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Research Article
Transportation Research Record
2020, Vol. 2674(5) 553–562
ÓNational Academy of Sciences:
Transportation Research Board 2020
Article reuse guidelines:
sagepub.com/journals-permissions
DOI: 10.1177/0361198120917162
journals.sagepub.com/home/trr
Exploring Benefits of Cargo-Cycles
versus Trucks for Urban Parcel Delivery
under Different Demand Scenarios
Giacomo Dalla Chiara
1
, Andre
´Romano Alho
2
, Cheng Cheng
2
,
Moshe Ben-Akiva
3
, and Lynette Cheah
4
Abstract
Urban deliveries are traditionally carried out with vans or trucks. These vehicles tend to face parking difficulties in dense
urban areas, leading to traffic congestion. Smaller and nimbler vehicles by design, such as cargo-cycles, struggle to compete in
distance range and carrying capacity. However, a system of cargo-cycles complemented with strategically located cargo-
storing hubs can overcome some limitations of the cargo-cycles. Past research provides a limited perspective on how demand
characteristics and parking conditions in urban areas are related to potential benefits of this system. To fill this gap, we pro-
pose a model to simulate the performance of different operational scenarios—a truck-only scenario and a cargo-cycle with
mobile hubs scenario—under different delivery demand and parking conditions. We apply the model to a case study using
data synthesized from observed freight-carrier demand in Singapore. The exploration of alternative demand scenarios
informs how demand characteristics influence the viability of the solution. Furthermore, a sensitivity analysis clarifies the con-
tributing factors to the demonstrated results. The combination of cargo-cycles and hubs can achieve progressive reductions
in kilometers-traveled and hours-traveled up to around densities of 150 deliveries/km
2
, beyond which savings taper off.
Whereas the reduction in kilometers-traveled is influenced by the the carrying capacity of the cargo-cycle, the reduction in
hours-traveled is related to to the cargo-cycle ability to effectively decrease the parking dwell time by reducing, for instance,
the time spent searching for parking and the time spent walking to a delivery destination.
Urban deliveries are traditionally carried out using motor
vehicles such trucks, a term that we will use to encompass
the use of vehicles such as vans, lorries, or others that are
similar. Such conventional urban delivery method is
increasingly under pressure. Because of the increase in
urban population and the rise of e-commerce, delivery
demand has not only increased, but also shifted toward
smaller consignments shipped at a higher frequency (1).
However, urban transport infrastructure supporting city
logistics distribution has often remained unchanged. In
particular, trucks often lack available curb-space for
parking and loading/unloading in urban areas, with the
consequence of increasing cruising for parking, dwell
times (as a result of increase in walking distance), and
unauthorized parking (2). With the increase in urban
population density, traffic congestion levels have also
increased, negatively affecting truck travel times (3). To
reduce the negative externalities caused by trucks in
urban areas, including road blockages caused by
unauthorized parking and noise and air pollution, city
authorities have often taken regulatory actions imposing
parking and travel restrictions for trucks in urban areas
(4). These factors lead to an increase in delivery costs,
negative social and environmental impacts, or both.
An alternative to the use of conventional trucks to
perform urban last-mile deliveries that has been deployed
in several cities around the world (5–7) relies on the com-
plementary use of cargo-cycles, sometimes supported by
mobile hubs. We use the term cargo-cycles as encom-
passing of two, three and four wheelers, which have
some component of human-based propulsion, often with
1
Department of Civil and Environmental Engineering, University of
Washington, Seattle, WA
2
Future Urban Mobility Interdisciplinary Research Group, Singapore-MIT
Alliance for Research and Technology (SMART), Singapore
3
Department of Civil and Environmental Engineering, Massachusetts
Institute of Technology, Cambridge, MA
4
Department of Engineering Systems and Design, Singapore University of
Technology and Design, Singapore
Corresponding Author:
Andre
´Romano Alho, andre.romano@smart.mit.edu
electric motor support. Mobile hubs are small de-
consolidation centers which, differently from traditional
urban consolidation centers, often do not require a fixed
infrastructure, except pre-existing parking space. In this
case, trucks carry consolidated delivery loads from a
depot to central areas, more specifically to the hubs,
from which cargo-cycles pickup the goods and delivery
locally to the final destinations.
The use of cargo-cycles is commonly assumed to be
comparatively more agile than trucks, as these vehicles
require a smaller parking space, can access areas and
routes not accessible to trucks, and can park closer to
destination. This is particularly advantageous in settings
of heavy parking and traffic congestion, as these vehicles
can lead to reduced cruising for parking and unauthor-
ized parking if due care is taken in their use and adequate
regulations are in place. On the other hand, the cargo-
cycle is disadvantaged with respect to trucks as it able to
carry less cargo and it reaches a lower maximum travel
speed, being more suited to deal with smaller consign-
ments and higher delivery densities (deliveries per km
2
).
In fact, past research showed mixed results on whether a
cargo-cycle-based distribution system can bring about
improvements compared with the traditional truck-based
system. According to a literature review, 47% of the
studies reviewed proposed the use of bicycles/tricycles as
alternative vehicles to perform last-mile deliveries instead
of the conventional alternative (8).
In this study we seek to answer the following research
question: how do delivery demand and parking conditions
relate to the benefits of using a cargo-cycle-based distribu-
tion system when compared with the conventional truck-
based system? In this setting, we investigate how demand
characteristics, particularly delivery density, parcel
weight, and parking conditions, particularly the excess
dwell time resulting from cruising for parking and walk-
ing, influence the performance of a system of cargo-cycles
with mobile hubs, when compared with truck-based
deliveries, in ‘‘last-mile’’ operations. For this purpose, we
develop a simulation method that allows replicating to a
suitable extent the delivery tours of a freight carrier in
urban areas for both a truck-based system and a cargo-
cycle-based system with mobile hubs, under different
delivery demand and parking conditions. The simulation
method is based on detailed data for delivery trips per-
formed by trucks of a parcel delivery company in down-
town Singapore. The simulation method first takes trip
records as an input to generate a delivery demand with
given characteristics (location, quantity, time window).
Then, given a delivery demand, it simulates two opera-
tional scenarios: (a) a truck-only distribution scenario
and (b) a cargo-cycle with mobile hubs distribution sce-
nario. The truck-based distribution estimates are com-
pared with the real data for validation. The data are also
used to infer the traveling, cruising, and parking dura-
tions. For the latter two we assume a reduction when
using cargo-cycles. For varying demand characteristics,
we compare the two scenarios using several performance
metrics (total daily travel distance and time; total daily
operating time, which includes travel time and dwell time;
and the number of vehicles required). We also perform a
sensitivity analysis on three key model parameters as to
better understand their contribution to predicted total
travel distance and operations time.
In the following section we review the relevant litera-
ture. Then, we describe the truck trips data and the simu-
lation model developed. Further, we present the results
of the model validation, of the simulated scenarios,
and of the sensitivity analysis. We conclude with a dis-
cussion of the results.
Literature Review
Whereas some research focuses on the potential and fac-
tors that can lead to or restrict the adoption of cargo-
cycles (9,10) our focus is on the operational impact of
adopting this alternative mode. In this domain, a large
part of the research focuses on environmental impacts
compared with conventional delivery operations. Sadhu
et al. analyzed the data collected from 2,000 cycle rick-
shaw trolley (CRT) drivers in Delhi in 2011 and showed
that CRTs for city goods movements reduced fuel use
and vehicle emissions (11). Saenz et al. compared the car-
bon footprint of a cargo-cycles logistics service with that
of a traditional system (12). The results showed that total
greenhouse gas emissions reduced between 51% and
72% in the cargo-cycles system. Figliozzi and
Tipagornwong presented a lifecycle emissions minimiza-
tion model, concluding that using small cargo-cycles in
dense service areas, for relatively small loads, and when
the depot is located close to the delivery area, can obtain
the lowest lifecycle emissions (13).
There is also considerable attention to financial
impacts and operational impacts. Melo and Baptista
developed a microsimulation model to replicate traffic
conditions and estimate cargo-cycles environmental and
operational impacts (14). The results showed that the use
of cargo-cycles improves mobility, environment, energy
consumption, and running costs if deployed at the appro-
priate spatial scale within the city. Marujo et al. exam-
ined the cost and environmental impacts of mobile-
depot-based delivery using GPS data (15). They showed
that the use of cargo-cycles and mobile depots in the last-
mile delivery led to significant reduction of greenhouse
gas emissions and local air pollution. In addition, the
mobile-depot-based delivery setup yielded a slight cost
reduction over the traditional setups. Nascimento et al.
assessed the economic viability of cargo-cycles from the
554 Transportation Research Record 2674(5)
carrier’s perspective, concluding possible benefits despite
several contextual barriers to adoption in Brazil (16).
Arnold et al. simulated a cargo-cycle system for B2C e-
commerce distribution in a comparative setting with
pickups from lockers, and with a hybrid system of cargo-
cycles and lockers against conventional deliveries (17).
The authors focused on a comparison of internal and
external costs across the delivery strategies. The study
revealed that diminishing external costs requires an
increase of internal costs by the carriers. Gruber and
Narayanan compared the travel time difference between
cargo-cycles and conventional vehicles in commercial
transport, applying a regression model (9). The results
indicate that when the trips distance is short, the cargo-
cycles and cars performances overlap. The increase of the
trip distance made cars more advantageous. However,
the time to park, which might be comparatively larger
for trucks and contributes to the vehicle dwell time, was
a neglected element in their research. Tipagornwong and
Figliozzi analyze the competitiveness of cargo-cycles
against diesel vans in urban areas using a cost model and
considering two demand scenarios (18). The results indi-
cate that (a) cargo-cycle competitiveness is affected by
drivers’ costs assumptions, and (b) specific local condi-
tions such as parking difficulties, low travel speeds, and
time-constrained delivery windows can contribute to
widen the gap between the delivery modes. Similar con-
clusions about the scenarios in which cargo-cycles are
more promising are achieved by Conway et al. (5) and
Choubassi et al. (19). The latter highlighted factors such
as high delivery density and congested areas as well as
the presence of depots within the delivery areas as contri-
buting factors to the comparatively better performance
of the cargo-cycles. To the best of our knowledge no
study could be found that explores in detail the relation-
ship between the performance which the system of cargo-
cycles can achieve and demand characteristics relating to
delivery density and parcel/delivery weight distribution.
Methods
Data Description
For estimating delivery demand, we used the records of
about 20,000 deliveries performed over 3 months by a
parcel delivery company in the central business district
(CBD) of Singapore, a 2.3 km
2
urban area. For each
delivery, the delivery weight (in kg), delivery destination
GPS location, and delivery priority level are available
(‘‘high’’ priority consignments have to be delivered by
noon, ‘‘low’’ priority consignments can be delivered any-
time). Moreover, each record reports the date when the
delivery took place, two timestamps at the start/end of
the delivery time, and a unique ID of the vehicle used to
perform the delivery.
Note that when a truck is parked, the driver might
perform more than one delivery, walking between differ-
ent delivery locations, before returning to the truck. We
therefore rely on the concept of ‘‘delivery groups,’’
explained as follows. Delivery locations are first mapped
to buildings. Then, deliveries that took place consecu-
tively in time, within the same building, and performed
from the same vehicle are aggregated into a single ‘‘deliv-
ery group,’’ which indicatively corresponds to a group of
deliveries performed in a single parking event, that is,
without moving the vehicle from the chosen parking
location. This process leads to a set of 8,300 delivery
groups from the original dataset of about 20,000 deliv-
eries. The total shipment weight delivered in a delivery
group is the sum of the individual weights of the deliv-
eries associated with that group. For simplicity, we
address delivery groups as ‘‘deliveries’’ throughout this
paper.
Travel distances between consecutive deliveries are
obtained by computing the shortest path between each
pair of locations, and the respective travel times are
obtained by querying the Here Maps API (here.com) for
the historical travel time between each pair of locations.
We estimated the vehicle dwell times from the individ-
ual deliveries start/end timestamps as show in Figure 1.
Consider two deliveries, named i1and i, performed at
two different locations. First, we estimate the arrival
time to location i(tarr
i) by adding to the previous deliv-
ery departure time ( tdep
i1) the estimated travel time from
location i1to i(we estimated travel times querying the
here.com API). Then, the dwell time for delivery iis
computed by subtracting from the recorded delivery end
time the new estimated delivery start time (dwell time =
tdep
itarr
i).
The resulting dwell time distribution is right skewed
with a mean and median dwell time of 7.6 and 16 min,
respectively (see histogram in Figure 2). It is important
to note that the resulting dwell time estimates include not
only the times to deliver the consignments but also other
activities including cruising for parking (i.e., time spent
searching for available parking), queuing, and walking
time to the consignee. Later in the paper we will assume
that, by using a cargo-cycle, the vehicle dwell times are
reduced—because a cargo-cycle can park closer to a
Figure 1. Method used to estimate vehicle dwell times.
Dalla Chiara et al 555
destination, therefore reducing walking time, and does
not cruise for parking—with respect to trucks.
Overview of Simulation Model
The simulation model developed consists of three sub-
models (Figure 3):
a demand generation model that generates deliv-
ery demand scenarios based on (i) a real-world
dataset of daily deliveries performed by a freight
carrier and (ii) a set of parameters (demand den-
sity and delivery weight distribution) characteriz-
ing the desired demand scenario;
a (tour) scheduling model that organizes the gen-
erated delivery locations into time- and distance-
efficient freight vehicle tours;
a vehicle assignment model that calculates the
minimum number of vehicles (either trucks or
cargo-cycles) required to perform all scheduled
tours.
Both demand-specific inputs characterizing the
demand scenarios and the desired operational scenario
(i.e., the way deliveries are performed) need to be speci-
fied to operationalize the model. It should be noted that
the proposed method to generate tours does not aim to
compute optimal delivery tours, for example, tours with
the minimum travel distance and time. Our intention is
to replicate, to some extent, the actual tours observed to
be performed by the freight carrier. Details on this com-
parison are provided in the Operational Scenarios
section.
Simulation Sub-Models
Sub-model 1: Demand Generation. Delivery demand for a
hypothetical weekday is generated based on the records
of 8,300 delivery groups. A demand scenario is charac-
terized by (i) a demand density (number of deliveries per
km
2
for the study area), and (ii) a delivery weight distri-
bution. To generate demand with a given number of
deliveries, we re-sample without replacement the original
dataset of delivery records. To simulate different delivery
weight distributions, we multiply the original weights of
the sampled deliveries by a positive constant. We assume
the dwell time remains as per prior estimates.
Sub-model 2: Tour Scheduling. In the tour scheduling sub-
model, deliveries are organized into tours. The procedure
to construct distance- and time-efficient feasible tours
consists of three steps: (1) Clustering (2), Feasible tour
search and (3) Tour merging. The procedure takes two
main inputs: first, the daily delivery demand generated in
the previous demand generation sub-model, and second,
the characteristics of the operational scenario simulated,
including the type of vehicle used (truck or cargo-cycle)
and the related vehicle constraints (e.g., vehicle weight
capacity, maximum speed). The Operational Scenarios
section provides a detailed description of the different
operational scenarios simulated.
In the Clustering step the objective is to group a given
set of deliveries into disjoint clusters, each representing a
potential tour. We use a hierarchical clustering algorithm
to generate a hierarchy of nested clustering solutions. At
the lowest level of the hierarchy, there are as many clus-
ters as the number of deliveries; at the highest level, all
deliveries are grouped into a single cluster; any intermedi-
ate level is generated by merging two clusters generated
at the lower level. We aim to generate clusters which are
not only geographically compact, but also which contain
Figure 2. Empirical distribution of the estimated dwell times.
Figure 3. Simulation flow (dashed: inputs/outputs, solid border:
models).
556 Transportation Research Record 2674(5)
deliveries of the same priority level. For this, the algo-
rithm requires as input a distance matrix Dwith elements
dij, which is a measure of dissimilarity between deliveries
iand j. We define dij as the weighted average of (i) the
geographical distance between iand jand (ii) a binary
variable taking value 0 if iand jhave the same priority
level, and 1 otherwise.
Once clusters are defined, in the next step of Feasible
tour search, a tour is feasible when it satisfies the follow-
ing constraints: (i) the total weight of all deliveries in the
tour is less than or equal to the vehicle weight capacity,
(ii) the total time it takes for a vehicle to perform the
tour is less than or equal to the maximum vehicle operat-
ing time (assumed shift time), (iii) the total tour-distance
traveled is less than or equal to the maximum vehicle
travel distance (important for cargo-cycle range), and
(iv) all high-priority deliveries are scheduled before noon.
The upper bounds in constraints (i)–(iii) are vehicle-
specific, and therefore depend on the mode of transport
(i.e., trucks or cargo-cycles). The tour travel distance and
time are computed by finding the shortest path covering
all delivery points of the tour, starting and ending at the
depot (for trucks) or at a consolidation hub location (for
cargo-cycles), and assuming an average travel speed. The
shortest path is found by solving a traveling salesman
problem for each cluster. To satisfy constraint (iv), if a
tour contains at least one delivery with high priority, we
limit the maximum tour time to the length of the morn-
ing shift, such that the whole tour can be completed
before noon. We iteratively search for the largest clusters
satisfying all the above constraints from the hierarchy of
clustering solutions. We therefore start from the top of
the hierarchy and iteratively check, for each cluster in
the clustering solution associated with the given level
of the hierarchy, whether its associated tour is feasible.
Feasibility leads to the cluster’s deliveries being assigned
to a tour; otherwise, we proceed to a lower level of the
hierarchy, that is, splitting the cluster into smaller nested
clusters. The search stops when all deliveries are assigned
to feasible tours. In the operational strategy that involves
the use of the cargo-cycles, deliveries need to be assigned
to the respective hub location. To do so, we start the
search for feasible tour not from the top of the hierarchy
of clustering solution, but from a lower level, specifically
from the level containing as many clusters as the desired
number of hubs, and then search for feasible tours within
each cluster.
Lastly, for Tour merging the objective is to overcome
tours that are undesirably small (containing few deliv-
eries), therefore forcing the vehicle to return to the depot
(or the consolidation hub in case of cargo-cycles) more
often than needed. To form larger tours, we consider all
possible pairwise combinations of the identified feasible
tours and identify those in which merged tours are (i)
feasible tours and (ii) their total tour travel distance and
time are smaller than those of the original tours com-
bined. We then model the possible tour combinations as
a bipartite graph in which nodes represent the tours and
links connecting pairs of tours represent the feasible com-
binations. Then, the maximum matching of the graph is
obtained to find the largest possible number of combina-
tions. We then merge the selected matches into single
tours. We repeat the pairwise merging of tours until there
are no additional feasible combinations.
Sub-model 3: Vehicle Assignment. After we generate efficient
and feasible tours, we find the minimum number of vehi-
cles needed to perform all tours. We start from an initial
number of vehicles equals to the number of tours. We
then solve two bin-packing problems sequentially, in
which tours are assigned (‘‘packed’’) to a minimum num-
ber of vehicles (‘‘bins’’), in which the ‘‘capacity’’ of each
vehicle consists of the length of a driver’s working shift.
We assume a driver shift starts at 7 a.m. and ends at
5 p.m. In the first bin-packing problem we assign high-
priority tours (i.e., tours that contain at least one high-
priority delivery, meaning the whole tour needs to be
completed by noon) to vehicles, assuming that each vehi-
cle has an initial ‘‘capacity’’ to perform the high-priority
tours within 5 h (assuming vehicles start at 7 a.m.), there-
fore guaranteeing that high-priority tours are dealt with
within the morning shift. In the second phase we add to
all vehicles 5 h of ‘‘extra capacity’’ (assuming the after-
noon shift starts at 12 p.m. and ends at 5p.m.) and assign
the remaining low-priority tours to vehicles solving a sec-
ond bin-packing problem. Finally, we retain only those
vehicles to which at least one tour was assigned. As a
result, we obtain the minimum fleet size needed to per-
form all tours, such that all high-priority tours can be
completed in the morning.
Operational Scenarios
We simulate the two operational scenarios: a truck-only
scenario (which is validated against the baseline scenario)
and a truck + cargo-cycle scenario. The first scenario
simulates a traditional distribution system relying solely
on trucks (scenario truck-only). Truck tours start and
end at a depot, located about 20 km from the area where
deliveries are performed. These tours are validated
against real data by computing the total travel distance
and operating time and comparing them against the
same metrics derived from the carriers’ data. We refer to
the real tours performed by the delivery company as the
baseline (scenario baseline) which we expect our algo-
rithm to replicate to a reasonable extent (scenario truck-
only).
Dalla Chiara et al 557
The alternative scenario simulates the use of a hybrid
system of trucks and cargo-cycles (scenario
truck +cargo-cycle): trucks are used to carry consoli-
dated loads from the depot to a mobile hub (hereafter
called ‘‘hub’’) located in the CBD; cargo-cycles pick up
the consolidated loads at the hubs and perform the indi-
vidual deliveries. Note that in this scenario, trucks are
used only to deliver consolidated loads to the hubs and
are not used for delivering to the final destinations, and
only cargo-cycles are used to perform the last leg of the
chain. Therefore, we do not consider the case of a mixed
cargo-cycle and truck scenario in which both vehicle
types are used for the last mile. Hubs are assumed to be
similar to containers in form and fit within a car parking
space. For the presented results, we assume the deploy-
ment of two hubs. Each hub contains a maximum of
nine cargo-cycle loads. The geographical locations of the
hubs change depending on the daily demand scenario
and are determined by the simulation model. Each hub
is placed at the geographical center of the demand cluster
of deliveries served by the hub. Given that cargo-cycles
are more compact and nimbler and therefore able to find
a parking space easily, their dwell times are assumed to
be 40% less compared with those of trucks. This is a
conservative assumption, as past research reports dwell
time savings of 75% (20). It should be noted that, as
mentioned earlier, our dwell time estimation includes the
time for cruising and queuing for parking. The original
maximum weight a cargo-cycle can carry is 205 kg.
However, recognizing that the heterogeneous volumes
and shapes of the delivery packages might not allow the
cargo-cycle to be loaded to maximum capacity, we
assume that only 70% of the maximum carried weight
can be loaded. Table 1 summarizes the input parameters
for each mode.
The two alternative operational scenarios are tested
on different demand scenarios, characterized by a given
demand density and delivery weight distribution. In the
baseline case, daily demand densities range between 25
and 90 deliveries per km
2
, the mean delivery weight is
5 kg and median weight is 1.1 kg. Each demand scenario
is simulated 50 times, and the resulting outputs are aver-
aged across simulation runs.
Performance Metrics and Sensitivity Analysis
The model outputs a set of metrics describing the opera-
tional performance of the simulated freight distribution.
These are: (a) total daily travel distance and time; (b)
total daily operating time, which includes travel time and
dwell time; and (c) the number of vehicles required.
Further, we analyze the sensitivity of some output
metrics—total travel time and operating time—to input
parameters. We test two vehicle-specific inputs: dwell
time reduction and capacity for the cargo-cycles and a
demand-specific input, the prior mentioned constant that
multiplies the original delivery weights. We take a one-
factor-at-a-time approach, changing one of the four
parameters each time, from 250% to 50% with a 10%
increment to the baseline value reported in Table 1, keep-
ing the other parameters fixed.
Results
Validating the Baseline Case
Figure 4 shows the output metrics for validation scenar-
ios (baseline and truck-only), using as input the observed
delivery demand. The baseline scenario illustrates the dis-
tance and time that the freight carrier took to perform
the deliveries, following the observed vehicle assignment
and delivery sequences in the data. The truck-only sce-
nario is obtained by simulation, using the same real
demand as input. Note in this case the demand genera-
tion model is not run and results are averaged by day of
the week. The daily travel distance of the simulated
truck-only scenario is on average 28% less, and the total
operating time is 12% less than in the baseline scenario.
This difference can potentially be explained by some fac-
tors which cannot be accounted for in the simulation,
such as:
Deviation in real-world delivery tours performed
by the drivers from optimal tours. These might
have been because of receiver-related factors (e.g.,
the receiver was not available at the moment of
the delivery) or driver-related factors (e.g., driver
might prefer to deliver bulky items first even if
they are not high priority as that would free space
in the van and ease subsequent load/unloads)
Exceptional traffic congestion conditions, parking
congestion conditions
Operational practices such as having smaller vans
re-loading from larger vans during daily operations
Naı
¨ve assumption of shortest path routes
Table 1. Assumptions of Mode-Specific Parameters
Type of vehicle
Model parameter Truck Cargo-cycle
Max. carried weight (kg) 600 kg 140 kg
Average speed (km/h) 30 km/h 15 km/h
Maximum distance
traveled (km) per tour
400 km 40 km
Maximum operating time
per day (h)
10 h 10 h
Dwell times change (with
respect to original dwell
times)
No change Reduced by 40%
558 Transportation Research Record 2674(5)
Despite this, we conclude that the simulation model
can replicate to a reasonable extent the performance of
carrier tour planning. Note that this comparison is per-
formed for averaged metrics across days of the week,
whereas for the following analysis we draw random deliv-
ery groups from any given day of the workweek.
Simulation-Based Analysis
Figure 5 shows the mean percentage change in travel dis-
tance and operating time obtained by using cargo-cycles
(truck +cargo-cycle scenario), versus the truck-only
scenario, for varying demand density conditions and dif-
ferent delivery weight multipliers. We observe a steep
decrease of both travel distance and operating time as
the demand density increases from 25 delivery/km
2
to
about 150 delivery/km
2
, reaching a 75% decrease in
travel distance and a 40% reduction in total operating
time (given the original weight distribution). After this
point, the savings in total operating time and travel dis-
tance stabilize as demand density increases.
We also observe that heavier weights influence the
performance, especially if combined with larger demand
densities. As the delivery weight distribution shifts
Figure 4. Daily total distance traveled (left) and the daily total operating time (right) for the baseline (observed data) versus truck-only
scenario (simulated).
Figure 5. Travel distance (left) and time (right) savings for cargo-cycle solution with respect to a truck-based solution.
Dalla Chiara et al 559
toward heavier weights, the improvements brought
about by the cargo-cycle system become less significant.
Figure 6 compares the average minimum fleet size
required to fulfill daily demand, for the two distribution
scenarios and for varying demand density. The smaller
fleet of cargo-cycles can be explained as a product of the
method, which minimizes the distance traveled and oper-
ating time but not the fleet size itself. Firstly, cargo-cycles
tours are shorter as these pick up the goods from hubs
located in the CBD, and do not require traveling back to
the depot. Since our method attempts to add multiple
tours to each vehicle, shorter tours can lead to a more
efficient use of vehicles. On the other hand, the method is
detrimental to the metrics of truck fleet required, because
longer tours leave less opportunities for matching in light
of existing schedules and maximum operating time per
day parameter.
Sensitivity Analysis
We perform a sensitivity analysis for three key para-
meters to better understand their contribution to pre-
dicted total travel distance and operations time, with
results illustrated in Figure 7. The selected parameters
are (1) the capacity of cargo-cycles, (2) cargo-cycle dwell
time, and (3) weight multiplier factor of parcels. In these
tests, we fix the demand density at 100 deliveries per km
2
.
Note that concerning total travel distance, this result is
highly dependent on the assumption of the depot loca-
tion; for example, in the truck +cargo-cycle scenario,
trips to/from the depot contributed on average 70% of
the total distance traveled.
The results for the selected parameters’ influence on
total travel distance demonstrate a major effect by the
assumed carrying capacity and minor effects from the
assumed dwell times and delivery weight. Looking at
reductions in capacity, a 50% reduction (to 70 kg) can
lead to 17% increase in travel distance. On the other
hand, increases in capacity have a small impact. With
regards to total operational time, the only critical factor
is the assumption of dwell time savings associated with
cargo-cycle use. This is justified mainly as the dwell time
contributes, on average, to more than 75% of total
operational time. In addition, the reduction in dwell
times can also increase the number of deliveries that can
Figure 6. Mean fleet size for truck-only (blue) and
truck +cargo-cycle (red) scenario.
Figure 7. Sensitivity analysis results.
560 Transportation Research Record 2674(5)
be completed in a single tour, which to some extent influ-
ences the fleet size required to fulfill the demand.
Conclusion
We developed and applied a quantitative method to
assess how delivery demand conditions relate to the ben-
efits of using a cargo-cycle-based distribution system
when compared with the conventional truck-based sys-
tem. We estimated the benefits in relation to total dis-
tance traveled, total operating time, and fleet size.
Decreases in all metrics can be justified as proxies for a
more sustainable freight distribution strategy. This
method is applied in a simulation context informed by a
dataset of real deliveries performed in the highly urba-
nized setting of Singapore’s CBD.
In line with recent experiments we found that the
introduction of cargo-cycles for last-mile distribution,
supported using mobile hubs, can lead to considerable
benefits. However, this is clearer under specific demand
scenarios, particularly at higher demand densities and
lower shipment weights. As delivery density increases
from low density to up to around 150 deliveries per km
2
the cargo-cycle based system shows increasing improve-
ments (reduction in total operating time and distance tra-
veled). Beyond this delivery density we observe a
stabilization of the cargo-cycle-derived performance
improvements compared with a truck-only system.
However, these results depend on several factors. Note
that actual delivery density would be equal or higher
than the used values, as we grouped deliveries performed
by the same vehicle in the same stop as the unit of analy-
sis. Furthermore, the starting baseline distribution of
observed delivery weights shows that most of the deliv-
eries recorded are light (median weight is 1.1 kg). Most
of the deliveries remain relatively light even when we
simulated heavier weights (by multiplying the original
delivery weights by a constant), but these hinder the ben-
efits of a system using cargo-cycles and mobile hubs. The
distribution of heavier weights was shown to sometimes
hinder any improvement, which supports the hypothesis
that this distribution system is more suited for parcel
deliveries, which is timely considering e-commerce
growth. Finally, we assumed that the cargo-cycles are
associated with a reduction in delivery times of 40% with
respect to trucks. Albeit conservative, this reduction
should be expected if the cargo-cycles are able to park
closer to the delivery destinations and are not affected by
parking congestion. To conclude, if demand density is
low, the benefits are expected to be fewer, and if a city is
not affected by heavy parking congestion, or the cargo-
cycles are affected as much as truck by parking conges-
tion, the benefits obtained by the use of cargo-cycles are
expected to be more limited.
Taking the baseline case for the freight carrier (with a
demand density between 25 and 90 deliveries per km
2
),
the total fleet size needed can be reduced by up to 36%.
Moreover, the total distance traveled can be reduced up
to 70%, and the total operating time up to 40%.
Considerations for operating costs and other influen-
cing factors were not explored in this research. For
instance, an important factor to take into consideration
when switching to a cargo-cycle with mobile hubs system
is the need for extra space closer to the delivery loca-
tions. Although these need not take the form of a fixed
infrastructure, the cargo-cycles still need to be parked
overnight and require charging stations. Moreover,
throughout the day the mobile hubs need to occupy the
space equivalent to a single parking slot. We cannot
ignore that larger scale deployment of cargo-cycles might
shift the traffic and parking management concerns to
curb-space management. We put forward as future
research the exploration of the impacts of wide-scale
adoption of cargo-cycles from a traffic flow and curb-
space management perspective, as well as giving consid-
eration to the impacts of street network design on the
presented results.
Acknowledgments
This work is supported in part by the SUTD-MIT International
Design Centre (IDC). We thank our partners, RYTLE GmbH
and United Parcel Service Singapore Pte Ltd (UPS), for sharing
data and insights. We also thank Ming Hong Chua and
Zhiyuan Chua for the support provided in data analysis and
processing. Any findings, conclusions, recommendations, or
opinions expressed are those of the authors only.
Author Contributions
The authors confirm contribution to the paper as follows: study
conception and design: Dalla Chiara, Alho, Cheah and Ben-
Akiva; simulation model development: Dalla Chiara and
Cheng; analysis and interpretation of results: Dalla Chiara,
Alho, Cheng and Cheah; draft manuscript preparation: Dalla
Chiara, Alho, Cheng and Cheah. All authors reviewed the
results and approved the final version of the manuscript.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with
respect to the research, authorship, and/or publication of this
article.
Funding
The author(s) disclosed receipt of the following financial sup-
port for the research, authorship, and/or publication of this
article: This work is supported in part by the SUTD-MIT
International Design Centre (IDC), under the grant
IDG21800101, under a project titled ‘‘Data-Driven Design of
Dalla Chiara et al 561
Last-Mile Urban Logistics Solutions to Address E-Commerce
Growth’’.
Data Availability
The data that support the findings presented here were used
under license for the current study. As such, the data are not
publicly available.
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