Improved public transport by data driven research

Transcription

1 Improved public transport by data driven research dr. ir. N. van Oort Assistant professor public transport 1

2 Developments in industry Focus on cost efficiency Customer focus Enhanced quality Main challenges: Increasing cost efficiency Increasing customer experience Motivating new strategic investments Data enable achieving objectives 2

3 Data sources GSM data; tracking travellers - Potential public transport services Vehicle data (AVL); tracking vehicles - Evaluating and optimizing performance Passenger data (APC); tracking passengers - Evaluating and optimizing ridership and passengers flows Combining data sources (APC and AVL) - Service reliability from a passenger perspective 3

4 The potential benefits Optimizing network and timetable design: The Netherlands: Potential cost savings: > 50 million Utrecht: 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 4

5 The challenge Data Information Knowledge Improvements - New methodologies - Proven in practice 5

6 Applied examples - Monitoring and predicting passenger numbers: Whatif - Benefits of enhanced service reliability - Optimizing planning and real time control Van Oort, N. and R. van Nes (2009), Control of public transport operations to improve reliability: theory and practice, Transportation research record, No. 2112, pp Optimizing synchronization multimodal transfers Lee, A. N. van Oort, R. van Nes (2014), Service reliability in a network context: impacts of synchronizing schedules in long headway services, Transportation research record - Improved scheduling Van Oort, N. et al. (2012). The impact of scheduling on service reliability: trip time determination and holding points in long-headway services. Public Transport, 4(1),

7 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 occupancy 7

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21 Origin Destination Matrix 21

22 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 22

23 Whatif: changing the schedule 23

24 Whatif results: Flows increased frequencies 24

25 Decision making 25

26 Passengers in decision support systems? Calculated 0% Expert judgment 13% Not 60% Qualitatively 27% Cost benefit analysis Transport models 26

27 Case: Uithoflijn Utrecht Transformation crowded bus line into tram line 27

28 Cost Benefit analysis required CBA > 1,0 YES NO + 28

29 Three step approach AVL data APC data Reliability ratio Vehicle performanc e Passenger impacts Travel time impacts Transport model Schedule adherence Additional travel time and variance Additional travel time and variance in travel time units 29

30 Result Service reliability effects are about 60% of all benefits! Ministry supported project. 30

31 Summary Data: increased quality of public transport Data: enhanced decision making Valuable data available Evaluating and controlling -> predicting and optimizing Data-> Information -> Knowledge -> Improvements Two applied examples Passenger data and whatif analysis Cost benefit analysis 31

32 Questions / Contact Niels van Oort N.vanOort@TUDelft.nl Research papers: 32