# Integrating Parcel Deliveries into a Ride-Pooling Service -
An Agent-Based Simulation Study

Fabian Fehn∗, Roman Engelhardt, Florian Dandl, Klaus Bogenberger and Fritz
Busch

(Chair of Traffic Engineering and Control, Technical University of Munich,
Arcisstr. 21, 80333 Munich, Germany
∗corresponding author<EMAIL_ADDRESS>
## Abstract

This paper examines the integration of freight delivery into the passenger
transport of an on-demand ride-pooling service. The goal of this research is
to use existing passenger trips for logistics services and thus reduce
additional vehicle kilometers for freight delivery and the total number of
vehicles on the road network. This is achieved by merging the need for two
separate fleets into a single one by combining the services. To evaluate the
potential of such a mobility-on-demand service, this paper uses an agent-based
simulation framework and integrates three heuristic parcel assignment
strategies into a ride-pooling fleet control algorithm. Two integration
scenarios (moderate and full) are set up. While in both scenarios passengers
and parcels share rides in one vehicle, in the moderate scenario no stops for
parcel pick-up and delivery are allowed during a passenger ride to decrease
customer inconvenience. Using real-world demand data for a case study of
Munich, Germany, the two integration scenarios together with the three
assignment strategies are compared to the status quo, which uses two separate
vehicle fleets for passenger and logistics transport. The results indicate
that the integration of logistics services into a ride-pooling service is
possible and can exploit unused system capacities without deteriorating
passenger transport. Depending on the assignment strategies nearly all parcels
can be served until a parcel to passenger demand ratio of 1:10 while the
overall fleet kilometers can be deceased compared to the status quo.
Keywords: integration of passengers and freight, mobility-on-demand, ride-
pooling, fleet control, parcel delivery, agent-based simulation

##  1 Introduction

The development of urban transportation is subject to constant change. In
recent years, passenger transportation has been subject to the influences of
new mobility services such as car-sharing, ride-hailing, ride-pooling, or on-
demand public transportation services, including autonomous shuttle buses. In
addition to passenger transportation, urban freight transportation is also
subject to disruptive developments. Many providers are relying on fast
delivery options, such as same-day or next-day delivery options, which are
increasingly provided by subcontractors. Furthermore, there has been a trend
towards more environmentally friendly delivery forms, such as the use of
bicycle couriers or CO2-compensated delivery options in recent years. The term
’crowd logistics’ is currently also heavily discussed and describes a
(typically fast) shipping service outsourcing the delivery to many
individuals, often private persons. The European Commission [1] compared the
influences of passenger and freight traffic in terms of total CO2 emissions in
the European Union and found that passenger and freight transportation account
for approximately 60% and 40%, respectively. More specifically, urban
passenger traffic accounts for 17% and urban delivery traffic for 6% of the
total amount of transportation-related CO2 emissions (Figure 1).

Figure 1: Freight and passenger shares of European CO2 emissions from
transportation111According to PRIMES and TREMOVE Models of the European
Commission [1]

Along with that, the attitude of city residents is also evolving. While it was
inconceivable a while ago that people would completely do without their
privately owned cars, many citizens now rely on a combination of public
transportation, sharing, and on-demand services. Generally, one can observe a
more ’eco-friendly’ lifestyle among consumers [2]. The so-called ’sharing
economy’ describes a social trend of our time, with people foregoing owning
things, preferring instead to rent them or share them with others. However,
there has been an increase in passenger kilometers traveled per day from 2,717
million km in 2002 over 3,080 million km in 2008 to 3,214 million km for the
year 2017 in Germany [3]. Moreover, online shopping increased significantly,
and accordingly, a drastic increase in home delivery traffic emerged. The
annual relative growth in parcel shipments in Germany was 10.9% in 2020
compared to 2019, with a total of 4.05 billion shipments. This is mainly due
to private customer shipments, which increased by 18.6%, whereas business
customer shipments even decreased by 5.2% [4].

Along with the mentioned developments in transportation services and social
trends, urban transportation problems have also evolved. In addition to the
more transport engineering related problems, such as increased travel times or
noise and pollutant emissions, transport planning problems, such as the lack
of space or the division of urban districts, become also present in many
European cities. Therefore, it is time to find and promote environmentally
friendly and space-saving forms of transportation. In particular, the
sustainable utilization of existing infrastructure plays a decisive role, as
this is not directly linked to the creation of new infrastructure with the
associated emissions and space consumption. With the introduction of
automation in urban transportation, a time-optimized usage of the existing
infrastructure could be realized, by shifting certain transportation tasks to
off-peak times. Furthermore, new cost structures could establish, since the
largest cost factor, the driver would be eliminated. This development could
accelerate the trend towards better utilization of existing infrastructure and
has therefore high disruptive potential.

One possible solution to the above-mentioned urban transportation problems,
making use of current social trends and ecological and technological
developments, is the integrated transport of passengers and freight in an
urban context. Especially, in the field of mobility on-demand (MoD), due to
its high temporal and spatial flexibility and centralized decision-making,
there seem to be promising opportunities to serve passengers and freight with
one fleet of vehicles. For this reason, this paper deals with strategies that
enable the integrated transportation of passengers and parcels in a MoD fleet.
This study evaluates the potential of such a service and assesses its impact
on different stakeholders and the overall transportation system. Furthermore,
this paper examines the real-world applicability and explores system
boundaries with the help of a case study.

The outline of this paper is as follows: first, it describes the state of the
art in research and real-world applications. Thereby, the different areas of
application of integrated transport of passengers and freight, as well as
existing models and solutions and case studies are discussed. The next chapter
describes the methodology for the simulation and case study in Munich,
Germany. Subsequently, it presents the obtained results and investigates Key
Performance Indicators (KPIs) for the customers and the service operator. Last
but not least, this paper discusses the findings and future research
directions.

##  2 State of the Art Analysis and Literature Review

###  2.1 Integrated Passenger and Freight Transportation and Real-World
Applications

The idea of transporting freight simultaneously with passengers is by no means
new. As early as 1610, the first documented stagecoach traveled between
Edinburgh and Leith, carrying passengers as well as smaller parcels on its
journey [5]. Nowadays, the joint transport of passengers and parcels is also
not uncommon. Today’s passenger aircraft typically also handle freight. The
combined transport of people and goods is also quite common in maritime
shipping and ferry service. However, much fewer services exist for combined
transportation in an urban context. Yet, intelligent solutions are needed here
in particular, as urban transport phenomena, such as increased travel times
[6], space shortages, noise emissions, and poor air quality have worsened
considerably in recent years, according to the German Ministry of Environment
[7]. For this reason, a couple of smart approaches have already emerged that
could lead to an improvement in the aforementioned combined transportation of
passengers and freight in an urban context. To classify the existing urban
concepts and to investigate existing simulation studies, this paper analyses
the existing concepts of integrated passenger and freight transportation. A
classification between the different forms can be made by distinguishing
between public transportation (rail- and road-based) and individual
transportation.

The integration of rail-based public transportation systems and freight
transportation can have many faces, thereby one of the most researched forms
is the railroad transportation sector [8, 9, 10]. The combined transportation
in urban subway systems, especially in off-peak times, is also a very
promising approach and has therefore already been examined in several research
papers [11, 12, 13]. Combinations of Personal Rapid Transit and Freight Rapid
Transit are also subject of research and combine the two transportation
streams on rail [14, 15]. Another interesting approach is to use urban
tramways [16]. The city of Zurich launched an operational service called
Cargo-Tram and E-Tram in 2003 [17], which acts as a recycling center on rails
and transports large and heavy waste and electronic devices in off-peak times,
however, without passengers on board. The Karlsruhe Institue of Technology and
the University Frankfurt am Main of Applied Sciences research integrated
passenger and freight transportation approaches in the ’LogIKTram’ and
’LastMileTram’ research projects [18, 19]. Another solution could be the
combined transport of passengers and freight in urban cable cars [20]. Road-
based public transportation systems are also a matter of discussion when it
comes to integrating passenger and freight transport. Especially, bus systems
are subject of current research [21, 22, 23, 24]. An interesting concept study
in this category is the ’Freight*Bus’, which according to the persons in
charge could change the economic and environmental costs of passenger and
freight transportation in modern cities [25]. One of the probably best-known
examples of a road-based, multi-purpose vehicle approach is the Toyota
e-Palette concept, which was presented in the context of the Olympic Games
2020 in Tokyo [26]. The e-Palette concept aims at combining multiple purposes,
such as passenger and freight transportation or mobile shopping facilities, on
one autonomous vehicle platform.

Apart from public transportation infrastructure, there is also the possibility
to integrate passenger and freight flows into private transportation. In the
case of crowdsourced delivery, the introduction of new vehicles or
infrastructure becomes superfluous, because these approaches rely on already
existing vehicles or passenger trips. The concept is a kind of ride-sharing
service for parcels, where the parcels are picked up and driven to their
destination by registered private individuals for a small fee [27, 28] or
bonus points [29]. Privately operated transportation services, such as taxi
services, should also be mentioned in this context and are themselves already
subject of research [30, 31]. Especially, centrally controlled MoD vehicle
fleets reveal a high potential for the combined transportation of passengers
and freight, as the decision-making (i.e. vehicle assignment and routing) is
bundled. In autonomous mobility on-demand (AMoD) systems, the driver is no
longer a cost- and time-limiting factor, thus enabling new application
scenarios for combined transportation [32]. Apart from the car as a transport
vehicle, the combined transportation of passengers and freight on two-wheeled
vehicles (i.e. bicycles and motorcycles) has also already been studied [33].

The combination of MoD for passengers and parcels is the core research field
of this paper and this research refers to it as ride-parcel-pooling (RPP), in
reference to the widely used term ride-pooling, which describes the joint and
simultaneous transportation of passengers with similar origin-destination
relationships in one vehicle.

###  2.2 Integrated Transport in Mobility-on-Demand Research

Several studies dealt with the control and the efficiency of mobility on-
demand services in recent years. Ride-hailing services can reduce the overall
needed fleet size compared to private vehicles [34] or car-sharing services
[35] because of higher temporal utilization, even without shared rides. To
also reduce vehicle kilometers in a mobility system, rides have to be shared
using ride-pooling services to overcome empty pick-up trips [36, 37].
Nevertheless, it has been shown that the efficiency of pooling, i.e. the
probability of finding shareable trips, heavily depends on fleet size and
especially the overall demand [38, 39]. While the utilization of fleet
vehicles can be increased by pro-actively distributing idle vehicles (i.e. re-
balancing) according to expected demand [40, 41], vehicles remain idle when no
passenger demand is present. The integration of parcel demand can fill these
idle items, however, new fleet control algorithms have to be developed. Most
state-of-the-art ride-pooling assignment algorithms heavily utilize explicit
time constraints on customer pick-ups and in-vehicle travel times enabling
graph-based approaches [42, 43, 44]. Nevertheless, these approaches become
computationally intractable if these time constraints are relaxed, which is
the case for most parcel assignment problems.

The integration of on-demand passenger and freight transportation is a heavily
discussed topic in the expert community. The underlying optimization problem
for the routing of vehicle fleets is a variant of the Vehicle Routing Problem
(VRP). The VRP is one of the most studied optimization problems in
transportation research and has already been addressed in numerous
publications. A very good overview of the different approaches can be found in
the literature reviews by Eksioglu et al. [45] for the general VRP and Kim et
al. [46] for the city VRP. In terms of joint transport of passengers and
freight, Cavallero and Nocera [47] conducted a concept-centric literature
review to classify the different integration concepts.

The present paper focuses on the integration of MoD of passengers and same-day
delivery of freight and classifies the existing literature on the subject
according to the categories: overall integration concept, passenger mobility,
and logistics service characteristics, optimization approach, and, last but
not least, the real-world examination of the results in a case study (Table
1).

This chapter collects the different existing approaches, case studies, and
mathematical models and algorithms dealing with the modeling of integrated
passenger and freight transport in MoD ride-pooling and ride-hailing services.
Mourad et al. [48] summarize models and algorithms for optimizing shared
mobility, which serves as an input for the literature collection in this
chapter and Table 1. In their paper the authors summarize the recent research
activities in the field, including different optimization approaches, to
provide guidelines and give promising directions for future research. In the
following, this paper gives a brief overview of the methodology and the
results of existing research, and the respective contribution to the
scientific discourse.

Ref. |  Author, Year |  Mode | MoD Service | Logistics Service | Optimization |  Case Study
---|---|---|---|---|---|---
|  |  |  hailing |  pooling |  imme-diate |  sche-duled |  static |  dyna-mic |
[31] |  B. Li et al., 2014 |  Taxi |  ✓ |  - |  ✓ |  - |  ✓ |  ✓ |  ✓
[49] |  L. Li et al., 2014 |  Multi |  - |  - |  ✓ |  - |  - |  ✓ |  -
[50] |  Ngoc-Quang et al., 2015 |  Taxi |  ✓ |  - |  ✓ |  - |  - |  ✓ |  ✓
[30] |  C. Chen et al., 2015 |  Taxi |  ✓ |  - |  ✓ |  - |  - |  ✓ |  -
[51] |  Ronald et al., 2016 |  MoD |  ✓ |  - |  ✓ |  - |  - |  ✓ |  ✓
[52] |  Soto Setzke et al., 2017 |  MoD |  ✓ |  - |  - |  ✓ |  - |  ✓ |  ✓
[53] |  C. Chen et al., 2017 |  Taxi |  ✓ |  - |  ✓ |  - |  ✓ |  ✓ |  ✓
[54] |  Kafle et al., 2017 |  Ped., Cycl. |  - |  - |  ✓ |  - |  - |  ✓ |  -
[55] |  Beirigo et al., 2018 |  AMoD |  - |  ✓ |  ✓ |  - |  - |  ✓ |  -
[56] |  Qi et al.,
2018 |  Car |  - |  - |  ✓ |  - |  ✓ |  - |  -
[57] |  Wang et al., 2018 |  Car |  - |  - |  ✓ |  - |  - |  ✓ |  ✓
[58] |  Arslan et al., 2018 |  Car |  ✓ |  - |  ✓ |  - |  - |  ✓ |  -
[59] |  Mourad et al., 2019 |  Multi |  - |  ✓ |  ✓ |  - |  - |  ✓ |  -
[60] |  Najaf Abadi, 2019 |  Taxi |  ✓ |  - |  - |  ✓ |  - |  ✓ |  ✓
[61] |  Y. Chen et al., 2020 |  Taxi |  ✓ |  - |  ✓ |  - |  - |  ✓ |  ✓
[62] |  Manchella et al., 2020 |  Taxi |  - |  ✓ |  ✓ |  - |  - |  ✓ |  ✓
[32] |  Schlenther et al., 2020 |  MoD |  - |  ✓ |  ✓ |  - |  - |  ✓ |  ✓
[63] |  Van der Tholen et al., 2021 |  AMoD |  - |  ✓ |  ✓ |  - |  - |  ✓ |  -
[64] |  Alho et al., 2021 |  MoD |  ✓ |  ✓ |  - |  ✓ |  - |  ✓ |  ✓
[65] |  Fehn et al., 2021 |  MoD |  - |  ✓ |  - |  ✓ |  ✓ |  - |  ✓
[66] |  Meinhardt et al., 2022 |  MoD |  - |  ✓ |  ✓ |  - |  - |  ✓ |  ✓
[67] |  Zhang et al., 2022 |  AMoD |  - |  ✓ |  ✓ |  - |  - |  ✓ |  ✓
Table 1: Existing Models in Literature and assorted research characteristics
(i.e. Transport Mode, Type of MoD Service, Optimization Approach, and Case
Study)

B. Li et al. [49] investigate a Share-a-Ride Problem (SARP) for passengers and
parcels sharing taxis. Therefore, the authors propose a reduced SARP and use
the Freight Insertion Problem (FIP) to insert parcel requests into the vehicle
routes. They evaluate their model with historical taxi data from San Francisco
and performed a numerical study of static and dynamic scenarios. L. Li et al.
[31] research a multi-agent cooperative inter-modal freight transportation
planning approach for multiple inter-modal freight transport operators. In a
simulation study, they show the potential of their cooperative planning
approach. Ngoc-Quang et al. [50] introduce a practical hybrid transportation
model for serving passengers and parcels in the same fleet of taxis. They show
the feasibility and efficacy of their time-dependent model using two different
heuristic algorithms. In the next step, the authors use a real-world data set
of the ’Tokyo-Musen’ taxi company to simulate the joint transport of
passengers and freight. Therefore, they introduce a practical hybrid
transportation model for Tokyo city to handle passengers and parcels in the
same vehicle. The paper adopts the model of Li et al. [31] to a real-life use
case. The simulation results are subsequently analyzed on the total benefit
for the taxi provider, the accumulated fleet distance over the day, and the
number of used taxis and served requests. Chen and Pan [30] propose a taxi
fleet-based crowd-sourcing solution to last-mile delivery for an e-commerce
delivery use case. Their approach relies on a two-phase decision model, first
an offline taxi trajectory mining, and second an online package for routing
and taxi scheduling. Ronald et al. [51] create a simulation of a VRP and a
FIP. They explore the integrated MoD passenger and freight transportation
problem using a simulation of an on-demand transportation scheme. They
investigate three different scenarios: 1) each shop has its delivery vehicles,
2) all shops share delivery vehicles, and 3) a co-modal scheme where
passengers and parcels can share vehicles. The authors show, that their co-
modality approach, using household survey data from Melbourne to generate
demand for passengers and parcel customers, can provide an improved experience
for both, operators and customers and that on-demand co-modality is more
resilient to uneven or unexpected demands, and provides more options for
travel compared to the other two scenarios. Soto Setzke et al. [52] suggest an
algorithm optimizing the assignment of drivers to transport requests, using
transport routes and time constraints as inputs. They evaluate their algorithm
based on a mobility data set from a major German city. Chen et al. [53] try to
exploit taxi trips by creating relays of multiple trips to integrate the
transport of parcels, without degrading the quality of passenger services.
They develop a city-wide parcel delivery system, leveraging a crowd of taxis
in Hangzhou, China. The authors introduce a two-phase framework, which in the
first phase mines historical taxi data offline and in the second phase uses an
online adaptive taxi scheduling algorithm to iteratively find near-optimal
delivery paths. Their results show, that over 85% of parcels can be delivered
within 8 hours, with around 4.2 trans-shipments/relays on average. Kafle et
al. [54] consider cyclists and pedestrians as crowdsource for last- and first-
mile operations. The distributors request deliveries by submitting bids to the
logistics operator. The authors find, that the total truck miles traveled and
the total cost can be reduced compared to pure-truck delivery. Beirigo et al.
[55] model a variation of the People and Freight Integrated Transport (PFIT)
Problem. They find that mixed-purpose fleets perform on average 11% better
than single-purpose fleets. Qi et al. [56] present new logistics planning
models and managerial insights. The proposed model uses open-loop car routes
to assign passenger vehicles to parcels and fulfill the last-mile delivery
job. Their findings suggest that crowd-sourcing shared mobility is not as
scalable as conventional truck-based logistics systems in terms of operating
cost. However, the results also show that reducing the truck fleet size and
exploiting additional operational freedom, like avoiding high-demand areas and
peak hours could be of interest. Wang et al. [57] use a crowd of connected
vehicles to pick-up and deliver parcels, by connecting them with a logistics
provider. In their simulation study, they used real-world car trips and
assigned the parcel trips accordingly. The authors believe that ride-sharing
will be a core service for connected vehicles, which they refer to as ride-
sharing as a service. Arslan et al. [58] consider a service platform for
crowdsourced delivery using existing journeys. The authors propose a rolling
horizon framework and searched for an exact solution to the matching problem.
Their results suggest that ad-hoc drivers have the potential to save up to 37%
of vehicle kilometers compared to traditional delivery systems. Mourad [59]
investigates in his PhD thesis how to synchronize people and freight flows. He
develops a matching algorithm using an Adaptive Large Neighborhood Search
(ALNS) heuristic and tests the approach in several experiments. Najaf Abadi
[60] develops an on-demand dynamic crowd-shipping system and tried to take
advantage of the unused capacities in taxis. In her PhD-thesis she
investigates the effects of an on-demand dynamic crowd-shipping system, using
the publicly available New York City taxi data and freight demand from
structural data. She studies the effects on trip cost, vehicle miles traveled,
and peoples’ travel behavior and finds that the proposed crowd-shipping model
has a substantial positive impact on the average total system-wide vehicle
miles savings for all scenarios, ranging from 47% to 50%. Chen et al. [61]
present a new parcel delivery scheme that takes advantage of multi-hop ride-
sharing. They tackle the assignment problem using a two-phase solution, which
first predicts the passenger orders and then plans the parcel delivery routes
using real-world data from the city of Chengdu. Their results suggest, that
the successful delivery rate may reach 95% on average daytime and is at most
46.9% higher than those of the benchmarks. Manchella et al. [62] develop a
deep reinforcement learning algorithm for joint passengers and goods
transportation using the publicly available New York City taxi data. The
proposed algorithm pools passengers and delivers goods using a multi-hop
transit method. The multi-agent simulations carried out in the paper show,
that the approach achieves 30% higher fleet utilization and 35% higher fuel
efficiency in comparison to 1) model-free approaches where vehicles transport
a combination of passengers and goods without the use of multi-hop transit,
and 2) model-free approaches where vehicles exclusively transport either
passengers or goods. Schlenther et al. [32] propose a methodology to simulate
the behavior of a vehicle fleet serving passengers and freight within an urban
traffic system and evaluate its performance using the simulation environment
MATSim. Their results suggest, that the vehicle miles traveled for freight
purposes increase due to additional access and egress trips. Van der Tholen et
al. [63] present a method to estimate the optimal capacity for the passenger
and parcel compartments of AMoD systems. They aim at creating an optimal
routing schedule between a randomly generated set of pick-up and drop-off
requests of passengers and parcels. Alho et al. [64] investigate the use of
MoD services performing same-day parcel deliveries. Therefore, they evaluate a
cargo-hitchhiking service for e-commerce using MoD vehicles in a simulation-
based approach. Their research aims at testing MoD-based solutions using an
agent- and activity-based simulation platform for the joint transport of
passengers and freight. The authors obtain e-commerce demand carrier data
collected in Singapore and investigate operational scenarios fulfilling the
deliveries with MoD service vehicles on existing passenger flows. The results
of a case study in Singapore indicate that the MoD services have the potential
to fulfill a considerable amount of parcel deliveries and decrease freight
vehicle traffic and total vehicle kilometers traveled without compromising the
quality of MoD for passenger travel. Fehn et al. [65] investigate the combined
transport of passengers and parcels using historical MoD trips and parcel
demand. The authors set up a static optimization problem for the combined
transport of MoD passengers and intra-city parcel shipments. Therefore, the
paper uses real-world individual transportation and parcel data from the city
of Munich and matches the parcel data in a static optimization approach on
pre-computed MoD passenger trips. The results show, that depending on the
simulated scenario about 80% of the distance traveled to provide the logistics
services could be saved, offering a 100% delivery rate. The study also gives
insights into the positive environmental impacts of such an integrated
transportation approach. Meinhardt et al. [66] study the joint transport of
passengers and freight using the Multi-Agent transport Simulation (MATSim)
software. The authors apply real-world data from Berlin in the simulation and
showed, that when assuming a relatively large vehicle fleet, passenger waiting
time statistics barely change. Furthermore, a rough cost analysis suggests a
large saving potential when using AMoD vehicles instead of a privately owned
vehicle fleet. Zhang et al. [67] propose an AMoD system performing joint ride-
sharing and parcel delivery. The distributed approach, setting up a mixed-
integer linear programming problem and solving it with a Lagrangian dual
decomposition method, shows near-optimal solutions in reduced computation
time. Nevertheless, due to the complexity of the problem, only 10 passenger
and 5 parcel requests could be modeled in reasonable computational time.

###  2.3 Research Gap and Contribution

As the literature review reveals, the range of possible combinations of
passengers and freight is very wide. It comprises different transportation
modes, like rail-based public transportation services or individual
crowdsourced approaches to the idea of central assignment of passengers and
parcels to a vehicle fleet, like in the case of MoD transportation services.
In this context, the solution approaches in the MoD area emerge as very
promising due to their high flexibility in terms of spatio-temporal network
coverage and relatively low capacity utilization when no passenger demand is
present.

Most recent studies focused either on temporally separated integration of
passenger and freight or the integration into ride-hailing service, where
passengers do not share the rides. Furthermore, they assume time windows for
the pick-up and drop-off of parcels, which simplifies the assignment, but does
not exploit the full integration potential of existing passenger trips. This
study focuses on the integration of freight transport into the operation of an
MoD ride-pooling service. Thereby, special focus is put on the simultaneous
transport of passengers and freight (i.e. passengers and parcels can be on
board the same vehicle). A dynamic simulation environment is proposed where
decisions to serve customers and or parcels have to be made online to model a
realistic setting. As can be seen in Table 1, the authors are only aware of
the approach of Alho et al. [64] to meet similar contributions. Nevertheless,
a different approach to integration is investigated in this study. While
recent studies focus on exploiting idle times of MoD fleets explicitly for
logistic services and/or modeled the logistics service as an as-soon-as-
possible delivery service (i.e. employing strict time constraints on parcel
pick-up and delivery), the goal of this study is to actively integrate parcel
pick-up and drop-off into vehicle routes. No explicit time constraints are
enforced on parcel pick-up and delivery, but the goal is the insertion into
vehicle schedules resulting from the underlying MoD service to minimize the
additional driven fleet kilometers for the integrated logistic service. To
solve the assignment problem of which parcel to assign when into the
dynamically evolving vehicle schedules, three heuristic assignment approaches
are proposed. The efficiency of this integration and the developed assignment
approaches is evaluated regarding the impacts on operator, customers and
traffic effects, based on real-world logistics demand, within a case study for
Munich, Germany.

##  3 Methodology

The methodology of this study is described in the following three sections: 1)
the different scenarios examined in this paper to integrate the parcel
delivery into the MoD service are defined, 2) the simulation framework and the
proposed algorithms to control the fleet of vehicles to serve passenger, as
well as parcel demand, is introduced and 3) the simulation inputs based on
real-world logistics and travel demand data for a case study in Munich,
Germany is described.

###  3.1 Service Definition and Scenarios of Integration

Figure 2: Illustration of the RPP Scenarios with Urban Logistics Depot

This research considers a Ride-Parcel-Pooling (RPP) service to investigate the
integrated transport of passengers and freight within the same MoD vehicle
fleet. The service consists of a MoD ride-pooling vehicle fleet and a central
operator for decision-making (i.e. vehicle assignment and routing). In the
envisioned RPP service, the transport of passengers has priority over parcel
transport requests, as the service for passengers should not deteriorate. The
service assumes that the parcels are transported from urban logistics hubs to
the customers. It is assumed that a pick-up or drop-off of the parcels at the
customer’s premises is possible at any time, which in reality could be
realized by small parcel lockers. Therefore, no explicit business hours are
modeled for the logistics service. Nevertheless, parcel pick-ups and
deliveries are integrated into passenger trips, therefore most parcel pick-ups
and deliveries will occur during daytime when most passenger demand is
present. The characteristics of the parcels in size and weight are assumed to
be boxes, which can be carried by one person and fit into the trunk of a
conventional passenger vehicle. The driving comfort of the passengers does not
deteriorate while parcels are on board the vehicle, as it is assumed that
parcels are transported spatially separated, in the trunks of the vehicles.
Furthermore, the service performs a typical logistics service and does not
guarantee a certain delivery period during the day. The goal of the operator
is to provide an additional service without compromising too much on the
additional vehicle kilometers covered by the fleet. It also allows the fleet
operator to achieve higher utilization and occupancy rates for the vehicle
fleet, and to keep the transport service running even during periods of low
passenger demand, which in turn could lead to higher customer satisfaction.
Additionally, the RPP service could contribute to the goal of the so-called
’livable city’, as two vehicle fleets (i.e. logistics fleet and ODM fleet)
become one and travel kilometers can be saved by pooling passengers and
parcels.

To be able to evaluate the RPP service, three scenarios are introduced.
Scenario No. 1 displays the current Status Quo, where passenger and parcel
requests are served by two independent vehicle fleets, one specialized in a
ride-pooling MoD service, and the other on a typical urban pick-up and
delivery logistics use case. In Scenario No. 2, denoted by Moderate RPP
Integration, the logistics service is integrated into the MoD service and
both, passenger and parcel requests, are served by the same MoD vehicle fleet.
However, there is the requirement, that no parcels are collected or delivered
during a passenger trip so that passengers do not experience the parcel pick-
up and drop-off (PUDO) or take detours due to the additional parcel transport.
Scenario No. 3, Full RPP Integration loosens this requirement and thus allows
the collection or delivery of a parcel during a passenger trip. In this
scenario, a passenger’s journey may be extended or a detour may have to be
accepted due to the additional parcel transport. Figure 2 illustrates the
respective simulation scenarios and gives a schematic overview of potential
assignments of requests to vehicles and the resulting routes. It is important
to note that a passenger request always consists of an origin-destination (OD)
pair and that a parcel request always starts at a parcel depot. Thus, for
parcel transport, either origin or destination consists of a depot location
and the respective other point corresponds to the pickup or delivery location
of a logistics end customer.

###  3.2 Agent-based Simulation and Fleet Control Strategies

To model the integration of logistics in an on-demand ride-pooling service,
the agent-based simulation framework ’FleetPy’ [68] is extended for this use
case. The framework consists of four main agents: 1) customers requesting
trips from the fleet operator; 2) parcels that need to be transported by the
service; 3) a fleet operator offering the service and assigning schedules for
its fleet of vehicles to pick-up and drop-off customers and parcels, and 4)
vehicles traveling along these assigned schedules within a street network and
fulfilling the corresponding pick-up and drop-off tasks. All symbols used in
the following can be found in Table 2.

Symbol | Description
---|---
$G$ | Street network, consisting of nodes ($N$) and edges ($E$)
$N$ | Network nodes
$E$ | Network edges
$r_{i}^{c}$ | Request of customer $i$
$o_{i}^{c}$, $d_{i}^{c}$, $t_{i}^{c}$ | Origin, destination, and request time of MoD customer $i$
$r_{i}^{p}$ | Request of parcel $i$
$o_{i}^{p}$, $d_{i}^{p}$ | Origin, and destination of parcel $i$
$v$ | MoD vehicle
$V$ | MoD vehicle fleet
$c_{v}^{c}$, $c_{v}^{p}$ | Vehicle capacity for customers, and parcels
$t_{max}^{wait}$, $t_{max}^{travel}$ | Maximum waiting, and travel time for a customer
${\Delta}$ | Detour factor
$tt_{i}^{direct}$ | Direct travel time for request $i$
$\psi_{k}$ | Feasible vehicle schedule
$R_{\psi}$, $P_{\psi}$ | Set of customer and parcel requests
$t_{b}$ | Time needed for boarding and alighting
$\phi(\psi_{k})$ | Objective function for the rating of vehicle schedule $\psi_{k}$
$d(\psi_{k})$ | Distance to drive to complete vehicle schedule $\psi_{k}$
$P$ | Assignment reward to prioritize passenger transport
$T_{repo}$ | Period of MoD fleet re-balancing
$T_{p}^{max}$ | Time when remaining parcels in the vehicles are delivered
$\tau_{th}$ | Parcel detour threshold parameter for assignment
Table 2: List of symbols for the methodology section

The street network $G=(N,E)$ consists of nodes $N$ and edges $E$ connecting
these nodes. Each edge is associated with a travel time and a distance. A
customer request $r_{i}^{c}=(o_{i}^{c},d_{i}^{c},t_{i}^{c})$ is represented by
its origin location $o_{i}^{c}\in N$, the destination location $d_{i}^{c}\in
N$ and the request time $t_{i}^{c}$. In this study, it is assumed that the
parcel delivery request was submitted at least the day before and is therefore
known in advance for the whole simulation period. Thus, a parcel request
$r_{i}^{p}=(o_{i}^{p},d_{i}^{p})$ is only represented by the corresponding
origin (pick-up) location $o_{i}^{p}$ and destination (drop-off) location
$d_{i}^{p}$.

The goal of the fleet operator is to assign schedules to its vehicles $v\in V$
to serve customer and parcel requests. A schedule $\psi$ describes the
sequence of pick-up and drop-off stops assigned to a vehicle. A schedule is
considered feasible, if:

  * •

the drop-off succeeds the pick-up for each request.

  * •

at no point during the schedule the maximum passenger capacity $c_{v}^{c}$ and
maximum parcel capacity $c_{v}^{p}$ is exceeded by on board passengers and
parcels, respectively.

  * •

for each customer request $r_{i}^{c}$, the waiting time (time between request
time $t_{i}^{c}$ and expected pick-up) does not exceed $t_{max}^{wait}$.

  * •

the in-vehicle time of each customer $r_{i}^{c}$ does not exceed
$t_{max}^{travel}=(1+\Delta)tt_{i}^{direct}$, with the direct travel time from
its origin to destination $tt_{i}^{direct}$ and a detour factor $\Delta$.

In case of modelling the Moderate RPP Integration, an additional constraint is
added for a schedule to be feasible:

  * •

while passengers are on board the vehicle, no stop is allowed where only
parcels are picked up or dropped off; the transport of parcels and a stop for
other passengers, however, is possible.

A feasible vehicle schedule $\psi_{k}(v;R_{\psi},P_{\psi})$ is defined as the
$k$-th feasible permutation of stops of vehicle $v$ to serve the set of
customer requests $R_{\psi}$ and parcel requests $P_{\psi}$ within the
schedule. Stops are associated with the location, boarding and alighting
customers or parcels and the time needed for boarding or alighting $t_{b}$. In
between stops, vehicles travel along the fastest route in the network $G$.

Schedules are rated by an objective function
$\phi(\psi_{k}(v;R_{\psi},P_{\psi}))$. The goal of the fleet operator is to
assign schedules minimizing the aggregated objective function for all of its
vehicles. In this study, the objective function is defined as:

$\phi(\psi_{k}(v;R_{\psi},P_{\psi}))=d(\psi_{k}(v;R_{\psi},P_{\psi}))-P(|R_{\psi}|+|P_{\psi}|).$
(1)

$d(\psi_{k}(v;R_{\psi},P_{\psi}))$ refers to the distance to drive to complete
the schedule. $|R_{\psi}|$ and $|P_{\psi}|$ are the number of customers and
parcels to be served with the schedule, respectively. $P$ is a large
assignment reward to prioritize serving customers and parcels over minimizing
the driven distance.

The high-level simulation flow is shown in Figure 3. The demand for the
simulation is split into a passenger request set and a parcel request set. It
is assumed that the operator has access to all parcel requests for the whole
simulation period and can freely decide when to serve which parcel. Passenger
requests, on the other hand, are revealed to the fleet operator dynamically
during the course of the simulation. Within each time step, the operator first
tries to accommodate new customer requests by inserting them into current
vehicle schedules. With a given frequency, namely every $T_{repo}$, re-
balancing trips to distribute idle vehicles according to expected demand are
computed. Based on new vehicle assignments, the decision is made to serve
specific parcels. Lastly, vehicle movements and boarding processes are
performed. Details on customer insertion, re-balancing, and the decision
process to serve parcels are provided in the following subsections. Because
the applied control strategy of ride-pooling fleets is explored in various
other research, the focus of this chapter is on the description of the
methodology to integrate parcel pick-up and deliveries into the existing ride-
pooling control strategy.

Figure 3: Flowchart of the simulation framework with different proposed parcel
assignment strategies.

####  3.2.1 Passenger Assignment and Re-Balancing

In order to assign new customer requests to vehicles and corresponding
schedules, a simple insertion heuristic is applied in this study. With the
currently assigned schedule $\psi_{k}(v;R_{\psi},P_{\psi})$ of vehicle $v$,
the pick-up and drop-off processes for a new customer request $r_{i}^{c}$ are
inserted at all possible positions within the currently existing sequence of
stops (drop-off must follow the pick-up stop). The new set of feasible
schedules can be enumerated again and results in schedules
$\psi_{\tilde{k}}(v;R_{\psi}\cup\\{r_{i}^{c}\\},P_{\psi})$ if a feasible
insertion can be found. The selected vehicle $v_{a}$ and schedule $\psi_{l}$
for serving the customer request is then determined by:

$v_{a},\psi_{l}=\mathop{\mathrm{argmin}}_{v,\psi_{\tilde{k}}}\phi(\psi_{\tilde{k}}(v;R_{\psi}\cup\\{r_{i}^{c}\\},P_{\psi}))-\phi(\psi_{k}(v;R_{\psi},P_{\psi}))~{}~{}~{}\forall
v,\psi_{\tilde{k}}~{},$ (2)

i.e. the vehicle schedule is assigned that decreases the change in objective
value the most when the new request is served. Each time a new customer
requests a trip, the schedules are updated iteratively. If no solution is
found by the insertion heuristic, i.e. no vehicle can serve the customer
within the given time constraints, the customer leaves the system unserved.

While more sophisticated algorithms to solve the ride-pooling assignment can
be found in the literature (e.g. [43, 69]), using this simple insertion
heuristic has the advantage that the assignment of customer and parcel
requests can be decoupled in different decision processes reducing the overall
complexity and thereby computational time.

To distribute idle vehicles dynamically according to expected demand in the
network, a re-balancing algorithm is applied in periodical intervals of
$T_{repo}$. For each zone in the network expected demand is determined and
idle vehicles estimated. A parameter-free re-balancing strategy based on [70]
is applied to distribute idle vehicles by solving a minimum transport problem.

####  3.2.2 Parcel Assignment

Because no explicit time constraints for parcel pick-up and delivery are
imposed in this study, different assignment strategies (in comparison to the
passenger assignment) were developed to assign parcels to the vehicle
schedules. While parcels could be served in times of low customer demand to
increase temporal vehicle utilization, the goal of this study is to evaluate
if parcel pick-up and delivery can be performed when vehicles pass by
occasionally to minimize the need for additional driven fleet kilometers.
Three different parcel assignment strategies are developed which will be
described in the following.

##### Combined Decoupled Parcel Assignment (CDPA)

In the first assignment strategy, both the origin as well as the destination
of a parcel are assigned at once. An assignment of a parcel request
$r_{i}^{p}$ to vehicle $v$ is only made if the detour to add the pick-up and
the drop-off into the currently assigned schedule
$\psi_{k}(v;R_{\psi},P_{\psi})$ is small compared to the distance of the
direct parcel route $d(o_{i}^{p},d_{i}^{p})$. The detour is measured by
comparing the distance that has to be driven to complete the schedule,
including the new parcel request
$\psi_{l}(v;R_{\psi},P_{\psi}\cup\\{r_{i}^{p}\\})$, with the distance not
considering the parcel, i.e. $\psi_{k}(v;R_{\psi},P_{\psi}))$. A possible
assignment is identified if:

$d(\psi_{l}(v;R_{\psi},P_{\psi}\cup\\{r_{i}^{p}\\}))-d((\psi_{k}(v;R_{\psi},P_{\psi})))<(1-\tau_{th})d(o_{i}^{p},d_{i}^{p}),$
(3)

with a threshold parameter $\tau_{th}\in\\{0,1\\}$ indicating the amount of
detour relative to a direct route to be accepted to serve the parcel
$r_{i}^{p}$. If $\tau_{th}$ approaches $1$ no detour is accepted to serve the
parcel.
Algorithm 1 sketches the procedure of assigning parcels with the CDPA
strategy. Let $P_{u}$ be the set of currently unassigned parcels and $V^{ca}$
the set of vehicles with schedules, that have been updated in the current
simulation time step. The method
insert$(\psi_{k}(v;R_{\psi},P_{\psi}),r_{i}^{p})$ returns the best feasible
insertion of $r_{i}^{p}$ in $\psi_{k}(v;R_{\psi},P_{\psi})$ with respect to
the objective function $\phi$. For each unassigned parcel, an insertion is
checked for each vehicle with an updated schedule (an insertion in other
vehicles would have already been checked in previous time steps). If a new
schedule fulfills the constraint of Equation 3, a candidate insertion is
found. In the end, the candidate schedule with the minimum objective value is
picked to be assigned. If no candidate is found, the parcel is tried to be
assigned again at a later time step.

Algorithm 1 CDPA Insertion

for all $r_{i}^{p}\in P_{u}$ do

$\psi_{best}=$ None

$v_{best}=$ None

for all $v\in V^{ca}$ do

$\psi_{\tilde{k}}(v;R_{\psi},P_{\psi}\cup\\{r_{i}^{p}\\})=$
insert$(\psi_{k}(v;R_{\psi},P_{\psi}),r_{i}^{p})$

if
$d(\psi_{\tilde{k}}(v;R_{\psi},P_{\psi}\cup\\{r_{i}^{p}\\}))-d(\psi_{k}(v;R_{\psi},P_{\psi}))<(1-\tau_{th})d(o_{i}^{p},d_{i}^{p})$
then

if
$\phi(\psi_{\tilde{k}}(v;R_{\psi},P_{\psi}\cup\\{r_{i}^{p}\\})<\phi(\psi_{best})$
then

$\psi_{best}\leftarrow\psi_{\tilde{k}}(v;R_{\psi},P_{\psi}\cup\\{r_{i}^{p}\\})$

$v_{best}\leftarrow v$

end if

end if

end for

if $v_{best}\neq None$ then

assignSchedule$(v_{best},\psi_{best})$

$P_{u}\leftarrow P_{u}\setminus\\{r_{i}^{p}\\}$

end if

end for

Algorithm 2 S-PA Origin Insertion

for all $r_{i}^{p}\in P_{u}$ do

$\psi_{best}=$ None

$v_{best}=$ None

for all $v\in V^{ca}$ do

$\psi_{\tilde{k}}(v;R_{\psi},P_{\psi}\cup\\{o_{i}^{p}\\})=$
insertOrigin$(\psi_{k}(v;R_{\psi},P_{\psi}),r_{i}^{p})$

if
$d(\psi_{\tilde{k}}(v;R_{\psi},P_{\psi}\cup\\{o_{i}^{p}\\}))-d(\psi_{k}(v;R_{\psi},P_{\psi}))<(1-\tau_{th})d(o_{i}^{p},d_{i}^{p})/2$
then

if
$\phi(\psi_{\tilde{k}}(v;R_{\psi},P_{\psi}\cup\\{o_{i}^{p}\\})<\phi(\psi_{best})$
then

$\psi_{best}\leftarrow\psi_{\tilde{k}}(v;R_{\psi},P_{\psi}\cup\\{r_{i}^{p}\\})$

$v_{best}\leftarrow v$

end if

end if

end for

if $v_{best}\neq$ None then

assignSchedule$(v_{best},\psi_{best})$

$P_{u}\leftarrow P_{u}\setminus\\{r_{i}^{p}\\}$

$P_{v_{best}}^{a}\leftarrow P_{v_{best}}^{a}\cup\\{r_{i}^{p}\\}$

end if

end for

Algorithm 3 SDPA Destination Insertion

for all $v\in V^{ca}$ do

$\psi_{best}=$ None

$r_{best}=$ None

for all $r_{i}^{p}\in P_{v}^{a}$ do

$\psi_{\tilde{k}}(v;R_{\psi},P_{\psi}\cup\\{r_{i}^{p}\\})=$
insertDestination$(\psi_{k}(v;R_{\psi},P_{\psi}\cup\\{o_{i}^{p}\\}),r_{i}^{p})$

if
$d(\psi_{\tilde{k}}(v;R_{\psi},P_{\psi}\cup\\{r_{i}^{p}\\}))-d(\psi_{k}(v;R_{\psi},P_{\psi}\cup\\{o_{i}^{p}\\}))<(1-\tau_{th})d(o_{i}^{p},d_{i}^{p})/2$
then

if
$\phi(\psi_{\tilde{k}}(v;R_{\psi},P_{\psi}\cup\\{r_{i}^{p}\\}))<\phi(\psi_{best})$
then

$\psi_{best}\leftarrow\psi_{\tilde{k}}(v;R_{\psi},P_{\psi}\cup\\{r_{i}^{p}\\})$

$r_{best}\leftarrow r_{i}^{p}$

end if

end if

end for

if $r_{best}\neq$ None then

assignSchedule$(v,\psi_{best})$

$P_{v}^{a}\leftarrow P_{v}^{a}\setminus r_{i}^{p}$

end if

end for

##### Subsequent Decoupled Parcel Assignment (SDPA)

The idea of the second assignment strategy is that – because no time
constraints are imposed on the parcel drop-off – the decision on when to drop-
off the parcel does not have to be made when the decision of the parcel pick-
up is made. This assumption can be made under the condition that the loaded
parcels do not significantly affect the energy consumption of the vehicles.
Therefore, the decision to pick-up a parcel is separated from the decision to
drop-off a parcel. The decision to pick-up a parcel is taken similar to the
CDPA strategy and summarized in Algorithm 2. The differences to Algorithm 1
can be summarized as follows: firstly, only the insertion of the origin
$o_{i}^{p}$ is tested for parcel request $r_{i}^{p}$ by the method
insertOrigin, secondly, the possible assignment is identified if:

$d(\psi_{l}(v;R_{\psi},P_{\psi}\cup\\{o_{i}^{p}\\}))-d((\psi_{k}(v;R_{\psi},P_{\psi})))<(1-\tau_{th})d(o_{i}^{p},d_{i}^{p})/2.$
(4)

Note that the threshold is divided by $2$ (compared to CDPA) to account for an
even split of the overall detour for parcel pick-up and drop-off. If a
feasible insertion of the origin of $r_{i}^{p}$ is assigned to vehicle $v$,
$r_{i}^{p}$ is added to the set $P_{v}^{a}$ to keep track of assigned parcel
pick-ups for each vehicle.
A similar approach is chosen to assign the parcel drop-off in any later
simulation time step and is sketched in Algorithm 3. For all vehicles with
scheduled pick-ups or on-board parcels, the insertions of drop-offs for all
parcels $r_{i}^{p}\in P_{v}^{a}$ is checked. A possible assignment is
considered if:

$d(\psi_{l}(v;R_{\psi},P_{\psi}\cup\\{r_{i}^{p}\\}))-d((\psi_{k}(v;R_{\psi},P_{\psi}\cup\\{o_{i}^{p}\\})))<(1-\tau_{th})d(o_{i}^{p},d_{i}^{p})/2.$
(5)

Thereby, $\psi_{l}(v;R_{\psi},P_{\psi}\cup\\{r_{i}^{p}\\})$ refers to the
schedule including origin $o_{i}^{p}$ and destination $d_{i}^{p}$ of parcel
$r_{i}^{p}$.
In the simulation, at first possible insertions of parcel drop-offs are tested
for vehicles with an updated schedule, then possible pick-up insertions are
created for parcel pick-ups analogously to the comprehensive parcel assignment
strategy. This strategy has the downside that it is not guaranteed to find a
drop-off for each parcel until the end of the simulation. However, parcels
should not remain on board the vehicles until the end. Hence, when a certain
simulation time $T_{p}^{max}$ is exceeded, all remaining on-board parcels are
scheduled to be dropped off by iteratively inserting them into the current
vehicle schedule.

Algorithm 4 SCPA Destination Insertion

for all $r_{i}^{c}\in R_{t}^{new}$ do

$\psi_{best,u}=$ None

$v_{best,u}=$ None

for all $v\in V$ do

$\psi_{\tilde{k}}(v;R_{\psi}\cup\\{r_{i}^{c}\\},P_{\psi})=$
insert$(\psi_{k}(v;R_{\psi},P_{\psi}),r_{i}^{c})$

if
$\phi(\psi_{\tilde{k}}(v;R_{\psi}\cup\\{r_{i}^{c}\\},P_{\psi}))<\phi(v_{best,u})$
then

$\psi_{best,u}\leftarrow\psi_{\tilde{k}}(v;R_{\psi}\cup\\{r_{i}^{c}\\},P_{\psi})$

$v_{best,u}\leftarrow v$

end if

end for

$\psi_{best}=\psi_{best,u}$

$v_{best}=v_{best,u}$

$r_{best}=$ None

for all $v\in V$ do

$\psi_{\tilde{k}}(v;R_{\psi}\cup\\{r_{i}^{c}\\},P_{\psi})=$
insert$(\psi_{k}(v;R_{\psi},P_{\psi}),r_{i}^{c})$

for all $r_{i}^{p}\in P_{v}^{a}$ do

$\psi_{l}(v;R_{\psi}\cup\\{r_{i}^{c}\\},P_{\psi}\cup\\{r_{i}^{p}\\})=$
insertDestination$(\psi_{\tilde{k}}(v;R_{\psi}\cup\\{r_{i}^{c}\\},P_{\psi}),r_{i}^{p})$

if
$d(\psi_{l}(v;R_{\psi}\cup\\{r_{i}^{c}\\},P_{\psi}\cup\\{r_{i}^{p}\\}))-d(\psi_{best,u})<(1-\tau_{th})d(o_{i}^{p},d_{i}^{p})/2$
then

if
$\phi(\psi_{l}(v;R_{\psi}\cup\\{r_{i}^{c}\\},P_{\psi}\cup\\{r_{i}^{p}\\}))<\phi(\psi_{best})$
then

$\psi_{best}\leftarrow\psi_{l}(v;R_{\psi},P_{\psi}\cup\\{r_{i}^{p}\\})$

$r_{best}\leftarrow r_{i}^{p}$

$v_{best}\leftarrow v$

end if

end if

end for

end for

if $v_{best}\neq$ None then

assignSchedule$(v,\psi_{best})$

if $r_{best}\neq$ None then

$P_{v_{best}}^{a}\leftarrow P_{v_{best}}^{a}\setminus\\{r_{best}\\}$

end if

end if

end for

##### Subsequent Coupled Parcel Assignment (SCPA)

In the last assignment strategy, the decision of assigning the drop-off of a
parcel is again made independently from the decision of assigning the pick-up.
While the decision of assigning the pick-up remains the same compared to the
subsequent independent parcel assignment strategy (Algorithm 2), the decision
of assigning the drop-off is coupled to the passenger assignment. The idea is
to assign passengers and thereby new vehicle schedules passing by the drop-off
location of on-board parcels. By that, the possible solution space for parcel
drop-off assignments increases. To this end, the formulation of the passenger
assignment is revisited.
Let $R_{t}^{new}$ be the set of new customer requests in time step $t$. In the
first step, the best possible solution for just serving a new customer request
$r_{i}^{c}\in R_{t}^{new}$ is calculated using the same insertion heuristic as
for the passenger assignment. The resulting schedule
$\psi_{l}(v_{a})=\psi_{l}(v;R_{\psi}\cup\\{r_{i}^{c}\\},P_{\psi})$ is used as
a benchmark for the decision to assign a parcel drop-off. In a second step,
instead of inserting a parcel drop-off into the overall best solution
$\psi_{l}(v_{a})$, an insertion of each on-board parcel requests $r_{i}^{p}$
is tried for feasible schedules in combination with the new request
$r_{i}^{c}$, resulting in the schedules
$\psi_{\tilde{l}}(v;R_{\psi}\cup\\{r_{i}^{c}\\},P_{\psi}\cup\\{r_{i}^{p}\\})\forall
v\in V_{r_{i}^{c}}$. A possible assignment of the parcel is found if:

$d(\psi_{\tilde{l}}(v;R_{\psi}\cup\\{r_{i}^{c}\\},P_{\psi}\cup\\{r_{i}^{p}\\}))-d(\psi_{l}(v_{a};R_{\psi}\cup\\{r_{i}^{c}\\},P_{\psi}))<(1-\tau_{th})d(o_{i}^{p},d_{i}^{p})/2,$
(6)

i.e. the driven distance of the best possible solution without parcel delivery
is only increased by at most a threshold factor compared to the direct
distance of the inserted parcels. If multiple of these options exist, the
vehicle schedule minimizing the objective $\phi$ is assigned. If none of these
options exist, only the best schedule for serving the customer is assigned.
The corresponding logic is sketched in Algorithm 4. For reasons of clarity and
comprehensibility, not the computationally most efficient version of the
algorithm is depicted in Algorithm 4. For example, all solutions from the
first customer insertion can be stored in a list, which makes the re-
computation of the insertion in the second loop over vehicles redundant.

###  3.3 Case Study for Munich, Germany

Figure 4: Street network, operating area and depots for the modelled RPP
service in the case study of Munich, Germany.

The proposed framework is applied and evaluated for a case study in Munich,
Germany. The considered service area of the MoD operator extends almost to the
freeway ring, which surrounds the city center. The street network $G=(N,E)$
with edge travel times for each hour of a usual working day is extracted from
a calibrated microscopic traffic simulation described in [71]. The street
network and operating area are shown in Figure 4.

Passenger demand for the MoD service is created by sampling from private
vehicle trip OD-matrices, extracted from the same microscopic traffic
simulation. 24 matrices for each hour of the day are available, which contain
around one million trips starting and ending within the service area. Poisson
processes are used to sample requests for the MoD service with Poisson rates
defined by the corresponding OD-entries times a penetration factor. In this
study, a penetration of 5% is applied, which can be interpreted as a MoD
service replacing 5% of the private vehicle trips within the operating area
resulting in approximately 50,000 trips per day. Origin and destination nodes
for the sampled requests are matched randomly onto intersection nodes within
associated zones defined by the OD-matrices. Using different random seeds for
the sampling process, three different sets of requests are created and used
for the simulations.

One month of real-world parcel shipment data of a local logistics provider was
available to create parcel demand for the RPP service. The data includes the
date of delivery, the local depot the parcel has been delivered from, and a
destination address. In the first step, the destination address is converted
into coordinates using the open-source geo-coding API ’Nominatim’ relying on
publicly available OpenStreetMap data. All deliveries outside of the operating
area of the MoD service are removed resulting in 56,000 parcels within the
operating area. Using the coordinates, parcel delivery destinations are
matched onto the nearest intersection node in the network. Maximally
$c_{v}^{p}$ parcels with same origin-destination relation are aggregated into
the same parcel request. Because no information about the size of the parcels
is given in the data, the size of a parcel request is set by the number of
aggregated parcels. All parcels are shipped from two depots, both positioned
outside of the MoD operating area (in the north and east of Munich). It is
assumed that if a RPP service as represented in this study is introduced,
corresponding depots have to be implemented within the operating area.
Therefore, two new depots in the northern and eastern part of the city are
defined and shown in Figure 4. Parcels that are shipped from the original
northern (eastern) depot are assumed that they have to be delivered from the
introduced inner-city northern (eastern) depot. One goal of the study is to
observe the system boundaries, i.e. which amount of parcel demand can be
served with given passenger demand. Therefore, the parcel demand data for the
whole month is used as input for the simulations. To be able to vary the ratio
between passenger and parcel demand is sub-sampled to shares of 1% to 50% of
the overall parcel demand. In the base scenario, a 10% sub-sample of the
parcel demand is used resulting in a ratio of approximately 1 parcel to 10
passenger requests to represent a service with a priority on serving passenger
demand. Analogously to the passenger demand, three different sets of parcel
requests are created using different random seeds within the sub-sampling
process.

Contrary to the simulations modeling the RPP service, the Status Quo is
modeled with two independent vehicle fleets serving as a baseline to evaluate
the efficiency of integrating parcel delivery into the MoD service. The first
vehicle fleet corresponds to the MoD service without delivering any parcels.
Hereby, the described simulations are conducted without any parcel demand. The
second fleet corresponds to the pure logistic service. Hereby, vehicles are
placed initially at each of the two depots. Parcels for the corresponding
demand scenario are inserted iteratively into their schedules, minimizing the
driven distance including their return to the depot at the end of the route.
The aggregate driven distance within these schedules is used to approximate
the fleet vehicle kilometer by the logistic service.

Within the simulation, the service parameters describing customer maximum
waiting time $t_{max}^{wait}$ is set to $10$ min while the maximum detour
factor $\Delta$ is set to $40$%. The fleet is operated with a capacity of
$c_{v}^{c}=4$ passengers and $c_{v}^{p}=8$ parcels. In the Status Quo
scenario, it is assumed that the logistics provider operates trucks with a
capacity of 100 parcels [72]. Re-balancing trips are performed every
$T_{repo}=15$ min and based on forecasts according to the zones and values of
the corresponding entries in the OD-matrices times the penetration factor of
5%. The fleet size of the RPP operator for the following simulations is
determined by first performing multiple simulations without parcel demand and
varying fleet size. Finally, a fleet size of 600 vehicles is chosen that
allows a service rate of approximately 90% served customers.

First simulations are performed for all parcel assignment strategies (CDPA,
SCPA, SDPA) as well as for Full RPP Integration and Moderate RPP Integration.
Within these simulations, the influence of the assignment threshold parameter
$\tau_{th}$ on various service Key-Performance-Indicators (KPIs) is evaluated.
In a second step, simulations with varying parcel demand penetration ranging
from $0$% to $50$% of the overall parcel demand data set are performed while
keeping $\tau_{th}$ constant to evaluate the number of parcels that can be
accommodated by the RPP service keeping the passenger demand fixed. Within the
SDPA and SCPA strategy remaining on board parcels are actively delivered after
$T_{p}^{max}=10$pm.

##  4 Results

Results of the simulation from the presented case study for Munich, Germany
are presented in the following. In the upcoming two subsections influences on
customer KPIs and operator KPIs are evaluated based on the different proposed
RPP service integration and parcel assignment strategies with a varying
threshold parameter $\tau_{th}$. In the third subsection, effects resulting
from different parcel demand levels are evaluated and compared to the Status
Quo.

###  4.1 Influence on Customer KPIs

A crucial question for the success of the proposed RPP service will be,
whether the ride-pooling fleet can still ensure sufficient service quality for
passengers despite the additional transport of parcels. The attitude in this
paper is that logistics services are subordinated to passenger requests. In
this context, for the scenario Moderate RPP Integration the pick-up or
delivery of parcels is only allowed while no passengers are on board, while
this constraint is lifted for the scenario Full RPP Integration. Nevertheless,
time-constraints regarding passenger pick-up and maximum detour, described in
the beginning of this chapter, have to be fulfilled in either scenario
ensuring a certain quality of service.

The quality of the mobility service from the customer’s perspective is
evaluated by average customer waiting and travel times. The simulation results
show, that waiting and travel times of the customers are rarely influenced by
the integration of freight transport into the MoD service. Figure 5(a) shows,
that the customer’s waiting times for all simulation scenarios and the
different assignment thresholds ($\tau_{th}$) are maximally increased by 2%.
Looking closer at the blue lines, which indicate the CDPA strategy for the
full (solid line) and moderate (dashed line) scenario, one can observe that
increasing values for $\tau_{th}$ tend to result in lower waiting times, as
the allowed detour for pick-up and drop-off of parcels becomes smaller. In the
cases of the SCPA and SDPA strategies, the trend seems to be less compliant.
The influences on customer’s travel times are shown in Figure 5(b). It becomes
apparent, that similar to the waiting times, the travel times are also only
marginally influenced by the additional transport of freight in the MoD
system. Looking at the blue lines for the CDPA strategy the increase in travel
time is constantly between 0.1% and 0.5% for the investigated assignment
thresholds ($\tau_{th}$). Taking into account the SCPA and SDPA strategies it
becomes clear that especially for small assignment thresholds a considerable
decrease in travel times can be observed.

(a) Waiting Time

(b) Travel Time

Figure 5: Impact of threshold parameter on waiting time and travel time.

Figure 6(a) and Figure 6(b) show the absolute waiting and travel time
distribution of the customers for the different RPP scenarios and the
assignment strategies. Generally, the customer assignment tends to insert new
passengers with waiting times close to the maximum allowed waiting time of
$10$ min. It once again becomes clear, that the difference in waiting and
travel times is relatively small comparing the Status Quo to the RPP
scenarios. This means that RPP has only little negative impact on the service
quality of the represented MoD provider. Looking at Figure 6(c) and Figure
6(d) the relative changes compared to the base scenario (Status Quo) of the
MoD ride-pooling service in terms of waiting and travel time become easier to
identify. Especially, the ratio of passengers experiencing low waiting times
(up to 3 minutes), resulting from preceding parcel pick-ups or drop-offs,
tends to decrease. This is especially the case for the moderate integration
because parcel insertion can mainly be done within passenger approaches of the
vehicles, while no customer is on board yet. The decrease of passengers
experiencing waiting times close to the maximum of $10$ min might be an effect
of longer assigned schedules by the additional parcel service, decreasing the
probability of finding a feasible insertion. For the travel time distribution,
a similar trend can be observed. Short and long travel times tend to be
slightly increased, whereas mid-range travel times (around 15 min) rather
experience a small decrease. This indicates the trend that parcels are mainly
inserted in short to medium-long trips.

Looking closer at the difference between the Full RPP Integration and Moderate
RPP Integration scenarios one can observe that from the customers’
perspective, i.e. waiting and travel times, no big advantage is gained by
limiting the parcel pick-up and drop-off to times, when no customer is on
board. The detours for parcel pick-ups and drop-offs, which have to be
accepted by the passengers, seem to get compensated by the additional freedom
within the creation of the vehicle schedules.

(a) Absolute Waiting Time

(b) Absolute Travel Time

(c) Waiting Time - Change to Status Quo

(d) Travel Time - Change to Status Quo

Figure 6: Waiting and travel time distribution of served customers within
different RPP assignment strategies. $\tau_{th}=0.80$ is considered in all
scenarios shown. (fleet size = 600)

###  4.2 Influence on Operator KPIs

(a) Served Parcels

(b) Served Customers

(c) Fleet KM

(d) Fleet Utilization

Figure 7: Impact of threshold parameter on the number of served parcels,
served persons, fleet kilometers traveled and fleet utilization.

In addition to the attractiveness of a mobility service for the customer, the
operator’s perspective plays a decisive role for success. It is important for
the provider of a MoD ride-pooling service, that the vehicle fleet can be
operated efficiently even after the logistics service has been integrated.
Figure 7(a) shows the number of served parcels given assignment thresholds
($\tau_{th}$) between 0.6 and 1.0. One can observe that generally, the number
of served parcels declines with an increasing assignment threshold, resulting
from more stringent pick-up or drop-off constraints for a parcel in a given
schedule. However, the CDPA reacts more sensitive to higher thresholds, than
the SDPA and SCPA strategies, which are both able to serve 100% of all parcels
for all investigated $\tau_{th}$ for the Full RPP Integration. Looking at
Figure 7(b) one can see the number of served customers for varying assignment
thresholds. It shows that for all strategies, a notable decline in service
rate for passengers cannot be observed. The SCPA and SDPA strategies are even
able to serve additional passengers compared to the Status Quo, which can
presumably be traced back to increased vehicle availability within the network
due to parcel pick-up and drop-off trips which can provide better coverage of
the service area. Figure 7(c) displays the overall fleet kilometers traveled
throughout the whole day. One can observe that the comprehensive (CDPA)
strategy even produces a lower driven distance than the Status Quo without the
integrated parcel delivery. Compared to the fleet KM to serve only the parcels
in the Status Quo ($2,614$ km on average), this equals a reduction share of
48% in traveled distance for $\tau_{th}=0.8$. This means, that only $1,252$ km
compared to the Status Quo were needed in the integrated transport approach.
However, the slightly lower number of served customers and parcels compared to
the Status Quo have to be considered. The SCPA strategy and especially the
SDPA approach, however, lead to considerably higher driven distances compared
to the Status Quo. Another interesting aspect in Figure 7(a) is that the
subsequent strategies (SDPA and SCPA) can accommodate nearly all parcels with
all thresholds for the Full RPP Integration. This means that all parcels are
picked-up because even with a high threshold the depots are very often located
on the route of the vehicle fleet. Nevertheless, at the end of the day the
collected but not yet dropped-off parcels, are then delivered, which also
contributes to the significantly increased vehicle kilometers of the fleet
using these strategies. Both, SDPA as well as SCPA, do not consider the
destination of parcels when picking them up, which can be a big disadvantage
when delivering them finally because parcel destinations might be distributed
throughout the whole network. For the Moderate RPP Integration the SDPA and
SCPA approaches produces less fleet distance with higher $\tau_{th}$ values,
which corresponds to the falling numbers of served parcels. Figure 7(d) shows
the fleet utilization, which of course leads to similar results as Figure
7(c). One can observe that depending on the chosen scenario and assignment
strategy the fleet utilization over the whole day varies between 54% and 61%.

The evaluations have shown that the Moderate RPP Integration performs
consistently worse than the Full RPP Integration: Significantly fewer parcels
can be served while the experience for passengers does not deteriorate much
applying the Full RPP Integration. Therefore, only full integration is
considered for further analysis. Additionally, the threshold parameter will be
fixed to $\tau_{th}=0.8$, the value where a first notable decline in served
parcels can be observed for the CDPA strategy.

Figure 8 shows the temporal distribution of pick-ups and drop-offs for
passengers and parcels for the different assignment strategies. Most passenger
demand starts in the morning at around 6am and lasts until the late evening at
around 8pm. It can be observed that parcel delivery only takes place, while
there is passenger demand to exploit the already existing passenger trips for
parcel delivery. Strategies using off-peak deliveries from other studies could
easily be added on top, which would increase the capacity for parcel transport
even further.

For the comprehensive (CDPA) strategy the temporal distribution for pick-ups
and drop-offs of parcels is similar indicating a rather fast delivery after
pick-up, resulting from the simultaneous assignment of pick-up and drop-off.
Therefore, the vehicle is actively routed toward drop-off locations. The
subsequent (SDPA and SCPA) strategies tend to collect the parcels right at the
beginning of the day at around 7am. The peak in the morning indicates, that it
seems to be easy to find routes including the logistics depots. The SCPA
strategy shows a higher success rate in delivering parcels during the day
compared to the SDPA strategy, as nearly all parcels could be served and the
number of drop-offs strongly declines after a peak around 11am. The SDPA
strategy, however, shows a strong peak in the number of drop-offs at 10pm,
when the parcels which are still on board the vehicles and could not be
delivered so far are driven to their destinations.

Figure 8: Time-dependent pick-ups and drop-offs of parcels for different
parcel assignment strategies and threshold parameters $\tau_{th}=0.8$. Full
integration is considered in all scenarios shown.

Figure 9 gives a detailed view of the temporal parcel occupancy states of the
vehicles throughout the day. If no parcels or passengers are on board,
vehicles are represented by a black color. White color is used for idle
vehicles. It can be observed that for the CDPA strategy only around 50 of 600
vehicles have parcels on board during the day, indicating a rather fast
delivery once a parcel is picked up, as stated previously. For the SDPA and
SCPA strategies, most of the vehicles are filled with parcels in the morning,
when they pass by the logistics depots and carry them around during the day.
In the case of SCPA most of the parcels can be delivered during the day
(Figure 8), which results in low occupation states at the end of the day.
Looking at the SDPA strategy, the occupation states at the end of the day are
still high, which results in a delivery peak at around 10pm to drop-off the
remaining parcels. In general, one can observe that the SCPA and SDPA
strategies result in higher overall vehicle utilization, indicated by the area
below the grey shape, compared to the CDPA strategy. This reflects the fact of
higher fleet kilometers and the higher number of served passengers and parcels
shown in Figure 7.

(a) CDPA

(b) SDPA

(c) SCPA

(d) Legend

Figure 9: Parcel occupancy states of moving vehicles for different parcel
assignment strategies. $\tau_{th}=0.80$ and full integration is considered in
all scenarios shown. (fleet size = 600)

###  4.3 Variation of Logistics Demand

(a) Served Parcels

(b) Served Customers

(c) Fleet KM

(d) Fleet KM Per Served Customer and Parcel

Figure 10: Impact of varying parcel demand on the number of served parcels,
served persons and fleet kilometers traveled. In all simulations
$\tau_{th}=0.8$ and full integration is considered.

To investigate the system boundaries of the introduced RPP service, the
logistics demand charged on the MoD fleet was varied. The overall data set of
56,000 parcel shipments is sub-sampled to shares of 1 to 50%.

Figure 10(a) shows the total number of served parcels depending on the applied
share of parcel demand. It becomes apparent that all strategies reach a
certain limit of parcel transport. The CDPA and SDPA strategies match the
Status Quo until a parcel demand of approximately 10%. The SCPA strategy is
even able to serve a parcel demand of up to 20% ( 11200 parcels per day). As
already observed, the SDPA and the SCPA approaches tend to pick up as many
parcels as possible in the morning. Nevertheless, contrary to the SCPA
strategy the SDPA strategy is not able to deliver the majority of parcels
throughout the day, leading to a fleet state of close to full load, indicated
by a horizontal plateau in Figure 10(a). CDPA and SCPA on the other hand can
also deliver most of the parcels during the service freeing up additional
capacity to serve further parcels.

Figure 10(b) displays the relative number of served customers. Similar effects
that have already been evaluated for the sensitivity of parameter $\tau_{th}$
in Figure 7(b) can be observed: For any strategy, no notable drop in served
customers can be observed, even for high parcel demand penetrations showing
the potential of free capacity for a parcel service during the MoD operation.
The higher fleet utilization seems to lead to a higher vehicle availability,
resulting in an even slightly higher share of served customers (at least when
using the assignment and re-balancing control strategies applied in this
study).

When it comes to the traveled distance of the vehicle fleet in Figure 10(c),
it can be seen that only the CDPA strategy results in similar traveled
distances compared to the Status Quo. The CDPA strategy actually even stays
below the Status Quo, saving driving distance through the integrated transport
of passenger and freight streams. The SCPA and SDPA tend to increase fleet
kilometers heavily and produce around 15,000 km and 30,000 km more total
distance when considering high parcel demand penetration rates. Until the
number of served parcels stabilizes at around $20$% penetration, Especially,
SDPA shows a strong increase in the driven distance indicating a bad scaling
behavior for finding effective routes for parcel pick-up and delivery. These
findings are also in line with the insights and discussion presented in Figure
7(c). However, similar scaling of fleet-driven distance with parcel
penetration can be observed for the CDPA strategy. In this regime, a very low
driven distance can be observed, especially for high parcel penetration rates
(even lower than the Status Quo). The down point of this strategy is that
significantly fewer parcels are served in this regime. Looking at Figure 10(d)
one can observe the fleet kilometers per served customer and parcel in
relation to the parcel demand penetration. This quantity also takes the number
of served customers and parcels into account when comparing fleet kilometers.
Thereby, ’better’ distance to request ratios can be achieved by either
transporting more passengers or parcels or both, while keeping the driven
distance low. All strategies show lower traveled distance per served request
with rising parcel demand, indicating a better integration. However, with high
parcel penetration efficient routes for parcel delivery can be found while all
parcels are served in the Status Quo, leading to the lowest values for fleet
kilometers per served customer and parcel in this scenario. These results
indicate that the modeled integration of parcel delivery into the MoD service
is only reasonable for parcel penetration rates of around 10%.

##  5 Conclusion

###  5.1 Summary

This paper investigates the integrated transport of passengers and freight,
assuming a mobility-on-demand ride-pooling service, called Ride-Parcel-Pooling
(RPP). Thereby, the operator of the vehicle fleet combines parcel delivery
into the passenger routes of the MoD service. This study uses two different
integration approaches, which are compared to the Status Quo consisting of a
separate logistics fleet and the MoD service serving passengers only. The
Moderate RPP Integration on the one hand, where parcels are only picked-up or
dropped-off when no passengers are on board, is not very beneficial, as it
results in lower numbers of served parcels and does not show better results in
customer service quality compared to the full integration. The Full RPP
Integration scenario on the other hand, shows similar results in passenger
waiting and detour times as the previous scenario and performs significantly
better in the number of served parcels. The assignment of parcels is
investigated using three different heuristic parcel assignment strategies
(CDPA, SCPA and SDPA). Each of these strategies aims at inserting parcels into
the schedules serving passengers with small detour for parcel pick-up and
delivery. Because no explicit time constraints on parcel pick-up and delivery
are employed, the assignment of parcel deliveries is separated from the parcel
pick-ups in the SCPA and SDPA strategy contrary to the CDPA strategy. The
results of a simulation case study for Munich, Germany suggest that the MoD
service is not deteriorated by the integration of logistics services and is
able to serve nearly all parcels until a parcel-to-passenger ratio of at least
1:10. The SCPA and SDPA strategies are able to transport more parcels than
CDPA and can even achieve an increase of served passengers compared to the
Status Quo, however they result in significantly higher driven distance. The
CDPA approach, however can decrease driven distances compared to the Status
Quo, where two vehicle fleets serve passenger and logistics demand
independently.

###  5.2 Discussion

The presented RPP service offers a good opportunity to increase the
utilization of MoD ride-pooling vehicle fleets. It could, assuming the
existence of a MoD service, already today complement existing logistics
services and extend the best practices in the sector. Thereby, it especially
offers a solution to use cases, where conventional logistics approaches can
not create any bundling effects, i.e. combining multiple parcels in one
logistics vehicle. Furthermore, RPP has the potential to reduce the number of
vehicles on the urban street network, by integrating passenger and logistics
flows in one fleet of vehicles.

This paper examines a forward logistics use case, meaning that the parcels are
transported from a depot to the recipient. In reality, this is the most
relevant, but not the only logistics form. Reverse logistics from the sender
to a consolidation center and courier services between two individuals could
be interesting for future analysis. In the case of integrating courier
services into the MoD ride-pooling service, a relatively cost-effective local
distribution system could be built up. Especially, considering developments in
autonomous driving, the integration into autonomous mobility-on-demand (AMoD)
services could further decrease operational costs, by eliminating the need to
pay a driver. This decrease in cost might further lead to decreased fares and
therefore amplified demand, enhancing scaling properties of ride-pooling
services that will also translate to more efficient options for parcel
delivery integration. In the future, one could even imagine multi-purpose
autonomous vehicles, which are able to convert passenger space into logistics
space and vice versa. Such a vehicle concept could further promote the
applicability of an integrated passenger and freight MoD service. A crucial
aspect here is the time taken to load and unload the parcels. This study
assumed that both loading and unloading are linked to a finely distributed
parcel locker station system or that the senders and recipients take over or
hand over the parcels at the vehicle, resulting in a relatively small boarding
time of one minute. In reality, significantly higher times could arise,
especially at the current stage without a high number of parcel lockers, which
could deteriorate passenger satisfaction in particular. Overall, RPP might be
a chance for local stores to compete with online shops, by offering fast
delivery and a sustainable way of shopping. If the service can succeed in
reducing the overall kilometers traveled in a city, by pooling passengers and
parcels, this could have substantial benefits for the city and its residents.
Not only it could induce a more livable city environment, by reducing the
number of cars and traffic load in cities, but lead to a more sustainable way
of city transportation without compromising on the customer experience, which
MoD services offer.

###  5.3 Future Work

Generally, one can observe that the integration of a secondary parcel demand
into the existing schemes of a MoD ride-pooling system is possible. The CDPA
approach already offers promising results, although this heuristic approach is
not of great complexity. The SCPA and SDPA strategies do still offer lots of
room for improvement. In theory, at least a similar fleet performance should
be achievable for a separated parcel pick-up and delivery assignment within
the SCPA and SDPA strategies compared to the CDPA strategy. Several
improvements can thereby be tested in the future for these strategies:
Firstly, the decision of an assignment in the SCPA and SDPA strategies is made
based on a threshold detour controlled by the parameter $\tau_{th}$ which is
the same for the pick-up and the drop-off. Results showed that it seemed to be
more probable to assign the pick-up compared to the delivery of a parcel. An
asymmetric assignment threshold for pick-up and delivery could thus amplify
efficiency. Secondly, the assignment of a parcel origin and therefore the
delivery of a parcel by a certain vehicle does not consider the destination of
parcels that are already assigned. It could be beneficial to assign parcels
with similar destinations to the same vehicle. And thirdly, the delivery of
parcels could be actively integrated into the re-balancing algorithm, i.e. if
a lack of vehicle supply is recognized in a certain zone, vehicles with parcel
deliveries in the corresponding zone could be prioritized for the
corresponding re-balancing trip. Furthermore, future research could
investigate the influences of a variation in loading and unloading times for
parcels for finding the influence on customer waiting and travel times and by
that customer satisfaction. Nevertheless, the overall goal should be the
development of an integrated control algorithm that optimizes vehicle
schedules and decides on parcel and passenger assignment in a unified
optimization framework.

Apart from that, the presented RPP service could be extended by integrating
vehicle idle times into the availability for logistics services. This could
increase the system capacity for logistics services strongly and already
developed efficient algorithms for planning routes for pick-up and delivery
could be exploited. Nevertheless, care has to be taken on when and how many
vehicles perform these pure logistics services because these vehicles will no
longer be available for passenger transport. Additionally, a further increase
in vehicle kilometers traveled is expected.

Looking at the RPP service definition, one could think of multiple extensions.
The current service intentionally did not impose pickup and delivery time
windows for parcels. However, this representation of parcels excludes certain
types of logistics services that could also be use cases for RPP, for example,
food and grocery, drugs and medicine, or high priority parcels. Furthermore,
the effects of different logistics service forms (e.g. same-day vs. next-day
delivery or forward vs. reverse vs. courier logistics) on the RPP performance
could be investigated. It would be particularly interesting to look at edge
cases that are currently financially not interesting for logistics companies
due to low bundling effects and to investigate whether RPP could remedy this
situation. Last but not least, constraints for the design of such a multi-
purpose vehicle could be examined. Depending on the use case the optimal
parcel capacity for a RPP vehicle could be determined in future studies.
Additionally, a dynamic allocation of capacity based on the current demand to
convert seats into parcel storage and vice-versa could be implemented.

## Conflict of Interest Statement

The authors declare that they have no known competing financial interests, or
personal relationships that could have appeared to influence the work reported
in this paper.

## Author Contributions

Study conception and design: FF, RE, FD, KB, FB; data collection: FF, RE;
analysis and interpretation of results: FF, RE, FD; simulation model: RE, FD,
FF; draft manuscript preparation: FF, RE, FD, KB, FB. All authors reviewed the
results and approved the final version of the manuscript.

## Funding

The German Federal Ministry of Transport and Digital Infrastructure provides
funding through the project “TEMPUS” with grant number 01MM20008K. The authors
remain responsible for all findings and opinions presented in the paper.

## References

  * [1] European Commission  “COMMISSION STAFF WORKING DOCUMENT - A call to action on urban logistics”, 2013  URL: https://op.europa.eu/en/publication-detail/-/publication/ffac1877-67ca-11e3-a7e4-01aa75ed71a1
  * [2] Pricewaterhouse Coopers  “A time for hope: Consumers’ outlook brightens despite headwinds - December 2021 Global Consumer Insights Pulse Survey”, 2021  URL: https://www.pwc.com/consumerinsights
  * [3] Claudia Nobis, Tobias Kuhnimhof, Robert Follmer and Marcus Bäumer  “Mobilität in Deutschland - Zeitreihenbericht 2002-2008-2017”, 2019, pp. 26  URL: https://www.bmvi.de/SharedDocs/DE/Anlage/G/mid-zeitreihenbericht-2002-2008-2017.pdf?__blob=publicationFile
  * [4] Bundesverband Paket Expresslogistik e..(BIEK)  “KEP-Studie 2021 – Analyse des Marktes in Deutschland”, 2021, pp. 6  URL: https://www.biek.de/files/biek/downloads/papiere/BIEK_KEP-Studie_2021.pdf
  * [5] Lay Maxwell G.  “Ways of the world : a history of the world’s roads and of the vehicles that used them”  New Brunswick, N.J.: Rutgers University Press, 1992, pp. 125–126  URL: https://trid.trb.org/view/368373
  * [6] Bob Pishue  “2021 INRIX Global Traffic Scorecard”, 2021, pp. 18  URL: https://inrix.com/scorecard/
  * [7] Federal Ministry Environment, Nature Conservation, Nuclear Safety and Consumer Protection (BMUV)  “Umweltbelastungen durch Verkehr”, 2022  URL: https://www.umweltbundesamt.de/daten/verkehr/umweltbelastungen-durch-verkehr#undefined
  * [8] Zhujun Li, Amer Shalaby, Matthew J. Roorda and Baohua Mao  “Urban rail service design for collaborative passenger and freight transport”  In _Transportation Research Part E: Logistics and Transportation Review_ 147, 2021, pp. 102205  DOI: https://doi.org/10.1016/j.tre.2020.102205
  * [9] Emilio Larrodé and Victoria Muerza  “Chapter 4 - Improving cost efficiency and environmental impact through the integration of light freight and passenger railway transport and last-mile distribution analysis”  In _Urban Freight Transportation Systems_ Elsevier, 2020, pp. 55–78  DOI: https://doi.org/10.1016/B978-0-12-817362-6.00004-5
  * [10] Walid Behiri, Sana Belmokhtar-Berraf and Chengbin Chu  “Urban freight transport using passenger rail network: Scientific issues and quantitative analysis”  In _Transportation Research Part E: Logistics and Transportation Review_ 115, 2018, pp. 227–245  DOI: https://doi.org/10.1016/j.tre.2018.05.002
  * [11] Yiming He, Qiang Wang, Hailong Chen and Chuanqi Gao  “Metro Passenger and Freight Transport: A Framework for Underground Logistics System”  In _2019 IEEE Intl. Conf. on Dependable, Autonomic and Secure Computing, Intl. Con.f on Pervasive Intelligence and Computing, Intl. Conf. on Cloud and Big Data Computing, Intl. Conf. on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech)_ , 2019, pp. 880–882  DOI: 10.1109/DASC/PiCom/CBDCom/CyberSciTech.2019.00160
  * [12] Fangting Zhou and Jin Zhang  “Freight Transport Mode Based on Public Transport: Taking Parcel Delivery by Subway as an Example”  In _ICTE 2019_ , pp. 745–754  DOI: 10.1061/9780784482742.083
  * [13] Alexandra G. Dampier and Marin Varbanov Marinov  “A Study of the Feasibility and Potential Implementation of Metro-Based Freight Transportation in Newcastle upon Tyne”  In _Urban Rail Transit_ 1, 2015, pp. 164–182
  * [14] Keith Cochrane, Shoshanna Saxe, Matthew Roorda and Amer Shalaby  “Moving Freight on Public Transit: Best Practices, Challenges, and Opportunities”  In _International Journal of Sustainable Transportation_ 11, 2016, pp. 00–00  DOI: 10.1080/15568318.2016.1197349
  * [15] Ezzeddine Fatnassi, Chaouachi Jouhaina and Klibi Walid  “Towards Creating Sustainable Urban Areas: A Shared Passengers and Goods On-Demand Rapid Transit Proposal”  In _Proceedings of 1st International Physical Internet Conference_ , 2014  URL: https://www.cirrelt.ca/ipic2014/pdf/1024a.pdf
  * [16] Katrien De Langhe, Hilde Meersman, Christa Sys, Eddy Voorde and Thierry Vanelslander  “How to make urban freight transport by tram successful?”  In _Journal of Shipping and Trade_ 4, 2019  DOI: 10.1186/s41072-019-0055-4
  * [17] City Zurich  “Cargo-Tram and E-Tram”, 2022  URL: https://www.stadt-zuerich.ch/ted/de/index/entsorgung_recycling/entsorgen/wo-wann-entsorgen/cargo-tram_und_e-tram.html
  * [18] Martin Kagerbauer, Lukas Barthelmes, Anna Reiffer and Emre Görgülü  “LogIKTram - Logistics Concept and ICT Platform for Urban Rail-based Freight Transport”, 2022  URL: https://www.ifv.kit.edu/english/26_1542.php
  * [19] Kai-Oliver Schocke, Petra K. Schäfer, Silke Höhl, Andreas Gilbert, Felix Kwast, Simon Lacoste, Simon Marhan and Klaus-Peter Wenz  “Bericht zum Forschungsvorhaben LastMileTram - Empirische Forschung zum Einsatz einer Güterstraßenbahn am Beispiel Frankfurt am Main”  In _Research Lab for Urban Transport, Frankfurt University of Applied Sciences_ , 2020  URL: https://www.frankfurt-university.de/fileadmin/standard/Hochschule/Fachbereich_1/FFin/Neue_Mobilitaet/Veroeffentlichungen/2020/Abschlussbericht_LastMileTram.pdf
  * [20] Martin Pernkopf and Manfred Gronalt  “An aerial ropeway transportation system for combined freight and passenger transport – a simulation study”  In _Transportation Planning and Technology_ 44.1 Routledge, 2021, pp. 45–62  DOI: 10.1080/03081060.2020.1851450
  * [21] Francesco Bruzzone, Federico Cavallaro and Silvio Nocera  “The integration of passenger and freight transport for first-last mile operations”  In _Transport Policy_ 100.C, 2021, pp. 31–48  DOI: 10.1016/j.tranpol.2020.10
  * [22] Veaceslav Ghilas, Emrah Demir and Tom Van Woensel  “The pickup and delivery problem with time windows and scheduled lines”  In _INFOR: Information Systems and Operational Research_ 54.2 Taylor & Francis, 2016, pp. 147–167  DOI: 10.1080/03155986.2016.1166793
  * [23] Niklas Arvidsson, Moshe Givoni and Johan Woxenius  “Exploring Last Mile Synergies in Passenger and Freight Transport”  In _Built Environment_ 42, 2016, pp. 523–538  DOI: 10.2148/benv.42.4.523
  * [24] Anna Trentini, Renaud Masson, Fabien Lehuédé, Nicolas Malhéné, Olivier Péton and Houda Tlahig  “A shared passengers & goods city logistics system”  In _4th International Conference on Information Systems, Logistics and Supply Chain_ , 2012, pp. 10p  URL: https://hal.archives-ouvertes.fr/hal-00861728
  * [25] Hugh Frost  “Freight*Bus, The bus that Delivers!”, 2008  URL: http://www.onroutebus.co.uk/
  * [26] Toyota  “ePalette Concept”, 2018  URL: https://www.toyota-global.com/pages/contents/innovation/intelligent_transport_systems/world_congress/2018copenhagen/pdf/e-Palette_CONCEPT.pdf
  * [27] Aliaa Alnaggar, Fatma Gzara and James H. Bookbinder  “Crowdsourced Delivery: A Review of Platforms and Academic Literature”  In _Omega, Elsevier_ 98.C, 2021  DOI: 10.1016/j.omega.2019.102139
  * [28] Afonso Sampaio, Martin Savelsbergh, Lucas Veelenturf and Tom van Woensel  “Chapter 15 - Crowd-Based City Logistics”  In _Sustainable Transportation and Smart Logistics_ Elsevier, 2019, pp. 381–400  DOI: https://doi.org/10.1016/B978-0-12-814242-4.00015-6
  * [29] Chengxi Liu, Qian Wang and Yusak O. Susilo  “Assessing the impacts of collection-delivery points to individual’s activity-travel patterns: A greener last mile alternative?” Special Issue on Supply chain elements in freight transport modelling; Guest Edited by Lóránt Tavasszy and Hanno Friedrich  In _Transportation Research Part E: Logistics and Transportation Review_ 121, 2019, pp. 84–99  DOI: https://doi.org/10.1016/j.tre.2017.08.007
  * [30] Chao Chen and Shenle Pan  “Using the Crowd of Taxis to Last Mile Delivery in E- commerce: a Methodological Research”  In _Proceedings of SOHOMA_ , 2015
  * [31] Baoxiang Li, Dmitry Krushinsky, Hajo A. Reijers and Tom Van Woensel  “The Share-a-Ride Problem: People and parcels sharing taxis”  In _European Journal of Operational Research_ 238.1, 2014, pp. 31–40  DOI: https://doi.org/10.1016/j.ejor.2014.03.003
  * [32] Tilmann Schlenther, Kai Martins-Turner, Joschka Felix Bischoff and Kai Nagel  “Potential of Private Autonomous Vehicles for Parcel Delivery”  In _Transportation Research Record_ 2674.11, 2020, pp. 520–531  DOI: 10.1177/0361198120949878
  * [33] John Howe  “’Filling the middle’: Uganda’s appropriate transport services”  In _Transport Reviews_ 23, 2003, pp. 161–176  DOI: 10.1080/01441640309890
  * [34] Daniel J. Fagnant, Kara. Kockelman and Prateek Bansal  “Operations of Shared Autonomous Vehicle Fleet for Austin, Texas Market”  In _Transportation Research Record: Journal of the Transportation Research Board_ 2536, 2015, pp. 98–106  DOI: 10.3141/2536-12
  * [35] Florian Dandl and Klaus Bogenberger  “Comparing Future Autonomous Electric Taxis with an Existing Free-Floating Carsharing System”  In _IEEE Transactions on Intelligent Transportation Systems_ 20.6, 2019, pp. 2037–2047  DOI: 10.1109/TITS.2018.2857208
  * [36] Roman Engelhardt, Florian Dandl, Aledia Bilali and Klaus Bogenberger  “Quantifying the Benefits of Autonomous On-Demand Ride-Pooling: A Simulation Study for Munich, Germany”  In _2019 IEEE Intelligent Transportation Systems Conference (ITSC)_ , 2019, pp. 2992–2997  DOI: 10.1109/ITSC.2019.8916955
  * [37] Claudio Ruch, ChengQi Lu, Lukas Sieber and Emilio Frazzoli  “Quantifying the Efficiency of Ride Sharing”  In _IEEE Transactions on Intelligent Transportation Systems_ , 2020, pp. 1–6  DOI: 10.1109/TITS.2020.2990202
  * [38] Remi Tachet, Oleguer Sagarra, Paolo Santi, G. Resta, Michael Szell, Steven H. Strogatz and Carlo Ratti  “Scaling Law of Urban Ride Sharing”  In _Scientific reports_ 7, 2017, pp. 42868  DOI: 10.1038/srep42868
  * [39] Aledia Bilali, Roman Engelhardt, Florian Dandl, Ulrich Fastenrath and Klaus Bogenberger  “Analytical and Agent-Based Model to Evaluate Ride-Pooling Impact Factors”  In _Transportation Research Record: Journal of the Transportation Research Board_ , 2020, pp. 036119812091766  DOI: 10.1177/0361198120917666
  * [40] Florian Dandl, Michael Hyland, Klaus Bogenberger and Hani S. Mahmassani  “Evaluating the impact of spatio-temporal demand forecast aggregation on the operational performance of shared autonomous mobility fleets”  In _Transportation_ 46.6, 2019, pp. 1975–1996  DOI: 10.1007/s11116-019-10007-9
  * [41] Arslan Ali Syed, Florian Dandl, Bernd Kaltenhäuser and Klaus Bogenberger  “Density Based Distribution Model for Repositioning Strategies of Ride Hailing Services”  In _Frontiers in Future Transportation_ 2, 2021, pp. 295  DOI: 10.3389/ffutr.2021.681451
  * [42] Paolo Santi, Giovanni Resta, Michael Szell, Stanislav Sobolevsky, Steven H. Strogatz and Carlo Ratti  “Quantifying the benefits of vehicle pooling with shareability networks”  In _Proceedings of the National Academy of Sciences of the United States of America_ 111.37, 2014, pp. 13290–13294  DOI: 10.1073/pnas.1403657111
  * [43] Javier Alonso-Mora, Samitha Samaranayake, Alex Wallar, Emilio Frazzoli and Daniela Rus  “On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment”  In _Proceedings of the National Academy of Sciences_ 114.3 National Academy of Sciences, 2017, pp. 462–467  DOI: 10.1073/pnas.1611675114
  * [44] Andrea Simonetto, Julien Monteil and Claudio Gambella  “Real-time city-scale ridesharing via linear assignment problems”  In _Transportation Research Part C: Emerging Technologies_ 101, 2019, pp. 208–232  DOI: 10.1016/j.trc.2019.01.019
  * [45] Burak Eksioglu, Arif Volkan Vural and Arnold Reisman  “The vehicle routing problem: A taxonomic review”  In _Computers & Industrial Engineering_ 57.4, 2009, pp. 1472–1483  DOI: https://doi.org/10.1016/j.cie.2009.05.009
  * [46] Gitae Kim, Yew-Soon Ong, Chen Kim Heng, Puay Siew Tan and Nengsheng Allan Zhang  “City Vehicle Routing Problem (City VRP): A Review”  In _IEEE Transactions on Intelligent Transportation Systems_ 16.4, 2015, pp. 1654–1666  DOI: 10.1109/TITS.2015.2395536
  * [47] Federico Cavallaro and Silvio Nocera  “Integration of passenger and freight transport: A concept-centric literature review”  In _Research in Transportation Business I & Management_, 2021, pp. 100718  DOI: https://doi.org/10.1016/j.rtbm.2021.100718
  * [48] Abood Mourad, Jakob Puchinger and Chengbin Chu  “A survey of models and algorithms for optimizing shared mobility”  In _Transportation Research Part B: Methodological_ 123, 2019, pp. 323–346  DOI: https://doi.org/10.1016/j.trb.2019.02.003
  * [49] Le Li, Rudy R. Negenborn and Bart De Schutter  “Multi-agent cooperative transport planning of intermodal freight transport”  In _17th International IEEE Conference on Intelligent Transportation Systems (ITSC)_ , 2014, pp. 2465–2471  DOI: 10.1109/ITSC.2014.6958085
  * [50] Ngoc-Quang Nguyen, Nguyen-Viet-Dung Nghiem, Phan-Thuan DO, Khac-Tuan LE, Minh-Son Nguyen and Naoto MUKAI  “People and Parcels Sharing a Taxi for Tokyo City”  In _Proceedings of the Sixth International Symposium on Information and Communication Technology_ , SoICT 2015  Hue City, Viet Nam: Association for Computing Machinery, 2015, pp. 90–97  DOI: 10.1145/2833258.2833309
  * [51] Nicole Ronald, Jie Yang and Russell G. Thompson  “Exploring Co-Modality Using On-Demand Transport Systems” Tenth International Conference on City Logistics 17-19 June 2015, Tenerife, Spain  In _Transportation Research Procedia_ 12, 2016, pp. 203–212  DOI: https://doi.org/10.1016/j.trpro.2016.02.059
  * [52] David Soto Setzke, Christoph Pflügler, Maximilian Schreieck, Sven Fröhlich, Manuel Wiesche and Helmut Krcmar  “Matching Drivers and Transportation Requests in Crowdsourced Delivery Systems”  In _Proceedings of the Twenty-third Americas Conference on Information Systems_ , 2017
  * [53] Chao Chen, Daqing Zhang, Xiaojuan Ma, Bin Guo, Leye Wang, Yasha Wang and Edwin Sha  “crowddeliver: Planning City-Wide Package Delivery Paths Leveraging the Crowd of Taxis”  In _IEEE Transactions on Intelligent Transportation Systems_ 18.6, 2017, pp. 1478–1496  DOI: 10.1109/TITS.2016.2607458
  * [54] Nabin Kafle, Bo Zou and Jane Lin  “Design and modeling of a crowdsource-enabled system for urban parcel relay and delivery”  In _Transportation Research Part B: Methodological_ 99, 2017, pp. 62–82  DOI: https://doi.org/10.1016/j.trb.2016.12.022
  * [55] Breno A. Beirigo, Frederik Schulte and Rudy R. Negenborn  “Integrating People and Freight Transportation Using Shared Autonomous Vehicles with Compartments” 15th IFAC Symposium on Control in Transportation Systems CTS 2018  In _IFAC-PapersOnLine_ 51.9, 2018, pp. 392–397  DOI: https://doi.org/10.1016/j.ifacol.2018.07.064
  * [56] Wei Qi, Lefei Li, Sheng Liu and Zuo-Jun Max Shen  “Shared Mobility for Last-Mile Delivery: Design, Operational Prescriptions, and Environmental Impact”  In _Manufacturing & Service Operations Management_ 20.4, 2018, pp. 737–751  DOI: 10.1287/msom.2017.0683
  * [57] Fangxin Wang, Yifei Zhu, Feng Wang and Jiangchuan Liu  “Ridesharing as a Service: Exploring Crowdsourced Connected Vehicle Information for Intelligent Package Delivery”  In _2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS)_ , 2018, pp. 1–10  DOI: 10.1109/IWQoS.2018.8624152
  * [58] Alp M. Arslan, Niels Agatz, Leo Kroon and Rob Zuidwijk  “Crowdsourced Delivery—A Dynamic Pickup and Delivery Problem with Ad Hoc Drivers”  In _Transportation Science_ 53.1, 2018, pp. 222–235  DOI: 10.1287/trsc.2017.0803
  * [59] Abood Mourad  “The synchronization of shared mobility flows in urban environments”, 2019  URL: https://tel.archives-ouvertes.fr/tel-02286031
  * [60] Shirin Najaf Abadi  “Designing an On-Demand Dynamic Crowdshipping Model and Evaluating its Ability to Serve Local Retail Delivery in New York City”, 2019  URL: https://academicworks.cuny.edu/cc_etds_theses/809
  * [61] Yueyue Chen, Deke Guo, Ming Xu, Guoming Tang, Tongqing Zhou and Bangbang Ren  “PPtaxi: Non-Stop Package Delivery via Multi-Hop Ridesharing”  In _IEEE Transactions on Mobile Computing_ 19.11, 2020, pp. 2684–2698  DOI: 10.1109/TMC.2019.2928808
  * [62] Kaushik Manchella, Abhishek Umrawal and Vaneet Aggarwal  “FlexPool: A Distributed Model-Free Deep Reinforcement Learning Algorithm for Joint Passengers I& Goods Transportation”  In _IEEE Transactions on Intelligent Transportation Systems_ , 2020  DOI: https://doi.org/10.48550/arXiv.2007.13699
  * [63] Max Tholen, Breno A. Beirigo, Jovana Jovanova and Frederik Schulte  “The Share-A-Ride Problem with Integrated Routing and Design Decisions: The Case of Mixed-Purpose Shared Autonomous Vehicles”  In _Computational Logistics_ Springer International Publishing, 2021, pp. 347–361
  * [64] André Romano Alho, Takanori Sakai, Simon Oh, Cheng Cheng, Ravi Seshadri, Wen Han Chong, Yusuke Hara, Julia Caravias, Lynette Cheah and Moshe Ben-Akiva  “A simulation-based evaluation of a Cargo-Hitching service for E-commerce using mobility-on-demand vehicles”  In _Future Transportation_ 1.3 Multidisciplinary Digital Publishing Institute, 2021, pp. 639–656
  * [65] Fabian Fehn, Roman Engelhardt and Klaus Bogenberger  “Ride-Parcel-Pooling - Assessment of the Potential in Combining On-Demand Mobility and City Logistics”  In _2021 IEEE International Intelligent Transportation Systems Conference (ITSC)_ , 2021, pp. 3366–3372  DOI: 10.1109/ITSC48978.2021.9564630
  * [66] Simon Meinhardt, Tilmann Schlenther, Kai Martins-Turner and Michal Maciejewski  “Simulation of On-Demand Vehicles that Serve both Person and Freight Transport” The 13th International Conference on Ambient Systems, Networks and Technologies (ANT) / The 5th International Conference on Emerging Data and Industry 4.0 (EDI40)  In _Procedia Computer Science_ 201, 2022, pp. 398–405  DOI: https://doi.org/10.1016/j.procs.2022.03.053
  * [67] Shiyao Zhang, Christos Markos and James J.Q. Yu  “Autonomous Vehicle Intelligent System: Joint Ride-Sharing and Parcel Delivery Strategy”  In _IEEE Transactions on Intelligent Transportation Systems_ , 2022, pp. 1–12  DOI: 10.1109/TITS.2022.3162609
  * [68] TUM - Chair Traffic Engineering and Control  “GitHub Repository for TUM-VT FleetPy Simulation Framework”, 2022  URL: https://github.com/TUM-VT/FleetPy
  * [69] Roman Engelhardt, Florian Dandl and Klaus Bogenberger  “Speed-up Heuristic for an On-Demand Ride-Pooling Algorithm”, 2020  DOI: 10.48550/arXiv.2007.14877
  * [70] Marco Pavone, Stephen L. Smith, Emilio Frazzoli and Daniela Rus  “Robotic load balancing for mobility-on-demand systems”  In _The International Journal of Robotics Research_ 31.7, 2012, pp. 839–854  DOI: 10.1177/0278364912444766
  * [71] Florian Dandl, Benedikt Bracher and Klaus Bogenberger  “Microsimulation of an autonomous taxi-system in Munich”  In _2017 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)_ , 2017, pp. 833–838  DOI: 10.1109/MTITS.2017.8005628
  * [72] DHL Global  “DHL and Streetscooter develop new electric drive vehicle with hydrogen technology”, 2019  URL: https://www.dhl.com/global-en/home/press/press-archive/2019/dhl-and-streetscooter-develop-new-electric-drive-vehicle-with-hydrogen-technology.html