1. Big Data meets Smart City – Optimization of Multimodal Flow
Networks
Sebnem Rusitschka, Nina Solomakhina, Michael Watzke, Steffen Lamparter, Silvio Becher
Siemens AG Corporate Technology, {name.surname}@siemens.com
Abstract
Smart cities are complex systems of resource infrastructures such as energy, transport, and information. The many
stakeholders of a smart city ecosystem, from infrastructure to service providers to end users, require a common under-
standing of these complexities and the potential synergies. Only if synergies in resource usage across all interdependent
infrastructure are leveraged, can the complexities associated with big data in smart cities be addressed, i.e. potentially
massive amounts of data coming from intelligent infrastructures and especially always connected end users giving way
to unnecessary data storage and potential profiling. The generic city node model introduced in this paper, with the ac-
companying definition of energy and transport networks as multimodal flow networks, which can be cross-optimized,
enables the exploration of such synergies on a technical as well as stakeholder, i.e. business model, level. Some of the
requirements for a smart data infrastructure, which follow after a high-level exploration of scenarios, are presented as
well as applications and stakeholders.
1 Introduction
In the recent years, the discussion around smart cities has
shifted from a sole technological to a more user-oriented
one. City officials worldwide are not questioning if the
cities will be smart, but it’s rather a question of how and
how fast. The increasing populations of the cities and how
citizens are interacting with technology in their day-to-
day life is demanding cities to make use of digitization.
However, still there seems to be a disconnect between
successful smart city pilots and their successful rollout.
After the technological feasibility has been shown for a
pilot use case, three questions still remain unanswered:
(1) What is the operator model? (2) What are the business
models? (3) Does it scale? We believe this is due to an
oversimplification too early in the process. Especially for
the smart city, a complex ecosystem of resources, infra-
structure, end users, and potentially massive amounts of
data to be exchanged, system thinking is required.
We propose a holistic approach based on a modular, ex-
tensible model of the city. The core principle is that the
city is made up of city nodes, e.g. buildings, connected by
infrastructure, e.g. transport, energy, information, which
facilitates the flow of goods and people in this complex
network (see Section 2). The model is not only deter-
mined by physical processes but also by stochastic and
behavioral processes taking into account the increased
liberalization of infrastructure businesses as well as end
user participation. The cross-optimization of energy and
transport becomes possible when the multimodal net-
works are represented as a multilayer virtual network and
combined with real-time data (see Section 3). This is the
very definition of big data: a scenario, in which high-
volume, high-velocity and high-variety information assets
that demand cost-effective, innovative forms of infor-
mation processing for enhanced insight and decision
making (as formulated by Gartner).
Following the same system-thinking principle we argue
that the smart city requires a smart data infrastructure,
which allows the synergetic usage of storage and compu-
ting resources of the entire intelligent infrastructure as
well as the involved stakeholders (see Section 4). Finally,
we discuss some scenarios where energy and transport
domains intertwine to expose both the technical and
stakeholder complexities as well the considerable syner-
gies and potential for optimization (see Section 5). We
conclude that these complexities and potentials need to be
tackled via system-thinking as opposed to through singu-
lar use cases in order to implement a smart data infra-
structure with feasible operator and business models and
give a short outlook on future work with the concepts de-
veloped in this paper (see Section 6).
2 Overview of the Generic City
Node Model
On a very high level the city is made up of city nodes, e.g.
private or commercial buildings, train stations, etc. Some
nodes are hubs such as central train station, airport, or in-
dustrial parks. City nodes are connected by infrastructure
for transportation, energy, and information. Each infra-
structure can be seen as a multimodal flow network, with
different modes of transportation: roads, railways lines, or
forms of energy: gas, heat, and electricity (see Figure 1).
Figure 1 Connectivity of city nodes within multiple net-
works: transportation, energy, and information
This work has been partially funded by the European Union under the
7th Framework Program. Grant agreement no: 619551.

2. In this model it is possible to determine key performance
indicators (KPIs) at the city level, such as CO2 neutrality,
and match these KPIs with the actual technological capa-
bilities of the city infrastructure and resources at the city
nodes. Each node is part of multiple infrastructures. A
city node is connected with the multimodal transport net-
work, allowing for the in- and outflow of people and
goods via car, or metro for example. The city nodes con-
tain resources that use different modes of energy and can
be used as buffers: Combined heat and power devices
(CHP) in buildings that can deliver heating or cooling and
use the waste energy in form of power to charge addition-
al parking electric vehicles in the neighbouring area. Al-
ternatively the excess power can be converted to gas or
heating and provided to the neighbouring nodes using the
multimodal network connections. The available excess
power then is directly dependent on the number of people
accommodated and goods stored or consumed in these
city nodes. Energy efficiency then becomes an optimal
flow problem across the various multimodal networks in
the city (see Section 3).
Current trends favour such a generalized view of a city
and its resources instead of siloed infrastructure planning:
Shift of focus towards the movement of people and goods
rather than vehicles and conversion of energy rather than
consumption of power, or water.
Diversification of supply, both in transportation, e.g. car
sharing, taxi-on-demand, as well as in energy, e.g. dis-
tributed energy resources at the consumer level.
Personalization of demand using technology to accom-
modate the differences between individuals’ needs, which
also change over time.
Stakeholders offering energy and mobility services within
a city need to take into account the synergies of their re-
spective capabilities in order to efficiently cope with the
increasing system dynamics through diversification and
personalization needs.
As will be discussed in Section 4, the information net-
work that the city nodes are also part of greatly enhances
the visibility into these dynamics to enable efficient oper-
ations in the face of changing system dynamics. The in-
creased visibility through digitization and data acquisition
is where big data meets smart cities, with high potential
applications but also with potentially negative side ef-
fects: This increased visibility about usage of resources
and infrastructure, would also reveal behavioural patterns
of their users. Concepts for integrated data security, at the
atomic level of the very data that is generated with each
user interaction, are indispensible. The massive amounts
of data associated with fully sensorized infrastructures,
always connected end users, and smart devices will re-
quire investments into data management and analytics ca-
pabilities of each stakeholder, potentially diminishing the
return on data.
3 Optimization of Multimodal Flow
Networks in a Smart City
The current European project BYTE1
concentrates on
capturing the positive externalities and diminishing the
negative externalities associated with big data. One of the
case studies, which shall expose such complexities, fo-
cuses on integrated management of multi-modal resource
infrastructures, i.e. optimizing public water supply, dis-
tributed heating, electricity network operations, and pub-
lic transport. Potential externalities to be addressed
through this case study include privacy, data security, as
well as those concerning access to data for cross-sector
optimization and policy decisions. The generic city node
model enables the systemic analysis as will be discussed
in the following sections.
3.1 Definition of Energy and Transporta-
tion as Multimodal Flow Networks
Flow networks are used in graph theory as well as opera-
tions research. A flow network is a directed graph (net-
work), in which each edge (arc) has a capacity and re-
ceives a flow. The amount of flow into a vertex (node)
equals the amount of flow out of it – unless it is a source
(producer/provider) or a sink (consumer/user). This is
called the conservation constraint, which is formulated by
Kirchhoff’s current law. Flows can represent people or
goods over a transportation network or electricity over a
distribution network.
The simplest form of problem to study in flow networks
is the maximal flow: the largest possible flow between a
source and a sink. In a multi-commodity flow problem,
multiple sources and sinks as well as various commodities
are considered. Various commodities (flow demands) are
then for example the various goods produced at different
factories to be delivered to multiple customers through the
same transport network. In a multimodal flow network,
with every origin/destination (O/D) pair a vector of flow
demands (dw
1
, dw
2
, …, dw
k
) where dw
i
is the flow demand
associated with O/D pair w and mode i. Additionally,
when each arc has a given cost and the objective is to
send a given flow from source to sink at lowest possible
cost is formulated as minimal cost flow problem.
Dynamic flow networks [1] include a temporal dimension
to model network flows over time. The flows can be de-
layed at nodes (stored) and network parameters such as
arc capacities can change over time. Hence, with dynamic
flow networks more realistic models can be achieved
which can represent congestion due to roads or distribu-
tion lines that become blocked or tripped.
The promise of multimodality of networks is that there is
potentially more flexibility to resolve the excess flow
(supply) through transition from the congested mode into
another mode, which has more capacity or possibility of
storage at nodes.
1
http://byte-project.eu/

3. 3.2 Combination of Virtual Networks with
Real-time Synchronized Data
In transport operations, the same infrastructure, link or
node can be used in different ways. The simple geograph-
ic representation of infrastructure does not take into ac-
count the possibilities of extensive multimodal networks
[2]. So-called virtual networks have been proposed to as-
sign flows between different routes as well as modes.
Multilayer virtual networks, as depicted in Figure 2, can
take into account the various interconnected or interde-
pendent infrastructures of a city such as multimodal ener-
gy and transport networks. Each arc is a virtual link with-
in one mode and there are transfer links between modes.
Each links has a specific cost for the particular use of in-
frastructure.
The cost functions can be relatively detailed and complex
[2] and take into account energy needs of the transport
and of the transported goods and people when these are
residing at city nodes. This initially “unnatural” view onto
the city complex, exposes where energy and transport
meet, e.g. electric cars parking (and charging) at commer-
cial building parking lots or goods stored at a city logis-
tics hub for transshipment (see Section 5). These origins
of energy flow demand highly correlate with mobility pat-
terns.
Figure 2 Representation of the multimodal flow network
of generic city nodes model via multilayer virtual network
Multilayer virtual networks can accommodate the combi-
nation of the characteristic models previously discussed in
Section 3.1. Nonetheless, the numerical analysis to solve
these models has high computational time requirements.
However, a digitized smart city will also emit synchro-
nized real-time measurements from sensors and intelligent
electronic devices installed along its infrastructure. Time-
synchronized and tagged measurements (e.g. geo-tag) al-
lows for observability of the flow networks and system-
wide comparability of state and flow anywhere at any
time in the system. Although smart cities will only be
gradually digitized, there is already a variety of sources of
data available such as in location-based services, digital
maps and navigation applications, smart meters. Each ad-
dition of real-time synchronized data makes numerical
analysis easier and faster. On the downside, however,
massive amounts of possibly information-sparse data
flows need to be managed and analyzed coming from var-
ious sources with different types and formats. Additional-
ly, these measurements not only reflect the usage of
transportation or energy modes but also allow the infer-
ence of behavioral patterns of users as well as transac-
tional patterns of commerce. This is a very dense example
of the promise and demise of big data. In Section 4, we
will discuss the need for a smart data infrastructure,
which captures the positive externalities and diminishes
the negative externalities associated with big data – espe-
cially in the dense setting of smart cities.
4 Smart Data Infrastructure is re-
quired
Building a digital copy of our infrastructures and move-
ments – this will have profound consequences. If the issue
is only addressed technically, as storage and computing
scalability issue, then we are missing the point that the
system that can be predicted and optimized includes us,
the users, the citizens. Again, the smart city complex cul-
minates the problematic issue very nicely. In the follow-
ing discussion on the characteristics of a smart data infra-
structure, hence, we are not only analyzing from a tech-
nical point of view but also from a socio-economical
standpoint.
4.1 Data Acquisition – Crux of the Matter
Real-time synchronized and geo-tagged data for use in
optimizing the planning and operations of city resources
and infrastructures becomes increasingly available. Digit-
ization of infrastructures in cities include lamp-posts be-
ing fitted with sensors that can transmit information about
cloud cover to offer hyper-local weather forecasting [3] to
smart meters for metering energy usage at city nodes. The
same or similar data on a modality of energy or transpor-
tation can have multiple different sources, e.g. smart
thermostats metering heating or cooling needs versus grid
measurement devices, or a navigation app collecting GPS
signals from its users versus a road equipped with elec-
tronic traffic counters.
Whether it is smart infrastructure, and external smart de-
vice or just smart phones with applications generating
time-synchronized geo-location data, the true value from
these numbers and patterns emerges when they are con-
nected to make cities sustainable and livable. There is also
huge potential in enabling citizen participation through
civic technology, i.e. apps or games, which allow citizens
to report damaged signs, or direct involvement in com-
munity projects [4]. These massive amounts of data and
accompanying algorithmic predictive possibilities could
allow targeting a city’s limited resources more efficiently
and tackling complex societal problems.
However, irrespective of the inefficiency to collect so
much data from so many sources to deduce the same in-
formation – there are many possibilities to misuse the
original data to deduce information that the data owners
did not or would not have allowed. The discussions
around profiling must be taken seriously as the majority
of users and citizens are currently unaware and unprotect-
ed. Smartness starts at the very beginning of the data
flow, at the data acquisition step. Data protection must be
integrative into the very sources of data, especially if they

4. are in personal use, e.g. smart phones, or capturing per-
sonal data, e.g. faces in traffic camera footage.
Atomic, dynamic configurability of data flows about
which data can be acquired for what purpose in what
granularity and time span and location must be enabled in
the intelligent devices or along the intelligent infrastruc-
ture of a city. The configuration or the effects thereof
must be represented in an easily comprehensible way for
the data originators. The configuration must be dynamic
in the sense that service providers are able to receive the
data in the required granularity and with the allowed pri-
vacy and confidentiality protection settings defined – on
demand and per service.
Privacy and confidentiality preserving data analytics are
required to enable the service provider to retrieve the
knowledge without violating the agreed upon granularity
of data or the allowed privacy or confidentiality settings.
4.2 Prescriptive Analytics – Put Knowledge
into Action
The combination of multilayer virtual networks to repre-
sent system dynamics of connected resource infrastruc-
tures combined with real-time data not only allows inter-
preting current situation but also predicting states in the
future given specific circumstances and evolving behav-
ioral patterns. Even this knowledge is of little use if not
put to action.
Prescriptive analytics is the combination of a predictive
model of system and data with optimization techniques as
to propose options of action given a future situation. Sim-
ulation needs to be used to verify and give operators con-
fidence before taking the actions. Additionally, real-time
data allows online verification of the predictions to enable
real-time corrective capabilities.
However, the potential smart cities use cases, such as
multimodal optimization of transport and energy to mini-
mize CO2 emission, are so complex and inter-
organizational that the possibly millions of insights per
second [5] cannot even be taken advantage of fast enough
to realize their value. Ultimately, the prescriptive analyt-
ics will be used for decision automation, for example as
used in the current self-driving car pilots [6].
4.2.1 Model- and Data-driven Analytics
Model- and data-driven analytics is at the core of the re-
quired smart data infrastructure as opposed to the purely
data-driven approach of big data. Data-driven analytics,
e.g. data mining and machine learning, can be is used to
reveal characteristics of the systems not known before or
to learn solely based on the data, when the programming
of rule-based algorithms are infeasible. Data-driven ana-
lytics is required when dealing with data-rich but theory-
poor domains such as online communities [7] and neuro-
science.
The city, however, is a planned, constructed, and engi-
neered system, consisting of increasingly digitized physi-
cal infrastructure. Models based on physical laws such as
the flow network models use known external knowledge
of the physical processes. At the same time, in today’s
complex systems and increasing dynamics through liber-
alized economic transactions, end user participation with
their shared resources – e.g. cars to provide transporta-
tion, or PV installation to provide energy – numerical
analysis to solve these models becomes very hard.
The digitization also extracts digital copies of domain
know-how entered by domain or planning experts using
software tools, e.g. how a distribution network for elec-
tricity is setup. Finally, the digitization of infrastructure
not only enables combining domain models, e.g. the con-
crete topology implementation, with physical models,
multimodal flow networks to explain the system with stat-
ic but hard facts, but also to analyze real-time data com-
ing from that infrastructure to discover unknown facts
caused by stochastic and behavioral processes such as end
user participation.
Finally, the power lies in semantically capturing the exist-
ing knowledge as well as the knowledge discovered from
model- and data-driven analytics. This continuous seman-
tic knowledge modeling allows continuous model im-
provement through real-time and historical data. Real-
time data, thus, is not only used for determining when to
take corrective actions according to the prescriptive ana-
lytics but also to improve models and the precision of the
prescribed actions. As such with model- and data-driven
analytics, more data leads to better models, and better
models lead to smarter data – enabling actionable
knowledge without invading privacy or compromising
confidentiality.
4.2.2 Decision Automation through Prescriptive Ana-
lytics
Decision support and decision automation have been de-
veloped at the enterprise business level for some time. In
this context decision automation refers to automating de-
cision making in recurrent business situations by automat-
ing workflows and business processes in combination
with employing business rules.
In cyber-physical systems, such as the resource infrastruc-
tures of a smart city, decision automation refers to both
planning and efficient operation of the infrastructures as
well as their efficient usage. Prescriptive analytics as
briefly discussed is the computational capability to make
predictions and combine these with optimization tech-
niques to propose actions for possible system situations
that require adjustments. The available real-time data and
data-driven analytics enable both online verification of
the models and decision automation as to when the pre-
scribed actions should be taken.
However, predictive models, optimization, and simulation
techniques are highly compute intensive. These tech-
niques massively benefit from large server farms, which
enable the parallel execution of so-called deep learning
models [8]. The extracted knowledge, i.e. patterns or ref-
erence values, which when encountered required a specif-
ic set of actions, can then be applied to real-time data in-
stream, i.e. as it enters the computing platform. This is
sufficiently for online decision automation, e.g. for plan-
ning and many cases of operational decisions in urban in-
frastructure management and usage.

5. 4.3 To Cloud or Not to Cloud
The question remains as to where to store smart city data
and where to execute the algorithms and computations
required for prescriptive analytics. Figure 3 depicts an
extension of the concept of cloud computing as a service
platform for smart cities presented in [9]. The main ra-
tionale is to utilize all available storage, computing, and
communication capacities available along the intelligent
infrastructure – be it transportation, or energy – of a smart
city.
The smart infrastructure [9] only concentrates on the addi-
tional data sharing potential of a cloud computing plat-
form. Similar to the 1970s mainframe era, where business
users sent their own data and did their own analysis on
managed mainframe computers via time sharing, tapping
into data of shared interest is facilitated once it is on the
same platform. In addition to data-as-a-service, the smart
data infrastructure, with state-of-the-art cloud service ca-
pabilities [10, 11] can facilitate the sharing of analytics
algorithms. Once predictive models and complex analyt-
ics algorithms are developed and deployed in the cloud,
they can be served as a utility to other users.
Figure 3 Smart data infrastructure [9] extended to utilize
all available storage and computing capacities including
those of smart devices
A cloud platform enables seamless deployment of analyt-
ics algorithms onto the massively distributed environment
of computer clusters through distributed data and network
management techniques. The same deployment tech-
niques can be used, to deploy patterns or entire trained
neural networks [12] onto intelligent electronic devices
along the smart city infrastructure. The so created capa-
bility of in-field analytics enables higher quality decision
making through local analysis and control that is in ac-
cordance with the knowledge discovered using system-
wide analysis in the cloud (or some other backend). Simi-
larly, the same distributed data and network management
techniques can be used to form ad-hoc computing clusters
for regional analysis, coordination, and control [13]. Such
in-field analytics enables the insights to be utilized in re-
al-time as an event is being captured by the intelligent
electronic devices through real-time synchronized and
geo-tagged measurements. Especially in congestion man-
agement and cross-optimization of multimodal energy and
transport networks such differentiated analytics capabili-
ties in a smart data infrastructure will be highly valuable.
5 Applications and Stakeholders
Transport is the biggest energy using sector followed by
manufacturing and households. The generic city node
model not only captures the multiple modes of energy and
transportation. Commerce, manufacturing and households
are modeled as the very city nodes. The movement of
goods and people, and the energy required are modeled in
a system-thinking manner. Optimization of the interde-
pendent multimodal infrastructures of energy and trans-
portation is the very task of a sustainable, livable city.
Scenarios in which energy and transportation intertwine
such as electric vehicle integration or energy-efficient city
logistics offer initial convergence areas for the application
of the systemic model, and the required smart data infra-
structure described here.
5.1 eCar and Renewables Integration
The “well-to-wheel” energy inefficiency in transportation
is indicative: losses amount to 70 percent either during
fossil power generation, transmission, and distribution for
the electrification of vehicles or when driving and idling
at traffic lights with petrol vehicles [14]. In light of these
inefficiencies, the integration of renewable energy sources
and the full-electric vehicle is a perfect technology match.
Given that the technological challenges are overcome, this
match not only enables zero-emission transportation, but
electric cars that are parked most of the time also repre-
sent a viable storage option for the peak supply from Re-
newables, which occur at noon via solar and at night via
wind during the main parking times. The matching be-
tween transportation and energy needs represents a new
arena which becomes populated by car manufacturers
who provide infrastructure or even energy supply [14]
alongside the incumbent energy sector players. The en-
trance of new players, such as car-sharing providers, also
transforms these traditional segments into new arenas.
The parking lots, where the physical connections between
the two intermodal networks happens via charging sta-
tions, are again owned by many other stakeholders, the
city, owners of commercial and industrial buildings or
dedicated car sharing stations.
5.2 Energy-efficient City Logistics
In energy efficient city logistics the flow of goods, vehi-
cles, and electricity is forecast, optimized, monitored, and
controlled both long-term and in real-time. City hubs and
logistics consolidation nodes play an important role for
coordination, as they are where goods are stored and sort-
ed for the following optimized in-city distribution. These
logistics consolidation hubs offer options for energy effi-
ciency, through cooling and heating, water management,
as well as electricity optimization. There are already en-
ergy demand response service providers targeting these
logistics centers to become part of so-called “energy sav-
ing fleet,” which then offer flexibility (i.e. saved energy
when power supply is low) to local utilities. Together
with fleets of smart electric trucks the margin of efficien-
cy increase is of course much higher per “user.” An inte-
grated positioning and on-trip vehicle re-routing based on

6. recent traffic, order, and weather data leads to improved
transparency in fleet management. The utilization of elec-
tric vehicles, energy management system, dynamic vehi-
cle routing, order management, and electric mobile city
hubs can reduce the environmental impact of commercial
traffic in urban areas. Integration of an order manage-
ment, for logistic processes, hubs and service models ena-
bles cities, logistics service providers, and logistics hubs
as well as major internet retailers to save resources and
gain efficiency, enable better quality of life in cities.
6 Conclusion and Outlook
The smart city is a complex system. When reduced to sin-
gle use cases the complexities as well as the synergies are
not taken into consideration. In order to expose require-
ments for a smart data infrastructure as well as for prob-
ing viable operator models, we have proposed a system-
thinking inspired generic city node model, which captures
the essence that the city is made up of interdependent re-
source infrastructures, which need to be optimized.
The accompanying smart data infrastructure, with the
main principles of (a) data and analytics as a service as far
as data and algorithms sharing is synergetic across appli-
cations and stakeholders, and (b) in-field analytics for re-
al-time prescriptive analytics that enables online decision
support and automation, reduces amount of raw data that
needs to be communicated, as well as enables atomic data
protection already within the smart devices.
The technological challenge is finding an efficient data
structure for the multilayer virtual network model of the
generic city nodes, which allows for efficient distributed
computations of the numerical analysis combined with
real-time data. The specification and implementation of
the concepts introduced here and their testing in simula-
tions and real-world setting is part of our ongoing and fu-
ture work.
On the operator model, or business model side, the chal-
lenge is how to start such a complex ecosystem. One of
the possibilities also discussed in [9] is so-called commu-
nity or city clouds. The trend towards open data on a Eu-
ropean level [15] might even accelerate the deployment of
city clouds as a platform due to the ease of data sharing. It
is highly advisable to employ a systemic model as intro-
duced in this paper, in order to expose stakeholder imbal-
ances and synergies to counteract or leverage them in the
design of a smart data infrastructure early on.
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