URBE: presentata la ricerca di Cascetta e Cartenì sulla pianificazione urbana dei trasporti

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Urban Freight transportation planning as a rational decision making process: a cognitive model and the false friends of eco-rationality

E. Cascetta, A. Cartenì

Department of Civil, Construction and Environmental Engineering University of Napoli Federico II

Department of Civil, Construction and Environmental

Engineering - University of Napoli Federico II

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Freight transportation planning as a rational decision making process: a cognitive model and lse friends of eco-rationality – Cascetta and Cartenì

1. Background

2. A cognitive rational decision-making process

3. Transport-related acceptance&equity measures for road pricing schemes

4. Exploratory results on a test network 5. Conclusions and research perspectives

Outline

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Urban Freight transportation planning as a rational decision making process: a cognitive model and the false friends of eco-rationality – Cascetta and Cartenì

1. Background

City Logistics is ‘‘The process for totally optimizing the logistics and transport activities by private companies in urban areas while considering the traffic environment, congestion and combustible consumption, with a view to reduce the number of vehicle on the cities, through the rationalization of its operations’’ (Taniguchi et al., 2001)

Most of the contributions in literature and real-case applications assume that the decision making process for city logistics are always “rational”

In this research we argued that unfortunately sometimes, this is not true because decisions on urban freight system may be “a-rational” and quantitative methods/tools are not used or are used in a purely “cosmetic”

way

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1. Background

Real-life experiences abound in decision making failures in City Logistics projects

Examples include:

Policies implemented/studied

 Travel Demand Management policies (e.g. road pricing schemes)

 rationalization of freight distribution (e.g. agglomeration, new distribution channel and/or paths)

 new transport infrastructures (e.g. transit-point)

 low-impact freight vehicles (e.g. electric/LPG light goods vehicles)

 …

Quantitative methods used for impact estimations

 unrealistic estimations in business plans (ex-ante)

 no ex-post evaluations

 …

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Naples (Italy) city logistics Plan (Cartenì, Cascetta, 2013)

A “false friends” of eco-rationality

 Transit-point and light goods vehicles for urban distribution

Aims

 Reduce traffic congestion

 Reduce traffic emissions (CO2 and PM10) Policies

 new transit points

 new vehicle paths

 no Heavy trucks for urban delivery

 Impacts estimation (sim. model)

 - 5% traffic congestion reduction

 + 10% traffic fuel consumption

 + 5% green gasses emission (eqiv. CO2)

 + 11% fine particles emission (PM10)

1. Background

5 Increase in

paths length

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n Freight transportation planning as a rational decision making process: a cognitive model and false friends of eco-rationality – Cascetta and Cartenì

1. Background

Pilot projects of City logistics New York City (Holguín-Veras, 2012)

 urban distribution only during the night (between 21:00 and 07:00)

North Rhine-Westphalia (Germany) (Eibner, 2012)

 cooperation for urban distribution

 2 local carriers

 pooling of delivery trips,

 better utilization of vehicles and reduction of empty trips.

 development of logistics solutions to control and optimize urban mobility

Failed because not accepted among the companies involved

consensus

barriers

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1. Background

Pilot projects of road pricing Tokyo (Japan) (Kato et al, 2009)

 business district of Tokyo (16 km2)

 €6.0 to 9.0 (800 to 1200 yen) daily charge for driving a vehicle within the charging

zone between 07:00 and 19:00 (week days)

7 Failed because not accepted among the (small) companies

involved (increase in transport costs, no interest in environment

impacts … no Public Engagement)

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1. Background

2. A cognitive rational decision-making process

3. Transport-related acceptance&equity measures for road pricing schemes

4. Exploratory results on a test network 5. Conclusions and research perspectives

Outline

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Planning interventions on a transport system means making decisions The quality of the decisions depends on the process followed to reach them

 rational choice (rationality) means acting in the best possible way

...where a proposal of minimal requirements of rationality are (Cascetta et al., 2015) :

 comparative

 considering more than one alternatives (e.g. not deciding, one of the available options, searching for other possibilities)

 aware

 being informed about the options (features), the context (physical and decisional) and other related choices (internal, horizontal and vertical coherence)

 impacts evaluation (costs, benefits, risks and opportunities)

 consistent

 comparing options with aims and constraints

 flexible

 e.g. choices can be changed due to the context (unpredictable)

 accepted &equitable (Public Engagement)

2. A cognitive rational decision-making process

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2. A cognitive rational decision-making process

Acceptance in city logistics

Many papers deals with the problem related to the consensus in introducing urban policies (e.g. road-pricing; rationalization of freight distribution)

(Gammelgaard, 2015; Grisolia et al. 2015; Levinson, 2010; Taylor ae al., 2010; Viegas, 2001)

The subject involved:

 Shippers (e.g. increase in distribution costs; brand-lost)

 Carrier (e.g. loss employment)

 Retailers (e.g. loss of competitiveness of their activities)

 Users and citizens

… no quantitative measures proposed in the literature

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2. A cognitive rational decision-making process

Acceptance in city logistics

Only few ex-post analysis - consolidated strategies for increasing acceptance are:

 Public Engagement

(e.g. Cascetta et alii, 2015)

 Information about the characteristics of the policy

(familiarity – e.g. Cools et al., 2011)

 Information about the social benefits

(Albalate and Bel, 2009; Odeck and Kjerkreit, 2010; May et al, 2010; Noordergraaf et al., 2014)

 Perception of an equitable policy:

 use revenues raised by a policy (e.g a toll) for new/revamped infrastructures and/or services for the freight deliverers

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2. A cognitive rational decision-making process

Equity in city logistics

Equity is concerned with the distribution of costs and/or benefits among members/users. Such benefits and costs (monetary or not) can be distributed in ways that people may see as reasonable or not (acceptance), depending on different criteria (Ecola and Light, 2009)

 Economists tend to use welfare-based and/or financial measures of equity based on microeconomic theory to characterize the impacts

 Transportation planners tend to evaluate a policy in terms of

transportation accessibility and environmental-impacts as

measures of equity

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2. A cognitive rational decision-making process

Road pricing schemes in operation London Congestion Charging Scheme (LCCS)

 £11.50 daily charge for driving a vehicle within the charging zone between 07:00 and 18:00, Monday to Friday

 Time restriction for High goods vehicles

Milan Congestion Charge

 €5.0 daily charge for driving a vehicle within the charging zone between 07:00 and 19:00, Monday to Friday

 No distinction among vehicle type (high vs. low emission)

 No distinction among length/duration of the trip

Not considered as an equitable policy (low acceptance) 13

(accessibility)

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2. A cognitive rational decision-making process

AIMS OF THIS FISRT PART OF THE RESEARCH

acceptance&equity measures for enlarge the consensus in city logistics policies

we focus on road pricing design for city logistics proposing

 transport-related acceptance&equity measures for road- pricing schemes

As an additional

criteria for city

logistics design

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1. Background

2. A cognitive rational decision-making process

3. Transport-related acceptance&equity measures for road pricing schemes

4. Exploratory results on a test network 5. Conclusions and research perspectives

Outline

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3. A transport-related acceptance&equity measure

The idea of introducing a toll (pricing) for the use of a road infrastructure and/ or a service in urban areas is one of the most common TDM policies aimed to reduce traffic congestion and/or pollutant emissions

Most common road-pricing schemes (Ecola and Light, 2009; Levinson, 2010)

 toll (pay for using a single road infrastructure or lane)

 cordon/area pricing (pay for crossing a cordon or entering in an area, e.g.

Historic city center)

Several applications of road-pricing across the world

 Singapore – Cordon, Time (peak vs. off-peak hours), Distance of the trip and Vehicle based

 London (UK) Cordon and Time-Based

 Milan (Italy) Cordon and Time-Based

 New York (USA) Bridge and tunnel crossing and Time-based

 …

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3. A transport-related acceptance&equity measure ITS technologies and road pricing

allow to extend and apply more sophisticated pricing schemes connecting the toll to:

 trip characteristics (OD-based; path-based; service-based)

 distance of the trips (e.g. $/km)

 travel time (e.g. $/minutes of network usage)

 congestion level (e.g. peak-hour vs. off-peak our)

 vehicle consumption\emission characteristics (e.g. electric vs.

traditional)

 vehicle size and loading factor

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rban Freight transportation planning as a rational decision making process: a cognitive model and e false friends of eco-rationality – Cascetta and Cartenì

3. A transport-related acceptance&equity measure State of the art

All the available applications aim to rationalized urban freight distribution and reduce car use and their impacts using different pricing strategies through different methodologies aimed to define the toll:

 Unconstrained optimization problem (e.g. first-best congestion pricing / marginal cost pricing)

(Ferrari, 2005; Tsekeris and VoB, 2009; Vickrey, 1969; Walters, 1961)

 Constrained multi-objective optimization problem (e.g.

second-best pricing)

 reducing/ constraining traffic congestion

 reducing/ constraining traffic emissions (e.g. PM10, CO2, CO)

 reducing/ constraining travel time

 constraining (generalized ) travel cost

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3. A transport-related acceptance&equity measure

Acceptance&equity measure

The OD net perceived utility (or surplus) could be considered as a measure of Acceptance&equity

In RUM (Random Utility Models), the EMPU (Expected Maximum Perceived Utility) variable s related to an OD pair can be considered as a measure of the OD net perceived utility (surplus)

(Cascetta, 2009) :

s = s(V) = E[max _j (U)] = E[max(V + e)] = ...... max(V + e) f(e) de

Where U is the vector of the perceived utility function related to all alternatives, V is the vector of the systematic utility and e is the vector of the residuals

If the residuals e are i.i.d. Gumbel variables, the EMPU can be expressed in closed form as a logsum (or inclusive) variable :

s = s(V) =θ ln Σ _j exp(V _j /θ) = θ ln Σ _j exp(Σ _i β _i ·X _i /θ)

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3. A transport-related acceptance&equity measure

Acceptance&equity measure s = s(V) =θ ln Σ _j exp(V _j /θ)

The s(V) logsum (or inclusive) variable related to an OD pair can be considered as a transport-related accessibility measure because:

 Increasing the trip distance/time/cost decrease s(V)

 Increasing the number of alternatives (e.g. paths, services) increase ^s(V)

EXAMPLE

s = -1.95

s = -1.65 V ₁ = -5

V 2 = -2

V ₁ = -5 V = -2 path1 path2

B

Path 1 (time, costs)

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High equity

Low equity s dispersion

3.2 A transport-related acceptance&equity measure

The dispersion (scatter) of OD inclusive variable (surplus) as a measure of equity for transport accessibility

21 Some possible indicators:

 Mean Absolute Deviation (MAD)

 standard deviation

 GINI coefficient

 …

EXAMPLE

d

₁

o ^d

2

V = -2

V = -1

PRICING (β·c = -2)

NO PRICING → MAD = 0.66

PRICING HP2 (β·c = -2)

d

₁

o ^d

2

V = -2

V = - 3

MAD=0.16 (-76%)

d

₁

o ^d

2

V = - 4

V = -2

V = -1

MAD=1.66 (+152%)

hypothesis 2 hypothesis 1

Pricing OD pairs with more opportunities (e.g. services, paths)

generally increase equity

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3.1 A transport-related acceptance&equity measure

The change in OD inclusive variable deriving from a road-pricing scheme could be considered as an inverse measure of acceptance (the smaller the change the larger the acceptance)

s = s(V) =θ ln Σ _j exp(V _j /θ) = θ ln Σ _j exp(Σ _i β _i ·X _i /θ) EXAMPLE

NO PRICING (θ=1) PRICING (β·c = -2)

s = -1.95

s = -1.65 V 1 = -5

V ₂ = -2

V 1 = -5 V ₂ = -2 V ₃ =-3 path1 path2

B

path1 path2 path3

s = -3.69

s = -2.59 V 1 = -5

V ₂ = -4

V 1 = -5 V ₂ = -4 V 3 = -3 path1 path2 B

path1 path2 path3

|Δs| = 1,74

|Δs| = 0,94 (more

acceptable)

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1. Background

2. Acceptance and equity in road pricing schemes

3. Transport-related acceptance&equity measures for road pricing schemes

4. Exploratory results on a test network 5. Conclusions and research perspectives

Outline

23

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4. Exploratory results on a test network

OD pair Urban Path Link Alternatives OD group

A-C

1 1, 2, 3 Urban +

suburban paths Long distance

2 1,4,7,5,3

B-C

3 6,4,2,3

Urban paths Long distance

4 6,7,5,3

D-C 5 8,5,3 Urban +

Short distance B

9 (suburban highway)

1 C

City centre

2 3

4 5

6 7 8

A

D

Test network the topology

• 4 OD pairs

• 10 arcs

• 6 urban paths

• 2 suburban paths ¹⁰

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25 Supply model

BPR separable cost functions

𝑐 _𝑖 (𝑓 _𝑖 ) = 𝑐 _0,𝑖 ∙ 1 + 𝛼 _𝑄 ^𝑓 ^𝑖

𝑖

𝛾

where:

𝑐 _𝑖 be the travel time (minutes) on arc i 𝑐 ₀ be the free-flow travel time on arc i 𝑄 _𝑖 be the capacity of arc i

𝑓 _𝑖 be the flow on arc i 𝛼 = 1,5

𝛾= 2

𝑑

_𝑜𝑑

= 1000 𝑣𝑒ℎ𝑖𝑐./ℎ𝑟

urban suburban

path 1 path 2

Demand model

4. Exploratory results on a test network

Assignment model

Elastic Stochastic User Equilibrium (SUE) for congestion road network

model attributes parameter

suburban vs.

urban path

suburban

In-vehicle travel time -1.8 (1/h) Fare

Alt. Specific Constant

-0.12 (1/€) +0.20 urban

Logsum

_paths

0.85 Urban Path choice

Travel time -1.8 (1/h) Monetary Cost -0.12 (1/€)

Within-day Static models with variable demand

+ a preload car demand

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4. Exploratory results on a test network

Road-pricing performance indicators

 Transportation system

 Total Travel Time TT = 𝑇𝑖𝑚𝑒 _𝑘 _𝑘 ∙ h _k (all modes)

 Total Generalized Cost GCC = 𝑉𝑇𝑇𝑆 ∙ 𝑇𝑖𝑚𝑒 _𝑘 _𝑘 + 𝐶𝑜𝑠𝑡 _𝑘 ∙ h _k

WHERE VTTS is the Value of the Travel Time Saved; Time

_k

is the total travel time per the path k; Cost

_k

is the total monetary cost (fuel + pricing); h

_k

is the freight flow on the path k

 Acceptance

 Average absolute variation ∆𝑠 of OD net perceived utility s

∆𝑠 = 𝑠 _𝑗 _𝑗 ^{𝑝𝑟𝑖𝑐𝑒} − 𝑠 _𝑗 ^{𝑏𝑎𝑠𝑒} / 𝑁 _𝑂𝐷

WHERE: s

_j^price

is the EMPU variable relative to the OD pair j and to a road-price scheme; s

_j^base

is the EMPU variable relative to the base scenario; Nod is the total number of OD pairs

 Equity

Mean Absolute Deviation (MAD) = 𝑠 _𝑗 _𝑗 ^{𝑝𝑟𝑖𝑐𝑒} − 𝑠 _𝑚 ^{𝑝𝑟𝑖𝑐𝑒} / 𝑁 _𝑂𝐷

where:

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6,00 8,00 10,00 12,00 14,00 16,00 18,00 20,00 22,00 24,00

10,00 15,00 20,00 25,00 30,00

p ri ci n g cost on p at h 5 (€ )

pricing cost on path 2 (€)

4. Exploratory results on a test network

A. Generally multiple solutions for a Road-pricing model

EXAMPLE

 Path-based road-pricing scheme (no price for highways)

 Argmin Average Travel Time (objective function )

 constraining waited average variation of the OD Generalized Cost - GC Gc _pricing,i – Gc _base,i / Gc _base,i < +100%

P is a vectors of the path-based pricing values (6 values of prices for 6 urban paths)

27 Possible P solution

∀𝑷 =

5 𝑝𝑟𝑖𝑐𝑒2

1 𝑝𝑟𝑖𝑐𝑒5 11

2 Min Total Travel Time

(%var. GC<100%)

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0%

10%

20%

30%

40%

50%

60%

-100% -80% -60% -40% -20% 0% 20% 40% 60% 80% 100%

% v ariat io n of g en. car c o st

4. Exploratory results on a test network

Not dominated solutions Dominated

solutions

B. Acceptance&equity measures for choosing the solution Road-pricing model (Argmin Average Travel Time )

Some possible solutions

b ^a

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4. Exploratory results on a test network

Road-pricing scheme (Argmin Total Travel Time)

 SOLUTION a: +18% Generalized Car Cost and; +14% Equity; 0.33 ∆𝒔 (acceptance)

 SOLUTION b: +18% Generalized Car Cost and; -12% Equity ; 0.40 ∆𝑠

29 OD

pair Alternatives Number of

Urban paths

SOLUTION a Average path

Price (€)

SOLUTION b Average path

Price (€)

A-C Urban + suburban paths 2 13 11

B-C Urban paths 2 6 10

D-C Urban + suburban paths 1 10 15

D-B Urban paths 1 2 3

To increase acceptability and equity: price the OD pairs with more

opportunities (e.g. services, paths)

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1. Background

2. Acceptance and equity in road pricing schemes

3. Transport-related acceptance&equity measures for road pricing schemes

4. Exploratory results on a test network 5. Conclusions and research perspectives

Outline

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5. Conclusions and research perspectives Findings

a) acceptance&equity measures for enlarge the consensus in city logistics policies

b) In RUM models, inclusive variable could be used allowing to price the OD pairs with more opportunities (less accessibility)

Research perspectives

a) Application to a real case study

 Some preliminary results relative to the case study of Naples (Italy) confirm the results obtained

d) Mathematical proprieties for the acceptance&equity measures and for the design problem (e.g. monotonicity of the s function;

domain of the solutions)

e) Comparison of alternative pricing schemes (link based; OD- based; path-based; vehicles-based …) wrt acceptability &

equity measures ³¹

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References

Albalate D.,Bel G.,(2009). What local policy makers should know about urban road charging:

lessons from world wide experience. PublicAdm.Rev.69,962–975

Cascetta E. (2009). Transportation System Analysis: Models and Applications. Springer, New York Cascetta E., Cartenì A., F. Pagliara, M. Montanino (2015). A new look at planning and designing transportation system: A decision-making model based on cognitive rationality, stakeholder engagement and quantitative methods. Transport Policy, pp. 27-39.

Cools M.,Brijs K.,Tormans H., Moons E., Janssens D., Wets G., (2011). The socio-cognitive links between road pricing acceptability and changes in travel be- haviour. Transp.Res.A 45,779–788.

De Palma A., Kilanin M., Lindsey R. (2007). Maintenance, service quality and congestion

pricing with competing roads. Transportation Research Part B, 41(5), 573–591.

doi:10.1016/j.trb.2006.10.001.

Ecola E., Light T., (2010). Equity and Congestion Pricing: A Review of the Evidence, RAND Corporation.

Ferrari P., (2005). Road pricing and users’ surplus. Transport Policy 12, 477–487

Grisolía J.M.,Ortúzar J., deD., (2010). For ecastingvs. observed out turn: studying choice infasterinter-island connections. Transp.Res.A44,159–168.

Grisolía J.M, López F., Ortúzar J.D., (2015). Increasing the acceptability of a congestion charging scheme. Transport Policy.

Kim, J. Schmöcker J.D., Fuji S., Noland R.B., (2013). Attitudes to wards road pricing and environmental taxation among US and UK students. Transp.Res.A48, 50–62

Levinson D., (2010).Equity effects of road pricing: are view. Transp.Rev.30,33–57.

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References

33 Odeck J., Kjerkreit A., (2010). Evidence on users'attitudes towards road user charges-across- sectiona lsurvey of six Norwegian toll schemes”. Transp.Policy 17,349–358.

Tsekeris t., Voß S., (2009). Design and evaluation of road pricing: state-of-the-art and methodological advances. Springer Science DOI 10.1007/s11066-008-9024-z

Verhoef E. T., & Rouwendal J. (2004). Pricing, capacity choice, and financing in transportation networks. Journal of Regional Science, 44(3), 405–435. doi:10.1111/j.0022-4146. 2004.00343.x.

Vickrey W. S. (1969).Congestion theory and transport investment. The American Economic Review, 59(2), 251–260.

Viegas J. M. (2001). Making urban road pricing acceptable and effective: Searching for

quality and equity in urban mobility. Transport Policy, 8(4), 289–294. doi:10.1016/S0967- 070X(01)00024-5.

Walters A. (1961). The theory and measurement of private and social cost of highway congestion.

Econometrica, 29(4), 676–699. doi:10.2307/1911814