v - UNIPA, DICGIM

Smart Transport for
Sustainable City
Francesco Marcelloni
Dipartimento di Ingegneria dell’Informazione
University of Pisa, Italy
E-mail: [email protected]
Alessio Bechini, Beatrice Lazzerini
PerLab
Projects
PerLab
“SMARTY” (SMArt Transport for sustainable citY) project funded
by “Programma Operativo Regionale (POR) 2007-2013” objective “Competitività regionale e occupazione” of the Tuscany
Region”
 Urban Sensing
 Social Sensing
 Analysis of GPS traces
“Metodologie e Tecnologie per lo Sviluppo di Servizi Informatici
Innovativi per le Smart Cities” project funded by “Progetti di
Ricerca di Ateneo - PRA 2015” of the University of Pisa
 GPS traces similarity
Francesco Marcelloni
The Smarty Project
PerLab
“SMARTY - SMArt Transport for sustainable citY”, funded by the Tuscany Region
in the framework of Bando Unico R&S - 2012
Francesco Marcelloni
The Smarty Project
PerLab
“SMARTY - SMArt Transport for sustainable citY”, funded by the Tuscany Region
in the framework of Bando Unico R&S - 2012
Francesco Marcelloni
Our role in the Smarty project
PerLab
• Urban Sensing
• Cooperative air quality monitoring based on low-cost sensors (uSense)
•
•
Privately owned by citizens
Deployed in places where they live and spend most of their time
• Low-cost system for smart urban parking
• Social Sensing
• Real-Time Detection of Traffic Congestions from Twitter Stream
Analysis
• Critical event detection from Facebook events analysis
• GPS trace analysis
• Real time traffic analysis
• Real-time detection of incidents
Francesco Marcelloni
Social Sensing
PerLab
• Tweet analysis aimed at
• Detecting traffic congestion
• Detecting if traffic congestion is caused by an external
event
•
•
•
•
•
Soccer match
Procession
Demonstration
Flash-mob
…
• Notifying (in real-time) users about traffic congestion
E. D'Andrea, P. Ducange, B. Lazzerini, F. Marcelloni, «Real-Time Detection of Traffic From Twitter Stream
Analysis», IEEE Transactions on Intelligent Transportation Systems , Vol.16, no.4, pp.2269-2283, Aug.
2015.
Francesco Marcelloni
Social Sensing
PerLab
• Tweet analysis aimed at
• detecting
road
traffic
congestions and accidents
• discriminating traffic event due
to an external cause (football
match,
procession,
demonstration, flash-mob, etc.)
• notifying (in real-time) the
users of the traffic event
• Facebook event analysis
aimed at
• monitoring the number of
partecipants along the time
• notifying the users when the
event is likely to be critical
Francesco Marcelloni
Traffic detection from Tweet analysis
PerLab
...I'mstuck
stuck in a 7km
km
...I'm
...I'm
stuck inina a7 7km
queue...
queue...
queue...
C
Tokenization
I7km
C
...I'mstuck
stuckininaF
km
Ia7km
...I'm
I7C
F
...I'm
stuck inF
a
F
F
queue...
A
queue...
RAF
queue...
SUM: Status Update Message
TRA
TTR
Stop-word
filtering
Fetch of
SUMs and
Pre-processing
Stemming
Classification
of SUMs
Stem filtering
Feature
representation
Elaboration of
SUMs
Text mining elaboration on a sample tweet
Text of a sample tweet
Sono bloccato in una coda di 7 km... il
traffico è incredibile stasera! Voglio
tornare a CASA!!!
English translation: I'm stuck in a 7
km queue... traffic is unbelievable this
night! Wanna get HOME!!!
Feature representation
[arriv, blocc, caos, cod, km, ...,
..., stasera, traffic, vers, vial]F
wq=ln(Ntr/Nq)
Tokenization
Stop-word filtering
tokens
<sono>, <bloccato> <in>, <una>, <coda>,
<di>, <7>, <km>, <il>, <traffico>, <è>,
<incredibile>, <stasera>, <voglio>,
<tornare>, <a>, <casa>
<sono>, <bloccato> <in>, <una>, <coda>,
<di>, <7>, <km>, <il>, <traffico>, <è>,
<incredibile>, <stasera>, <voglio>,
<tornare>, <a>, <casa>
Stem filtering
<blocc>, <cod>, <7>, <km>, <traffic>,
<incredibil>, <stasera>, <vogl>,
<torn>, <cas>
Stemming
<bloccato> , <coda>, <7>, <km>,
<traffico>, <incredibile>, <stasera>,
<voglio>, <tornare>, <casa>
F relevant stems selected in the learning phase
[0, wblocc, 0, wcod, wkm, ..., wstasera, wtraffic , 0, 0]F
Francesco Marcelloni
[arriv, blocc, caos, cod, km,...,
stasera, traffic, vers, vial]F
<blocc>, <cod>, <7>, <km>,
stems <traffic>, <incredibil>, <stasera>,
<vogl>, <torn>, <cas>
Traffic detection from Tweet analysis
PerLab
• Binary classification problem
• traffic vs. non-traffic tweets
• balanced 2-class dataset of 1330 tweets
• best accuracy: 95.75% using an Support Vector Machine (SVM)
classifier
Prec 
TP
TP  FP
Rec 
TP
TP  FN
F -score  1  2 
Francesco Marcelloni
Prec Rec
  2  Prec   Rec
Traffic detection from Tweet analysis
PerLab
• Multi-class classification problem
•
•
•
•
•
traffic due to external event vs.
traffic congestion or crash vs.
non-traffic
balanced 3-class dataset of 999 tweets
best accuracy: 88.89% using an SVM classifier
Francesco Marcelloni
Traffic detection from Tweet analysis
PerLab
• Real-time detection of traffic events
• monitoring campaign of areas of the Italian road network
•
70 traffic events detected during September and early October 2014
• comparison with official Traffic News Channels
•
•
Autostrade per l’Italia
CCISS Viaggiare informati
4 traffic events detected on September, 26th,
2014
• 2 late detection events
• 2 early detection events
Francesco Marcelloni
Facebook event analysis
PerLab
• Real-time monitoring of events using Facebook
• Critical event: at least Ke persons probably will attend the event
• Ke is determined based on the event features and context
IF
2/3 * Num. Sure + 1/3 Probable > Ke
THEN the event is critical
• Analysis on the trend of the possible attendees
{"type":"EventoFacebookCritico","eventoFb":{
"idFb":"365145446986497",
"nome":"open day #master #alta #formazione",
"descrizione":"una giornata di incontri ed orientamento per futuri studenti dei nostri master e corsi di alta
formazione:\n\n- presentazione delle attività didattiche\n- workshop con coordinatore e docenti \n- incontro con
ex alunni\n\nl\u0027\u0027\u0027\u0027open day è aperto a tutti.\n\ninizio corsi novembre
2014\niscrizioni ai corsi entro il 31 ottobre 2014 \n\npossibilità di colloqui individuali. sede: roma. \nper
partecipare all\u0027\u0027\u0027\u0027open day è necessario registrarsi online http://goo.gl/rgg8wy",
"owner":"75874409682",
"location":"accademia di costume e di moda",
"startTime":"2014-10-25T11:00:00+0200",
"endTime":"data_stimata : 2014-10-25T13:00:00",
"pointWKT":"POINT((12.468175254952 41.901237903208)",
"partecipazione":{"attending":"22","maybe":"2","declined":"6"}},
"tipoEventoClassificato":"Arte","angleIndex":{"timeInMs":67442172,"estimatedAttending":22}},
Francesco Marcelloni
GPS trace analysis
PerLab
to exploit vehicle GPS traces as
traffic sensors
Francesco Marcelloni
GPS trace analysis
PerLab
• Spatiotemporal GPS traces analysis aimed at
• Detect road traffic congestions and accidents
• Notify the users of a traffic alert containing
• Affected area
• Critical traffic levels
o slowed traffic
o very slowed traffic
o blocked traffic
o incident
• Detected velocity of vehicles
E. D'Andrea, F. Marcelloni, «Detection of Traffic Congestion and Incidents from GPS Trace
Analysis», submitted to an International Journal.
Francesco Marcelloni
GPS trace analysis
PerLab
• Approach
• Matching of GPS traces on the road segments of the digital map of the
city
• Development of an expert system for traffic and incident detection
GPS traces
(latitude, longintude,
velocity, timestamp,
vehicle id )
Digital map
Pre-Processing
- establish vehicles
travel direction,
- perform routing,
- match GPS
traces on digital
map
Segment Traffic
Classification
- assign a traffic
label to each
segment
Traffic Alert
Notification
- perform a spatiotemporal analysis
for traffic and
incident detection
Traffic alert
(magnitude,
estimated velocity,
congested segments)
Francesco Marcelloni
GPS trace analysis
PerLab
 Road segment classification based on the velocity of vehicles in
traffic states in sj
the segment with respect to the traffic code velocity
blocked
vblock
very
slowed
flowing
slowed
P2 % × vcode
j
P1 % × vcode
j
absent
vcode
j
space
alert for incident with queue
T=1
S
very
slowed
very
slowed
blocked
absent
very
slowed
very
slowed
very
slowed
blocked
absent
very
slowed
very
slowed
very
slowed
blocked
absent
T=2
time
 Spatiotemporal
analysis of near
classified segments in
consecutive time
intervals
T=3
very
slowed
Francesco Marcelloni
GPS trace analysis
PerLab
• Experimental results
• Used SUMO (Simulation of Urban Mobility)
• Simulations of GPS traces of 50000 cars and 48 incidents in Pisa, Italy
• using SUMO (Simulation of Urban Mobility) framework
• Incidents were correctly detected
• Incident detection rate: 91.6%
• Average detection time: < 7 minutes
• It is also possible to detect the congestion propagation in roads
close to the incident
Francesco Marcelloni
GPS trace similarity
PerLab
• Objective
• To understand how much two GPS
traces are similar to each other
• Current methods
• Exploit the concept of Point of
Interest
• Not suitable to our aims
• New concept of similarity based on
closeness of the paths
• Applications: car pooling
Francesco Marcelloni
Data Mining for Big Data
PerLab
• Analysis of a large amount of data
collected from different types of sensors
• Data mining algorithms for big data
• In particular, accurate and interpretable
classification and regression systems.
• Speed-up close to linear
Francesco Marcelloni
Questions?
PerLab
Francesco Marcelloni
Department of Information Engineering
University of Pisa, Italy
E-mail: [email protected]
Francesco Marcelloni