Journal of Software Engineering and Applications, 2013, 6, 97-101
doi:10.4236/jsea.2013.63b021 Published Online March 2013 (http://www.scirp.org/journal/jsea)
97
Integrated Mobility: A Research in Progress
Gianmario Motta1, Antonella Ferrara1, Daniele Sacco1, Linlin You1, Gianpaolo Cugola2
1Dept. of Industrial and Information Engineering, University of Pavia, Pavia, Italy; 2Dept. of Electronic and Information Engineering,
Politecnico of Milan, Milan, Italy.
Received 2013
ABSTRACT
We here present an ongoing research project abou t an integ rated real time mobility assistan t, which has been developed
for the call “Personal Integrated Mobility” of the EU framework program. The assistant is aimed to support personal
mobility in a near future scenario, where green, shared or public transports are replacing the current private, carbon
transportation system. The assistant handles itineraries, which are based on go als of time, en erg y/po llu tion and co st, and
supports users both before and during the trip. The serv ice will gather and interpret an y relevant source of information
on transport resources and their availability. Information includes user generated content and social data. Given the
globalization of users and the hourly peaks of mobility, the assistant is a cloud service, which integrates existing tech-
nologies; however a global extension requires a federation of clouds and, above all, the interpretation and validatio n of
social data. These can be considered major research challenges.
Keywords: Smart Cities; Cloud Computing; Mobility Man agement; Mobility Integrator; Service System
1. Introduction: The Personal Mobility
Isssue
The world population is increasingly city-based. First
most people live in cities: 3.6 billion nowadays and in
2050 68% of the world population [1]. Second, cities
consume 80% of worldwide energy production and gen-
erate 67% of energy-related greenhouse gases [2]. Third,
64% of travel kilometers are urban and the travel within
urban areas is expected to triple by 2050 [3]. Hence, ur-
ban mobility is a challeng e.
In 2011 the mobility maturity of 66 cities worldwide
was assessed by 11 criteria [4], ranging from public
transport share to average travel speed and trans-
port-related C02 emissions. With a mobility score from 0
to 100 EU had the best regional performance with an
average of 71.4 points, with seven out of the 18 analyzed
cities scoring above 75 poin ts, while Eastern/South East-
ern Europe performed a regional average of 64.0 points.
Also Asian cities e.g. Hong Kong, Singapore and
Shanghai reach the high and middle-high level [4]. Smart
city projects are focusing on environment issues. Am-
sterdam Smart City [6], is a cradle for twenty pilot pro-
jects developed in cooperation with 72 partners aimed at
reducing emissions of carbon dioxide. Lyon [7] carries
on an integrated mobility project for people, travelers and
freights, starting in 2012, with the goal of reducing emis-
sions. In Portugal, MOBI.E [8] is a modeling framework
for smart mobility, includes a network for sharing elec-
trical vehicles. In Malaga [9] a project is deploying a
fleet of electrical vehicles. All these projects intend to
reduce emissions and energy saving, with a positive so-
cial and economic impact [10,11].
What shall be a future ideal scenario? According to
Horizon 2020 [5] in future smart cities transport shall be
smart, green, and integrated. To support an intelligent
mobility, cities have to g et more and more wired and the
smart phone users have to grow from the current 55-60%
to 100% of mobile phones. This, with the increasing
number of smart terminals such as smart TV and in-ve-
hicle devices, will enable users to be served by an inte-
grated mobility information serv ice [12].
However, a future, ideal scenario requires also a new
mobility service concept. Mobility Integrator is an inno-
vative approach that integrates Information & Planning,
Transport Services, Infrastructure and Traffic Manage-
ment. In short, it shall provide a smooth and convenient
integrated mobility platform. Let us mention some pro-
jects that fall within these objectives.
Instant Mobility [13] intends to manage mobility for
different stakeholders, based on future internet infra-
structure (FI-WARE). It enables travelers to view real-
time traffic status and public transport availability and
optimize their routes according to current personal pref-
erences and constraints. The service also supports local
authorities, public transport operators and professional
drivers to optimize traffic and promote car sharing and
pooling. In addition, logistic is enhanced and optimized
for fleet management.

Integrated Mobility: A Research in Progress
98
TRIPZOOM [14] implements a new approach to urban
mobility by sharing personal mobility patterns via social
networks. The research & development project SUNSET
is part of the European Commission’s Seventh Frame-
work program Smart Cities & Sustainability (DG Con-
nect) and consists of 9 partners from 4 European coun-
tries. It encourages citizens to use sustainable forms of
transportation and intends to generate a win-win game
for all involved stakeholders. E.g. citizens can optimize
their mobility needs using recommendation and person-
alized traffic services from the city authority. The city
authority can accurately assess current infrastructure use
and induce mobility patterns by an incentive system.
Third-party service providers can tap into the data to cre-
ate new offerings and integrate with others through
common incentive structures. Generally, TRIPZOOM
focuses on sharing trip information on social networks
and optimizing the personal mobility according to incen-
tives and suggestions derived by mobility sensing. It can
be seen as an attempt to integrate Traffic Management
and Information and Planning.
In Future Urban Mobility program [15] developed by
the Massachusetts Institute of Technology (MIT) and the
National Research Foundation of Singapore, SimMobil-
ity model is designed to use personal mobile computing
and communication devices to provide high quality in-
formation about the state of the transportation network to
users and system managers, working as data collectors
and computing engines. The main objective of SimMo-
bility is to guarantee the promise those urban citizen de-
mands, such as energy consumption and transportation
flows are derived from human needs. Currently, only a
framework has been developed. The intent is to create a
model that serves as a decision-supporting tool for urban
planners and policy makers. It gathers, processes, stores
and shares the data from different sources including per-
sonal devices, but it is mainly focused on traffic man-
agement in the urban area.
2. An Integrated Mobility Assistant
Our mobility proposal, called IRMA (Integrated Real-
time Mobility Assistant), targets individuals in whole
lifecycle of their mobility. Work started in Department of
Information and Industrial Engineering of Pavia Univer-
sity to develop an App that could assist travelers to meet
their schedule even with of transport disruptions [16].
From further investigations emerged a need of a more
comprehensive architecture. First, the mobility App had
to be able to process any useful information source; sec-
ond, it had to be universal and usable in any urban area,
thus implying a Service Oriented Architecture in a cloud
environment. Such integrated architecture, which has
defined for call 6.6 of the European Framework Program,
called “Personal Integrated Mobility”, is presented here.
Here below illustrate first the function a l concept.
IRMA handles itineraries, performed b y one person or
a group, that are conceived as an oriented graph [17],
whose connections are represented by nodes, while en-
ergy or pollution requirements are modeled as resource
constrains as it happens with project modeling techniques
[18,19]. Finally, itinerary cost is considered as third
variable. Itinerary is a master information that is inte-
grated both before and during the trip, by additional in-
formation about:
 Transport map, that records transport resources
which are / will be available e.g. train or under-
ground;
 Transport status, that records future and current
availability and delay; e.g. train timetable, train
delay, underground load, road traffic and devia-
tion from st andard t ravel t ime;
 Relevant information from social network, that is
handled as a text message (traffic jams, road
bumps etc.)
The processing cycle of IRMA conceptually is a Big
Data version [21,22] of the classic BI schema [20], that
adds semantic interpretation/ transformation of social
information [23,24]. In short, IRMA aggregates a net-
work of information sources, typically spread in clouds,
to a personal information service. In Table 1 we summa-
rize the functional coverage of IRMA versus the before
mentioned Instant Mobility (IM) TRIPZOOM (TZ) and
Future Urban Mobility (FUM). The table lists main fea-
tures of integrated mobility solutions, namely the stake-
holders which are targeted, the data sources that are in-
volved, and, finally, the services that are provided to the
individual users. The focus of IRMA are the services to
the individual mobile users. IRMA manages the whole
individual mobility lifecycle of an itinerary, which goes
from A to B through multiple connections. Such itin erary
can be analyzed, forecasted and assisted using historical
and real-time data- More than that, mobility users can get
more effective and efficient assistants to manage their
itineraries by two dimensions in terms of before the trip
and after the trip. IRMA is a cloud-based solution for
personal mobility. The services are built upon a Future
Internet-based infrastructure by which various data
sources are integrated. Upon that infrastructure, different
stakeholders’ views are served: users, transport providers
and municipalities.
2.1. The Overall Architecture
IRMA architecture includes various elements, namely (a)
mobility analysis (b) mobility forecasting (c) mobility
assistant (d) terminals (e) communication services (f)
sources which is shown in Figure 1. Let us shortly de-
scribe these elements.

Integrated Mobility: A Research in Progress
99
Table 1. Functional coverage of IRMA: a comparison.
Projects
Features IM TZ FUMIRMA
Mobility User Part
Municipality
Stake
holders Transport Prov i de r
Transportation
Management Systems
Traffic Management
Systems
Vehicles
Data Sources
Social
Networks
Mobility analysis Part
Individual Mobility
Planning
Services
Individual real-time
assistant
Figure 1. IRMA Architecture.
2.2. The Individual Mobility Analyzer
It stores a mobility map and related mobility data. The
mobility map describes mobility resources within the
urban area, by route, time, and mode. In turn mobility
data describe the mobility load by route, time and date.
Users can analyze mobility across any urban area, while
municipalities and transport enterprises can analyze ac-
tual versus expected mobility performances. These his-
torical data are an input to the individual mobility fore-
caster.
2.3. The Individual Mobility Forecaster
We assume that the urban area is divided in cells. The
forecast formulates a discrete event framework that gives
the ability to anticipate the effects on the itinerary by
predicted or unpredicted future events (e.g. football
match, exhibition, holiday, accident etc.). Forecasted
travel duration is an input to the mobility assistant, where
the user can choose the best route, taking into account the
dynamics of transportation modes and user constrains.
2.4. The Personal Mobility Assistant
It shall assist the end user to plan, configure, monitor,
alarm, and reschedule mobility across multiple mobility
options. It manages and supports the mobility itinerary
by two phases in the mobility life cycle with different
modules/services.
Before the trip (Planning). Request Handler proc-
esses the mobility request of each user. Request may
concern an indiv idual trip or a mobility calendar. It help s
the user to define the optimal mobility plan by accessing
mobility forecast and mobility maps through the Infor-
mation Retrieval sub service. The user will choose and
confirm the ideal option as an ind ividual itinerary. In the
Figure 2, you see simplified screens where transport
options that are sorted by time or by energy efficiency.
Transport time is based on the forecaster that adjusts the
standard time on the traffic load projections related to
transport modes, the specific daytime, date, and the route
from A to B.
During the trip. During the trip the user receives in-
formation about disruptions (Event Notifier) and use the
assistance to choose a viable alternative (Compensation
Engine). An informal representation of the user’s view is
in Figure 3. Event Notifier is a set of instances that are
activated when the user confirms and actually starts the
trip. It concerns all connection s of the individual itinerary
and process relevant disruption information provided by
communication services. Compensation Engine proc-
esses mobility alternatives in front of a disruption or a
change, by browsing on mobility analyzer the closest
option (as an alternative the request handler could fetch a
plan B in advance).
2.5. Terminals
IRMA shall be used by a variety of mobile and fixed
terminals, which include smart phone/tablet/ laptop
Figure 2. Before a trip - Planning a trip - illustrative screens.

Integrated Mobility: A Research in Progress
100
Figure 3. During the trip - Reschedule a trip - illustrative
screens.
computers, smart TV to support ageing people, web ap-
plicati o n s on board di s p lays, etc.
3. Integrating the Mobility Assistant with
Information Sources
A SOA - EDA (Service Oriented Architecture – Event
Driven Architecture) infrastructure enables services that
access field information on mobility. Such collection
involves various data so urces. Mobility data include time
tables & delays of planes, trains, buses, underground;
availability of taxi, carpooling, parking. Traffic data en-
compass data gathered from traffic & environment man-
agement systems. In turn, vehicle data include data on
mobility from bus, trains, cars, bikes etc. Finally, crowd
data comprise alarms and messages refined by ad hoc
schema coming from Twitter, Facebook, YouTube, etc.
Through a communication layer, the system gets in-
formation on traffic and mobility, with various purposes,
e.g. access to historical data, on demand access, real-time
access. The access on historical mobility data is intended
to collect historical mobility information that will be
processed by Analyzer and Forecaster modules; sources
may include traffic systems and transport systems. On
demand access concerns information about availability of
transport resources e.g. trains, urban public transportation,
carpooling, bikes. Finally, real-time access gets informa-
tion to moving users; information is taken on the delay or
disruption that happens while users are moving. Sources
are data and services of the systems the manage mobility,
namely transport operators, municipalities, mobility us ers,
that have been mentioned before. The itineraries of
IRMA users may be also a statistical source, if data are
cleaned and made anonymous in order to protect privacy.
4. Conclusion
We have illustrated the key concepts IRMA, i.e. an inte-
grated mobility assistan t, which we regard as a stage 2 of
smart cities. It is in a design in progress and only small
lab demonstrators have been made. Generally, IRMA
looks feasible, because it mainly integrates existing
technologies; however, it goes beyond the state of the art
on several aspects, among which data integration as a
key one. Let us consider this issu e.
IRMA uses heterogonous information from multiple
sources. Hence, the correspondences among similar enti-
ties (instances and concepts) in different datasets have to
be found in order to make a virtually integrated knowl-
edge base available. Tis implies to develop scalable
methods to select, triplify, match, and integrate mobility-
related open data and user generated content, so that
these data can be plugged in the data warehouse of the
analytic IRMA services. Two research directions will be
explored, as efficient techniques for the on-the-go
matching of linked data schemas and quality-driven
methods to fuse semantic data coming from different
sources will be defined by extending techniques applied
to service descriptions extracted from Web sources [27],
or by adopting a Human Computation approach to im-
prove urban-related information via user-centric contri-
butions [28].
IRMA is expected to impact on environment, business
and users. As far as environment is concerned, the ser-
vice will lower energy and emissio ns, since it will enable
to use public/shared transport systems even with complex
connections. Also, a variety of stakeholders can benefit
from IRMA. In business perspective, municipalities and
transport providers can analyze and forecast mobility in
terms of time, route, connection, and mode, and, there-
fore, can optimize transportation resources. Finally, mo-
bile users can define their travel and can actually travel
in an optimal way, without any previous knowledge of
any transportation system.
Finally, as future goal, with a cloud based service as
IRMA, users will, ideally, plan their mobility in any ur-
ban area across the world. Also, a global coverage im-
plies several advances in clouds, as interoperability
among cloud offerings with federating applications run-
ning on different clouds [25], and combine pull and push
approaches by integrating Service Oriented and Event
Driven architectures. Finally, IRMA can be platform for
a global virtual ticket. Simply, the users buy a mobility
ticket, that is nothing else but a credit, alike a SIM re-
charge, that can be spent on the itineraries managed by
IRMA.
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