ABSTRACT

The SMILE Project aims at optimizing and armonizing the processes of management of urban traffic and distribution of goods, through an efficient use of existing systems and infrastructures and by providing involved actors with information and guidelines. The goal is to relieve the pressure on traffic in urban centers and increase the quality of service for the operators, not only in a smart city and smart mobility perspective, but also in what we may call a “smart working” one.
To this purpose, SMILE is a telematic platform that exploits existing systems specifically in the context of urban mobility and logistics to gather and normalize information coming from such systems, and make them available to an application layer to develop innovative services oriented towards the business operators, the public administration, and citizens, in a smart mobility-smart city perspective.

PRINCIPAL INVESTIGATOR
CRISTIANO CERVELLERA

DATA SHEET
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   TYPE AND DATE
   PAR FAS 2007-2013
   STARTING DATE – Sep 23, 2013
   ENDING DATE – Feb 17, 2016

COST
TOTAL COST € 2.900.000,00
TOTAL
FUNDING €

CNR ISSIA
COST € 210.000,00
FUNDING € 75.000,00

MAIN OBJECTIVES

The goals of the project and the addressed scenario correspond to three technologic challenges for the SMILE platform:

  1. To integrate existing technologies and systems in order to acquire, normalize and correlate heterogeneous data.
  2. To create functionalities, algorithms and services to be shared with end users and supporting a smart mobility in the urban context, optimizing the existing infrastructures.
  3. To employ new technologies, either integrated in or supporting existing ones, that add value to services for the collectivity, always in a smart city perspective.

ISSIA MAIN ACTIVITIES

The main activities of the CNR-ISSIA Unit in the SMILE project will be aimed at the development of algorithms for prediction of the state of traffic and detection of congestions and critical situations on the urban mobility network. Then, specific algorithms based on machine learning from data will be developed for the automatic generation of rules for decision support in order to improve the efficiency of last-mile logistics services.

The research will be carried out through the following specific activities:

  • analysis of the specific goals of the scenarios (urban mobility and last mile logistics) and of the platform functionalities;
  • study of the available information sources and evaluation, through a suitable statistical analysis, of the set of most significant variables;
  • development of statistical learning algorithms suited to deal with the available information sources;
  • solution of issues related to the heterogeneity and amount of input data;
  • definition of ad-hoc algorithms to manage input data and generate predictive rules, with a study on their integration in the decision support systems developed in the project;
  • test of the performance of the developed algorithms through simulation.

 

 

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SOURCE OF FOUNDING

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PARTNERSHIP

SELEX ES SPA
AITEK SPA
SOFTECO SPA
D’APPOLONIA SPA
University of Genoa