Jason BaldersonJason BaldersonJoseph Kariampuzha

Joana AdjaidooKofi Owusu-Boakye

Alexis Saint-Jean

Special thanks to

Ernest Chrappah

Wanda Butler

•Explore and provide insight on hidden relationships between the tickets issued, adjudication cases, and dismissed tickets.

•Data elements analyzed:

•Issuing agency,

•Violation code,

OBJECTIVE:

•Violation code,

•Violation type, adjudicated/contested, dismissed, reason for dismissal,

•Fine amount

•Etc.

•Provide MBA students an opportunity to gain applied practical experience in the field of data mining while exposing them to career options in government.

61%

39%

15%

85%

DMV Dataset Reduction

System Generated

Adjudicated

Non Actionable

ABREAST

EXPIRED METER

FAIL DISP CURR TAGS

FAIL SECURE DC TAGS

NO PARKING ANYTIME

No Standing anytime

Violation Types

Violation Types (ctd)

Adjudicators

1 2 3 4 5 6 7 8 9 10 11 12

Adjudicators (ctd)

1 2 3 4 5 6 7 8 9 10 11 12

Hearing Type

Time of Day – Photo Moving

Filter Settings

- NEW Ticket Type: (Photo Moving Violation)

Time of Day – Parking

Filter Settings

- NEW Ticket Type: (Parking Violation)

Economic Value

Economic Value (ctd)

Classification Tree - Hour

•Potential for revenue maximization by reducing the percentage of “automatic dismissals”.

•Around-the-clock enforcement of traffic and parking violations.

•Improved data-reporting systems:

SUGGESTIONS:

•Improved data-reporting systems:

•Easier to understand

•Regular evaluations

•Increased program efficiency

•Early warning system

•Training process for new adjudicators.

QUESTIONS QUESTIONS