Transit Smart Card Data Research: Thoughts and Perspectives

Transcription

1 Transit Smart Card Data Research: Thoughts and Perspectives Brendon Hemily, Ph.D. 7 November, 2014

2 SUMMARY OF ACADEMIC RESEARCH USING TRANSIT SMART CARD DATA

3 Methodological Challenges Data quality challenges Volume of data: ability to process Factoring up samples Determining geographical location Especially if no check-out Distortions caused by pricing Card expiry

4 Service and Operational Planning Network and route design Frequency and schedules Vehicle and crew scheduling Origin-Destination Matrices Provides real desire lines Appealing to researchers because enables optimization Optimize travel times for most customers More direct links / reduced # of transfers

5 Analyses Using Smart Card (OD) Data Path choice / transit assignment (including impact of transfers, network choices, crowding, information, etc.) Transfer patterns Route / vehicle loading Service reliability Variations by time of day, day of week, etc. Residence location Inference of socio-demographic information Comparison with Travel Surveys: validity check Retention rates Impact of weather Shopping vs mode access behavior

6 Use of SC Data for Operational Planning / Control and Performance Monitoring Monitoring, Analysis and Prediction (Decision Support System) SC Data provides patterns of customer behavior as backdrop for analyzing: Service reliability as experienced by customer Modifications to operational plans Real-time intermodal coordination Incident management: Traveler natural responses Contingency plans Emergency information provision Transfer management Much of this may be of more interest to rail systems Passenger Kilometres: Operator Contracts, Allocation of Revenues

7 EU Standard on Quality of Service in Public Transport (EN 13816/ EN 15140) : Broader Perspective on Performance Monitoring and Effectiveness

8 APPLICATIONS THAT TRANSIT ORGANIZATIONS ARE LOOKING FOR: TRANSIT PRICING

9 Reality Prior to a Smart Card deployment: Affects heart of revenue management: CRITICAL Function Risks are huge But uncertainty is great Little accessible information on smart card use, customer behavior, and impacts to assist planning of SC deployment No information on behavioural impacts switch between media changes in patterns Little basis for risk management: Transit is conservative by nature Little incentive to innovate fare policy

10 Alternative Perspective on Research Needed by Transit Agencies: Fare Pricing (Structure) Price Elasticities: Fares vs Ridership Trade-Off! Traveler Response to Transportation System Changes: Transit Pricing and Fares TCRP Volume 95 Chapter 12 (2004) By Mode By Trip Purpose (Work, School, etc.) (Service Elasticities: Fares vs Service Trade-Offs)

11 Key Pricing Decisions where analysis would help Revenue risk vs. Ridership potential (and Equity, by type of customer, by jurisdiction) Pass Multiplier (Monthly, Weekly, Daily) Price increments per zone Short trips? Max allowed time for journey Round-trip price Age break-offs (child vs. student, student vs. postsecondary) Peak vs Off-peak

12 Innovative Pricing where analysis would help Evening and/or weekend fare Weekend pass University or Employer based discounted annual pass (U-Pass, Eco-pass): utilization rates by time of day Summer pass for students Student freedom pass (after 4PM / weekends) Co-pricing with sports / entertainment events Passes to condominium buyers (in lieu of parking) Social fares (unemployed) Loyalty schemes Shared-use mobility co-pricing (bike-share, car-share)

13 FOOD FOR THOUGHT

14 Challenges for Researchers Need Data Publicly available (Open Data) Private Data (requires partner) To deploy applications: Need Partners Requires communication channel Quid pro quo? Access to data (sometimes that is only objective) Identification of user needs Interest / ability to test, evaluate, (and deploy)

15 A Personal Perspective on the Public Transit Industry In U.S. The Cutting Edge Innovators or Optimizers Best Practices Incrementalists Standard Practice Satisficers Survival Mode or Low-level tolerated subsistence 5% 10-15% 50% 30-35%

16 Applying Knowledge / Applications from SC Data Who is target audience to date? By and large = Innovators / Optimizers Future Challenge: Develop and disseminate methodologies and tools that would encourage satisficers to adopt best practices Disseminate knowledge to make agencies with best practices to become aware of cutting edge

17 Some Cause for Optimism Societal / industrial discussions of Dashboards, Big Data, Business Intelligence Creative approaches to applications, data fusion, methodologies Transit industry (in general) is slowly waking up to trying to use ITS data THANK YOU!