Big Datain public transport:

Big Datain public transport:

Dr. Niels van Oort

Assistant professor Public Transport

Opportunities to enhance cost efficiency and quality

Opportunities to enhance cost efficiency and quality

Developments in publictransport industry

Developments in publictransport industry

• Focus on cost efficiency

• Customer focus

• Enhanced quality

Main issue:

• Increasing cost efficiency

• Increasing occupancy

Trends

• Big Data availability

• Enhanced knowledge about passenger behaviour

Omnitrans software enables optimization proces

The potential benefitsThe potential benefitsOptimizing network and timetable design:

The Netherlands:

Potential cost savings: > €50 million

• Utrecht: € 400.000 less yearly operational costs

• The Hague: 5-15% increased ridership

• Amsterdam: ~10% increased cost coverage

• Tram Maastricht:> €4 Million /year social benefits

• Tram Utrecht: : €200 Million social benefits

Which questions to answer?Which questions to answer?

• What are the main transfer points and directions?

• What is the best spot to insert bus lanes or traffic light priority?

• What direct connections to offer?

• Where can I optimize my trip times?

What are passenger impacts

of design choices?

DataData InformationInformation KnowledgeKnowledge ImprovementsImprovements

The challengeThe challenge

The opportunityThe opportunity

Improving quality

Reducing costs

Customer satisfaction

Ridership

Cost coverage

Big Data

- Monitoring and predicting passenger numbers: Whatif

- Improving speed and service reliability

ApplicationsApplications

Passenger dataPassenger data

Connecting to transport model:

• Evaluating history

• Predicting the future

• Elasticity approach (quick and low cost)

• Whatif scenario’s– Stops: removing or adding

– Faster and higher frequencies

– Route changes

• Quick insights into– Expected cost coverage

– Expected occupancy

fictitious data

fictitious data

fictitious data

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fictitious data

fictitious data

fictitious data

fictitious data

fictitious data

fictitious data

fictitious data

fictitious data

OD-patterns (1/2)OD-patterns (1/2)

fictitious data

OD-patterns (2/2)OD-patterns (2/2)

Cost coverage Cost coverage

fictitious data

Whatif results: Flows reroutingWhatif results: Flows rerouting

Whatif results: Flows increased frequenciesWhatif results: Flows increased frequencies

Fast and reliable servicesFast and reliable services

Today:- Much attention to quality: e.g. speed and reliability

- Much focus on efficiency

Enhanced service reliability serves both objectives!

Data illustrates opportunities

We developed a tool to find bottlenecks and

potential savings

Projects in:

e.g. Amsterdam, Utrecht, The Hague, Groningen

Schedule adherence

Speed

Dwell time

Bron: GVB

Questions? Demo?Questions? Demo?

Visit us at F19

Niels van Oort

NvOort@Goudappel.nl

https://nielsvanoort.weblog.tudelft.nl/

Thank you for your attention!

Speaking about attention… We are looking for these persons:

There is a suprise waiting for them at our booth #F19.

Big Data.... Small Gestures....

Big Impact!