1 21.09.2016

Ing. Chiara Bersani, DIBRIS, University of Genoa

Big Data, Smart Analytics, the Future Now

Intelligent Transport System (ITS) and risk

applications on dangerous good transport

AGENDA

BIG DATA in transportation sector

DELAB research and activities

Dangerous Good Transportation Information System

Risk definition and loss prevention in Dangerous Good Transportation

Enhancing safety of transport by road by on-line monitoring of driver emotions

A smart railcar prototype for dangerous good transportation

Conclusion

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BIG DATA IN

TRANSPORTATION

SECTOR

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BIG DATA IN TRANSPORTATION SECTOR

4

Big data is already being used in transportation sector,

for example, to improve road traffic managements and

the planning of public transit services.

This presentation explains how new technologies can

enable the use of big data in freight transport (and in

particular in dangerous good transport) to provide safer,

cleaner and more efficient transportation system.

5

Risk assessment processes use big data and large

scale predictive analytics to assess risks dynamically

and report automatically, empowering personnel to

identify issues, taking necessary preventive measures

to address them, avoiding

a related shutdown incident or accident.

BIG DATA AND SMART ANALYTICS

TO PREVENT RISK

DELAB

RESEARCH AND

ACTIVITIES

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DELAB’S RESEARCH

DELAB is a joint laboratory between:

DIBRIS (Department of Informatics,

Bioengineering, Robotics and Systems

Engineering) of the University of Genova

ENI, the biggest Italian oil Company

• DELAB research focuses on Dangerous Goods

Transportation (DGT) integrating intelligent

systems in order to prevent accidents to people

or infrastructures and damages to the

environment.

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DELAB MAIN ACTIVITIES

8

Real time monitoring architecture for vehicles carrying

dangerous goods

Support tools for staff and drivers including training,

resource management and advanced data mining

Decision Support Systems (e.g. orders and fleet

management)

Risk definition

DANGEROUS

GOOD

TRANSPORTATION

INFORMATION

SYSTEM

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DGT INFORMATION SYSTEM (1)

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On board architecture

Transmission system

Database

GIS-based Applications

DSS

A suggested On-board architecture might be based on:

a collection of analog/digital sensors

a “road box” containing a functional unit (concentrator)

interfaced with sensors and equipped with a GPS

antenna and a GPRS transmitter/receiver

ON-BOARD ARCHITECTURE (1)

Products temperature

and pressure

Electronic Counter Emergency

button

Canbus

ON-BOARD ARCHITECTURE (2)

GIS APPLICATION

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The approach to the GIS layer must include:

effective graphic interface

high scalability

methods to retrieve information from the interface

ability to perform geographic calculation

Available information includes:

Maps

Geocoding / Reverse geocoding

Routing

Proximity researches

BIG DATA SOURCES

14

WHAT IS TIP?

Since 2002 a transmission system between trucks and

remote servers has been carried on.

In the last few years continuous improvements have led

our proposal to the acknowledgement as an Italian

communication standard “de facto”.

Actually the system involves more than 400 vehicles in

Italy and new transmissions from foreign countries are

increasing.

TIP (Transport Integrated Platform) is a web portal, with

secure and selective access, accessible via the Internet

by both internal users (Eni S.p.A.) and by external parties

(e.g. suppliers of transportation).

TIP GOALS

TIP helps Eni S.p.A. to provide a high quality service, to

improve loss prevention strategies, respecting laws and

rules of safety and environmental protection and for the

protection of the health of the workers and the public.

Because transport services are almost completely

outsourced, it is necessary to develop instruments for

continuous monitoring of processes and performance,

and to ensure an adequate level of control of this

important aspect of the supply chain.

TIP SECTIONS

Audit

Operative

control

Anomalies

control

Control

Center

Route

planning

Railcar

management

Training

Document

management

Remote

monitoring

Technical

Management

Service

stations

Accident

reporting

Quality

Performance

indices

Administration

Emergency

REMOTE MONITORING

This module is equipped with

real-time monitoring of

transport through the

representation of data in

tabular form and on geo-

referenced maps and

interactive maps.

This allows the users to

constantly monitor the

operations and to extract and

export data.

RISK DEFINITION

AND LOSS

PREVENTION IN

DANGEROUS

GOOD

TRANSPORTATION

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RISK IN THE DG TRANSPORTATION

DG transportation accidents are perceived as

low probability–high consequence (LPHC)

events and data seem to support this

perception.

Risk is the primary ingredient that separates DG

transportation problems from other

transportation problems.

In the context of DG transport, risk is a measure

of the probability and severity of harm to an

exposed receptor due to potential undesired

events involving a DG (Alp, 1995). The exposed

receptor can be a person, the environment, or

properties in the neighborhood. 20 21.09.2016

RISK DEFINITION

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Basic risk equation = probability of an event

multiplied by the consequence of that event

where

Expected frequency of an accident event which involves

DG material

Exposure: Consequences to people (e=1), property(e=2), or

the environment (e=3) is determined by what, where and

when the material is spilled.

THE FREQUENCY ANALYSIS

The frequency analysis involves

(a) determining the probability of an

undesirable event;

(b) determining the level of potential receptor

exposure, given the nature of the event;

(c) estimating the degree of severity, given the

level of exposure.

There are two primary means to estimate the

accident, release, and incident probabilities:

historical frequencies and

logical diagrams (fault tree and event tree

analysis). 22 21.09.2016

CONSEQUENCE MODELING AND

EXPOSURE ANALYSIS

The consequences are a function of the impact

area (or exposure zone) and on the type of

exposure within the impact area:

population,

property, and

environmental assets.

Big data and Smart Analytics are

fundamental to quantify individual risk and

people involved.

Individual risk is defined as the yearly death

frequency for an average individual at a certain

distance from the impact area. 23

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DYNAMIC INDIVIDUAL RISK EXPOSURE

Unlike traditional methodologies to compute population

exposed in case of accident, the use of Big Data

created by the mobile network represents an

innovative and smart approach.

To process and observe Big Data created by the mobile

users can provide information on the real behavior based on

millions of mobile events that occur on mobile network 24/7

365 days a year. The data can be extrapolated to provide a

reliable value in real time of the total population in specific

area.

INDIVIDUAL RISK ACCEPTANCE CRITERIA

ENHANCING SAFETY OF

TRANSPORT BY ROAD BY

ON-LINE MONITORING OF

DRIVER EMOTIONS

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MONITORING SYSTEM

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Biometric Data

Behavioral Data

PHYSIOLOGICAL SIGNALS

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SEW

Smart T-shirt

Smartphone Server

Physiological data are acquired and previously elaborated by a wearable system.

This system is a specific T-shirt equipped with two kinds of sensors that are fully

integrated into the fabric structure. There is one sensor of breathe (a strain gauge)

and two piezoelectric sensor for electrocardiography (ECG) data.

Furthermore T-shirt is equipped with a device (SEW3) dedicated to the acquisition,

pre-processing, storage, and/or transmission of data; the device is inserted into the

pocket of the garment.

The SEW3 device transmits both ECG wave and sample data.

OTHER EXPERIMENTS

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Increase applications of ECG mesurament, for example changed electroted positions

Informations by Eye Blinks Rate

Integration of an Okulus Rift in the pre-existing system

Integration with traffic, weather and cartographic parameters

More driving sessions

A SMART RAILCAR

PROTOTYPE FOR

DANGEROUS GOOD

TRANSPORTATION

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A SMART RAILCAR PROTOTYPE FOR

DANGEROUS GOOD TRANSPORTATION

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Architecture of the hardware system

Development of the software

Simulation of real scenarios

WEB GIS software module

Analysis of the system performances

CONCLUSION

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RESULTS AND CONCLUSION

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REAL-TIME AND HIGH-SPEED PROCESSING FOR BIG DATA ANALYSIS AND

PREDICTION

DSS DEVELOPMENT

PREVENTION MAJOR RISK

BIG DATA COLLECTION - moving

objects (vehicles and human…) -

roadside sensors – other sources of

information (social medias, etc.)

SMART ANALYSIS

Data analysis and prediction

OPTIMIZATION

Utilize analyzed data or insights

NAVIGATION

Innovative services for

public authorities and

fleet manager

The main product is a big data analytics and optimization platform

targeted to transportation and logistics industries.The proposed

platform has already been integrated into ICT-systems of the main

important petrol company in Italy. The solution allows for the capture of

vast amount of data, its aggregation from different sources and the use

of analytics for generating decision-support information.

REAL-TIME ANALYTICS

AND PREDICTION

Chiara Bersani

DIBRIS

Department of Computer Science, Bioengineering, Robotics and System Engineering

University of Genoa

via Opera Pia 13 16145 Genova

Italy

Chiara Bersani, PhD

Chiara Bersani@Unige.it