1. Travel Time Perception and Learning in Traffic Networks
Roger B. Chen
UIC Seminar, May 19, 2008

2. Introduction
The study of user decisions (interactions) in traffic networks has two motivations:
Scientific: Traffic systems are examples of socio- economic systems.
Practical: We desire the ability to devise strategies and policies for managing traffic systems.
Microscopic Decisions
Macroscopic Properties
Collectively
Varying levels of
Information and
Technological Capabilities
User Interactions
Consequences
guiding systems towards more socially desirable paths and system states

3. Background Assumed equilibrium under various user behaviors
Interdependence Between User Behaviors
and Network Performance
Assumed equilibrium under various user behaviors
User Equilibrium
Stochastic User Equilibrium
Day-to-day adjustment processes of travelers
Stochastic Process
“Tatonnement” Process
Simulate network response, user response, or both
Mathematical
Programming
(Sheffi 1984)
(Cascetta and Cantarella 1989, 1991, 1996)
(Freitz et al. 1994)
Analytical
Laboratory Experiments
Real Users
Simulated Network Performance
Mahmassani and Herman; Helbing et al. 2001, 2002

4. Background Behavioral Decision Theory
Integration of Travel Experiences and Learning
Behavioral Decision Theory
“Gestalt Characteristics”
Integration of Experiences (Transportation)
Weighted Average of Travel Times
Myopic Adjustment Departure Time
Weighted Average of Travel Times and ATIS
Updating of Travel Time and Uncertainty
Only salient experiences count
(Ariely et al. 2000)
(Horowitz 1982)
(Mahmassani and Chang
1980, 1996)
(Ben-Akiva, de Plama
and Kaysi 1982)
Kaysi 1991; Jha, Peeta, and Madanat 1998,

5. Research Objectives Simulation
Model the interdependence between user decisions and system performance.
Simulation Experiments:
Updating of perceived travel times
Timing of Learning: Starting and Stopping
Extend Past Studies:
Further consider travel time learning
Triggering Mechanisms
Perceived Uncertainty
Capturing their effects on network performance
Simulation
Learning may be costly, and
not occur continuously.

6. Attitudes and Perceptions
Modeling Framework
Environment
Agent-Based
Simulation
Framework
Te,d
Traffic Network
d=d+1
Experienced Travel
Time on day d
Integrating Travel Time δd
Learning/ Updating?
Route Choice
Travel Time Updating
Triggering
Mechanism
yes
Perceived Travel
Time no
yes
Te1
Te2 Tp
Te3
Switch Routes?
Decision
Mechanisms no
Experienced Travel
Times
Attitudes and Perceptions
Triggering
Mechanism
User

7. Mean Updated Travel Time
Travel Times
Experienced Travel Time
Perceived Travel Time
Gaussian
Error Due to Perception
Objective Travel Time
Same Variance
Error Due to “not knowing”
The performance of the system
Mean Updated Travel Time

8. Bayesian Updating Updating the Mean Updating the Variance Sample Size
Weighted Average
Prior Mean
Mean of the sample
Sample Variance
With Every New Experience, Updated
Variance Decreases
Confidence Increases with the Number
of Experiences
Inverse of Variance is a Measure
Of Confidence
As Confidence Increases, New Experiences have
less Impact

9. Trigger Mechanisms Unrealistic Learn Selectively
Condition
Description
Number of Days
Difference in Experienced Travel Times
Confidence of Updated Travel Time
Update at every
Mn-th day
Unrealistic
Update if an experienced
travel time
is relatively “salient”
Learn
Selectively
Update until confidence for all routes reaches a desired level
Similar to New Users becoming Commuters

10. Route Switching Travel Time “Savings” Threshold [0,1]
Perceived Best Travel Time on Day d
Users will choose the “Best” Path (assumption)

11. Test Network Links near the centre have smaller capacities
compared with links on the border 7 8 9 6 4 5 1 2 3
link
tmin
capacity b E 1 20
360
0.1
0.95 2 12 3 15
240
0.12 4
180
0.15 5 6 10
150 7 8 9 30 11
O-D
Routes
Demand
1-8 6 60
1-9 2 40
9-8 10
1-5 1
5-8
1-4
4-8
7 OD Pairs
Link Cost Function

12. Given Objective Travel Time
Simulation Procedure
Perceived Mean
Initialize
Perceived Variance
Calculate
Link Flows
d = d+1
Initial Route Choice
Determine Objective Travel Times
n = n+1
n = N?
Given Objective Travel Time
Draw Experienced
Travel Time no
Switch
Routes?
Choose Best
Route
Update?
yes
Bayesian Rules
Update Perceived
Travel Time

13. Traffic System States
Convergence
Oscillating
Non-Convergence

14. Varying Inter-Update Periods
WHY?
Lower initial uncertainty perception (β=0.5)
may delay convergence. β = variance per unit of time
High initial uncertainty perception (β=5.0),
updating may not decrease uncertainty
fast enough for convergence.
Lower usage levels show
greater propensity towards convergence
compared to high levels.
As the number of days between updates increases…(all else being equal)
Optimum Point?
Also, the number of updates to reach
convergence decreases.
the number of days until convergence
decreases then increases;
As the number of days between updates increases, sample (DS) gets BIGGER
Mean will stabilize more rapidly
Variance will decrease more rapidly
However, number of days until convergence does not decrease monotonically

15. Differences in Experienced and Updated Travel Times
All other factors equal
OD Pair 2
Travel Times
=0.07
Not a UE
Increasing
Tolerance
of Differences
before
UPDATING
Increasing
Propensity towards
Convergence
=0.90

16. Differences in Experienced and Updated Travel Times
All other factors equal
OD Pair 3
Travel Times
=0.07
Same Trend – Different OD Pair
=0.90

17. Terminating Based on Confidence
OD Pair 2
Travel Times
Not a UE
λnd = 0.05
Increasing
Confidence to
Terminate
Updating
Decreases
Propensity towards
Convergence
λnd = 0.90

18. Conclusions for Route Choice Dynamics
Individuals’ perception of travel times and mechanisms for updating them greatly affect convergence.
Perceived confidence in the updated travel times is an important factor and should not be ignored.
The results also show that there are system-wide properties common to all cases regardless of triggering mechanism.
The results call into question some of the behavioral assumptions invoked in stochastic and deterministic equilibrium assignment models.
Travel Time Perception and Updating Affects Convergence
Should Not Be Ignored
System-wide properties common to all cases
Calls into questions behavioral assumptions in UE and SO Assignment Models

19. Extensions and Future Studies
Consider Different Learning Types and Risk
Perform Laboratory Experiments to Provide Empirical Insight
Estimate Parameters in Learning Model
Chen and Mahmassani 2008 forthcoming
Interdependency between User Decisions and System Performance
Empirical Data on Route Choice
Econometrically Estimating the Weights for Travel Time Updating