National Datawarehouse for Traffic Information – Big Data supplier

National Datawarehouse for Traffic Information – Big Data supplier

Els Rijnierse

Contents

• Introducing NDW

• Experiences with our big data

• Challenges, choices and changes

Posting

• The last slide will ask you to post your impression, to share what struck you most with all conference attendees

NDW is a Collaborative venture

• 24 Road authorities

National

6 out of 12 provinces

Cities, either independent or in an alliance• Covering >6000 km road network

(total Dutch road network is 130.000 km)

Introducing NDW

What is our aim?

• Develop and maintain a joint database for traffic data. Up-to-date, complete and unambiguous with known quality

• Create efficiency by working together and sharing information

• Stimulate effective use of this data for:- real time traffic management - real time traffic information - analyses, policy making and research

Introducing NDW

Trafficmanagement

Central source for all road authorities

Introducing NDW

Objectives

• Less traffic jams• Predictability• Safer roads• Less emission• More collaboration

Data voor doorstroming

Happy road users

Introducing NDW

NDW

Supervisory BoardSupervisory Board

Demand

Road authoritiesRoad authorities

Service providersService

providers

Supply

Participating goverments

(IDP)

Participating goverments

(IDP)

Commercial parties(EDP)Commercial parties(EDP)

System provider (external)

Selection fromdata

IndividualData need

Common data need

Individual datasupply

AccountabilitySupervision

Infrastructuresupply

Introducing NDW

Data types - 1

• Traffic flow per lane per vehicle class on 14818 measuring sites

• Travel time (realised or estimated) per lane on 9424 measuring sites

• Traffic speed per lane per vehicle class on 13410 measuring sites

(measuring sites may produce more kind of data)

Introducing NDW

Every minute, traffic data from more than 24,000 measuring sites is collected, processed and within 75 seconds distributed to the users

Data collection

Introducing NDW

Some figures on figures

• Over 24,000 measurement sites• Giving aprox. 460,000 figures on speed, flow and travel

time each minute• => >27 Million per hour• => >600 million per day• => >240 billion per year

+ meta data on these figures

Real-time traffic data (February 2012: 5 cm snow)

Introducing NDW

Data types - 2

• Road works, planned and actual• Reports of congestion and accidents • Status (open/closed) of bridges• Near future: Status (open/closed) of peak lanes and

regular lanes

Introducing NDW

On occurrence data on availability of the road is collected

Cooperation between CBS en NDW

• NDW collects and distributes raw data, we do not aim to do any statistical analysis.

• CBS started with small NDW datasets (1 day) and is now working on a larger set (3 months) to determine new methodology

• Conclusion:

Forget everything you learned about statistics

Experiences

When to start calculating (Experiences with big data – 1)

When using big data:• This traditional way of working does not produce statistics

quicker. • This requests huge datastores for raw data storage• Strongly advised is starting with statistical analyses the

moment data is streaming in and storing only aggregated in between results

• Adapt you algorithms to be able to handle correct any unpredictable gaps in the raw data that will occur

Experiences

Traditional statistical methodology: gather and store everything and perform the statistical analyses on certain times.

Technical issues (Experiences with big data - 2)

• Traditional relational databases but also statistical tools (SPSS/SAS/R) are not fast enough, run far out of memory and do not have enough performance for quick retrieval of raw data.

• When using a data storage technique suitable for fast recovery of raw data then some coding and programming has to be done on the raw data.

• Recalculating because of wrong choices or methods takes an increasing amount of time as the amount of raw data grows quickly every day.

Experiences

Challenges, Choices, Changes

Devils Triangle

Challenges, Choices, Changes

Contents awareness

IT knowledgeStatistical knowledge

Challenges, Choices, Changes

ChallengeGovernment policy is that public data are open data, which means our raw data are on the WWW (www.ndw.nu/datalevering)Anybody can download them and produce surveys, statistics, tables, draw conclusions and publish these (long) before statistical office does.

Be aware of publicity this might cause, discussions on ‘the truth’ and the status of a response or statement from the statistical office.

Take on the challenge of producing real time statistics

Challenges, Choices, Changes

Challenges, Choices, Changes

ChoiceTraditional storage of raw data used for statistics is at thestatistical office.

Big data should be left at their origin and withdrawn when

needed.

Challenges, Choices, Changes

Challenges, Choices, Changes

ChangeLook for appropriate IT infrastructure and develop a new way of handling data

Challenges, Choices, Changes

www.sendsteps.comPrepare to react; keep your phone ready!

TXT 1

2

Text to +316 4250 0030

Type Session <space> WS3 <space> your answer

Internet 1

2

Go to sendc.com

Log in with Session

Posting messages is anonymousNo additional charge per message

3 Type WS3 <space> your answer

When using Big Data for our statistics the biggest change in our way of working will be….

Internet Go to sendc.com and log in with Session Type WS3 <space> Your answer

TXT Send to 06 4250 0030: Session Type WS3 <space> Your answer

Challenges, Changes and Choices when using these amounts for Statistics

Forget everything you learned on statistics: How to produce 1 representative figure on traffic intensity from this:

When to start calculating

Immediately soon after data is available, continuously

Were to store what (intermediate or raw) data

Raw data at the providers, intermediate results at the statistical office

Tools and IT techniques

No more SPSS, R, and SAS, but programming and working with new tools

Algorithms Asymptotically, the time complexity as well as the space complexity should be of a lower order, because of the large volumes of data

As/When open data is government policy:

Be aware of others producing also statistics, quicker and with other conclusions