The role of bounded rationality in travel
choice behavior and implications fortransport modeling
Theo Arentze
Urban Planning group
Eindhoven University of Technology,
The Netherlands

Travel demand models

Micro simulation models

Activity-basedmodelsAggregate trip-basedmodels

Tour-basedmodelsDaily activity-patterns

Predicting people’s
response to policies isnotoriously difficult

Trip records

OD trip matrix

Dynamic/static traffic simulation/assignment models

Travel demand models

Aggregate trip-basedmodelsMicro simulation models

Activity-basedmodels

Tour-basedmodelsDaily activity-patterns

New model
development started inearly nineties
Models are now making
the transition topractice

Trip records

OD trip matrix

Dynamic/static traffic simulation/assignment models

Outline of my presentation• Brief review of activity-based modeling• objectives, approach and new developments• Bounded rationality in travel behavior• human biases• towards dynamic models• New modeling approaches• habitual behavior and spatial search• learning and wellbeing

Why activity-based modeling?
• New demands from transport planning and policymaking• Switch in focus to travel demand measures• Importance of temporal factors (flexible work hours) andtask combination• Integration of policies: land-use and transport planning• More comprehensive evaluation of policies

Activity-based versus trip-based approach
Trip-based

Activity-based

Focus is on trips

Focus is on activities

Unit is a trip

Unit is a day

Space-time constraints ignored

Space-time constraints taken
into accountLow resolution time and place

High resolution time and place

Decision unit is individual

Decision unit is household

Predicts when, where, transport
mode

Predicts which activities, when,
where, for how long, tripchaining and transport modeAlbatross example of an activity-based model• Rule-based• Continuous time scale• Within household-interaction• Space-time constraints• National level• Computation time− 10 % of population – 2.1
million agents− More than 4000 postcode
areas− Around 8 hours computation
time on a standard PC

Albatross example of an activity-based model• Albatross abroad• Feathers – Belgium• Under development• Seoul• Indonesie• Model is static – time span isone dayNew developments in activity-based modeling• From static to dynamic models• expand time frame from one day to multiple days
• include life trajectories and long-term mobilitydecisions• Include social networks and social interactions• social influence in decision making• group decision making – negotiation• New survey methods and data sources• tracking of movements with GPS or mobile phone
positioning• social media – big data

Incorporating bounded rationality in models oftravel demand

Time is ripe
• Cumulative evidence from psychology andbehavioral economics• See recent book of Daniel Kahneman (2011) – Thinking,
Fast and Slow

• Human biases are well documented and tools fordata collection and modeling available• Modern survey technologies facilitate a move fromone-day to multiple days data collection• Wide use of smart phones allows new in-situ datacollection methods

Aspects of bounded rationality
Biasesare well-documented
Search	  &	  info	  acquisi2on

Needs	  and	  resources

Learning	  &	  judgment
Evalua2on	  &	  decision

Environ-­‐ment	  (social	  and	  physical)

Sensitivity to losses
Over responding topeak experiences
Sticking with habits
Impact of emotion

Ac2vity-­‐travel	  paAerns	  &	  subjec2ve	  wellbeing

Memory distortions
Limited search andeffort…….

Accounting for biases requires a change from static to dynamic
modeling and involves all 4 areas

A model with bounded rationality

Search	  &	  info	  acquisi2onNeeds	  and	  resources

Learning	  &	  judgment

Memory biases
Evalua2on	  &	  decision

Environ-­‐ment	  (social	  and	  physical)

Heuristics

Mobility	  paAerns	  &	  subjec2ve	  wellbeing

Learning & judgment model predicts how an individual learns andmakes judgments about risks based on experiences

A model with bounded rationality

Search	  &	  info	  acquisi2on

Needs	  and	  resources

Learning	  &	  judgment

Limited search
Evalua2on	  &	  decision

Environ-­‐ment	  (social	  and	  physical)

Mobility	  paAerns	  &	  subjec2ve	  wellbeing

Search-and-information-acquisition model predicts the search foralternatives and formation of choice-sets

A model with bounded rationality

Search	  &	  info	  acquisi2on

Needs	  and	  resources

Learning	  &	  judgment
Evalua2on	  &	  decision

Environ-­‐ment	  (social	  and	  physical)

Reluctance to change
Framing
Emotional weighting

Mobility	  paAerns	  &	  subjec2ve	  wellbeing

The evaluation-and-decision making model, given the choice-sets andjudgments of risks, predicts an individual’s choices

A model with bounded rationality

Search	  &	  info	  acquisi2on

Needs	  and	  resources

Learning	  &	  judgment

Memory biases
Evalua2on	  &	  decision

Environ-­‐ment	  (social	  and	  physical)

Emotional weighting

Mobility	  paAerns	  &	  subjec2ve	  wellbeing

The model of subjective-wellbeing predicts an individual’s satisfaction
with the (transport and location) options he has

New modeling approaches (1)Habitual behavior and spatial searchHabitual behavior• Over time individuals develop particular routines for
implementing their activities• A routine has the form of a script that defines− departure time
− location (destination)− duration− where from (origin – trip chaining)− main transport mode
− route• For each activity there may be multiple scripts alternative ways of implementing an activity• In habitual mode, individuals select the scripts thatbest fit the current needs and constraints

Habitual behavior
Example of an agent’s set of scripts
Activity	  Start time	  Location	  Duration

Where from	   Mode	  Route	  work	  7 am	  TUe	  8 hours

Home	  Walk - Train	   local train	  work	  8 am	  TUe	  8 hours	  Home	  Car	  highway	  work	  7.45 am	  TUe	  8 hours	  Home	  Car	  local route	  groceries	  morning	  Aldi	  20 min.

Home	  Car	  shortest	  groceries	  lunch-break	  AH	  10 min.

Work place	   Walk	  shortest	  groceries	  early afternoon	   Market	  30 min.

Home	  Walk	  shortest	  ……	  …..	  
touring -walk	  7 am	  Neighborh.	   20Home	  Walk	  -	  touring -walk	  afternoon	  Wood 1	  2 hours

Home	  Car	  via Reusel	  touring -walk	  afternoon	  Wood 2	  1 hour

Home	  Car	  via Oisterwijk	  Alternative ways to implement the work activity – referred to as Scripts

Habitual behavior
Utility of a script

Activity component

Travel component

Decision rule– consider the scripts that meet the following condition
Threshold constr.
– choose the script that maximize U
Adaptation rule
Increase threshold if time budget is exceeded
Decrease threshold if time budget is not fully usedSpatial search• If dissatisfied with current set of scripts then theagent starts exploration
The probability that a location i is discovered isspecified as
The Boltzman model
Attributes considered
Universal choice set
Hidden utility for theagent

Lack of information- Limited effort- Limited access to info sources

Limited search can be modeled by means of τAn implementation

New modeling approaches (2)Effect of memory
and emotion onlearning andsatisfaction

How do travelers judge the likelihood of a risky
event?
How do travelers judge degree of satisfaction
with choice alternatives?

How do travelers judge the likelihood of a risky
event?

Arentze, T.A. (2013) Incorporating human memory biases in travel-behavior models ofjudgment and learning: availability and fluency heuristics. Paper presented at the HKSTSAnnual Conference, December 2013, Hong Kong.

• This is a relevant question• knowing how travelers make likelihood judgements isimportant for understanding their choice behavior• Naïve model• people count occurences and store frequency data inmemory – their judgments are unbiased• However, this is not in line with evidence. Twofundamental biases in human likelihood judgements
are well-known (Lichtenstein et al., 1978)

EvidencePrimary bias
• small probabilities are overestimated and large
probabilities are underestimated• this eplains why rare events may have a large
impact
1
0.9
0.8
0.7
0.6
q 0.5

A
RP

0.4
0.3

RT

0.2

p0.1
00

0.2

0.4

0.6
p0.81

Evidence
Secondary bias
• events that are more vividly imagined areoverestimated• this explains why a salient event such as a planecrash tends to have much more impact than a morecommon event
10.9
0.8
0.70.6
q 0.5

salient

0.4

base0.3

p0.2
0.1
000.2

0.4

0.6
p0.81

Theory• Availability / fluency heuristic• first formulated by Tversky and Kahneman (1973)• supported by numerous empirical studies• Tversky and Kahneman (1973)• people use a byproduct of memory processes to judge
the likelihood of some event• that is, the ease with which examples of the event canbe retrieved from memory is used as criterion• the easier examples come to mind the more likely theevent is judged to be• This heuristic explains the primary and secondarybiases (Hertwig et al. 2005)

Memory model• ACT-R cognitive architecture provides a model ofmemory encoding and retrieval processes (Andersonet al. 2004)(

Aik = ln ∑ j∈k (tij ) d)

Aik
Qik =
∑ j Aij• This model explains the primary bias

Memory model• Extension of the ACT-R memory model to accountfor effect of arousal on memory(Aik = ln ∑ j∈k (tij ) d ( S ))

Aik
Qik =
∑ j Aij• This extended model also explains the secondarybias

How do travelers judge degree of satisfaction
with choice alternatives?

Wielens, N.J. and T.A. Arentze (2014) The role of affective experiences in travelers’assessments of risks and subjective wellbeing: an experience sampling approach. Paper
prepared for the HKSTS Annual Conference, December 2014, Hong Kong.

• This is a relevant question• knowing how travelers arrive at satisfaction judgements
is important for understanding subjective wellbeing andhabitual behavior• Naïve model• decision utility is the same as experienced utilityØ utilities can be derived from choice behavior• However, Kahneman (2000) points to known biases:• neglect of duration of episodes• dominance of end outcome of episodes• disproportional impact of peak experiences

Model• Again, the memory model of ACT-R offers a way todescribe this process(

Aij = ln (tij ) d ( S ))U i = ∑ j Aij ⋅ U ij
• This model explains the disproportional impact ofextreme events on satisfaction

Data collection(

Aij = ln (tij )d (S ))

U i = ∑ j Aij ⋅ U ij

• Experience sampling• Small questionnaire on the smartphone completed
on every trip
• data of the trip (mode, route, purpose, etc.)• emotional state of the traveller during the trip(arousal and valence)• satisfaction judgement (experienced utility)• In-situ measurement of affective experiences oftravelers (Ettema et al. 2014)

Implications for policy making and modeling• Policy making - theory stresses:• importance of reliability of transport services on
satisfaction and risk assessment– avoid negative peak experiences• importance of avoiding losses in the behavior changetargeted− losses generate negative emotion• Transport modeling• the memory-based models of learning and jugement
can be incorporated in dynamic travel-demand models

Conclusion• It was argued that• taking bounded rationality into account matters –human biases are well-documented• to realize this, static models should be replaced bydynamic models• I highlighted new modeling approaches in areas of• habitual behavior and spatial search• learning and wellbeing

Conclusion• The new approaches are only in its infancy• New data collection and estimation methods areneeded to estimate parameters of• habitual behavior and search• learning and wellbeing• New agent-based platforms are needed to develop
full-scale applications

Thank you for your attention!

Literature cited•••••••••

Arentze, T.A. (2013) Incorporating human memory biases in travel-behavior models of judgment and learning:
availability and fluency heuristics. Paper presented at the HKSTS Annual Conference, December 2013, HongKong.
Wielens, N.J. and T.A. Arentze (2014) The role of affective experiences in travelers’ assessments of risks andsubjective wellbeing: an experience sampling approach. Paper prepared for the HKSTS Annual Conference,December 2014, Hong Kong.
Arentze, T.A. and H.J.P. Timmermans (2009), A Need-Based Model of Multi-Day, Multi-Person Activity
Generation, Transportation Research B, 43, 251-265.
Lichtenstein, S., P. Slovic, B. Fischhoff, M. Layman, B. Combs (1978) Judged frequency of lethal events. Journalof Experimental Psychology: Human Learning and Memory, 4, 551-578.
Tversky, A. and D. Kahneman (1973) Availability: A heuristic for judging frequency and probability. Cognitive
Psychology, 5, 207-232.
Anderson, J.R., D. Bothell, M.D. Byrne, S. Douglass, C. Libiere and Y. Qin (2004) An integrated theory of themind. Psychological Review, 111(4), 1036-1060.
Hertwig, R., T. Pachur and S. Kurzenhäuser (2005) Judgments of risk frequencies: tests of possible cognitive
mechanisms. Journal of Experimental Psychology: Learning, Memory and Cognition, 31, 621-642.
Kahneman, D. (2000) Evaluation by moments: past and future. In: D. Kahneman, E. Diener, N. Schwartz (Eds.)
Well-being: The foundations of Hedonic Psychology. Russel Sage Foundation, New York, 3-25.
Psarra, I., F. Liao, T.A. Arentze and H.J.P. Timmermans (2014) Modeling context-sensitive, dynamic activity travel
behavior by linking short- and long-term responses to accumulated stress: results of numerical simulations.
Transportation Researcg Record, 2014, vol 1, 28-40.