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Assesment of the potential of cargo bikes
and electrification for last-mile parcel
delivery by means of simulation of urban
freight flows
Carlos Llorca
*
and Rolf Moeckel
Abstract
Background: The paper presents a simulation model for freight. In the paper, this model is applied to understand
the impacts of electric vans and cargo bikes for the last-mile delivery of parcels. Cargo bikes are electrically assisted
vehicles that distribute parcels from micro depots located close to the final customers by means of short tours. The
parcels are sent from the major distribution center to micro depots in vans (called feeders).
Materials and methods: An agent-based model is used for the purpose of the paper. The model is based on the
disaggregation of commodity flows to represent trucks (for all commodities) and individual shipments (for parcel
deliveries). The model represents microscopically every freight vehicle in the study area.
Results: The simulation of various scenarios with different shares of cargo bikes and electric vans assesses the
impacts of electrification and cargo bikes. The use of cargo bikes to deliver parcels allows to reduce the number of
motorized vehicles, although the presence of large parcels requires that at least half of deliveries by vans are still
required. The shift to cargo bikes represents a slight increase in the total operating time to deliver the parcel
demand. With low shares of cargo bikes, the total distance traveled increases, since the reduction of van tours
cannot compensate the additional feeder trips from distribution centers to micro depots. The cargo bikes also do
not reduce the number of vehicles for the served area, but modify the composition of vehicle types. Low noise,
smaller, low emission vehicles increase, while delivery vans are reduced.
Conclusion: Both cargo bikes and electric vans are able to reduce CO2 emissions, even after accounting for the
emissions related to electricity production.
Keywords: Last mile delivery, Urban logistic, Electric vehicle, Cargo bike, Freight model
1 Introduction
Both passenger and freight transport contribute to con-
gestion of the transport system, road emissions, noise,
and safety issues, among others. A major components of
urban freight traffic is parcel deliveries. The increase of
online shopping in recent years contributed to a rapid
increase of the number of parcels delivered. As a
consequence, the number of delivery vehicles and the
impact on the transport system increased rapidly.
Various solutions have been proposed to make the de-
livery of parcels more efficient and environmentally
friendly, including electric delivery vehicles, drones, au-
tonomous robots or cargo bikes. The use of cargo bikes
for last-mile deliveries has been introduced in some cit-
ies already, but scientific studies of actual implementa-
tions are still in an early stage. This paper models freight
flows, while paying particular attention to the distribu-
tion of parcels. The goal of this research is to understand
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* Correspondence: carlos.llorca@tum.de
Modelling Spatial Moility - Technical University of Munich, Arcisstr. 21, 80333
Munich, Germany
European Transpor
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Llorca and Moeckel European Transport Research Review (2021) 13:33
https://doi.org/10.1186/s12544-021-00491-5
the effects of a partial substitution of vans with combus-
tion engines by electric vans or cargo bikes.
1.1 Freight models
Compared to person travel demand modeling, freight
modeling is substantially less developed. Limitations in
data availability, heterogeneity of freight transport op-
tions and lack of interest to intervene with freight flows
are important reasons why freight modeling is less com-
mon than person travel modeling.
Most freight vehicles do not travel in simple trips from
A to B and back to A, but in tours that connect many
stops. Holguín-Veras et al. [20] provided an overview of
the state-of-practice of urban tour-based truck models
(models that represent explicitly deliveries of freight
units by vehicles). Noteworthy are also the operational
tour-based models for Calgary, Alberta [22], Portland,
Oregon [12], Guatemala City [19], Rome [40] and Phoe-
nix, Arizona [26]. Such models have been found to be
behaviorally richer and allow for the explicit representa-
tion of distribution centers, warehouses and intermodal
facilities, which are used by more than half of all trucks
[13]. However, truck surveys are commonly required for
tour-based models, and stochastic variations between
runs may pose an additional burden for such models
[12]. Tavasszy et al. [45] developed the SMILE freight
model that explicitly accounts for production, warehous-
ing and transportation of goods. Chow et al. [9] devel-
oped a sophisticated framework for freight modeling in
California, but readily admitted that this framework has
data requirements that will be difficult to fulfill due to
privacy issues.
Sometimes, urban tour-based models are combined
with regional freight flow models, which may simplify
the routes of vehicles compared to tour-based models.
The most common example for regional freight models
is the Commodity Flow Model. Such a model tries to
represent the economic motivation behind a freight flow,
which is exchanging goods between producers and con-
sumers. Leontief [29] developed the framework in which
economic interactions are captured in Input/Output
matrices. Annual tons of commodity flows are converted
into truck trips using daily truckload equivalents from
truck intercept surveys.
A recent review of freight modeling in the twentieth
century [30] stressed the need for modeling commodity
flows rather than just trucks. Research in Europe has
been summarized by De Jong et al. [10], concluding that
the ideal freight model should have two levels of reso-
lution: a detailed high-resolution version shall be applied
to very specific policy questions, while a fast and low-
resolution version could answer simpler day-to-day pol-
icy questions.
1.2 Electrification and cargo bikes for city logistics
Traditionally, last-mile deliveries are carried out by vans,
also called Light Commercial Vehicles (LCV). The use of
electric vehicles has been proposed as a solution to the
environmental impacts of urban logistics, by reducing
noise and emissions. Giordano et al. Giordano et al. [15]
assessed the life-cycle of battery electric and diesel vans
for urban parcel delivery. They used average demand in-
dicators and randomly generated trip lengths to evaluate
the costs and externalities of both types of vehicles. The
conclusions reported the need of incentives or taxation
to accelerate the fleet conversion and reduce emissions.
From the carriers’perspective, Quak et al. [42] compared
diesel and electric delivery vans. They identified several
limitations to the electrification, including the higher
purchase costs and the lack of appropriate infrastructure.
Martins-Turner et al. [32] performed agent-based simu-
lations of electric and conventional delivery vehicles.
They simulated an entire fleet that is converted from
diesel to electric vehicles. The study simulated the deliv-
ery of food in Berlin (Germany). The results demon-
strated a significant reduction of CO
2
emissions, even
after accounting for the emissions caused by electricity
production.
While most previous studies identify relevant reduc-
tions in emissions, the impacts on traffic congestion and
on the operation of fleets do not change much. The
routing of delivery vehicles remains similar, with the ex-
ception of the new constraint of limited vehicle ranges
and recharge times [17,47]. Regarding the limited range,
Martins-Turner et al. [32] found in their simulations
that 56% of vehicles could operate during 1 day without
any recharging.
One particular type of electric vehicle (more precisely,
an electrically assisted vehicle) is the cargo bike. The de-
livery of parcels using cargo bikes instead of diesel vans
has been proposed to solve some of the current issues of
urban logistics. Melo and Baptista [34] pointed out the
limited use of cargo bikes for very specific deliveries,
mostly smaller parcels. The combination of delivery vans
and cargo bikes has been researched mostly in opera-
tions research [2]. The advantages of cargo bikes with
respect of motorized delivery vehicles are: 1) they are
smaller, so they can ride more easily on narrow streets
and find parking locations faster and closer to the recipi-
ent, 2) they are electric assisted vehicles, and therefore,
they do not produce noise nor direct emissions, 3) vehi-
cles purchase costs and vehicle maintenance costs are
lower, while labor costs are about the same ([28,44];
Traject Mobility [31]). On the other hand, cargo bikes
have significantly smaller capacity and their batteries
limit their range. Driver fatigue could be an issue for
cargo bikes [28], and the maximum speed is generally
lower than for conventional vans [18,34].
Llorca and Moeckel European Transport Research Review (2021) 13:33 Page 2 of 14
The distribution of goods by cargo bikes requires
smaller distribution centers located in proximity to cus-
tomer locations [27,33]. These distribution centers are
called micro depots that receive deliveries from large
distribution centers by vans. Parcels are delivered out of
these micro depots using cargo bikes.
Several studies analyzed the potential impacts of cargo
bikes using freight models. Predominately, these studies
solved vehicle routing problems [4,27,34,38,39,49].
Such tour-based models optimize the design of the tours
to deliver a given number of parcels subject to vehicle
characteristics. Previous studies [3,8,49] suggest that
cargo bikes have a capacities of 10–25 parcels (typically
5 to 15% of light trucks) and speeds of around 10–25
km/h. In the dense area of Seoul (South Korea), the use
of cargo bikes with a substitution rate of about 3 bikes
per truck could reduce costs by 14.1% (improving the
service time thanks to reduced walking distances from
the vehicle to the customer) and emissions of carbon
pollutants by 10%. According to Zhang et al. [49], an al-
most complete substitution of vans by cargo bikes for
commercial clients could reduce cost and emissions de-
rived from parcel distribution by 28 and 22%, respect-
ively. On the other hand, a study for Antwerp [4] found
an increase in operational costs for providers that use
cargo bikes, which could encourage providers to pro-
mote self-pick-ups that transfer part of the cost to their
customers. A simulation for a small area in Munich [39]
also identified significant reductions in distance traveled
by motorized vehicles. Alternatively, some authors pro-
posed analytical cost functions based on average delivery
costs [44,46]. They do not represent individual ship-
ments and simplify variability of the demand, but they
are faster in terms of optimizing fleet sizes or composi-
tions. In any case, neither analytical nor simulation tools
have not explained the impacts of cargo bikes in the
whole supply chain.
1.3 Research motivation
The lack of open or accessible data on parcel delivery
demand under real-life conditions makes the analysis of
potential shifts from diesel vans to electric vans or cargo
bikes difficult. Previous research [28] identified the need
to analyze potential impacts of using cargo bikes in city
centers, and the potential demand distribution of cargo
bike customers. Moreover, the existing research did not
quantify the impacts of cargo bikes with respect to the
entire supply chain including non-urban flows.
In the paper, we first develop a method to generate
freight demand without using commercial or privately-
owned data sources. The developed model is used to
compare different shares of electrification and use of
cargo bikes for the distribution of parcels in urban areas.
2 Methodology
The first part of this section describes the freight model
and the second explains its application to the case study
to the metropolitan area of Munich (Germany). The
third section summarizes the calibration and validation
of the model and the fourth proposes a set of scenarios.
2.1 Freight model
A freight model called FOCA (Freight Orchestrator for
Commodity flows Allocation) was developed for this re-
search. The model represents every long-distance freight
flow (only flows by road vehicles are assigned to a net-
work) starting at, ending at or crossing the study area.
The model also represents urban freight distribution
centers for a subset of the study area, namely the ana-
lysis area. Therefore, the model combines two levels of
resolution and detail as proposed by De Jong et al. [10].
By combining a long-distance and an urban model we
were able to understand the magnitude of policies and
measures (e.g. electrification) for the entire distribution
chain, and not only in the urban environment as if it
was an isolated element.
The model is based on the disaggregation of commod-
ity flows [35] into microscopic freight units (either
trucks or parcels, depending on the level of analysis).
The study area is divided into zones (larger zones, cover-
ing the entire study area), and the analysis area is further
divided into micro zones (smaller zones, only in the ana-
lysis area).
2.1.1 Long-distance freight model
This module converts commodity flows into long-
distance freight trips between zones of the study and as-
signs them to the network. Here, only the flows by truck
are assigned, while flows by rail, air or water are merely
reported. The steps are shown in Fig. 1. Although mod-
eling long-distance trucks is not required to analyze
urban last mile processes (which is the core of this
paper), the total demand of parcels is obtained from the
total volume of parcels sent or received to/from the ana-
lysis area via long-distance freight trips. Also, represent-
ing the entire commodity flow allows assessing the
impact of cargo bikes on the entire delivery chain.
First, the annual flows (step 1 in Fig. 1) are converted
to daily flows by dividing them by an annualization fac-
tor (2). The annualization factor is calculated by eq. 1to
distinguish weekdays from weekends. The temporal dis-
aggregation is based on truck counts and are assumed
not to vary among commodities.
fannualization;i¼365 AADT trucks
ADTtrucks;i
ð1Þ
Where:
Llorca and Moeckel European Transport Research Review (2021) 13:33 Page 3 of 14
AADT
trucks
is the average annual daily traffic count
of trucks summed up across every traffic count
station in the study area
ADT
trucks,i
is the average daily traffic count of trucks
summed up across every count station in the study
area during day i
Next, the daily flows in tons that travel by truck are
converted into long-distance trucks by average payload
factors (for various distance bins, accounting for higher
average loads for longer trips). In addition, empty trucks
are generated based on average percentages of empty
trucks by commodity (3).
For every truck that starts or ends in the analysis area,
origin and destination micro-locations are assigned. Se-
lected commodities, such as minerals, oil or machineries
are distributed door-to-door (4a). A micro-zone within
the trip end zone is selected by a weighted random
choice (5a). The weight of each zone is calculated based
on employment by industry and make/use coefficients
[35]. Make/use coefficients describe how many goods of
a given commodity are produced/consumed per em-
ployee of a given industry. This results in the delivery
(or pick up) of goods that are produced or consumed by
different industry types. Geographical x/y coordinates
within the micro zone are sampled randomly.
Other commodities are shipped via distribution cen-
ters (4b). The long-distance truck trip ends at a distribu-
tion center (5b) that can handle the given commodity
type near the shipment’s final destination. Goods are
reloaded on smaller trucks and sent to their final destin-
ation in the urban freight model.
A departure time is chosen for each long-distance
truck (6a,b). A list of long-distance truck trips is
Fig. 1 Long-distance truck model
Fig. 2 Urban freight model
Llorca and Moeckel European Transport Research Review (2021) 13:33 Page 4 of 14
generated (7a,b) that serves as an input to the traffic as-
signment model described further below (8).
2.1.2 Urban freight model
The flows that are shipped through distribution centers
are processed in the urban freight module, as defined in
Fig. 2.
For each distribution center, the flows of the same
commodity that arrive are summed up (step 9 in Fig. 2)
and disaggregated into shipments on smaller trucks.
Trips between distribution centers and customers de-
pends on the commodity and are defined in two differ-
ent ways:
1) All commodities that are not parcels (10a) are
shipped from a distribution center by short-distance
trucks to their final destination. The volumes proc-
essed at each distribution center are disaggregated
into short-distance (and smaller) trucks (11) by an
average payload factor for short-distance trips.
Similar to long-distance trucks, a micro location
(12) is chosen for the receiving trip end (based on
employment and make/use coefficients), as well as a
time of departure (13).
2) For post and parcels (10b), the volumes are
disaggregated from tons into individual parcels (15).
Parcel sizes are generated randomly, based on a
given parcel weight distribution. The customer type
(private customer, business customer or parcel
shop/warehouse) is selected for each parcel.
For each parcel of the private and business customer
groups (16a), a time window is assigned (17) and a geo-
graphical x/y coordinate is selected. Business customer
locations are assigned (18) similarly to non-parcel com-
modities (using employment numbers and make/use co-
efficients). Private recipient locations are micro zones
selected by population. A service time is added for each
delivery by adding a fixed time plus a distance-
dependent term (which depends on the distance be-
tween the parking location on the road network and the
recipient). Delivery tours are organized to deliver those
parcels (21a). We use the transport simulation model
MATSim (Multi Agent Transport Simulator) [21] and
its extension “freight”[50] to generate delivery tours.
With this extension, we create carriers at each distribu-
tion center and generate delivery services for each one
of the parcels. This extension creates and iteratively op-
timizes the tours to deliver every parcel to every private
or business recipient.
The use of cargo bikes (20b) is designed as a deviation
from the distribution by van. Cargo bikes are assumed
to be electrically supported by require peddling by the
rider, which reduces electricity consumption. To
distribute parcels with cargo bikes, we define intermedi-
ate, subsidiary distribution centers (micro depots) that
receive parcel shipments from a major distribution cen-
ter. Goods are sent from a distribution center to the mi-
cro depots with vans (22c) before the parcel delivery
time windows. After that, parcels are delivered to the
final recipient by cargo bikes (22b).
Parcels delivered to or picked-up at parcels shops or
warehouses are distributed by van (16b).
2.1.3 Traffic assignment
The trips of long-distance trucks, short-distance trucks
and parcel delivery vehicles are jointly assigned with
MATSim (step 8 in Fig. 1and 23 in Fig. 2). Moreover,
the trips made by private cars generated by the passen-
ger travel demand model MITO [36] are loaded and
jointly assigned with trucks. The output of MATSim in-
cludes individual route choices of freight and passenger
vehicles in the study area.
2.2 Case study: a distribution center in Munich (Germany)
This model is applied to the study area of Germany to
test the impacts of cargo bikes for the last-mile delivery
in several districts in central Munich. The analysis area
is defined as the city of Munich (Fig. 3). The analysis is
focused on one distribution center located to serve the
districts of Altstadt, Maxvorstadt and Ludwigvorstadt.
The following data sources are used to apply the
model:
Commodity flows estimated by the German Federal
Ministry of Transport and Digital Infrastructure
(Verflechtungsprognose 2030) [6] for the base year
2010.
Truck load factors and empty truck shares by the
German Office for Motorized Transportation [24].
Employment and population of Munich in form of a
synthetic population for the analysis area [37].
Make/use coefficients for Germany published by the
European Commission [14].
Location of distribution centers of the major parcel
delivery companies from openstreetmaps.org.
Road network from openstreetmaps.org.
In absence of observed data, the following assumptions
for further model parameters were done. These assump-
tions were discussed with eight stakeholders of the Ger-
man parcel industry and confirmed as reasonable:
Share of individual customers (business or private)
and parcel shop delivery/pick up services: parcels
that are received by customers are split into 40%
private customers (home), 40% business customers
(companies) and 20% parcel shops. Outgoing parcels
Llorca and Moeckel European Transport Research Review (2021) 13:33 Page 5 of 14
are shipped via parcel shops (80%) or picked-up at
business customer locations (20%). No parcels are
picked-up at home locations.
Time windows for deliveries: 8:00 to 17:00.
Van capacity: 100 parcels.
Cargo bike capacity: 20 parcels, based on Zhang
et al. [49].
Cargo bike speed: 20 km/h.
Micro depots for cargo bikes are assumed to be
allocated in a grid of 1000 m × 1000 m.
2.3 Model calibration and validation
We compared the simulated truck volumes with truck
counts on major roads in Germany. The traffic counts
were downloaded from the Federal Highway Research
Institute [5]. Figure 4a shows a comparison between
simulated and observed average daily truck volumes in
528 traffic count stations distributed across Germany.
The percent Root Mean Squared Error (RMSE) is 32%.
The R-squared coefficient of simulated vs. observed
counts is 0.72. Similarly, we compared hourly traffic
counts. Figure 4b shows the average hourly truck vol-
ume in every traffic detector. A departure time distribu-
tion was calibrated to resemble the hourly distribution
of truck counts on major roads.
After comparing the truck traffic counts, we compared
the simulated number of parcels delivered in the study
area with the observed values. According to the German
association of parcel logistics (BIEK - Bundesverband
Paket und Expresslogistik), there were 240,000 delivered
parcels in the city of Munich in 2016 [7]. Based on the
global growth rate in this period, we extrapolated this
value to 184,000 parcels in 2010. The average parcel
weight was approximately 7.5 kg. To match the simula-
tion results with this value, we calibrated a parcel weight
distribution (used in the step 15 of Fig. 2).
2.4 Scenarios
To understand the impact of electrification of delivery
vehicles and the introduction of cargo bikes, we
Fig. 3 Boundaries of the city of Munich (Germany). The catchment area of the distribution center is marked in red (background
map: OpenStreetMaps.org)
Llorca and Moeckel European Transport Research Review (2021) 13:33 Page 6 of 14
Fig. 4 Comparison of simulated and observed truck counts
Table 1 Scenario definition with shares of cargo bikes and electric vehicle vans and daily delivered parcels
Scenario
ID
Share
of EV
vans
(%)
Share
of
parcels
by
cargo
bike
a
(%)
Parcels delivered directly to the final customer Parcels
to
shops
(by
van)
Total
number
of
parcels
by cargo bike by van
0 0 0 0 12,242 13,330 25,572
1 0 20 1390 10,852
2 0 40 2727 9515
3 0 60 4094 8148
4 0 80 5423 6819
5 0 100 6783 5459
6 25 0 0 12,242
7 25 20 1390 10,852
8 25 40 2727 9515
9 25 60 4094 8148
10 25 80 5423 6819
11 25 100 6783 5459
12 50 0 0 12,242
13 50 20 1390 10,852
14 50 40 2727 9515
15 50 60 4094 8148
16 50 80 5423 6819
17 50 100 6783 5459
a
share of parcels to private and business customers under 10 kg; due to larger parcels that are not delivered by cargo bike, the actual share of cargo bikes may be lower
Llorca and Moeckel European Transport Research Review (2021) 13:33 Page 7 of 14
developed the scenarios defined in Table 1. The scenario
zero is a base scenario with only diesel vans and no
cargo bikes. The scenarios 1 to 5 assume increasing pro-
portions of cargo bike deliveries, from 20% to 100% of
suitable parcels, in 20%-point intervals. In these scenar-
ios, all vans are diesel. Scenarios 6 to 11 assume increas-
ing shares of cargo bikes from 0% to 100%, while 25% of
the fleet of vans is electric. Scenarios 12 to 17 are
equivalent to the previous ones but assume a 50% share
of electric vans. The assumed total weight processed by
the distribution center is 212 ton per day. The median
density of parcels delivered directly to the final customer
is 320 parcels/km
2
, with a maximum value of 3048 par-
cels/km
2
at the densest location. There are 17 micro de-
pots and parcels shops in the catchment area, placed in
a 1 km grid.
3 Results
This section compares the simulated scenarios by differ-
ent indicators to describe the efficiency of the last-mile
delivery, the traffic volumes and the environmental im-
pacts within the catchment area of the analyzed distribu-
tion center. Results shown in Figs. 5,6and 7focus on
the scenarios with different shares of cargo bikes and do
not show the differences due to the van fleet compos-
ition. Later for the calculation of CO
2
emissions, both
the share of cargo bikes and the share of electric vans
are considered.
Due to their limited capacity, we assumed that parcels
heavier than 10 kg cannot be transported by cargo bikes.
As a consequence, the actual share of parcels delivered
by cargo bike does not exceed approximately 55%, even
in the scenario with a 100% share of cargo bikes for eli-
gible deliveries (Fig. 5a). The number of tours by vehicle
type is shown in Fig. 5b. The number of tours by cargo
bike increases linearly with the number of parcels that
need to be delivered. The same happens for the number
of feeder tours, although the absolute number is much
lower, because feeders have a larger capacity and are as-
sumed to be fully loaded on their way to micro depots.
With an increasing share of cargo bikes, the number of
vans decreases. Due to their large capacity, however, the
reduction of van tours is smaller than the growth of
cargo bike tours.
The share of time spent on parcel deliveries (in-
cluding travel and service time) develops almost pro-
portionately to the share of parcels by vehicle type
(as shown in Fig. 6a). The total time required for de-
livery (excluding warehouse processing times) in-
creases slightly with a higher share of cargo bikes
(the scenario with 100% cargo bikes resulted in 6%
more time compared to the scenario without cargo
bikes). Due to the slower speed of cargo bikes,
Fig. 5 General indicators for parcel distribution by vehicle type
Llorca and Moeckel European Transport Research Review (2021) 13:33 Page 8 of 14
however, the share of distance traveled by cargo bike
does not reach the same percentage as time, and
reaches no more than approximately 25% (Fig. 6b).
The scenarios that required the highest number of
vehicle-km were those with a 20% and 40% share of
cargo bike share. In these scenarios, the distances
traveled by van do not decrease significantly, but
cargo bikes and feeders add extra distance. When the
share of cargo bikes is high (80% or 100%), the total
distance traveled is not much longer than in the base
Fig. 6 Operational indicators for parcel distribution by vehicle type
Fig. 7 Link counts of urban freight vehicles by road type and freight vehicle type in the catchment area of the analyzed Munich
distribution center
Llorca and Moeckel European Transport Research Review (2021) 13:33 Page 9 of 14
scenario without cargo bikes, though the share of dis-
tancebymodeisaffected.
Apart from the previously mentioned changes in dis-
tance and time, we assessed the differences in traffic
counts on all links of the catchment area of the analyzed
distribution center. Figure 7divides the road links intro
two groups: major roads include urban segments of mo-
torways, primary, secondary and tertiary roads (based on
the classification obtained from OpenStreetMaps.org).
Minor roads include residential roads. In Fig. 7, we in-
cluded the truck flows transporting other commodities
(e.g. construction materials, food, etc.) in the bars filled
in dark gray. The truck volumes of other commodities
that are not parcels remain constant in all scenarios. Ac-
cording to Fig. 7, the traffic volume increases signifi-
cantly, but only on minor roads and when the share of
parcels by cargo bikes is low (20% to 40%). It remains
similar to the base case (0% cargo bikes) when the share
of cargo bikes is high (80% to 100%). On minor roads,
the vans for parcel delivery are the most frequent freight
vehicle, while trucks are the dominant freight mode on
the major road network.
The FOCA model also reports the distance traveled
by long-distance trucks to transport the parcels be-
tween this distribution center and distribution centers
of other cities. That distance is not included in the
figures, as it is the same for all scenarios, and is equal
to 6245 km. By comparison, the total distance traveled
for the last-mile delivery tours ranges from 5000 km
to 6000 km, representing approximately the same
distance.
To analyze the CO
2
emissions, we multiplied the dis-
tance traveled by vehicle type with average emission fac-
tors according to the following assumptions:
Diesel vans’fuel consumption of 10 l/100 km (based
on Krause et al. [25]
Electric vans’electricity consumption of 30 kWh/
100 km (based on Weiss et al. [48])
Cargo bikes’electricity consumption of 3 kWh/100
km (based on Saenz et al. [43])
CO
2
emissions of fuel-powered vehicles of 3.170 kg/l
(based on DIN EN 16258:2012 [11])
CO
2
emissions for electricity consumption in
Germany in 2018 of 0.518 kg/kWh (based on Icha
and Kugs [23])
Given the above-mentioned data, the emission factors
for the different vehicles are 317 g CO
2
/km for diesel
vans, 155 g CO
2
/km for electric vans and 16 g CO
2
/km
for electric cargo bikes. Using these emission factors, we
calculated the emissions generated by the delivery tours.
In Fig. 8we assessed the scenarios with different shares
of cargo bikes (x-axis) in combination with a different
share of electric vans (3 subplots). The comparison with
Fig. 6b reveals a linear relationship between distances
traveled by vans and emissions (due to the use of simpli-
fied emission factors in g CO
2
/km). The use of cargo
bikes represents, in all cases, a relevant reduction of
emissions in comparison to the base scenario with no
cargo bikes. The emissions generated by electricity con-
sumption of cargo bikes are almost irrelevant, despite
Fig. 8 CO
2
emissions per day
Llorca and Moeckel European Transport Research Review (2021) 13:33 Page 10 of 14
the long operating times and distances. The contribution
of feeder trips is also small in relative terms. A replace-
ment of 50% of diesel vans with electric vans (without
the use of cargo bikes) is similar in terms of CO
2
emis-
sions to the use of cargo bikes for as many parcels as
possible.
The long-distance diesel trucks that transported the
parcels from (or to) the analyzed distribution center pro-
duced under these assumptions approximately 3960 kg
of CO
2
.
4 Discussion and conclusions
One of the challenges of freight modeling is the lack of
good data [9,16,41]. This affects the availability of ac-
curate information about the number, the spatial and
temporal distribution and the size of delivered parcels.
These data are known to logistics companies who con-
sider this information as a business secret. Unfortu-
nately, these data are commonly not accessible for
research purposes. In this paper, we present the develop-
ment of a model for freight mostly based on openly ac-
cessible data. The model is solely based on data sources
that can be downloaded by request or are directly open
data. The model disaggregates national commodity flow
data in tons to the finer scale of vehicles (for all com-
modities) and individual transported shipments (for
parcels).
The number of parcels in a certain analysis area
matches the observed data, although the model results
may deviate if a very small geographical area is analyzed.
While the modeled spatial distribution, which is propor-
tional to population and employment density, seems rea-
sonable, further work is needed to fine-tune the
temporal distribution: currently, the model represents an
average day and ignores seasonal effects. In absence of
systematic data of parcel volumes by time, it could be
useful to analyze online shopping or parcel tracing re-
lated web search trends. This information could be also
used to extrapolate the volumes of 2011 (when the last
commodity flow data was released) to the present.
The use of a freight model at different scales and levels
of detail allowed us to analyze the scenarios related to
the last-mile delivery within the entire supply chain [10].
The results confirmed that the urban distribution of par-
cels is a relevant component of the entire parcel distri-
bution in terms of vehicle-km traveled (and
consequently also emissions). Although the share of vol-
umes and number of vehicles of this commodity is rela-
tively low in comparison to the other commodities, its
impact is relevant particularly in urban areas.
The impacts of the introduction of cargo bikes were
identified in the paper. The smaller size of cargo bikes
requires the introduction of a reasonable weight thresh-
old, as the heaviest parcels cannot be delivered by cargo
bike. We assumed this threshold to be 10 kg, and it re-
sulted in an actual share of parcels transported by cargo
bikes of up to 55%. As a result, a relatively high number
of van tours is still required. As the heavier parcels are
assumed to be uniformly distributed in the catchment
area, the van tours remain somewhat long and the dis-
tance traveled by vans does not decrease drastically (al-
though the time is cut to 50% due to the reduced
number of stops). On the other hand, the presence of
cargo bikes results in a high number of tours (the cargo
bike tours are short) and a relative low number of feeder
trips (from large distribution centers to micro depots).
The distance traveled and operating time of feeder trips
is minor, compared to the delivery tours of either vans
or cargo bikes.
Without cargo bikes, the model estimates that there
are 266 van tours with an average distance of 20 km (the
dimensions of catchment area of the distribution center
are approximately 7 × 4 km). The average tour duration
is 2:06 h. Assuming 8 h of service operation, at least 90
vehicles per day are required (the loading at the distribu-
tion center is not included in the calculations). When we
simulate the use cargo bikes for as many parcels as pos-
sible, the number of van tours is reduced to 200, but 345
additional cargo bike tours and 70 feeder tours are re-
quired. The duration of a van tour is reduced to 1:20 h.
Cargo bike tours are, on average, 0:51 h long, while
feeder tours last only 0:21 h. For the same operation
time of 8 h, the minimum number of vans drops to 40
with the introduction of cargo bikes, and up to 38 cargo
bikes are required to serve the entire demand. Approxi-
mately 3 additional vans would be needed for the feeder
tours. As derived from the previous calculations, cargo
bikes may be used to distribute every parcel lighter than
10 kg, resulting in a decrease of the number of vans from
90 to 43, but adding 38 cargo bikes. The total person
hours required increase by 6% for the cargo bike sce-
nario. The results are obtained with cargo bikes with a
capacity of 20 parcels. However, due to many different
cargo bike designs there is no consensus with value
should be, ranging from 10 [3] to 40 parcels [44]. Fur-
ther work needs to explore the sensitivity to this
parameter.
The paper analyzed the potential changes in traffic
volumes with the introduction of cargo bikes. The re-
sults do not show relevant changes in volumes of freight
vehicles, especially on major roads and arterial streets.
On residential streets, the scenarios with a low share of
cargo bikes result in an increase of total volumes com-
pared to a scenario without cargo bikes. With a high
share of cargo bikes, this effect is no longer relevant.
However, despite the low impact on the total vehicles
volume, the introduction of cargo bikes represents an
average change in the vehicle composition: the share of
Llorca and Moeckel European Transport Research Review (2021) 13:33 Page 11 of 14
cargo bikes increases up to 25% of all freight vehicles.
However, the share of freight vehicles was, on average,
only 1% of the total number of vehicles, including cars
and motorcycles. Therefore, the impact of different sce-
narios on the overall urban traffic is small. However, se-
lected locations may be affected noteworthy by changes
in the last-mile delivery processes.
Regarding the impacts on CO
2
, the use of cargo bikes
has the potential to reduce total emissions. The reduc-
tion is almost proportional to the reduction of distance
traveled by vans (the emissions caused by the electricity
production to run cargo bikes are much smaller). Alter-
natively, the same effect in emission reduction could be
achieved if part of the diesel vans was substituted by
battery-powered electric vans. In the paper, it was as-
sumed that the logistics of the parcel delivery by van
does not change (diesel vs. electric), which seems
plausible given the relatively short length of tours that
are within the usual range of electric vans. The re-
sults of the emission assessment are an additional
data point to reduce emissions caused by last-mile
parcel deliveries.
The paper simulated various scenarios with increasing
cargo bike shares. However, we exogenously assumed
the share of cargo bikes of each scenario (with the ex-
ception of the fact that parcels heavier than 10 kg could
be only delivered by van) and analyzed the effects on de-
livery tours, distance traveled, traffic and emissions. The
actual choice between cargo bikes and electric vans de-
pends, however, on a combination of many factors. From
the point of view of logistic providers, the introduction
of cargo bikes may increase labor costs (longer operation
times with same wage according to Sheth et al. [44]) and
at the same time, additional fixed costs to build micro
depots (including rent of space). On the other hand,
purchase and maintenance costs of cargo bikes are sig-
nificantly lower than for vans, but more cargo bikes are
required to substitute one van (According to the Ameri-
can Transportation Research Institute [1], operational
costs of cargo bikes are around one fourth of vans).
From the point of view of the city administration, the
presence of cargo bikes (or electric vehicles) can contrib-
ute to reduce emissions (note that this research modeled
CO
2
emissions, but similar results would be obtained if
local NO
x
, PM or other emission factors were calcu-
lated). On the other hand, if the bicycle infrastructure is
insufficient, the use of cargo bikes is unlikely to benefit
traffic conditions (as bikes will occupy motor vehicle
lanes and probably reduce the average travel speeds).
While the paper presents a model that can be used to
assess those indicators under different assumptions, fur-
ther cost-benefit analyses that include many points of
view are needed. In any case, the results this paper sug-
gest that in absence of specific policies for the
promotion of cargo bikes or without restrictions or pri-
cing measures for motorized traffic, the attractiveness of
cargo bikes is not obvious compared to other measures,
such as the introduction of electric vans.
The results of the paper cannot be extrapolated to
other geographical areas without proper adaptations. If
the distance between the distribution center and the
served area is higher (in our example the distribution
center was inside the served area), the average trip
length of van tours and feeder tours would be higher as
well (this is not the case for cargo bike tours, as long as
micro depots are located close to the zones they serve).
Another limitation of the model is the simplified def-
inition of the bicycle network, which is motivated by the
use of the software MATSim. Road links that allow the
access to bicycles do not differ much from each other in
our model. However, a more realistic approach should
include additional link attributes, such as the type of in-
frastructure, the maximum speed or the possibility of
overtaking slower bicycles.
Despite the above-mentioned limitations, the FOCA
model provides a framework that is suitable for policy
analyses in a field where observed demand data com-
monly are unobtainable. Further research will apply this
model to test alternative scenarios, including road pri-
cing, emission fees or parking restrictions for freight
vehicles.
Acknowledgements
The authors would like to thank Kamil Moreau for processing the traffic
count data.
Authors’contributions
CL, RM: study design, CL: model development, CL, RM: result analysis, CL:
manuscript writing. The authors read and approved the final manuscript.
Funding
This research is part of the project Potenziale für Lastenradtransporte in der
Citylogistik (RadLast), funded by the German Federal Ministry of Transport
and Digital Infrastructure within the National Cycling Plan 2020 (NRVP) (Grant
agreement VB1806). The research was also completed with the support of
the Technical University Munich –Institute for Advanced Study, funded by
the German Excellence Initiative and the European Union Seventh
Framework Programme under grant agreement n° 291763. Open Access
funding enabled and organized by Projekt DEAL.
Availability of data and materials
The model developed in this paper is an open-source model that can be
downloaded directly from the GitHub repository https://github.com/
msmobility/foca. Input data and simulation results are available from the au-
thors on request.
Competing interests
The authors declare that they have no competing interests.
Received: 26 June 2020 Accepted: 21 May 2021
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