Project 2 - Supply and Demand Gap Analysis

Aim

The Uber Supply and Demand Gap Analysis involve creating a dashboard that provides a comprehensive overview of supply and demand trends across the entire Uber network. This dataset includes data on the number of drivers, pickup points, trip status, and request and drop time. The dataset was downloaded from Kaggle. The objective is to identify and analyze the problems in Supply-Demand of Uber trips from the city to the Airport and vice-versa across various hours of the day and find out possible solutions to address the problem.

Introduction

Uber is a ride-hailing technology platform that has become a popular and convenient way to get around in many cities all over the world. With its prevalence, uber has created a unique opportunity to analyze the supply and demand of the platform. This analysis can be used to identify potential gaps in the market and develop strategies to improve the efficiency of uber services.

Uber provides its services through mobile applications. The current dataset taken has the details for five days in the month of July 2016. We will use a dataset from uber to create visualizations and analyze the data given for five days in July 2016. The main objective of this project is to analyze the dataset and give recommendations based on our findings

Problem Statement

In this project, we find out the performance of Uber, how many request id is placed from the pickup point of the Airport and city, how many Drivers are present, how many trips are completed, and how many trips are can canceled find out the availability/unavailability of the cab. 

Methodology

• The dataset contains information for the period 11/July/2016 to 15/July/2016.

Data Exploration:

Data exploration is the first step of data analysis and it is used to explore and visualized data to take any necessary actions. Based on our understanding of the data we can go with the appropriate data cleaning.

Data Importing:

To Import the data into Power BI, Let’s start with the Get Data option under the home tab. As this is a CSV file, select the Text/CSV option from the drop-down list and select the file named. After selecting the file, data will be displayed Click on Load and save data.

Data Cleaning:

After importing there are 6745 rows and 6 columns. We need to clean the data with the help of the Power query editor.

To open the power query editor by clicking on Transform data under the home tab and go to the power query editor

In the power query editor, go to the view tab, and enable column distribution, column quality, and column profile. It will help you to find out missing values, any data errors, any data type mismatch, any outliers

 

 

 

Data Visualization:

I have used the Power BI tool to visualize the data available to gain meaningful insights and better understand the dataset. I have used different visualizations like bar graphs, line graphs which are available in the tool.

Analysis:

The dataset contains the following columns

Request id: A unique identifier of the request

Time of request: The date and time at which the customer made the trip request

Drop-off time: The drop-off date and time, in case the trip was completed

Driver id: The unique identification number of the driver

Pick-up point: The point from which the request was made either airport or city

Status of the request: The final status of the trip, which can be either completed, canceled by the driver, or no cars available

The dataset contains a null value in the driver id and the drop timestamp has been replaced with the value 0. The column Requested timestamp and Drop timestamp is split as date and time.

By using the conditional column, the New Column (Hour of the day) is added to know the demand of the cabs based on the hours of the day and also another two columns are added one is supply that is the total trip completed and another one is gap that is total count except trip completed this is use the analysis the supply and gap in pickup points.

The slicer is used in the dashboard to analysis the data based on our need. The slicer contains Pickup Points, Date and the Hour of the day data.

From this plot we can see that the time slot between 05 to 09 and 17 to 21 are particularly peak time with high demand. On 05 to 09 there are high demand for cab in the city and  17 to 21 there are high demand for cab on airport.

In this plot we can analysis the no cars are available at the peak time of 17 to 21.

More cancelation is done at the time of 05 to 09.

To carry out further analysis, we create time slots based on the number of trips. From our observations from previous plots, we notice that the times between 5.00 to 9.00 and 17.00 to 21.00 have high request and hence are of our interest.

We can make various time slots like:

• 00 to 5.00 — Early Morning
• 00 to 10.00 — Peak Morning
• 00 to 17.00 — Normal Day Time
• 00 to 22.00 — Peak Evening
• 00 to 0.00 — Late Night

From the above plot, we came to know that at the peak time of morning there are more cancellation done and at the peak time of the evening there are large number of No Car Available.

From the above, we can see the more cancellations and done on the city.

No Cars Available are more in the airport pickup point.

The dataset contains details about 6,745 requests, out of which only 2.83k trips were completed which 41.97% rides were successful and 58.03% of rides were unsuccessful.

Out of the unsuccessful rides 1.26k were due to cancellation of the rides and 2.65k rides were due to unavailability of the cars.

• There are totally 301 drivers available and 6745 requested are done as per the given dataset.
• There are 41.97% of trips are successfully completed and the remaining 58.03% and unsuccessful due to No Cars Available 39.29% and Cancelled 18.74%
• The more request is done on the period on 05 to 09 at the city and 17 to 21 at the airport.
• Considering the status of the requests in the city at the peak time slop we can see that the unsuccessful requests are mainly due to the cancellation of the rides.
• Considering the status of requests in the airport peak hours we can see that unsuccessful requests are mainly due to the unavailability of the cars.

 

 

 

 

 

 

Insights:

We observed that in the Peak Morning Slot where the demand for City to Airport trips are high, there are maximum number of trip cancellations leading to great Gap in the Demand & Supply.

Out of the Demand or Total Requests of 2103, the Supply or Trips Completed were only 854, with high Gap of 1249.

This is because the trip to Airport from the City usually takes a longer time. Once the driver is in the Airport, he will have a longer idle time depending on the on the flight’s patterns. In the mornings, a lot of flights usually leave the city and less flights arrive. Also, it doesn’t make any economic sense to come back empty from the Airport to the City.

All these factors lead to high cancellation rate for trips from City to Airport.

We observed that in the Peak Evening Slot where the demand for Airport to City trips are high, there are maximum number of “No Cars Available” leading to great Gap in the Demand & Supply.

Out of the Demand or Total Requests of 2342, the Supply or Trips Completed were only 784, with high Gap of 1558.

This is because by the night, a lot of flights including the international ones start arriving at the airport. This creates a high demand for the cars. Also, the Uber driver partners start retiring for the day as the dawn proceeds leading to a high non availability of the cars.

These factors lead to high “No Cars Available” issue for trips from Airport to City.

 

 

 

 

 

Recommendations:

By increasing the count of the cabs can resolve this gap

By given rewards to the customer takes the trip on the peak time can avoid the cancellations of the trip.

Incentivize to the drivers who takes trip from Airport to City in the night so that more drivers from the city reach the Airport thus increasing the availability of the cars.

Conclusion:

The Uber supply and demand gap is an ongoing problem that needs to be addressed in order to ensure that the company continues to be successful. Uber has already taken steps to increase the number of drivers in certain areas, as well as to better incentivize drivers with higher fares. However, the gap is unlikely to be closed completely, and Uber will need to continue to develop strategies to bridge the gap between supply and demand. Additionally, Uber will need to continue to monitor and adjust its pricing model to ensure that riders anddrivers both receive fair and equitable compensation. Finally, Uber should also focus on improving the overall user experience in order to ensure that riders will continue to choose them as their preferred ride-sharing service.