Urban freight distribution policies: joint accounting of non-linear attribute effects and discrete mixture heterogeneity

1

Valerio Gatta* and Edoardo Marcucci** DIPES/CREI, University of Roma Tre

"Transport, Spatial Organization and Sustainable Economic Development” - Venice - September 18-20, 2013

XV Conference of the Italian Association of Transport Economics and Logistics

Outline2

Research goals

Survey and Data description

Main results

Conclusions

Research goals3

Urban Freight Transport (UFT). Main agents: retailers, transport providers, own account

Policy makers are interested in knowing, before implementing a given policy, the most likely reactions

One-size-fit-all policies are usually implemented with mixed results

Study context

Lack of appropriate data (elicitation costs & low interest of agents ► in-depth investigation of transport providers’ preferences

Policy makers usually evaluate policies assuming linear effects on agent’s utility for attribute variations.

Not only inter-agent but also intra-agent heterogeneity Joint analysis of heterogeneity & non-linear effects

Contributions to UFT literature

Survey and data description (I)

4

Stated Ranking Exercise in Rome’s Limited Traffic Zone Volvo Research Foundation (2009), “Innovative solutions to

freight distribution in the complex large urban area of Rome”

Project

Advancement from stakeholder consultation to final attribute selection criteria

Attribute definition Levels and ranges selection Progressive design differentiation by agent-type with updated

priors (efficient design, 3+1 waves)

Main steps

Survey and data description (II)

5

Attribute levels and ranges

Example of a ranking task

Attribute Number of levels

Level and range of attribute(Status Quo underscored)

Loading/unloading bays (LUB) 3 400, 800, 1200

Probability of free l/u bays (PLUBF) 3 10%, 20%, 30%

Entrance fees (EF) 5 200€, 400€, 600€, 800€, 1000€

  Policy 1 Policy 2 Status QuoLoading/Unloading bays 400 800 400Probability to find L/U bays free 20% 10% 10%Entrance fee 1000 € 200 € 600 €

Policy ranking

Survey and data description (III)

6

66 units Total number of observations: 1128

The sample of transport providers

Transport provider agent distribution by main freight sector 1) Food(fresh, hotels, restaurants)

2) Personal and house hygiene (pharmaceuticals, watches)

3) Stationery(paper, toys, books, CDs)

4) House accessories(computers, dish-washer)

5) Services(flowers, animal food)

6) Clothing(cloth, leather)

7) Construction(cement, chemicals)

8) Cargo(general cargo)

Models estimated7

M1 - Multinomial logit model with linear effects(attributes linear and normalized)

M2 – Multinomial logit model with non-linear effects(effects coding)

M3 – Latent class model with linear effects(the same specification as in M1)

M4 – Latent class model with non-linear effects(the same specification as in M2)

Comparison between models through WTP measures(confidence intervals based on Delta method)

Discrete choice models

Main results (I)8

Model fit: adj.Rho2 = 0.252 Coefficients statistically significant, with the expected sign Tariff plays the lion part in explaining preferences SQ adversion

M1 – MNL, linear effect, attributes linear and normalized

Variable Coefficient St. Err. t-statLUB 0.558 0.061 9.16

PLUBF 0.435 0.069 6.31EF -1.170 0.069 -16.85

ASC_Alt1 0.686 0.173 3.97ASC_Alt2 0.709 0.159 4.46

Main results (II)9

Better fit (adj.Rho2 = 0.281) All the reported coefficients are statistically significant PLUBF is linear

M2- MNLwith non-linear effects (effects coding)Variable Coefficient St. Err. t-stat

LUB2 0.238 0.077 3.07LUB3 0.491 0.077 6.40

PLUBF 0.613 0.076 8.05EF1 2.220 0.146 15.19EF2 1.586 0.116 13.62EF4 -1.131 0.109 -10.40EF5 -3.260 0.224 -14.56

ASC_Alt1 1.041 0.212 4.92ASC_Alt2 0.972 0.184 5.28

Main results (II)10

M2 - MNLwith non-linear effects (effects coding)

Part-worth utilities for different policy attributes for transport providers

In line with prospect theory (Kahneman & Tversky, 1979)

Main results (III)11

Better fit (adj.Rho2 = 0.377) – almost equal class membership probabilities

Class 1 comprises more price-sensitive agents Agents in Class 2 are more interested in LUB and PLUBF

M3 - LC with linear effects (same specification as in M1)

Variable Coeff. t-stat Coeff. t-statLUB 0.545 4.90 0.920 13.67

PLUBF 0.203 1.54 0.966 10.67EF -2.271 -13.55 -0.747 -10.55

ASC_Alt1 0.839 2.94 0.847 3.30ASC_Alt2 0.740 2.99 0.869 3.63

CLASS 1 CLASS 2

Estimated latent class probabilities: Class1 = 0.50; Class 2 = 0.50

Main results (IV)12

Better fit (adj.Rho2 = 0.423), C1 price sensitive; C2 Bays sensitive The same considerations of M3 apply here

M4 - LC with non-linear effects (same specification as in M2CLASS 1 CLASS 2

Estimated latent class probabilities: Class1 = 0.50; Class 2 = 0.5

Variable Coeff. t-stat Coeff. t-statLUB2 0.236 1.63 0.339 3.64LUB3 0.405 3.03 0.982 10.37

PLUBF 0.480 3.61 1.277 10.23EF1 4.105 12.60 1.724 9.43EF2 3.003 11.53 1.138 8.92EF4 -2.204 -8.56 -0.363 -2.71EF5 -6.079 -9.39 -2.818 -12.57

ASC_Alt1 1.534 4.15 0.735 2.24ASC_Alt2 1.359 4.20 0.770 2.61

Discriminant socio-economic variables to explain class membership (CART model):

Number of customers (145)Number of deliveries per day(4,5)

Main results (V)13

WTP comparison  M1 M2 M3 M4

Policy    Class 1 Class 2 Class 1 Class 2

+ 400 l/u bays -95(± 9)

-113(± 16)

-48(± 9)

-246(± 16)

-52(± 13)

-487(± 145)

+ 800 l/u bays -191(± 18)

-142(± 17)

-96(± 18)

-492(± 33)

-62(± 12)

-676(± 210)

+ 20 units ofprob. bays free

-149(± 20)

-143(± 16)

-36(± 22)

-517(± 34)

-57(± 15)

-750(± 218)

Impact of Non-linear effects: M1 vs M2Overall efficiency loss for P1, P2, P3 = 18€, 49€, 6€

Impact of Heterogeneity: M1 vs M3Overall efficiency loss for P1, P2, P3 = 198€, 396€, 481€

Impact of joint Heterogeneity &Non-linear effects: M1 vs M4Overall efficiency loss for P1, P2, P3 = 435€, 614€, 693€

P1

P2

P3

Conclusion14

The results obtained are relevant both from a theoretical as well as practical and policy-oriented perspective

The paper represents a first attempt at bridging the gap between theory, applied research and data needs

Relevant biases could characterize the results obtained if non-linearity & heterogeneity are not duly accounted for

There is a need for a sophisticated agent-specific model treatment to implement well-tailored and effective policies.

Final remarks

Similar investigation on retailers and own-account Dealing with: i) interactive choice models; ii) Bayesian

estimation methods; iii) sample size increment

Future research

Thanks for your attention!15

Questions? Questions?

Questions?

Questions?

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