Smart Transport for Sustainable City

Transcription

1 Smart Transport for Sustainable City Dipartimento di Ingegneria dell Informazione University of Pisa, Italy Alessio Bechini, Beatrice Lazzerini

2 Projects SMARTY (SMArt Transport for sustainable city) project funded by Programma Operativo Regionale (POR) 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

3 The Smarty Project SMARTY - SMArt Transport for sustainable city, funded by the Tuscany Region in the framework of Bando Unico R&S

4 The Smarty Project SMARTY - SMArt Transport for sustainable city, funded by the Tuscany Region in the framework of Bando Unico R&S

5 Our role in the Smarty project 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

6 Social Sensing 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 , Aug

7 Social Sensing 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

8 Traffic detection from Tweet analysis...i'm stuck in a 7 km...i'm...i'm stuck stuck in in a a 77 km km queue... queue... queue... Tokenization Stop-word filtering...i'm stuck in a 7 km...i'm...i'm stuck stuck in in a a 7 7 km km queue... queue... queue... TTRAFFI TRAFFI RAFFI CCC SUM: Status Update Message Fetch of SUMs and Pre-processing Stemming Stem filtering Classification of SUMs Feature representation Elaboration of SUMs 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!!! Text mining elaboration on a sample tweet tokens <sono>, <bloccato> <in>, <una>, <coda>, <di>, <7>, <km>, <il>, <traffico>, <è>, <incredibile>, <stasera>, <voglio>, <tornare>, <a>, <casa> Tokenization <sono>, <bloccato> <in>, <una>, <coda>, <di>, <7>, <km>, <il>, <traffico>, <è>, <incredibile>, <stasera>, <voglio>, <tornare>, <a>, <casa> Stop-word filtering w q =ln(n tr /N q ) Feature representation [arriv, blocc, caos, cod, km,...,..., stasera, traffic, vers, vial] F [0, w blocc, 0, w cod, w km,..., w stasera, w traffic, 0, 0] F Stem filtering <blocc>, <cod>, <7>, <km>, <traffic>, <incredibil>, <stasera>, <vogl>, <torn>, <cas> F relevant stems selected in the learning phase [arriv, blocc, caos, cod, km,..., stasera, traffic, vers, vial] F stems <bloccato>, <coda>, <7>, <km>, Stemming <traffico>, <incredibile>, <stasera>, <voglio>, <tornare>, <casa> <blocc>, <cod>, <7>, <km>, <traffic>, <incredibil>, <stasera>, <vogl>, <torn>, <cas>

9 Traffic detection from Tweet analysis 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 2 2 Prec -score 1 Prec Rec Rec

10 Traffic detection from Tweet analysis 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

11 Traffic detection from Tweet analysis 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, late detection events 2 early detection events

12 Facebook event analysis Real-time monitoring of events using Facebook Critical event: at least K e persons probably will attend the event K e is determined based on the event features and context IF 2/3 * Num. Sure + 1/3 Probable > K e THEN the event is critical Analysis on the trend of the possible attendees {"type":"eventofacebookcritico","eventofb":{ "idfb":" ", "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 "owner":" ", "location":"accademia di costume e di moda", "starttime":" t11:00: ", "endtime":"data_stimata : T13:00:00", "pointwkt":"point(( )", "partecipazione":{"attending":"22","maybe":"2","declined":"6"}}, "tipoeventoclassificato":"arte","angleindex":{"timeinms": ,"estimatedattending":22}},

13 GPS trace analysis to exploit vehicle GPS traces as traffic sensors

14 GPS trace analysis 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.

15 GPS trace analysis 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)

16 time GPS trace analysis Road segment classification based on the velocity of vehicles in traffic states in s the segment with respect to the traffic code j velocity very blocked slowed flowing absent slowed v block P 2 % v j code P 1 % v j code v j code space Spatiotemporal analysis of near classified segments in consecutive time intervals T=1 T=2 alert for incident with queue S very slowed very slowed very slowed very slowed very slowed blocked blocked absent absent T=3 very slowed very slowed very slowed very slowed blocked absent

17 GPS trace analysis Experimental results Used SUMO (Simulation of Urban Mobility) Simulations of GPS traces of 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

18 GPS trace similarity 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

19 Data Mining for Big Data 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

20 Questions? Department of Information Engineering University of Pisa, Italy