Presentation MSc thesis, University College London

A Psychological Approach to Understand Decisions
About Time in Public Transport: Evidence from lab
experiments in London, UK and Santiago, Chile
ITS PUC Transportation Seminar
Pablo Guarda1,2 Paula Parpart1 Nigel Harvey1
Juan Carlos Mu˜noz2
1Department of Psychology and Language Sciences, University College London (UCL)
2Institute of Transportation Studies (ITS), Pontiﬁcal Catholic University of Chile (PUC)
December 19, 2017

Outline
1 Introduction
2 Theoretical framework
3 Method
4 Results
5 Conclusions
6 Further Research

Introduction Context
How do travelers’ make decisions in public transport?
Decision attributes
• Monetary cost
• Physical eﬀort (e.g. while walking)
• Time spent (e.g. while waiting and traveling)
• Service reliability (e.g. due to time variability)
• ...
Pablo Guarda (UCL and PUC) ITS PUC Transportation Seminar December 19, 2017 1 / 38

How do travelers’ make decisions in public transport?
Decision attributes
• Monetary cost
• Physical eﬀort (e.g. while walking)
• Time spent (e.g. while waiting and traveling)
• Service reliability (e.g. due to time variability)
• ...
Decision-making models
• Random utility maximization
• Cumulative prospect theory
• Heuristics

How can Psychology inform travel behavior models used in
transportation science?
1 Descriptive approaches for travelers’ decision making (e.g based on
bounded rationality)
2 Integration of diﬀerent underlying cognitive processes in travelers’
decision-making (e.g. active learning and time perception)
3 Novel experimental methods to elicit risk preferences in decisions
about time (Ashby and Rakow, 2017)
4 Theories to understand the inﬂuence of time variability and
uncertainty on travelers’ decisions (Avineri, 2006)

Introduction Study Overview
Study overview
Research questions
1 Understand how travelers trade-oﬀ waiting and in-vehicle times
2 Understand how time variability inﬂuences travelers’ decisions

Introduction Study Overview
Study overview
Research questions
1 Understand how travelers trade-off waiting and in-vehicle times
2 Understand how time variability influences travelers’ decisions
Main objectives
1 Bringing knowledge from Psychology to develop a new framework
to study travelers’ decision-making
2 Applying different choice paradigms to elicit risk preferences in
travel decision-making
3 Experimentally testing and assessing the consistency of travelers’
behavior predicted by random utility theory (RUT)

Theoretical framework Literature review
Relevant studies in Psychology
1 Kahneman, D. and Tversky, A., 1979. Prospect theory: An analysis of
decision under risk. Econometrica, 263–291.
2 Leclerc, F., Schmitt, B.H. and Dube, L., 1995. Waiting Time and Decision
Making: Is Time like Money? Journal of Consumer Research, 22, 110–119.
3 Antonides, G., Verhoef, P. and Aalst, M. Van, 2006. Consumer Perception
and Evaluation of Waiting Time. Journal of Consumer Psychology 12,
193–202.
4 Hertwig, R. and Erev, I., 2009. The description-experience gap in risky
choice. Trends in Cognitive Sciences 13, 517–523.
5 Ashby, N.J.S. and Rakow, T., 2017. When time is (not) money: preliminary
guidance on the interchangeability of time and money in laboratory-based
risk research. Journal of Risk Research 0, 1–16.

Theoretical framework Literature review
Relevant studies in Transportation science
1 Noland, R.B. and Polak, J.W., 2002. Travel time variability: a review of
theoretical and empirical issues. Transport Reviews: A Transnational
Transdisciplinary Journal 22, 39–54.
2 Ben-Elia, E., Erev, I. and Shiftan, Y., 2008. The combined eﬀect of
information and experience on drivers’ route-choice behavior. Transportation
35, 165–177.
3 Fosgerau, M. and Engelson, L., 2011. The value of travel time variance.
Transportation Research Part B: Methodological 45, 1–8.
4 Raveau, S., Guo, Z., Mu˜noz, J.C. and Wilson, N.H.M., 2014. A behavioural
comparison of route choice on metro networks: Time, transfers, crowding,
topology and socio-demographics. Transportation Research Part A: Policy
and Practice 66, 185–195.

Theoretical framework Research Hypotheses
Research hypotheses in contexts of riskless choice
Time dominance (H1)
• H1: People prefer more routes with shorter than longer journey
times as long as the latters does not have a better trade-oﬀ
between waiting and in-vehicle times.

Research hypotheses in contexts of riskless choice
Time dominance (H1)
• H1: People prefer more routes with shorter than longer journey
times as long as the latters does not have a better trade-off
between waiting and in-vehicle times.
Compensatory behavior in preferences for waiting and traveling (H2)
• H2A: People prefer more routes with better than worse trade-off
as long as the journey times of the routes are the same
• H2B: People prefer more routes with longer than shorter journey
times as long as the formers have a better trade-off

Research hypotheses in context of risky choice
Aversion to time variability (H3)
• H3A: People prefer more routes with deterministic waiting time
than variable waiting time.
• H3B: People prefer more routes with deterministic in-vehicle time
than variable in-vehicle time

Research hypotheses in context of risky choice
• H3A: People prefer more routes with deterministic waiting time
than variable waiting time.
• H3B: People prefer more routes with deterministic in-vehicle time
than variable in-vehicle time
Aversion to waiting time variability (H4)
• H4A: People are more averse to variability in waiting time than to
variability in in-vehicle time
• H4B: People prefer more experiencing variability in in-vehicle time
than in waiting time

Method Task and materials
Method
• Materials
– Virtual environment programmed in PyQt

Method
• Materials
• Cognitive task
– Participants were asked to make a choice in 14 decision scenarios.
– Each scenario presented two bus routes with diﬀerent time attributes
– Only waiting and in-vehicles were manipulated across the scenarios.

Method
• Materials
• Cognitive task
– Participants were asked to make a choice in 14 decision scenarios.
– Each scenario presented two bus routes with diﬀerent time attributes
– Only waiting and in-vehicles were manipulated across the scenarios.
• Experimental conditions
– Within-subjects: Decisions-from-experience (experiential choices) vs.
Decisions-from-description (descriptive choices)
– Between-subjects: Manipulation of the level of information provided to
participants (Less/More Informative)

Method Participants
Participants
City: Santiago, Chile
Place: Computer Lab (Engineering), PUC
Date: June 2017
Participants: 36 university students
Duration: 35 minutes

Method Participants
Participants
City: Santiago, Chile
Place: Computer Lab (Engineering), PUC
Date: June 2017
Duration: 35 minutes
City: London, United Kingdom
Place: CogSys Lab (Psychology), UCL
Date: July 2017
Duration: 40 minutes *

Method Participants
Experimental design
36 Participants
From Santiago,
(CL)
36 Participants
From London
(UK)
Sample
Selection

Method Participants
Experimental design
36 Participants
From Santiago,
(CL)
36 Participants
From London
(UK)
18 CL
18 CL
Random
Assignment
Sample
Selection

Method Participants
Experimental design
36 Participants
From Santiago,
(CL)
36 Participants
From London
(UK)
Random
Assignment
Sample
Selection
18 CL
+
18 UK
18 CL
+
18 UK

Method Participants
Experimental design
Less Informative
Experiential Choices
(LI, EC)
Less Informative
Descriptive Choices
(LI, DC)
More Informative
Descriptive Choices
(MI, DC)
More Informative
Experiential Choices
(MI, EC)
Experimental Conditions
(Between, Within)
36 Participants
From Santiago,
(CL)
36 Participants
From London
(UK)
18 CL
+
18 UK
18 CL
+
18 UK
Random
Assignment
Sample
Selection

Method Experiential Choices
Experiential choices
• Decision Task
– Participants were presented with animated bus trips in two routes and
then asked to choose the route they liked most.

• Decision Task
• The decision scenarios were divided in 3 blocks
– Training block: dominated alternatives (2)
– Block 1: deterministic time attributes (8)
– Block 2: variable time attributes (6)
– The order of the scenarios was randomized within each block

• Decision Task
• Each decision scenario included four stages
– Learning stage
– Decision stage
– Consequential stage
– Conﬁrmation stage

• Decision Task
• Each decision scenario included four stages
– Learning stage
– Decision stage
– Consequential stage
– Conﬁrmation stage
• Between-subjects conditions
– Less-Informative: Unknown waiting and travel times
– More-Informative: Known waiting time and unknown travel time

Experiential choices - Non-informative condition (unknown waiting time)

Experiential choices - Informative condition (known waiting time)

Method Descriptive choices
Descriptive choices
• Decision task
– Participants were presented with the time attributes of two bus routes
and then asked to choose the one they like most

Descriptive choices
• Decision task
• Decision scenarios
– The same set of scenarios included in the experiential choices
– ”1 second - 1 minute rule”
– They were not divided in blocks and their order was randomized.

Descriptive choices
• Decision task
• Decision scenarios
– The same set of scenarios included in the experiential choices
– ”1 second - 1 minute rule”
– They were not divided in blocks and their order was randomized.
• Between-subjects conditions
– Less-informative: Prospects showing probabilities of the time outcomes
of each route
– More-informative: Tables showing the waiting and in-vehicle times of
each route

Descriptive choices - Non-informative condition (prospects)

Descriptive choices - Informative condition (tables)

Results
Results
Levels of analysis
1 Descriptive analysis of response times, certainty levels and consistency
between experiential and descriptive choices
2 Comparison of choice proportions within decision scenarios
3 Comparison of choice proportions between decision scenarios
4 Route choice model

Results Descriptive analyses
Response times in experiential and descriptive choices
0
5
10
15
20
01 02 03
Experimental Block
AverageReactionTimebyScenario[s]
Santiago London
Descriptive choices
0
5
10
15
20
01 02 03
Experimental Block
AverageReactionTimebyScenario[s]
Santiago London

Certainty level in experiential choices
Between-subjects condition
0
20
40
60
80
100
01 02 03
Experimental Block
CertaintyLevel(%)
Less−Informative More−Informative
City
0
10
20
30
40
50
60
70
80
90
100
01 02 03
Experimental Block
CertaintyLevel(%)
Santiago London

Choice consistency
Santiago
London
0 20 40 60 80 100
Choice Consistency by Participant (%)
City
Santiago London

Consistency between experiential and descriptive choices
0
20
40
60
80
100
01 02 03
Experimental Block
ChoiceConsistency[%]
Santiago London

Results Comparison within scenarios
Decision scenarios and predicted behavior by RUT

Choice proportions within scenarios (experiential choices)

Choice proportions within scenarios (descriptive choices)

Results Comparison between scenarios
Comparison between scenarios: Time Dominance (H1)
Table 1: Mixed eﬀects logistic regression examining predictors of picking the domi-
nating option in decision scenarios S1 and S2. Odd ratio (OR) and 95% conﬁdence
interval (CI).
Predictor OR z p CI
Constant 41.09 4.74 0.00 [8.84, 191.07]
Time Dominance (Travel vs. Waiting) 2.04 0.57 0.57 [0.18, 23.12]
W-S Condition (Descriptive vs. Experiential) 0.06 -3.60 0.00 [0.01, 0.28]
W-S Condition x Time Dominance 1.61 0.36 0.72 [0.12, 21.47]

Comparison between scenarios: Compensatory vs.
non-compensatory behavior (H2A)
Table 2: Mixed effects logistic regressions examining predictors of picking the shorter
waiting option (with better trade-off ) in scenarios S3 and S4. Odd ratio (OR) and
95% confidence interval (CI).
Predictor OR z p CI
Constant 8.20 4.98 0.00 [3.58, 18.78]
Difference in Proportion of Waiting Time (Low vs. High) 0.89 -0.25 0.81 [0.34, 2.32]
Difference in Proportion of Waiting Time x W-S Condition 0.83 -0.30 0.76 [0.24, 2.85]

Comparison between scenarios: Compensatory vs.
non-compensatory behavior (H2B)
Table 3: Mixed effects logistic regressions examining predictors of picking the longer
journey option (and with better trade-off ) in scenarios S5-S8. Odd ratio (OR) and
95% confidence interval (CI).
Predictor OR z p CI
Constant 1.71 1.96 0.05 [1.00, 2.92]
W-S Condition x Utility Gain in Longer Journey Option 1.00 0.00 1.00 [0.46, 2.20]
Utility Gain in Longer Journey Option (Moderate vs. High) 1.09 0.29 0.77 [0.62, 1.90]

Results Route choice model
Route choice model
• Model
– Binary logit model (BL)
• Explanatory Variables
– Average waiting time (tw = 1, 2, 3, 4, 6, 7, 8, 9)
– Average in-vehicle time (tv = 1, 2, 4, 6, 7, 9)
– Standard deviation of waiting time (±2, ±4, minw = 0, maxw = 8)
– Standard deviation of in-vehicle time (±2, ±4, minv = 2, maxv = 10)
• Levels of analysis
– Within-subjects conditions: experiential vs. descriptive choices
– Between-subjects conditions: less-Informative vs. more informative
– Cities: London vs. Santiago

BL estimation results: experiential choices
Table 4: Binary logit model (BL) estimation results in experiential choices. Disaggre-
gation by between-subjects condition (less/more informative)
Between-Subject Conditions (Experiential Choices)
Variable (t-test) Less-Informative More-Informative Both
Average Waiting Time (θµ
w) −0.515 (−3.9) −0.867 (−5.7) −0.678 (−6.9)
Average Travel Time (θµ
v ) −0.524 (−4.4) −0.777 (−5.7) −0.639 (−7.3)
Variability Waiting Time (θσ
w) −0.244 (−3.7) −0.299 (−4.3) −0.271 (−5.7)
Variability Travel Time (θσ
v ) −0.124 (−1.9) −0.082 (−1.2) −0.103 (−2.2)
Ratio Average Waiting/Travel (γµ
w,v) 0.98 1.12***
1.06**
Ratio Variability Waiting/Travel (γσ
w,v) 1.96*
3.66***
2.62***
Risk Premium Waiting (pw) 0.47***
0.34***
0.4***
Risk Premium Travel (pv) 0.24*
0.11 0.16**
Akaike Information Criteria (AIC) 666 642 1306
Log-likelihood -328 -316 -648
Adjusted McFadden’s pseudo-R2
(ρ2
) 0.06 0.09 0.07
Observations 504 504 1,008
Notes: Log-likehood Ratio (LR) Test. Signiﬁcance levels: ∗
p<0.1; ∗∗
p<0.05; ∗∗∗
p<0.01
Null Hyphotesis 1 (H1
0 ): γi
w,v = 1, i = µ, σ. Null Hyphotesis 2 (H2
0 ): θσ
i = 0, i = v, w.

BL estimation results: descriptive choices
Table 5: Binary logit model (BL) estimation results in descriptive choices. Disaggre-
gation by between-subjects condition (less/more Informative)
Between-Subject Conditions (Descriptive Choices)
Variable (t-test) Less-Informative More-Informative Both
w) −1.574 (−6.5) −1.984 (−6.0) −1.746 (−9.0)
v ) −1.306 (−6.1) −1.630 (−5.7) −1.440 (−8.4)
w) −0.356 (−4.9) −0.321 (−4.4) −0.335 (−6.6)
v ) −0.241 (−3.5) −0.428 (−5.6) −0.329 (−6.5)
w,v) 1.21***
1.22***
1.21***
w,v) 1.48*
0.75 1.02
0.16***
0.19***
Risk Premium Travel (pv) 0.18***
0.26***
0.23***
Akaike Information Criteria (AIC) 579 541 1119
Log-likelihood -285 -265 -554
(ρ2
) 0.18 0.24 0.2
Observations 504 504 1,008
p<0.1; ∗∗
p<0.05; ∗∗∗
p<0.01
0 ): γi
0 ): θσ
i = 0, i = v, w.

BL estimation results: London vs. Santiago
Table 6: Binary logit model (BL) estimation results. Dissagregation by within-subject
condition (experiential/descriptive choices) and city (Santiago, Chile or London, UK)
Experiential Choice Descriptive Choice
Variable (t-test) Santiago London Santiago London
w) −1.203 (−6.2) −0.354 (−2.9) −2.183 (−5.8) −1.630 (−6.5)
v ) −1.079 (−6.2) −0.381 (−3.5) −1.678 (−5.2) −1.441 (−6.5)
w) −0.390 (−5.2) −0.180 (−2.8) −0.480 (−5.8) −0.210 (−3.2)
v ) −0.276 (−3.9) 0.047 (0.7) −0.436 (−5.4) −0.240 (−3.6)
w,v) 1.12***
0.93 1.3***
1.13***
w,v) 1.42*
-3.83***
1.1 0.88
0.51***
0.22***
0.13***
Risk Premium Travel (pv) 0.26***
-0.12 0.26***
0.17***
Akaike Information Criteria (AIC) 608 675 479 600
Log-likelihood -299 -332 -235 -295
(ρ2
) 0.14 0.05 0.32 0.15
Observations 504 504 504 504
p<0.1; ∗∗
p<0.05; ∗∗∗
p<0.01
0 ): γi
0 ): θσ
i = 0, i = v, w.

Conclusions Context of riskless choice
Conclusions in contexts of riskless choice
Time dominance (H1)
• Participants had a higher preference for dominating options in
both experiential and descriptive choices. However, they were less
rational when experiencing commuting times.

Conclusions Context of riskless choice
Conclusions in contexts of riskless choice
Time dominance (H1)
• Participants had a higher preference for dominating options in
both experiential and descriptive choices. However, they were less
rational when experiencing commuting times.
Compensatory behavior in preferences for waiting and traveling (H2)
• Participants in general preferred more routes with better than
worse trade-oﬀ when the journey times of the routes are equal.
However, they seems insensitive to changes in the trade-oﬀ
between waiting and traveling times.
• Thus, participants may have used an heuristic such as picking the
alternative with lower proportion of waiting time instead of
compensating waiting and traveling times.

Conclusions Context of risky choice
Conclusions in contexts of risky choice
• Participants preferred more routes with no variability in waiting
times.
• London participants were less sensitive to variability in in-vehicle
times.
• In the experiential choices, there was no evidence of aversion to
variability in in-vehicle time.

Conclusions Context of risky choice
Conclusions in contexts of risky choice
• Participants preferred more routes with no variability in waiting
times.
• London participants were less sensitive to variability in in-vehicle
times.
• In the experiential choices, there was no evidence of aversion to
variability in in-vehicle time.
Aversion to waiting time variability (H4)
• People are more averse to variability in waiting time than to
variability in in-vehicle time
• The degree of aversion does not increase as the level of variability
is higher

Further Research Limitations
Limitations
Internal validity
• Low statistical power (because of low budget and logistic
constraints)

Limitations
Internal validity
constraints)
External validity
• The sample of participants is not representative of the high
diversity of public transport users

Limitations
Internal validity
constraints)
External validity
• The sample of participants is not representative of the high
diversity of public transport users
Ecological validity
• In the experiential choices, the animated trips lasted seconds. In
the descriptive choices, the routes have short journey times
• Travelers do not passively learn about their available routes
• Omission of factors that mediates the impact of waiting and
in-vehicle times on travelers’ decisions: e.g. weather

Further Research Extensions
Extensions
• Check whether longer/shorter journey times in the animated trips
changes the conclusions
• Let participants actively learn about the routes

Extensions
Descriptive choices
• Add new decision scenarios with diﬀerent journey times and levels
of variability.
• Present time attributes using other formats, e.g. time intervals to
represent variability.

Extensions
Descriptive choices
• Add new decision scenarios with diﬀerent journey times and levels
of variability.
• Present time attributes using other formats, e.g. time intervals to
represent variability.
Experimental manipulations
• Add transfers, other modes of transportation and the cost of each
option, among others.

References
References
Antonides, G., Verhoef, P, and Aalst, M. V. (2006). “Consumer
Perception and Evaluation of Waiting Time”. Journal of Consumer
Psychology 12, pp. 193–202.
Ashby, N. J. and Rakow, T. (2017). “When time is (not) money:
preliminary guidance on the interchangeability of time and money in
laboratory-based risk research”. Journal of Risk Research 0, pp. 1–16.
Avineri, E. (2006). “The eﬀect of reference point on stochastic network
equilibrium”. Transportation Science 40, pp. 409–420.
Ben-Elia, E., Erev, I., and Shiftan, Y. (2008). “The combined eﬀect of
information and experience on drivers’ route-choice behavior”.
Transportation 35, pp. 165–177.
Fosgerau, M. and Engelson, L. (2011). “The value of travel time
variance”. Transportation Research Part B: Methodological 45, pp. 1–8.

References
References
Hertwig, R. and Erev, I. (2009). “The description-experience gap in risky
choice”. Trends in Cognitive Sciences 13, pp. 517–523.
Kahneman, D. and Tversky, A. (1979). “Prospect theory: An analysis of
decision under risk”. Econometrica: Journal of the econometric society,
pp. 263–291.
Leclerc, F. et al. (1995). “Waiting Time and Decision Making: Is Time like
Money? Is Time like Money?” Journal of Consumer Research 22,
pp. 110–119.
Noland, R. B. and Polak, J. W. (2002). “Travel time variability: a review
of theoretical and empirical issues”. Transport Reviews: A
Transnational Transdisciplinary Journal 22, pp. 39–54.
Raveau, S. et al. (2014). “A behavioural comparison of route choice on
metro networks: Time, transfers, crowding, topology and
socio-demographics”. Transportation Research Part A: Policy and
Practice 66, pp. 185–195.

Acknowledgements
Acknowledgements
This research was beneﬁted from the support of:
• Bus Rapid Transit Centre of Excellence, funded by the Volvo Research
and Educational Foundations (VREF)

Presentation MSc thesis, University College London

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