Ppt gu scs team2_a_metro project

Ppt gu scs team2_a_metro project

• 1.
  Predicting Post-Safetrack MetroReliability GU SCS Data Science Capstone Project September 10, 2016
• 2.
  over 250m ridersannually 118 miles of track Facts over 13 disruptions per day Problem Statement
• 3.
  Problem Statement highly publicizedsafety lapses & deferred maintenance 1 Year timeframe estimated $60,000,000 price tag improved safety & reliability?
• 4.
  Hypothesis The DC MetroSystem is a pivotal transportation asset for Washington DC and the surrounding regions. The SafeTrack project is meant to increase system safety and reliability. While technical and operational disruptions are inevitable, we believe that available data can provide insight into how frequently Metro riders will experience post-SafeTrack disruptions and ultimately improve their Metro commute expectations. Scenario #1 Improvement Scenario #2 Improvement Scenario #3 Improvement To quantify the outcome, we will explore several scenarios to provide riders with a clearer picture of their post-safetrack commute. Scenario #4 Improvement Scenario #5 Improvement
• 5.
  Data Ingestion &Wrangling System Operations Data: used to determine system behavior under optimal conditions Disruption Data: historical data used to analyze the frequency and effect of technical and operational disruptions (ie: delays) Ridership Data: in conjunction with operational datasets, ridership data used to quantify and extrapolate the scope of Metro delays.
• 6.
  The Data ON TIME ONTIME ON TIME DELAYED DELAYED DELAYED Planned Operating Schedule Disruption Data Data_Source: wmata.com Data_Scope: Provided operating data under a perfectly efficient system with no delays or disruptions Data_Scope: Provided 5 years of daily disruption logs, including; cause of disruption and minutes delayed Data_Source: opendatadc Planned Operating Schedule and Disruption Data provided a basis for comparing pre and post-safetrack system behavior LN CAR DEST MINLN CAR DEST MIN RD 6 RD 6 RD 6 RD 6 RD 6 RD 6
• 7.
  The Data 24,335 records between April 2012- July 2016 All Metro lines represented in the dataset Description of disruption cause. Translated as technical or operational Delay, in minutes
• 8.
  Computation & Analysis:Limitations AccuracyLocation Station - To - Station ‘Garbage in - Garbage out’ concept Opted to take a two-pronged approach: 1.) Build data product 2.) Develop simulation based on available data Completeness Compounding Delays
• 9.
  Computation & Analysis:Methodology 1 Calculated the number of minutes of trips per day on each line. Broke daily delays into five tiers based on severity. Scenario:1 Scenario:2 Scenario:3 Scenario:5 Tier 2 Tier 3 Tier 4 Tier 5 Tier 1 Tier 2 Tier 3 Tier 4 Tier 5 Tier 1 Tier 2 Tier 3 Tier 4 Tier 5 Tier 1 Tier 2 Tier 3 Tier 4 Tier 5 Tier 1 Tier 2 Tier 3 Tier 4 Tier 1 Scenario:4 Built in compounding delays based on expected train departures. Injected random noise into the system. 2 3 4
• 10.
  Results of SimulatedScenario
• 11.
  A Look UnderThe Hood [software system demo]
• 12.
  Results Created visualizations of thevarious simulations Analyzed results to determine the shape of the data
• 13.
  Results Current 9861.402 102.51522 Scenario#1 9868.713 97.10936 Scenario #2 9854.400 108.57028 Scenario #3 9852.256 102.1384 Scenario #4 9850.429 101.7149 Scenario #5 9848.057 104.1241 Current 8121.386 95.954 Scenario #1 8117.496 97.341 Scenario #2 8115.761 99.953 Scenario #3 8114.653 104.407 Scenario #4 8104.47 99.702 Scenario #5 8093.36 98.429 Current 5280.572 100.5566 Scenario #1 5261.651 92.5748 Scenario #2 5262.043 114.093 Scenario #3 5020.293 41.431 Scenario #4 5014.868 41.251 Scenario #5 5013.92 40.980 Current 6762.053 97.839 Scenario #1 6765.053 97.839 Scenario #2 6759.09 103.266 Scenario #3 6562.22 52.973 Scenario #4 6552.85 53.316 Scenario #5 6540.79 48.947 Current 6811.311 108.8495 Scenario #1 6815.311 108.8495 Scenario #2 6816.787 105.2023 Scenario #3 6809.531 108.5713 Scenario #4 6810.966 97.1970 Scenario #5 6809.322 98.0109 Current 11149.5 97.3886 Scenario #1 11159.6 98.4512 Scenario #2 11146.33 99.5911 Scenario #3 11138.77 112.8393 Scenario #4 11132.07 97.0613 Scenario #5 11123.83 101.226
• 14.
  Conclusions Scenario #1 Scenario#2 Scenario #3 Scenario #4 Scenario #5 Noticeable improvements in time and probability of delay was not realized until higher scenario parameters were introduced. Analysis of the results indicates that SafeTrack repairs must reduce disruption severity and probability by roughly 30% - 50% for Metro riders to experience improved trip safety and reliability.
• 15.
  Conclusions Improvements in Stochastic System Biases& Assumptions Data Quality Springboard for Future Work SafeTrack’s improvements may not be noticed if they do not overcome the system’s random noise Recognizing biases and stating assumptions is key to data science The importance of accurate data cannot be overstated Our software can be generalized and adapted
• 16.
  Questions? ?