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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.

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

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  1. Urban freight distribution policies: joint accounting of non-linear attribute effects and discrete mixture heterogeneity Valerio Gatta* and EdoardoMarcucci* * 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

  2. Outline • Research goals • Survey and Data description • Main results • Conclusions

  3. Research goals • Study context • 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 • Contributions to UFT literature • 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

  4. Survey and data description (I) • Project • 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” • Main steps • 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)

  5. Survey and data description (II) • Attribute levels and ranges • Example of a ranking task

  6. Survey and data description (III) • The sample of transport providers • 66 units • Total number of observations: 1128 Transport provider agent distribution by main freight sector • Food(fresh, hotels, restaurants) • Personal and house hygiene (pharmaceuticals, watches) • Stationery(paper, toys, books, CDs) • House accessories(computers, dish-washer) • Services(flowers, animal food) • Clothing(cloth, leather) • Construction(cement, chemicals) • Cargo(general cargo)

  7. Models estimated • Discrete choice models • 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)

  8. Main results (I) • M1 – MNL, linear effect, attributes linear and normalized • Model fit: adj.Rho2 = 0.252 • Coefficients statistically significant, with the expected sign • Tariff plays the lion part in explaining preferences • SQ adversion

  9. Main results (II) • M2- MNLwith non-linear effects (effects coding) • Better fit (adj.Rho2 = 0.281) • All the reported coefficients are statistically significant • PLUBF is linear

  10. Main results (II) • M2 -MNLwithnon-linear effects (effects coding) • In line with prospect theory (Kahneman & Tversky, 1979)

  11. Main results (III) • M3 - LC with linear effects (same specification as in M1) Estimated latent class probabilities: Class1 = 0.50; Class 2 = 0.50 • 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

  12. Main results (IV) • M4 - LC with non-linear effects (same specification as in M2 Estimated latent class probabilities: Class1 = 0.50; Class 2 = 0.5 Discriminant socio-economic variables to explain class membership (CART model): Number of customers (145) Number of deliveries per day(4,5) • Better fit (adj.Rho2 = 0.423), C1 price sensitive; C2 Bays sensitive • The same considerations of M3 apply here

  13. Main results (V) • WTP comparison P1 P2 P3 • 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€

  14. Conclusion • Final remarks • 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. • Future research • Similar investigation on retailers and own-account • Dealing with: i) interactive choice models; ii) Bayesian estimation methods; iii) sample size increment

  15. Thanks for your attention! • Questions? • Questions? • Questions? • Questions? • Questions?

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