Market update IT TRANS Karlsruhe: Big data opportunities in public transport
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Transcript of Market update IT TRANS Karlsruhe: Big data opportunities in public transport
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
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OD-patterns (1/2)OD-patterns (1/2)
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OD-patterns (2/2)OD-patterns (2/2)
Cost coverage Cost coverage
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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
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!