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MDOT MTA’s Capital Budget: A Data-Driven Impact Analysis Name of Speaker Leah Visakowitz, GIS/Data Analyst, Maryland Transit Administration, Baltimore, Maryland
MDOT MTA’s Capital Budget: A Data-Driven Impact Analysis
Name of SpeakerLeah Visakowitz,
GIS/Data Analyst,Maryland Transit Administration,
Baltimore, Maryland
Call for Projects - Background
• Definitive start and end date
• No existing fund
• >$50,000
Call for Projects - Background
PIS
Oct 18-Dec 18 Oct 18-Feb 19 April 19 April 19 May 19
Evolution of the PIS
Criteria
Safety Efficiency Reliability Customer Service
Crime Maintenance Schedule Asset Condition Customer Experience
Crime Resolution Maintenance Cost Disability Access Call Center Experience
Preventable Accidents Hardware/Software Upgrades Bike/Ped Access Customer Access to Info
Workplace Injuries Data Collection/Analysis Failures
Non-Cash Fare Payment Revenue Hour Loss
Customer Travel Time
Operating Cost
Employee Productivity
Energy Consumption
Water Consumption
Air Pollution
Environmental Hazard
Criteria
Safety Efficiency Reliability Customer Service
% of crimes reduced % change in maintenance time Change in condition score % complaints reduced
% change in resolution rate Maintenance cost reduction ($) # of disabled riders affected Improves call center (yes/no)
% of accidents reduced Upgrades Hardware/Software (yes/no) # of riders affected # of riders affected
% of workplace injuries reduced Improves data collection/analysis (yes/no) Change in # of failures
% change in non-cash riders Revenue dollars lost
Reduction in customer travel time (hours)
Operating cost reduction ($)
Person hours saved
Annual energy savings ($)
Annual water savings ($)
GHG reductions (MTCO2-E)
Cost of env hazard ($)
Data Collection
No
No
No
Yes
Yes
Yes
Central data repository?
Does data exist somewhere?
Can it be estimated?
Consistent Analysis
Quantitative Result
Something
Not Measured
Measuring Impacts
Assume MaxPrecise
CalculationCost Indicator
BinarySME
Guesstimate
Project Scoring
$0.00
$200,000.00
$400,000.00
$600,000.00
$800,000.00
$1,000,000.00
$1,200,000.00
$1,400,000.00
0 5 10 15 20 25
Maintenance Cost
0.00%
0.20%
0.40%
0.60%
0.80%
1.00%
0 1 2 3 4 5 6 7 8 9
Customer Complaints
Do
llars
Sav
ed
Projects in order of impactProjects in order of impact
% C
om
pla
ints
Red
uce
d
Summary Sheets
Score with cornerstone break-down
Project Info
Criteria, Value, Score
Other considerations
Selection Meeting
Selection Meeting
Results
• Not all projects with a high score were selected
• Some projects with low scores were selected
• A lot of partially funded projects
• Critical needs addressed
Feedback/Challenges
• Lack of reliable data
• Project Manager participation
• Scoring system that works for variety of projects
• Addressing risk
• Asset condition considerations (capacity, support, criticality)
