Improving public transport and rail design, operations and service by Big Data Integrating Passenger Information, London 2014 Dr. Niels van Oort Assistant professor Public Transport
Improving public transport and rail design, operations and service by big data
Jan 27, 2015
Verbetering van het openbaar vervoer en railontwerp, operaties en service door Big Data en Reisinformatie
In maart 2014 op de conferentie Integrating Passenger Information in London presenteerde Niels van Oort zijn visie op het verbeteren van openbaar vervoer en railontwerp, operaties en service door Big Data. Met het gebruik van Big Data ontstaan er veel nieuwe mogelijkheden om het openbaar vervoer te verbeteren met als voordeel grote kostenbesparingen en/of verbeterde klantwaardering.
DAT.Mobility was sponsor van dit evenement en oogstte veel waardering met haar innovatieve aanpak om allerlei soorten data te integreren tot informatie. Niet alleen informatie die de informatie naar de reiziger verbetert, maar ook extra inzicht geeft aan de vervoerders over haar performance indicatoren, zoals betrouwbaarheid en punctualiteit. Hiermee was DAT onderscheidend.
In maart 2014 op de conferentie Integrating Passenger Information in London presenteerde Niels van Oort zijn visie op het verbeteren van openbaar vervoer en railontwerp, operaties en service door Big Data. Met het gebruik van Big Data ontstaan er veel nieuwe mogelijkheden om het openbaar vervoer te verbeteren met als voordeel grote kostenbesparingen en/of verbeterde klantwaardering.
DAT.Mobility was sponsor van dit evenement en oogstte veel waardering met haar innovatieve aanpak om allerlei soorten data te integreren tot informatie. Niet alleen informatie die de informatie naar de reiziger verbetert, maar ook extra inzicht geeft aan de vervoerders over haar performance indicatoren, zoals betrouwbaarheid en punctualiteit. Hiermee was DAT onderscheidend.
Welcome message from author
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Improving
public transport and rail
design, operations and service
by Big Data
Integrating Passenger Information, London 2014
Dr. Niels van Oort
Assistant professor Public Transport
Dutch approach
• Using Big Data to improve public transport
• Direct approach: privacy issues tackled
• Reducing costs
• International orientation
• Paradigm shifts– Passenger -> Traveller
– Trip -> Journey
– Evaluation -> Prediction
– Two practical applications
Developments in public transport and rail industry• Focus on cost efficiency• Customer focus• Enhanced quality
Main challenges:Increasing cost efficiencyIncreasing customer experienceMotivating new strategic investments
What do we want to know?
What do you actually want to know?
Where people are– When, at what moment– How much– How long are they there– How did they get there– Where did they come from– Where are they goingand– Why are they on the move?
Understanding peoples behaviourandPredicting future scenarios
Big Data
GSM data; tracking travellers
- Potential public transport services
Crowd data
- Evaluating and optimizing pedestrian flows
Passenger data (APC); tracking passengers
- Evaluating and optimizing ridership and passengers flows
Combining data sources
- Passenger experience
Big Data
The potential benefits
Optimizing network, timetable design and services:
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
DataData InformationInformation KnowledgeKnowledge
ImprovementsImprovements
The challenge
EvaluationEvaluation ForecastsForecasts
- Monitoring and predicting passenger numbers: Whatif- Monitoring and analysing traveller behaviour
Applications
Passenger flows and whatif
Passenger 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 ridership
Occupancy train line Zwolle-Emmen
fictitious data
Whatif scenarios
Adjusting
- Speed
- Fares
- Time of operations
- Number of stops
- Routes
- Frequency
Illustrating impacts on (indicators):- Cost coverage- Occupancy- Ridership- On time performance- Revenues- Fuel consumption
Whatif: changing the schedule
Results: Flows rerouting
fictitious data
Results: Flows increased frequencies
fictitious data
Measuring traveller behaviour
Smart app
pagina 20
Multimodal journey planner
Monitoring traveller behaviour
• Knowing (and affecting) traveller behaviour– Multimodal
– Door-door
– Trip purpose
• Activity-based interaction– Real time information
– Personalized information
– Gamification
• Real time questionnaires
Summary
• Big Data offers many opportunities
• Indivual mobility pattern towards aggregated mobility pat terns
to optimize design and services
• Paradigm shifts
– Passenger -> Traveller
– Trip -> Journey
– Evaluation -> Prediction
Related Documents
