Sep. 14, 2020
The 19 th Behavior Modeling in Transportation Networks

Mechanism Design &Behavior Modelling
Hiroshima University
Makoto Chikaraishi

A class of problems we address• There are a lot of examples that your decision, or
allocating some limited resources to you, would
bring negative benefits to others.
• Some examples– School choice:• Your successful enrollment would prevent othersʼ enrollment.

– Land use:
• Parking lots in CBD prevent others to utilize the land in different ways.

– Car use:
• Your car use would worsen traffic congestion.

– Public transit use:
• Your public transit use would worsen crowding in public transit.

– Taxi/Ride hailing:• Your taxi/ride-hailing use may increase othersʼ waiting time.
2

Generalized cost

No intervention,
No social welfare maximization
Marginalsocial cost
Marginal
privatecost
Social optimumMarket equilibrium
Note: This figure is not exactly
correct when considering queueing
congestion and actual road capacity.

DemandFunction(=WTP)
Traffic volume

Market equilibrium

Social optimum31. Price-based regulations


Static/dynamic road pricing
Evolutionary road pricing

Generalized costThree types of interventions
Social optimump*Market equilibrium

2. Quantity-based regulations


Tradable bottleneck permits
Tradable mobility/travel credits

Generalized cost

Traffic volume

Social optimumMarket equilibrium

Traffic volume

3. Other regulations/incentivesTwo-sided market
Personalized incentives

In particular, the 3 rd type demands further behavioral studies.
41. PRICE-BASEDREGULATIONS
5

Price-based regulations: Idea

Generalized cost

Road pricing: Ask all road users to pay p*

Social optimump*Market equilibrium

Traffic volume
6

Generalized cost

A simple example
19.5

Social optimum

14.5

p*=4

11.0
10.5

Market equilibrium

10.0

1

2

3

4

5

6

7

8

9

10

Traffic volume

7

Problems:
1. Spatio-temporal generalization– [Spatial] Not one single road section, but network.
– [Temporal] Demand is not fixed, but varying acrosstime of day, day of week, etc.
(c.f.,松井寛(編)︓交通ネットワークの均衡分析̶最新の理論と解法̶,⼟⽊学会, 1998)

2. Considering queues of vehicles on roads– Flow congestion  Queuing congestion(c.f., 桑原雅夫: 交通流理論―流れの時空間変化をひも解く―, 交通⼯学研究会, 2020)

3. Difficulty in observing demand function– Achieving social optimum without demand
information• Quantity-based regulations with a proper mechanism design(e.g., Akamatsu et al., 2006; Akamatsu and Wada, 2017)8

2. QUANTITY-BASEDREGULATIONS
9Quantity-based regulations: Idea

Generalized cost

Limited number of permits will be issued

Social optimumMarket equilibrium
Note: This figure is not exactly
correct when considering queueing
congestion and actual road capacity.

Traffic volume
How should we allocate permits to road users?
 Mechanism design10What is Mechanism Design?
• Mechanism Design– Find a mechanism that maximizes some objective function(e.g., social welfare) to a problem that involves multiple
self-interested agents.
• Examples of objective functions– Maximizing social welfare, maximizing revenue, etc.

– Applications••••

Emission trading scheme
Fishing quotas
Government bond auction
Allocating transportation resources/services• Two key ideas in designing the mechanism– Strategy-proof: nobody has an incentive to tell a lie(incentive compatible)– Efficient resource allocation: maximizing social welfare11Tradable bottleneck permits (TBP)
Akamatsu et al. (2006); Akamatsu (2007)• Tradable bottleneck permits (TBP)
Destination

Origincapacity: 𝜇

The number of
permits issued: 𝜇• Applications to transportation systems– Airport slot allocations (Schummer and Vohra, 2013)– Car Sharing (Hara and Hato, 2018)– Tradable mobility credits (Yang and Wang, 2011)– Tradable bottleneck permits (Akamatsu et al., 2006; Akamatsu, 2007)• Common features– Transport managers/operators do not need to observe
usersʼ demand preferences.
– The strategy-proof and efficient mechanism.

12Basic settings𝑖
1,2, … , 𝑛 : Individual (bidder)𝑣 : Individual 𝑖ʻs private value (equal to his bid) [strategy-proof]𝑥 ∈ 𝐴: option (𝐴 : a set of possible outcomes)𝑡 : Actual payment
Utility function: quasi-linear function
𝑈 𝑎, 𝑡 ; 𝑣
Example1st vehicle𝑣 𝑥𝑡

Our goal:∀ 𝑎, 𝑡Private value

Payment

Private value

Payment

45 – travel timep

2nd vehicle

40 – travel timep

3rd vehicle

38 – travel timep

4th vehicle

33 – travel timep

5th vehicle

28 – travel timep

6th vehicle

25 – travel timep

7th vehicle

20 – travel timep

8th vehicle

16 – travel time

p

𝑥∗ 𝑣

14.5 – travel time

𝑣 𝑥

∈

19.5

Social optimum

14.5

p*=4

11.0
10.5

Market equilibrium

10.0

1

9th vehicle

arg max[efficient allocation]
Generalized cost

•
•
•
•
•

p

Willingness to pay (=demand function)

2

3

4

5

6

7

8

9

10

Traffic volume

13

Groves mechanism• Grove mechanism:𝑡ℎ 𝑣

Constant foreach individual

𝑣 𝑥 𝑣

※ 𝑣

𝑣

Total private
value of otheragents• Grove mechanism is a truthful mechanism, i.e., amechanism where bidding the true valuation is adominant strategy.

14

Pivotal Mechanism• In Pivotal Mechanism, we further assume:
ℎ 𝑣

max

𝑣 𝑥

∈

• And thus the payment will be
𝑡

𝑣 𝑥 𝑣

max
∈

𝑣 𝑥

• When negative externalities exist, obviously
𝑣 𝑥 𝑣

max
∈

𝑣 𝑥

 𝑡

0

• Thus, all agents will not receive any incentive
under pivotal mechanism.
15

VCG Mechanism• In VCG mechanism, we further assume:𝑥𝑦 ⟹ 𝑣 𝑥𝑣 𝑦
∀𝑥, 𝑦 ∈ 𝐴𝑥
∅ ⟹ 𝑣 𝑥0∀𝑥 ∈ 𝐴𝑣 𝑥
0 ∀𝑥 ∈ 𝐴𝑣 𝑦∀𝑥, 𝑦 ∈ 𝐴𝑥 ⊂𝑦 ⟹ 𝑣 𝑥• Under VCG mechanism,𝑣 𝑥∗𝑡Social welfare
with agent i

where

𝑥∗
𝑥

∗

𝑣 𝑥∗
Social welfare
without agent iarg max ∑ 𝑣 𝑥 : optimal allocation with agent i∈arg max ∑∈𝑣 𝑥 : : optimal allocation without agent i
16

A simple example
1. Assume that we have 9 permits that will be allocated to road users.
2. For the 1st vehicle,

𝑡

∑

𝑣 𝑥∗

𝑣 𝑥∗

∑

130.5

134.5

4

Generalized cost

4.
3. Similarly, for other vehicles, 𝑡
4. Thus, all road users should pay 4 to get the permit under VCG mechanism,and this is equivalent to the optimum pricing.
19.5

Private value

14.5

11.0
10.5
10.0

1

2

3

4

5

6

7

8

9

10

1st vehicle

45 – travel time

2nd vehicle

40 – travel time

3rd vehicle

38 – travel time

4th vehicle

33 – travel time

5th vehicle

28 – travel time

6th vehicle

25 – travel time

7th vehicle

20 – travel time

8th vehicle

16 – travel time

9th vehicle

14.5 – travel time17

Traffic volume

Major problems• The mechanisms only take into accountexternalities to persons who join the system.
– It would not work for environmental externalities(externalities to persons who are not in the system suchas non-car users), health damages (externalities to otherlife domains), impacts on urban form, etc.

• Transportation may be too fundamental to optimize
the system without paying attention to other social
systems.
• And some others (will be discussed in later slides)18

3. OTHER REGULATIONS/INCENTIVES
19

Introduction
The above price-based and quantity-based
regulations provide solid theoretical foundations,while further discussions and considerations areneeded for practical implementations.
1. Problems we should further considerMechanism design versus matching (Budish, 2012)
Preference elicitation cost (Nie, 2012; Hara, 2018)
Social acceptability (many references)

2. Necessary extensionsMultiple travel modes
Multiple social goals
Flexible transport supply (two-sided market)

3. New possible directions


Personalized incentives
20

Matching vs. mechanism design
Budish, E., 2012. Matching “versus” mechanism design. ACM SIGecom Exchanges, 11(2), 4-15.

•

Matching versus mechanism design– Mechanism design: to find a mechanism that maximizes some
objective function– Matching: to find a mechanism that satisfies various good
properties•

Applied researchers and practitioners may prefer “matching”,
because
1. Difficult to pin down the objective.
e.g., we often aim for measures of both efficiency and fairness.

2. Difficult to pin down the true constraints of the problem.
e.g., the budget could be somewhat flexible.

3. A lack of tools to “maximize social welfare s.t. constraints”•

Applied researchers may prefer to use “good properties”
approach rather than “maximize objective” approach.
21

Flexible transport supply
Traditional market:
Fixed
SupplyFlexible
DemandFlexible
SupplyFlexible
DemandEmerging market:

22

Two-sided market
Example: Ride hailing service

Flexible
Supply(=driver)Flexible
Demand(=user)Charge for drivers

Charge for users
Platform(Uber, Gojek, etc.)

23How different are daily fluctuations andweekly rhythms in time-use behavior
across urban settings? (Watanabe et al., In press)•

Hypothesis tested:– High car dependency and compact activity space increase theflexibility in activity-travel decisions, resulting in larger daily
fluctuations in discretionary activities (i.e., higher unobserved
intra-individual variations). However, long-distance commuting inpublic transport creates difficulty for the residents to engage inboth work at their office and leisure activities in theirneighborhoods within a day, forming salient weekly rhythms indiscretionary activity engagements (i.e., workday time-use
significantly affects non-working day time-use).

In future, transport supply could be flexible enough to meetsuch fluctuated travel demand, and some modest
positive/negative incentives would be good enough to manage
the mismatch between transport supply and demand.
24

Multi-sided market
USERs

SERVICES

DRIVERs(Merchants)

A

B

C

PLATFORMS

Activity‐Travel
Behavior

Urban Form

Safira, M., Chikaraishi, M. (2020) Impact of Online-based Food Delivery Service on Individualsʼ Eating Behavior:
A Case study on the Multi-service Transport Platforms (MSTPs) in Indonesia, submitted to TRB annual meeting.

25Personalized incentives
Trigger the desired behavioral change by providing
personalized incentives• Personalized incentives– incenTrip (https://incentrip.org/)– TRIPOD (Azevedo et al., 2018)– Zhu et al. (2020)26

incenTrip

Tang and Hu (2019)27

TRIPOD

28

Zhu et al. (2020)29

Other possible incentivization
Bella Mossa program:
Incentive + gamification
for health benefits

30

Other possible incentivization
EMPOWER (http://mobility-apps.eu/)

 Awareness-based intervention

 Nudging active participation
 Activating social interactions
31

Or even do we really need an incentive or
penalty to internalize externalities?
Hara, Y., Yamaguchi, H.: Behavioral
change under COVID-19 state-ofemergency declaration in Japan(unpublished).
– The Japan government did not
impose strong restrictions on
travel during the COVID-19
pandemic. Just “request”.
– A clear decrease in travel demand
after the declaration of the stateof emergency without penalty.
People may be a bit cleverer thanthe model assumes.

⽇本経済新聞︓https://www.nikkei.com/article/DGXMZO
57774880X00C20A4EA1000/32

Other possible considerations
Cooperative Token for Bottlenecks in Disaster Restoration Period(Hara and Chikaraishi, in progress)
1. People often have a will to behave in a cooperative way
particularly during disaster without any payment/incentive.
2. In such a case, applying payment/incentive schemes could
even worse the outcome (e.g., Gennzy and Rustichini, 2000).
3. Other “softer” interventions which nudge people to behave
in a socially better way would be preferable.

33

Important behavioral aspects
Behavioral
effects

Key references

Explanation

Potential effect in TC context

Loss
aversion

Kahneman and Tversky(1979)

Losses weigh more than equivalent gains

A higher propensity to reduce credit usage in asituation of credit shortage than of credit surplus

Endowment
effectThaler (1980) and
Kahneman et al. (1991)

People ascribe more value to objects orresources when they are in their possession

Increased reluctance to trade credits

Framing

Tversky and Kahneman(1981) and Levin et al.
(1998)The presentation of an equivalent situationor outcome in a different format leads to adifferent outcome

Credit‐spending patterns depend on the framingof the policy by participants and regulating bodies

Mentalaccounting

Thaler (1999) and
Health and Soll (1996)

Money and resources are psychologically
categorized based on different codes andlabelsCredits are not equal to the money that they
represent; the suggested budget limit may
encourage credit conservation

Endowment
effect under
uncertainty

Kahneman & Tversky(1979)/van Dijk & van
Knippenberg (1999)

Endowment effects tend to be stronger intrades that involve uncertainties

Uncertainty over the future credit price and travel
may encourage credit conservation

Complexity
aversionTversky and Kahneman(1974)

People tend to act less rationally and rely
more on decision heuristics in complex
decision contexts

The more people encounter difficulties in
estimating credit costs, the more people will
make decisions that satisfy rather than optimize

Regretaversion

Bell (1982) and Loomes
and Sugden (1982)

People anticipate the possibility of regret
felt if an alternative choice option would
result in a better outcome and try to avoid
choice options with larger anticipated regret

In TC decision‐making contexts with increasinglevels of uncertainty, regret aversion might play amore prominent role

Immediacy
effectKeren and Roelofsma(1995) and Green andMyerson (2004)

People tend to attach greater value toimmediate rewards than to equivalent
rewards that arrive latter

People may overspend their credits at the start ofa TC period

Learning
effectErev and Barron (2005)

People learn from their past decision
through feedback

Credit spending may change over time based on
how satisfied people are with earlier outcomes

Behavioral effects in tradable credits (TC): Source: Dogteron et al. (2017)34

Summary• Price-based regulations– Need aggregate demand function• Auction-type approach– Demand function is not needed• Personalized incentives– Need individual level demand function (or even it
would vary depending on time-of-day, day-of-week, etc.)

Increasing needs for“revitalizing” behavioral studies.

35References
•
•
•

•
•
•
•
•

•

•
•

Akamatsu, T., Wada, K., 2017. Tradable network permits: A new scheme for the most efficient use of
network capacity. Transportation Research Part C: Emerging Technologies 79, 178-195.
⾚松隆, 佐藤慎太郎, Nguyen, X.L., 2006. 時間帯別ボトルネック通⾏権取引制度に関する研究. ⼟⽊学会論⽂集Ｄ62, 605-620.
Azevedo, C.L., Seshadri, R., Gao, S., Atasoy, B., Akkinepally, A.P., Christofa, E., Zhao, F., Trancik, J.,
Ben-Akiva, M., 2018. Tripod: sustainable travel incentives with prediction, optimization, andpersonalization, the 97th Annual Meeting of Transportation Research Board.
Budish, E., 2012. Matching" versus" mechanism design. ACM SIGecom Exchanges 11, 4-15.
原祐輔 (2015) オークション理論とメカニズムデザイン [http://bin.t.utokyo.ac.jp/summercamp2015/document/key_hara.pdf]
Hara, Y., Hato, E., 2018. A car sharing auction with temporal-spatial OD connection conditions.
Transportation Research Part B: Methodological 117, 723-739.
Nie, Y., 2012. Transaction costs and tradable mobility credits. Transportation Research Part B:
Methodological 46, 189-203.
Tang, Q., Hu, X., 2019. Chapter Eight - Triggering behavior changes with information and incentives:
An active traffic and demand management-oriented review, in: Ben-Elia, E. (Ed.), Advances inTransport Policy and Planning. Academic Press, pp. 209-250.
Watanabe, H., Chikaraishi, M., Maruyama, T. (In Press) How different are daily fluctuations and weekly
rhythms in time-use behavior across urban settings? A case in two Japanese cities, Travel Behavior
and Society (Accepted).
Yang, H., Wang, X., 2011. Managing network mobility with tradable credits. Transportation ResearchPart B: Methodological 45, 580-594.
Zhu, X., Wang, F., Chen, C., Reed, D.D., 2020. Personalized incentives for promoting sustainable travel
behaviors. Transportation Research Part C: Emerging Technologies 113, 314-331.

36