Transit Labs

DataPalooza 6/3/2014 : Standards for Sharing Data Part 2

Transcript

1. Datapalooza
2. Inefficiencies in Public Transportation Long Wait Times Inaccessibility Energy Consumption Cost Overcrowded Vehicles Safety Demand is increasing, funding is decreasing. 30 miles 2
3. The Transit Labs model Data Collection - Tablet Based Counters - GPS Locators - Accurate Data Automated Reporting - Monthly Reporting - Centralized Data - Built in Standardized Algorithms - Immediate Response on Specific Irregularities Analyzing Data to Reach Actionable Conclusions Using Real-time Visualizations - Performance Metrics - Benchmarking - Peer Analysis - Modeling/Predictive Analysis - No Human Capital Required to Generate Analysis Accessible Data Empowering Stronger Decision Making by Key Stakeholders - Transit Operators - Public Officials - City Council - County Level - State - Federal - Media - Business Community - Taxpayers Data Collection Real-time Reporting Real-time Analysis Real-time Decision-Making Real-time 3
4. Use Cases Metropolis Transit (MT) 4
5. Our Revenue Miles Between Failures is Lower Than Our Peers. Let's look at three ways to figure out why we're having more failures than everyone else. Performance Metrics Graphs (web) 5
6. 1) Cross-Check Safety Incidents with Vehicle Age Solution: Our safety incidents involve older vehicles than our peers. Need newer vehicles. Performance Metrics Graphs (web) 6
7. 2) Cross-check Vehicles Series with Mechanical Incident Solution: 50% of Metropolis's failures come from 4000 series vehicles, while peers are dealing with a 10-25% failure rate for that series. We have a maintenance issue. Performance Metrics Graphs (web) 7
8. 3) Visualize Where Failures Are Occurring Solution: Failures are occurring along a particular route. Need to investigate and make track/road repairs. Performance Metrics Maps (web - selected failure) Performance Metrics Maps (web - filtered to route) 8
9. Operating Cost per Passenger Mile is Higher Than it Should Be Vehicle Miles/Gallon of Fuel Consumed are not in-line with our peers Solutions: 1) Investigate efficiency of fuel consumption Percent of Labor Hours that are Overtime is much higher than our peers Solution: 1) Hire more people 2) Equalize distribution of hours among personnel Operating Expenses/Vehicle Hour are in-line with our peers Solutions: 1) Decrease service during off-peak hours 2) Increase fares for off-peak hours 3) Optimize routes 9
10. Data Sets National Transit Database (NTD) APCs and AVLs GTFS GTFS Real-time Census 10
11. Data Standardization - Pain 11
12. Stakeholder Buy-In - More pain 12
13. Transportation Verticals Transit Freight Highway Aviation 13
14. Panel Discussion 1. Data Sources, Accuracy and Formats / Standards 2. Data Consumers, Aggregation and Rendition 3. An Open Source Future 14
15. Transit Labs, LLC 1133 15th Street NW, 12th Floor Washington, DC 20005 Dag Gogue dag@transitlabs.com (202) 999-6561 15