1 S.I.MO.NE. INNOVATIVE SYSTEM FOR METROPOLITAN AREA MOBILITY MANAGEMENT Fabrizio Arneodo R&D Program Manager 5T S.r.l. Torino, Italy 16° ITS World Congress.

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S.I.MO.NE.

INNOVATIVE SYSTEM FOR METROPOLITAN AREAMOBILITY MANAGEMENT

S.I.MO.NE.

INNOVATIVE SYSTEM FOR METROPOLITAN AREAMOBILITY MANAGEMENT

Fabrizio ArneodoR&D Program Manager

5T S.r.l.Torino, Italy

16° ITS World Congress

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• Background information: Turin TOC

• S.I.MO.NE. Project: architecture and goals

• Gathered Data: Floating Car Data

• Reference Systems: graphs

• Aggregator module

• Conclusions: Field Trial and Expected Beneficts

• Background information: Turin TOC

• S.I.MO.NE. Project: architecture and goals

• Gathered Data: Floating Car Data

• Reference Systems: graphs

• Aggregator module

• Conclusions: Field Trial and Expected Beneficts

OutlineOutline

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HERE

WE ARE!

• 2006 Winter Olympics Host City

• 900.000 citizens / 130Km2

• ~ 0.62 car / inhabitant ratio

• ~ 3.6M daily movements

• ~ 600 traffic light intersections• 1.5M inhabitants / 837Km2 (metropolitan area)

• 2006 Winter Olympics Host City

• 900.000 citizens / 130Km2

• ~ 0.62 car / inhabitant ratio

• ~ 3.6M daily movements

• ~ 600 traffic light intersections• 1.5M inhabitants / 837Km2 (metropolitan area)

The city of Turin (Italy)The city of Turin (Italy)

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5T (Telematics Technologies for Transport and Traffic in Torino) The mission

• Design, deployment and management of systems for mobility management;

• Development and management of system for Public Transport (P.T.) monitoring.

• Traffic monitoring and control comprehensive of traffic light junctions management.

• Development and application of telematic system for fining and ticketing activities related to P.T. and other mobility services.

• R&D in ITS Field, participation in European and National research projects and partnerships.

• Traffic and transport data collections to support traffic planning at Regional, province and local level.

• Design, deployment and management of systems for mobility management;

• Development and management of system for Public Transport (P.T.) monitoring.

• Traffic monitoring and control comprehensive of traffic light junctions management.

• Development and application of telematic system for fining and ticketing activities related to P.T. and other mobility services.

• R&D in ITS Field, participation in European and National research projects and partnerships.

• Traffic and transport data collections to support traffic planning at Regional, province and local level.

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5T Traffic Operation Center (1/2)5T Traffic Operation Center (1/2)

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• Real-time control/monitoring over:– ~255 traffic light controlled intersections– 38 parking venues (18,000 stalls)

– 1,300 vehicles public transport fleet– 26 variable message signs– ~200 displays at bus stops– Several hundreds cameras

• Real-time control/monitoring over:– ~255 traffic light controlled intersections– 38 parking venues (18,000 stalls)

– 1,300 vehicles public transport fleet– 26 variable message signs– ~200 displays at bus stops– Several hundreds cameras

5T Traffic Operation Center (2/2)5T Traffic Operation Center (2/2)

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S.I.MO.NE Project: Main Goals

• Design and development of technologies for Floating Car Data Design and development of technologies for Floating Car Data (FCD) gathering;(FCD) gathering;

• Development of innovative solutions for the interoperability Development of innovative solutions for the interoperability among different actors operating in the Traffic Management among different actors operating in the Traffic Management field and design of standard de-facto communication field and design of standard de-facto communication protocols;protocols;

• Enhancement of already operating Traffic Operation Centres Enhancement of already operating Traffic Operation Centres (TOCs) upgrading Traffic Models with the usage of FCD;(TOCs) upgrading Traffic Models with the usage of FCD;

• Design of Decision Support System (DSS) for access criteria Design of Decision Support System (DSS) for access criteria impact evaluation (Limited Traffic Zones);impact evaluation (Limited Traffic Zones);

• Creation of enablers for Value Added Infomobility Services.Creation of enablers for Value Added Infomobility Services.

• Design and development of technologies for Floating Car Data Design and development of technologies for Floating Car Data (FCD) gathering;(FCD) gathering;

• Development of innovative solutions for the interoperability Development of innovative solutions for the interoperability among different actors operating in the Traffic Management among different actors operating in the Traffic Management field and design of standard de-facto communication field and design of standard de-facto communication protocols;protocols;

• Enhancement of already operating Traffic Operation Centres Enhancement of already operating Traffic Operation Centres (TOCs) upgrading Traffic Models with the usage of FCD;(TOCs) upgrading Traffic Models with the usage of FCD;

• Design of Decision Support System (DSS) for access criteria Design of Decision Support System (DSS) for access criteria impact evaluation (Limited Traffic Zones);impact evaluation (Limited Traffic Zones);

• Creation of enablers for Value Added Infomobility Services.Creation of enablers for Value Added Infomobility Services.

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S.I.MO.NE Project: Architecture

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Gathered Floating Car Data (FCD)Gathered Floating Car Data (FCD)

• Raw Data (RD): speed, position, time of measurement (usually

according to NMEA 0183). Position without any map-matching

elaboration;

• Raw Data Public Transport (RDPT): similar to RD, generated by

Public Transport Automatic Vehicle Monitoring (AVM) systems;

• Map-matched Raw Data (MRD): still consists in speed, position

and time, but position is map-matched and associated to the

reference system used by Fleet Manager;

• Travel Time (TT): aggregated information, the travel time for

each single element of the geographic reference system.

• Raw Data (RD): speed, position, time of measurement (usually

according to NMEA 0183). Position without any map-matching

elaboration;

• Raw Data Public Transport (RDPT): similar to RD, generated by

Public Transport Automatic Vehicle Monitoring (AVM) systems;

• Map-matched Raw Data (MRD): still consists in speed, position

and time, but position is map-matched and associated to the

reference system used by Fleet Manager;

• Travel Time (TT): aggregated information, the travel time for

each single element of the geographic reference system.

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Different Reference Systems: GraphsDifferent Reference Systems: Graphs

• For G1 TMC has been chosen in the project, because TMC is a standard and currently the main Italian Fleet Managers are able to provide TT data aggregated on TMC reference;

• G2 graph used by the Aggregator has to be detailed as much as possible, in order to aggregate RD and RDPT;

• G3 graph used by the Supervisor functionality is defined to represent the main roads of the real roads network;

• it has been decided to adopt G3 graph also in the Aggregator and so G3 and G2 are the same;

• G4 graph used by the Information Policy Manager is designed in order to provide information, in the project has been chosen TMC;

• For G1 TMC has been chosen in the project, because TMC is a standard and currently the main Italian Fleet Managers are able to provide TT data aggregated on TMC reference;

• G2 graph used by the Aggregator has to be detailed as much as possible, in order to aggregate RD and RDPT;

• G3 graph used by the Supervisor functionality is defined to represent the main roads of the real roads network;

• it has been decided to adopt G3 graph also in the Aggregator and so G3 and G2 are the same;

• G4 graph used by the Information Policy Manager is designed in order to provide information, in the project has been chosen TMC;

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Aggregator module: Data fusion and completionAggregator module: Data fusion and completion

• The data sent by several Fleet Managers are collected by the “Aggregator”, in order to carry out Data fusion and Data completion;

• Data are filtered and then projected on the geographic reference system used by the Aggregator (G2);

• RD and RDPT are elaborated by a map-matching algorithm that associates the GPS coordinates (latitude, longitude) to the relevant basic element of the geographic reference system, typically an arc of the graph;

• RD data are also elaborated to identify the traces of the vehicles, identifying the start and the end of each single performed travel using Key-on and Key-off signals, to be used by an algorithm to create O/D (Origin/Destination) matrix;

• For MRD and TT data the Aggregator translates the received geo-reference information and matches it with the relevant

element in its own geographic reference system.

• The data sent by several Fleet Managers are collected by the “Aggregator”, in order to carry out Data fusion and Data completion;

• Data are filtered and then projected on the geographic reference system used by the Aggregator (G2);

• RD and RDPT are elaborated by a map-matching algorithm that associates the GPS coordinates (latitude, longitude) to the relevant basic element of the geographic reference system, typically an arc of the graph;

• RD data are also elaborated to identify the traces of the vehicles, identifying the start and the end of each single performed travel using Key-on and Key-off signals, to be used by an algorithm to create O/D (Origin/Destination) matrix;

• For MRD and TT data the Aggregator translates the received geo-reference information and matches it with the relevant

element in its own geographic reference system.

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Aggregator module: Travel Time (TT) calculation (1/2)Aggregator module: Travel Time (TT) calculation (1/2)

• To calculate TT on basic geographic element of the Aggregator graph or on aggregation of basic geographic elements (functional classes), the following data are needed:– Start and end points over the arc (Map Matching);– Start and end timestamps;– Calculate ∆T=Tend-Tstart

– Calculate ∆L=Lend-Lstart;

• To calculate TT on basic geographic element of the Aggregator graph or on aggregation of basic geographic elements (functional classes), the following data are needed:– Start and end points over the arc (Map Matching);– Start and end timestamps;– Calculate ∆T=Tend-Tstart

– Calculate ∆L=Lend-Lstart;

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Aggregator module: Travel Time (TT) calculation (2/2)Aggregator module: Travel Time (TT) calculation (2/2)

• The aggregation process calculates TT on a per arc basis starting from incomplete TT-FCD relevant to portion of arc; this is executed on the base of two approaches:

1. Mean TT on minimal element (portion) of the arc: the arc TT is the sum of minimal element TT

2. Mean TT on arc: mean of all TT-FCD value is calculated, then the speed is calculated to get the TT of the complete arc.

• The aggregation process calculates TT on a per arc basis starting from incomplete TT-FCD relevant to portion of arc; this is executed on the base of two approaches:

1. Mean TT on minimal element (portion) of the arc: the arc TT is the sum of minimal element TT

2. Mean TT on arc: mean of all TT-FCD value is calculated, then the speed is calculated to get the TT of the complete arc.

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Aggregator module: Minimal requirementsAggregator module: Minimal requirements

• The TT-FCD are aggregated on a per time frame basis and on geographic reference system elements (single arcs, functional class, etc.);

• minimal requirements for good data aggregation and TT calculation are:

– sampling of data has to be performed according to a “distance” based criteria and not on “time“ base, since the total number of samples have to depend from travelled distance and not from travel time spent;

– sampling data frequency has to enable Travel Time calculation on arcs, average length of 90 metres in the urban areas and 150 metres in the suburban one;

– a sampling step of 30 metres shall guarantee a coverage of 94% of the total road network.

• The TT-FCD are aggregated on a per time frame basis and on geographic reference system elements (single arcs, functional class, etc.);

• minimal requirements for good data aggregation and TT calculation are:

– sampling of data has to be performed according to a “distance” based criteria and not on “time“ base, since the total number of samples have to depend from travelled distance and not from travel time spent;

– sampling data frequency has to enable Travel Time calculation on arcs, average length of 90 metres in the urban areas and 150 metres in the suburban one;

– a sampling step of 30 metres shall guarantee a coverage of 94% of the total road network.

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Final Field Test sperimentationFinal Field Test sperimentation• The project will be finally validated with Field Trials

that:

• Will cover the areas of Turin, Bologna, Cagliari, Genoa and Florence cities, (roughly 3.500.000 of inhabitants);

• Will use data coming from a fleet of 830.000 vehicles;

• Will involve main Italian private leader companies operating in the market: Centro Studi Sistemi di Trasporto (CSST- FIAT Group), Infoblu (Autostrade per l’Italia Group), Magneti Marelli and Telecom Italia (tema.mobility consortium);

– Will test usage of FCD together legacy fixed-sensor traffic monitoring equipments;

– Will demonstrate the improvement of traffic monitoring via usage of FCD, that increase data accuracy and geographic coverage;

– Will test usage of standard protocol with large amount of real time information;

– Will prove the scalability of the architecture facilitating the growth of FCD market and the set-up of new TOCs.

• The project will be finally validated with Field Trials that:

• Will cover the areas of Turin, Bologna, Cagliari, Genoa and Florence cities, (roughly 3.500.000 of inhabitants);

• Will use data coming from a fleet of 830.000 vehicles;

• Will involve main Italian private leader companies operating in the market: Centro Studi Sistemi di Trasporto (CSST- FIAT Group), Infoblu (Autostrade per l’Italia Group), Magneti Marelli and Telecom Italia (tema.mobility consortium);

– Will test usage of FCD together legacy fixed-sensor traffic monitoring equipments;

– Will demonstrate the improvement of traffic monitoring via usage of FCD, that increase data accuracy and geographic coverage;

– Will test usage of standard protocol with large amount of real time information;

– Will prove the scalability of the architecture facilitating the growth of FCD market and the set-up of new TOCs.

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Project BenefitsProject Benefits• The adoption of the S.I.MO.NE. architecture will bring the

following main benefits:

– Cost reduction: FCD will reduce traffic data collection costs since less fixed-sensor equipments will be needed;

– Data accuracy and enhancement of geographic coverage;

– Service enablers: providing real-time info to drivers (e.g. road works, traffic limitations, traffic flows, congestions);

– New opportunities for ITS market growth.

• The adoption of the S.I.MO.NE. architecture will bring the following main benefits:

– Cost reduction: FCD will reduce traffic data collection costs since less fixed-sensor equipments will be needed;

– Data accuracy and enhancement of geographic coverage;

– Service enablers: providing real-time info to drivers (e.g. road works, traffic limitations, traffic flows, congestions);

– New opportunities for ITS market growth.

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Thanks for your attention !Thanks for your attention !