valerio gatta * and edoardo marcucci * * dipes/crei, university of roma tre
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Urban freight distribution policies: joint accounting of non-linear attribute effects and discrete mixture heterogeneity. Valerio Gatta * and Edoardo Marcucci * * DIPES/CREI, University of Roma Tre. - PowerPoint PPT PresentationTRANSCRIPT
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?
Questions?