● Led as project manager to analyze 2017 DC taxi trips including 1.1 M data, 150 taxi companies and 39 variables. ● Processed and cleaned the dataset by using Python. Used Tableau to analyze and do visualization. ● Successfully built the model to detect demand and supply pattern in each neighborhood. Stood out from 69 teams and got invited to present the result to Department of For-Hire Vehicles data team in DC.

1. Data Challenge
TeamDC19036

2. Wei Chen
MSBA Student
Liya Zhang
MSBA Student
Project Team1
Yunjia Jiang
MSBA Student
Tariq Haque
MSBA Student

3. Agenda
• Introduction
• Exploratory Analysis of Data
• Manual Anomaly Detection
• Using Machine to Detect Anomaly
• Recommendations
• Future Studies

4. 2 Challenge for Taxicabs
Dynamic
Fare
Fixed Fare

5. 3
11M
2017 Trip Data from
DFHV
Time, fare amount, origin,
destination, mileage, duration,
etc.
42 Variables
53
Zip codes
Data Description
150
Taxi companies

6. 4 Neighborhoods
• Divided DC into 217
neighborhoods
• These are small regions
considered to possess
uniform characters
• Added neighborhood
information for each trip

7. 5 Hourly Trip Frequency
11 AM : 10 PM

8. 6 Average Trip Distance by Hour
Longest distance
happens at
3 to 7 am

9. 7
Customers take taxis
most frequently on
Weekly Trip Frequency

10. 8 Weekly Trip Frequency by Hour

11. 9 How do we decide price?
• Match supply with ridership/demand
• Study pattern in ridership and identify anomalies
○ Hourly and Weekly demand pattern in each neighborhood
○ Identify anomalies in demand pattern by comparing with overall pattern

12. 10
0
Anomaly 1

13. 11 Anomaly 2

14. 12 Anomaly 3

15. 13 Anomaly Detection Code
Comparing 36,456 with
168 data points

16. 14 Recommendations
Anomalies for the Union Station Neighborhood
● High Ridership: Experiment by increasing fares and assess
the impact on ridership
● Low Ridership: Experiment by providing discounts and assess
the impact on ridership

17. 15 What to do in future?
➔Conduct our analysis on complete dataset
➔Apply machine learning to cluster
neighborhoods based on their characteristics
(hospitals, train station etc)
◆ Match this information with areas showing
anomalies to efficiently identify reasons for
anomalies
➔Collect drivers’ real-time geolocation
information for supply and analysis

18. 15