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THE GROWSMARTER PROJECT
2015-2019
Microdistribution of freight in Barcelona
Jaume Roca, CENIT

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Index
 Context and scope of the measure
 Implementation of the service and evaluation
Problems that have occurred/learnings
Recommendations

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Context of the measure
City Logistics in Barcelona
• Barcelona is one of the densiest cities in
Europe with more than 1,6 million
inhabitants in 100km² which leads to heavy
traffic conditions.
• Approximately between 11,000 and 12,000
freight vehicles enter the city each day to
perform delivery activities.
• These vehicles carry out around 40,000
loading and unloading operations
(Analysis carried out at the from AreaDUM
app), this accounts for 15% of city traffic and
40% of pollution
• In some areas of the city such as Ciutat
Vella (the old quarter of the city) strict
regulations apply. Only in short time
delivery windows vehicles are allowed to
perform operations.

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Context of the measure
Framework in which the measure is implemented

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Scope of the measure
Objectives
• Introduce a more efficient and effective freight transport system in the city
center by means of a urban distribution of goods platform.
• Analyze the administrative management of such a servi. Analyze the business
model of the service.
• Analyze the reducion in CO2 due to the introduction of this service
• Test a new sensoric system to track position of new vehicles and also to
monitor pollutants and other environmental parameter using the bicycles of the
service.

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Scope of the measure
How does the microplatform work?
URBAN CONSOLIDATION CENTER
LOGISTIC OPERATORS
RETAILERS
Last mile distribution is performed
using electric tri-cycles (or other
sustainable vehicles) using the required
technology to track all parcels
delivered
The UCC is the place to tranship all
packages from the vans to the
sustainable vehicles. A public tender
will be made to choose the the
operator that will run the service.
Any logistic operator can contract the
services of the last-mile operator

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Implementation of the service
What stakeholders are needed?
• Public administration: municipality, mobility services Department, heritage,
townscape, municipal institute of ICT
• Research and academia: CENIT(center for innovation in transport)
• Freight forwarders: DHL, TNT, SEUR, etc
• Last Mile Operator: Vanapedal

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Implementation of the service
Steps followed
1. Find engagement in the different stakholders to promote such initiative. The
municipality is an important stakeholder.
2. Analyse the area in which such service could be placed in order to guarantee
a sufficient number of daily deliveries.
3. Decide which administrative regulation to contract the services of the Last
Mile Operation.
4. Set up the microplatform and start operations.
5. Monitor the activity of the Company to analyse the impact on the city.

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Implementation of the service
Area of delivery
PILOT
ESTACIÓ FRANÇA

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Current status of the measure
Pilot in Barcelona
Since January the Company Vanapedal is
operating from the Estació de França facilities.
Currently, up to 9 vehicles (bicycles and
tricycles) are being used.
They have a contract with the city council in
which the latter ceases the space for free to
the former in exchange for their business
data.
A new sensoric system developed by I2CAT
installed in three different bikes is making
measurements of position of vehicles as well
as environmental parameters such as pollution

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Baseline of the measure / evaluation
How are we determinig the baseline?
The ideal situation would be to perform
an ex-ante and ex-post evaluation of the
situation, this means before and after the
implementation of the measure.
Ex-ante: Evaluate the how all deliveries
were performed before using the last-mile
operator service. This evaluation is very
complicated since each carrier has
distinct strategies when using the service,
and it is difficult to quantify how many
kilometers were they performing or the
number of vans reduced.
Ex-post: This evaluation is easier, since
we are collecting the daily operation of
the last mile operator. We are currently
monitoring the number of deliveries,
kilometers performed, and other
indicators.

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Baseline of the measure / evaluation
How are we determinig the baseline?
From the information gathered in the ex-post evaluation we have several data from
wich we will infer the data of the ex-ante situation, which will be the baseline.
The tricycles offers flexibility. Whereas in average a tricycle needs 16.5 km to
perform daily deliveries, a van would need approximately 25km to do the same.
o Number of heavy vehicle kilometers in the area: Calculated considering the
number of trike kilometers in the area.
o Average emissions per heavy vehicle kilometer: knowing the type of heavy
vehicles that circulate in that area, and using the Corinair book, we will
calculate the emissions
o Average energy use per heavy kilometer in the site: Idem to the previous
KPI.
o Number of heavy vehicle kilometers shifting to renewable fuels due to
measure 9.2: calculated through the values of the first KPI.
o Number of heavy vehicle kilometers reduced due to measure 9.2: idem.

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Baseline of the measure / evaluation
DIA
Codigo
Tipo
Vehículo
Nombre JORNADA zona
TOTAL
EXP
EXP
SALIDAS
EXP
LLEGADAS
Q km
01-feb
TRICICLO
1 CONDUCTOR 1 52 52 51 35 13,00
01-feb
TRICICLO
2 CONDUCTOR 2 60 60 55 56 15,00
01-feb
TRICICLO
3 CONDUCTOR 3 47 47 44 50 16,00
01-feb
TRICICLO
4 CONDUCTOR 4 s/d s/d s/d s/d s/d
01-feb
TRICICLO
5 CONDUCTOR 5 33 33 31 275,00 23,00
01-feb
TRICICLO
6 CONDUCTOR 6 34 34 32 260,00 21,00
01-feb
TRICICLO
7 CONDUCTOR 7 s/d s/d s/d s/d s/d
01-feb
TRICICLO
8 CONDUCTOR 8 s/d s/d s/d s/d s/d
01-feb
TRICICLO
9 CONDUCTOR 9 50 50 49 s/d s/d
01-feb 0 276 276 262 676 88,00
How is data collected
• A simple template was facilitated to the operator in order to obtain relevant information

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On-board Sensor Units
Monitoring Parameters
Sensors intended to show the variation of the environmental parameters
Installing in the bikes
•Sound level
•Position
•Temperature
•Atmospheric pressure
•Relative humidity on the air
•Particles (PM10, PM2.5)
•Gases (CO, NO2, O3, SO2)

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On-board Sensor Units
Data publication
•Stored data can be analyzed and used to:
•Track and evaluate the performance of the
microdistribution service
•Study mechanisms and alternatives to optimize
the service
•Measure environmental impact

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Baseline of the measure / evaluation
First results of the evaluation
• The number of daily expeditions (63,713 expeditions in 8 months).

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Baseline of the measure / evaluation
First results of the evaluation
In January the average distance covered was of 63 km / day (6 tricycles)
while in june it was of 173 km/day (8 tricycles).

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Baseline of the measure / evaluation
First results of the evaluation
The average for expedition is 21 km/tricycle

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Baseline of the measure / evaluation
First results of the evaluation
• The average distance between two
expeditions is lower than 295
meters.
• This distance includes the trip from
the micro platform to the distribution
zone, so the average distance for
each expedition “in operation” is less
than 100 meters.

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First results of the pilot
Results for first 5 months
• The number of heavy
vehicle km shifting to
renewable fuels has been of
23,350km
• 6,213kg of CO2 have been
saved due to this shift

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Problems that have ocurred / learnings
Issues encountered
Difficulty in finding a public location that suits the requirements of this
service. The location must be close to the delivery area but also have a good
connection with the primary road network, so that carriers can easily reach the
microplatform.
Defining the administrative way to manage the service contract was also
discussed during long time. The former solution was to contract the service in
exchange of money, but the city council changed their priorities and stated that
these services will have to be financially self-sustained in the near future. For
this reason, in the end, the chosen solution was to cease for two years for free
the space. In the future, with the data gathered though the project, it is possible
that a public tender to cease the space will be done, although there is nothing
confirmed yet.
The Company operating the service is currently facing difficulties, since other
companies are performing similar services in the city.
If logistic companies find it financially sustainable to develop their own
sustainable delivery system, these service will no longer be needed

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Problems that have ocurred / learnings
Learnings up to this point
To intervene a private sector such as the freight logistics from the public
perspective is not straight-forward. When ceasing a public space to one company
instead of another the authority is creating distortion in market competition.
After several years of tests in this field, the city council started to notice that
these services need to be financially sustainable and cannot depend forever
from public funding. Additional regulatory measures can be used to force
carriers to shift to sustainable modes.
The urban logistics sector is currently facing rapid changes due to increased
demand and is difficult to predict how will companies manage to cope with that.
Innovative technologies will arise and more sustainable vehicles will be available
in the market and all these changes will have an impact on the city.
We haven’t already looked after the data from sensors. The main learning we
wish to obtain is that using moving sensors is a way to better understand
differences in pollutants inside and outside restricted areas.

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Recommendations
The location of the microdistribution is crucial. Ensure there is enough density
of small shipments in the area.
Municipalities can give a competitive advantage to these services by reducing
time delivery windows
Create a stakeholder platform to discuss freight delivery issues in the city
Plan a clear way of contracting the services. The aim is not to create conflict
Monitor the service to ensure that public resources are being correctly used
Engage logistic stakeholders to use the microplatform
The microplatform should be financially sustainable. The future scenario should
be that no public intervention is required

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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant
agreement no 646456. The sole responsibility for the content of this presentation lies with the GrowSmarter project and in no way
reflects the views of the European Union.
Jaume Roca
jaume.roca-guitart@upc.edu
Thanks for your attention

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