Uber-Pickup-analysis-using-Spatial-Temporal-Analysis-and-Geo-Spatial-Clustering (Streamlit Web App)

Uber Pickup analysis using Spatial Temporal Analysis & Geo-Spatial Clustering

• This project explores the spatio-temporal patterns of taxi-service apps and taxi pick-up data and uses geo spatial clustering to make decisions from calculating pricing to finding the optimal positioning of cabs/drivers to maximize profits of the cab-share business.

1. Temporal Analysis & Business analysis with Competitors 2. Airports in depth analysis:

3. Base Station & Identified hubs 4. Active Vehicle analysis:

Business Need:

• According to Gartner, by 2022, more than a quarter billion connected vehicles will form a major element of the Internet of Things. Connected vehicles are projected to generate 25GB of data per hour, which can be analyzed to provide real-time monitoring and apps, and will lead to new concepts of mobility and vehicle usage.

• With the emerging app-based on demand taxi services , the competition in the market is increasing. Thus companies are using quantitative analysis of their app and taxi demands for neighborhoods of cities.

Major Analysis Points:

1. Spatio-temporal analysis :Exploring trip data

   • Getting inference about the number of trips per hour/day/week/Month.
   • Number of trips completed per cab
   • How different base stations are performing each month.
   • Which base stations are best for different perspectives like businesses - may help us do price surge because of demand.

2. Geo Spatial Clustering: A better perspective:

   • Use clustering techniques to find various spatial hotspots.
   • Strategically place the driver’s in good locations(within these clusters) where in probability of getting a ride request are huge.
   • optimal placing of their vehicles at different time of the day.
   • Use these centroids for optimal pricing by analyzing which cluster deals with maximum requests, peak times etc.

3. Business analysis with Competitors:

   • Business Growth in cummulative months
   • Competiton with other Cab Services like Lyft , Skyline , Fedral etc.

4. Active Vehicle Analysis:

   • No of active vehicles & trips
   • Trips per Vehicle

5. Web based Dashboard (Built Using Streamlit)

Data:

Used public uber trip dataset to discuss building a real-time example for analysis and monitoring of car GPS data. The Uber trip dataset, which contains data generated by Uber from New York City. Source : Kaggle