Introduction and research question The city of Turin participated as partner to the NOVELOG Project (New Cooperative Business Models And Guidance For Sustainable City Logistics – EC H2020). This paper provides an Agent-Based Model (ABM) on urban freight transport in the Turin Limited Traffic Zone (LTZ). Focus: retailers’ behaviour during the freight provision process. Aim: give support to policy design by the City of Turin within the NOVELOG framework, in order to reduce the freight-related congestion and polluting emissions. Research question: to what extent NOVELOG-based policies implemented by the city of Turin may foster more ecological behaviours of retailers during the process of freight provision?

Methodology and data Why ABM?: consideration of stakeholders heterogeneity, monetary and non monetary incentives, social networks dynamics. Software: NetLogo The model • Empirically based (data provided by the city of Turin). • 762 agents (30% of the 2,542 retailers in Turin LTZ). • Non-spatial retailers professional network (Bass 1969). • Output: effect of policies on agents’ behavioural change

from non ecological to ecological, and on PM10 emissions. “Ecological behaviour” = use of own-account transport

with low pollutant vehicle (Euro 5) or use of third-party transport providers.

“Non-ecological behaviour” = use of own-account transport with high pollutant vehicle.

Agents’ incentives for behavioural change: • personal motivation for environmental issues; • comparison of perceived service quality and its price; • network influence. Empirical data 1. Vehicles crossing the entry points of the LTZ of Turin within

10 days in 2013: vehicle Euro class, own-account or third-party transport.

2. Commercial activities within the LTZ in Turin: commodity sector, location and size.

3. PM10 emissions of different Euroclass commercial vehicles. Table 1. Retailers features (Maggi 2007, Danielis et al. 2013,

Turin data)

Policies and results

“Pull” approach for policies = incentives for proactive attitudes by the beneficiaries, instead of punishing rules violation. 1) Price policy: indirect incentive to retailers to shift from the non-ecological group to the ecological group:

• Soft own-account policy: focus on light decrease of the price of own-account with low pollutant vehicle (Euro 5). • Strong own-account policy: focus on strong decrease of the price of own-account with low pollutant vehicle (Euro 5). • Third-party policy: focus on strong decrease of the price of use of third party transport option.

Where indirect incentive = set of requirements that the retailer should comply with, in order to obtain a NOVELOG permit similar to the one of logistic operators: move within LTZ from 6am to 12pm, use dedicated bus lanes, use loading/unloading areas within pedestrian zones => reduction of the total costs for freight provision.

2) Motivation policy (last two columns): intervention on the agent intrinsic motivation for ecological behaviour (+ 10%) and

on the desire to increase its reputation within the network (Fowler and Christakis 2010). Educational campaign, eco-labeling.

Table 2. Results of policies implementation

In the no-policy scenario agents tend to become “adopters”. Policies simulation improves the timing of adoption. • Initial shock given by the policy: share of adopters increases. The best policy is n. 4 => increase of adopters from 57% to

60%. Decrease of emissions from 22 to 20.18 PM10 g/km. • Time needed to reach +20% adopters: the best policy is n. 3 (focus on strong decrease of costs for using own account

transport with ecological vehicle). • Addition of motivation policy to price policies: all effects are amplified, but the best results are given by policy n. 4. • Motivation policy alone (first row): strong improvement (time unit decreased from 1.83 to 1.54) • Policy 2 has positive effects only over time, not as initial shock. • Emissions always decrease by 34% when increasing the percentage of adopters by 20% (from 22 to 14.52 PM10 g/km).

Conclusions The incentive to shift to third-party transport is fully effective only if coupled with an intervention on the motivational level. In absence of motivation policy agents react better to a policy that provides strong incentives to shift to a more ecological vehicle within the own-account option. Motivation policy alone has very positive effects on adoption of ecological behaviour. In case of combination of price and motivation policies, the best results are given if a policy incentivizing the shift to third-party transport is applied. Soft incentives for more ecological vehicles in own-account transport are more effective overtime than as initial shock.

Further information NOVELOG Project (funded by the European Commission H2020 Programme for Research and Innovation. Grant agreement No 636626). Aim: designing a more efficient and less pollutant freight provision system within many European cities, promoting collaboration among stakeholders for sustainable city logistics. 28 partners (12 Cities, 16 Universities and research institutions). http://novelog.eu/

Literature cited Bass F., 1969. A new product growth for model consumer durables. Management Science. 15 (5). Danielis R., Maggi E., Rotaris L., Valeri E., 2013. Urban Freight Distribution: Urban Supply Chains and Transportation Policies. In Ben-Akiva M., Meersman H., Van de Voorde E. (eds). Freight Transport Modelling. Emerald Group. Fowler J. H. and Christakis N. A., 2010. Cooperative behaviour cascades in human social networks. Proceedings of the National Academy of Sciences 107.12: 5334-5338. Maggi E. 2007. La logistica urbana delle merci. Aspetti economici e normativi. Polipress: Milano.

Simulation of dynamics of retailers’ freight provision through an Agent-Based Model: the case of Turin Elena Vallino (University of Turin and Venice International University), Elena Maggi (University of Insubria and Venice International University), Elena Beretta (Polytechnic of Turin) IAERE Conference, 15-16 February 2018, University of Turin

Contact elena.vallino@unito.it elena.maggi@uninsubria.it elena.beretta@polito.it

Policy Price levels Initial share of adopters

of ecological behavior

(PM10 g/km)

Unit of time to reach

+20% adopters

(price policies)

Time reduction

(price policies)

Unit of time needed to

reach +20% adopters

(motivation policy)

Time reduction

(motivation policy)

1. No policy TP: 2.5

OAE: 5

OANE: 3.5

57%

(22) 1.83 - 1.54 -

2. Soft own-account policy

TP: 2.3 OAE: 3

OANE: 3.5

55%

(23.43) 1.7 -7.1% 1.41 -9%

3. Strong own-

account policy

TP: 2.3 OAE: 2

OANE: 3.5

59% (21)

1.56 -14% 1.4 -9%

4. Third-party policy

TP: 1.5 OAE: 4.5

OANE: 3.5

60% (20.18)

1.68 -8.19% 1.32 -14.28%

Note: “Adopters” = agents that shifted from non-ecological to ecological behaviour, either through the purchase of a Euro 5 vehicle in own-account, or through the decision to shift to the third-party option. TP = third-party transport. OAE = own-account with ecological vehicle. OANE = own-account with non-ecological vehicle. Prices of freight transportation are expressed into levels within a 0-5 range.

Frequency of goods supply

Distribution of retailers

Percentage of use of own-account

Many times a day 1.2% 0%

Daily 51% 70%

Many times a week

5.1% 65%

Weekly 19.5% 50%

Many times a month

3.4% 28%

Seasonal 19.8% 12.5%