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 Econom Development” - Venice - September 18-20, 2013 XV Conference of the Italian Association of Transport Economics and Logistics
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Valerio Gatta * and Edoardo Marcucci * * DIPES/CREI, University of Roma Tre
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 Presentation
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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%
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