BIG DATA FOR MODELLING 2.0

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www.ptvgroup.com Seite 1 www.ptvgroup.com

Lorenzo Meschini - CEO, PTV SISTeMA COST TU1004 final Conference

Paris, 11 May 2015

BIG DATA FOR MODELLING 2.0

ENHANCING MODELS WITH MASSIVE REAL DATA INTEGRATION

BIG DATA FOR MODELLING 2.0

ENHANCING MODELS WITH MASSIVE MOBILITY DATA INTEGRATION

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www.ptvgroup.com Seite 2

“A collection of data too massive to be handled efficiently by traditional

databases tools and methods”

Big Data IS NOT only related to non-trivial sizes of data, but it IS rooted in

the push to discover hidden/useful insights in data.

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VOLUME is the sheer size of the data being collected

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VELOCITY is the speed at which data is flowing into a business’s infrastructure and

the ability of software solutions to receive and process that data quickly

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VARIETY refers to different data format incoming into your platform, and the challenge to be able to take raw, (un)structured data and organize it.

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Three challenges besides data availability from a business point of view:

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STORE: can you store the vast amounts of data being collected?

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PROCESS: can you organize, clean, and analyze the data collected?

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ACCESS: Can you search and query this data in a organized manner?

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Once you get beyond storage and management, you still have the enormous task of

creating actionable business intelligence (BI) from the datasets you’ve collected.

There are so many types of analytic models, and different ways of providing infrastructure for this process. But the analytics solution must scale, too.

Ultimately, analytics tools rely on a great deal of reasoning and analysis to extract data patterns and data insights, but this capacity means nothing for a business if they can’t then create actionable intelligence.

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Big Data Plans are Underway for Most Organizations

RDBMS Still Dominates the Broader IT Industry

SOME STATISTICS (2015)

Almost All Orgs Expect Their Storage Needs to Grow Exponentially

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www.ptvgroup.com Seite 7 The market already offers world or continent wide services and solutions based on individual vehicle and/or people mobility trajectories or movements

Raw data sources

 Vehicle Trajectories form black boxes for insurance applications or vehicle location systems

 Vehicle Trajectories from navigation systems

 Crowd sourcing from Mobile phone apps

 Localization of mobile phones

Offered services / products

 Real time traffic monitoring & information

 Performance measures

 Maps

 Speed profiles and travel times on road segments

 Travel time matrices

 Observed od matrices

 Trajectories

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www.ptvgroup.com Seite 8 Public transport data are currently collected and stored on a local base:

Raw data sources

 Service plans

 PT vehicle trajectories from AVL and AVM systems

 PT events (delay/cancellation/rerouting)

 Tickets emission/collections

 Crowd sourcing from Mobile phone apps

Services produced are currently often limited within the entities collecting the data

 Real time information

 Performance and Level of Service measures

 Clearing

 Service planning (schedule)

Some companies are trying to bring services to a global level

 Aggregating local data

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Challenges

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Collecting data on PT worldwide: data are (owned?) by different authorities that won't provide them

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Go multimodal: collecting Bike, pedestrians counts

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Mode of transport identification

 car, bike, PT can be very similar in urban contexts  Same trip, several transport systems

Enablers

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Open data

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Crowd sourcing

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Internet of things

Opportunities

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“Smart cities”

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Big Data (historical) on PuT

Computer Science

Transportation Engineering

Pure

“statistical/machine

learning” approach

“Modelling” approach

+

Calibration by data

Modelling 2.0

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 Input: from same raw FCD data that provide today speed profiles

 Output: calibrated traffic models + route choice

DATA DRIVEN MODELS - TODAY

FCD raw trajectories

Optima

Data Driven

• Network graph • Traffic zones

• Available flow counts

Demand

• OD matrices

Route choice

• Turning ratio (by destination zone) Network attributes • Free flow speeds • Capacities

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 Input: from same raw FCD data that provide today speed profiles

 Output: calibrated traffic models + route choice

DATA DRIVEN MODELS - TOMORROW

Multi modal trips

Optima

Data Driven

• Network & Service graph • Traffic zones

• Multi modal flow counts

Demand

• OD matrices

• Modal split

Route choice

• Turning ratio (by destination zone) Network attributes • Free flow speeds

• Transit Capacities

• Waiting times • Acess / Egress /

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DATA DRIVEN MODELS – FUNCTIONAL OVERVIEW

Observed Vehicle trajectories Zones (Origin destinations) Graph Map Matching & speed calc.

Link speeds by day type Day types definitions Splitting rates by destination and day type Assignment matrix estimation Assignment matrix by day type Zones (Origin destinations) Graph Assignment matrix by day type Initial OD matrix OD matrix correction Corrected OD matrices by day type Zones (Origin destinations) Initial Graph Corrected OD matrices by day type speed, capacity and jam density

correction

Corrected Graph Flow measures Link speeds by _{day type}

ASSIGNMENT MATRIX UPDATE OD MATRIX UPDATE GRAPH UPDATE Observed matrices by day type

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MODELLING 2.0 – AN EXAMPLE

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Creation of a graph model for Transport Assignment

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Running the Big Data analysis tools you discover, from FCD probes for example, that some streets should be included into the model because they are deeply used !!!

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Running online Big Data tools you can update in real time parameters of your model, for example for the route choice model the turn probabilities at a given intersection.

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www.ptvgroup.com Seite 15 Big data can contribute to enhance calibrating and validating all our models

 Trip generation

 Trip distribution

 Mode choice

 Route choice

 Supply calibration

We need to conceive new calibrating methodologies

 Capacity and flow level recognition

 Transport system & mode recognition

 Path choice recognition

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www.ptvgroup.com Seite 16 Lorenzo Meschini

CEO, PTV SISTeMA

Realtime Solutions Director, PTV Group

[email protected]