High Level Design
Overview
Getting on a cab or an Uber / Lyft is one way to get around in New York. An average of over a million trips are made every day in the city either with a medallion (“yellow”) cab, boro (“green”) cab, or a ride-hailing service car (“for-hire vehicle / FHV”), such as Uber and Lyft. With such a large amount of data, there are countless kinds of analysis that can be performed to uncover meaningful, actionable insights about taxis and FHVs in New York. This dashboard is specifically designed to provide high level information, such as the number of trips made with Taxi vs Uber and Uber-like services, most popular pickup/dropoff neighborhood, the busiest hours and day of the week, and many more.Goals
The goal of this dashboard is to provide an understanding of the key statistics and insights about the trips people in New York make everyday with taxis or FHV cars. With these insights in hand, a lot of actions can be done to improve the efficiency and the quality of service of taxis in New York. For example, by understanding the busiest hours and the places where most people hail / order a taxi, better strategies can be formulated to make sure there are enough cars to serve all passengers (add more cars/drivers, promote ride-sharing, etc). These insights will also be especially valuable for other stakeholders in the city, including business owners and real estate dealers. Having an understanding of the volume of taxi trips made from / to different places and at different times will greatly help these stakeholders to make various types of decisions, such the type of business to provide, the ideal location for the business, and the pricing strategy.
Objectives
- Understanding the current state of Taxi vs FHV through high level KPIs.
- Increasing revenue stream by better understanding busy hours and places.
- Collecting key insights about passengers’ payment.
KPI Architecture
| Objectives | KPIs | Measures | Data Source |
| Understanding the current state of Taxi vs FHV through high level KPIs. | Number of Trips by Taxi Type, over Time | Count(TripID) | Fact Taxi Trips |
| Total Fare | Sum(TotalAmount) | Fact Taxi Trips | |
| Average Fare | Avg(TotalAmount) | Fact Taxi Trips | |
| Average Distance (in miles) | Avg(TripDistance) | Fact Taxi Trips | |
| Average Duration (in minutes) | Avg(TripDuration) | Fact Taxi Trips | |
| Increase revenue stream by better understanding busy hours and places. | Number of Trips by the Time of the Day (hourly) | Count(TripID) | Fact Taxi Trips |
| Average Number of Trips by the Time of the Day and the Day of the Week | Count(TripID) | Fact Taxi Trips | |
| Number of Trips by Pickup Borough | Count(TripID) | Fact Taxi Trips | |
| Number of Trips by Pickup Neighborhood | Count(TripID) | Fact Taxi Trips | |
| Most Popular Route (neighborhood to neighborhood) | Count(TripID) | Fact Taxi Trips | |
| Top 5 Pickup Neighborhood, by Taxi Type | Count(TripID) | Fact Taxi Trips | |
| Top 5 Dropoff Neighborhood, by Taxi Type | Count(TripID) | Fact Taxi Trips | |
| Collecting key insights about passengers’ payment. | Number of Trips by Tip Percentage Bucket | Count(TripID) | Fact Taxi Trips |
| Average Tip Percentage by the Time of the Day | Avg(Tip) | Fact Taxi Trips | |
| Number of Trips by Payment Method | Count(TripID) | Fact Taxi Trips | |
| Number of Trips by Rate Type | Count(TripID) | Fact Taxi Trips | |
| Number of Shared Rides vs Non-Shared Rides | Count(TripID) | Fact Taxi Trips |
Entities Relationship Diagram
Plugins & Scripts
1. Custom Filter
2. BloX
3. Narratives
4. Color Heatmap
5. Custom Map
6. Widget Title Style
7. Reduced Text Widget Height
Data Connectors
