least in thisstudy.

Results indicate that the degree ofvariation tends to differ between
mandatory and flexible activities.

Discussion

Discussion
Uncertainty in travel times was only
implemented for a certain corridor in thestudy area and consequently not allindividuals face this uncertainty
The behavioral mechanisms in Albatross
kept constant. Assuming that in short run
people do not adopt to uncertain speed. If
it continued then they might structurally
adapt their departure time.

Future ResearchFuture research agenda
Spatial variability analysis.
The effect of uncertainty in speed at theindividual level.
Examine the effect of travel time variability
on different segments of the population.

Questions?
More Information?
Soora Rasouli
Harry Timmermanss.rasouli@tue.nl
Eindhoven University of Technology
Urban Planning GroupThe Netherlands