Uber Trip Analysis Dashboard by Onanibosi BabajideUber Trip Analysis Dashboard by Onanibosi Babajide

Uber Trip Analysis Dashboard

Onanibosi Babajide

Data Analyst

Microsoft Excel

Microsoft Power BI

Transportation

Uber Trip Analysis Dashboard

Project Overview

A comprehensive Power BI dashboard for analyzing Uber trip data to gain insights into booking trends, revenue generation, and trip efficiency. This project helps stakeholders make data-driven decisions by visualizing key performance indicators and trends in an interactive dashboard.

Dashboard Previews

Dashboard 1: Overview Analysis

Dashboard 2: Time Analysis

Dashboard 3: Details View

Business Requirements

The project provides three interconnected dashboards:

Dashboard 1: Overview Analysis

Provides a high-level summary of key performance indicators and trip metrics.

Dashboard 2: Time Analysis

Focuses on temporal patterns with visualizations segmented by different time intervals.

Dashboard 3: Details Tab

Offers granular access to individual trip records with drill-through capabilities.

Key Performance Indicators

The dashboards track the following KPIs:

Total Bookings: Number of trips booked over a given period

Total Booking Value: Total revenue generated from all bookings

Average Booking Value: Average revenue per booking

Total Trip Distance: Total distance covered by all trips

Average Trip Distance: Average customer travel distance per trip

Average Trip Time: Average duration of trips

Features & Visualizations

Overview Analysis Dashboard

Dynamic Measure Selector: Toggle between different KPIs using a disconnected table

Payment Type Analysis: Breakdown of metrics by payment method (Card, Cash, Wallet)

Trip Type Analysis: Comparison between day and night trips

Vehicle Type Analysis: Grid view showing KPIs across different vehicle categories

Daily Booking Trends: Line chart showing total bookings by day of the week

Location Analysis:

Most frequent pickup points

Most frequent drop-off points

Farthest trip details

Top 5 booking locations

Most preferred vehicle by location

Time Analysis Dashboard

Global Dynamic Measure: Filter that updates all charts based on user selection

10-Minute Interval Analysis: Area chart showing bookings in 10-minute intervals

Day of Week Analysis: Line chart showing booking trends from Monday to Sunday

Hour and Time Heatmap: Matrix grid showing peak booking hours across days

Details Dashboard

Grid Table: Comprehensive view of all trip details

Drill-Through Functionality: Access detailed records from other dashboard visuals

Full Data View Bookmark: Toggle between filtered and complete datasets

Enhanced User Experience

Dynamic Titles: Chart titles update based on selected measures

Interactive Slicers: Filters for date, city, and other parameters

Enhanced Tooltips: Show additional details on hover

Data Details Bookmark: Provides explanations for metrics and data sources

Clear Filters Button: Reset all selections with one click

Export Functionality: Download raw data for external analysis

Dataset Description

The analysis is based on the "Uber Trip Details" dataset containing over 100,000 trip records with the following fields:

Trip ID

Pickup Time

Drop Off Time

Passenger Count

Trip Distance

PULocationID (Pickup Location ID)

DOLocationID (Drop-off Location ID)

Fare Amount

Surge Fee

Vehicle Type

Payment Type

Tools & Technologies

Power BI: Primary tool for dashboard development

Power Query: Data cleaning and transformation

DAX: Advanced calculations and measures

Excel: Source data management

DAX Measures

Core KPI Measures

// Total Bookings
Total Bookings = COUNT('Trip Details'[Trip ID])

// Total Booking Value
Total Booking Value = SUM('Trip Details'[fare_amount]) + SUM('Trip Details'[Surge Fee])

// Average Booking Value
Average Booking Value = DIVIDE([Total Booking Value], [Total Bookings], 0)

// Total Trip Distance
Total Trip Distance = SUM('Trip Details'[trip_distance])

// Average Trip Distance
Average Trip Distance = DIVIDE([Total Trip Distance], [Total Bookings], 0)

// Average Trip Time
Average Trip Time = 
AVERAGEX(
    'Trip Details',
    DATEDIFF('Trip Details'[Pickup Time], 'Trip Details'[Drop Off Time], MINUTE)
)

Time Intelligence Measures

// Bookings by Hour
Bookings by Hour = 
CALCULATE(
    [Total Bookings],
    USERELATIONSHIP('Trip Details'[Pickup Time], 'Time'[DateTime])
)

// Booking Value by Day of Week
Booking Value by Day of Week = 
CALCULATE(
    [Total Booking Value],
    USERELATIONSHIP('Trip Details'[Pickup Time], 'Date'[Date])
)

// Pickup Time in 10-min Intervals
Pickup Time Interval = 
FORMAT(
    TIME(
        HOUR('Trip Details'[Pickup Time]),
        FLOOR(MINUTE('Trip Details'[Pickup Time])/10)*10,
        0
    ),
    "hh:mm"
)

Dynamic Measure Selection

// Selected Measure (for dynamic selection)
Selected Measure = 
SWITCH(
    SELECTEDVALUE('Measure Selector'[Measure]),
    "Total Bookings", [Total Bookings],
    "Total Booking Value", [Total Booking Value],
    "Total Trip Distance", [Total Trip Distance],
    [Total Bookings] // Default value
)

// Dynamic Chart Title
Dynamic Chart Title = 
VAR MeasureName = SELECTEDVALUE('Measure Selector'[Measure])
RETURN
    "Analysis by " & MeasureName

Location Analysis Measures

// Most Frequent Pickup Point
Most Frequent Pickup = 
VAR TopLocation = 
    TOPN(1, VALUES('Locations'[Location Name]), 
        CALCULATE([Total Bookings], 
            USERELATIONSHIP('Trip Details'[PULocationID], 'Locations'[LocationID])), 
        DESC)
RETURN
    SELECTEDVALUE(TopLocation, "None")

// Most Frequent Drop-off Point
Most Frequent Dropoff = 
VAR TopLocation = 
    TOPN(1, VALUES('Locations'[Location Name]), 
        CALCULATE([Total Bookings], 
            USERELATIONSHIP('Trip Details'[DOLocationID], 'Locations'[LocationID])), 
        DESC)
RETURN
    SELECTEDVALUE(TopLocation, "None")

// Farthest Trip Distance
Farthest Trip = 
    MAXX('Trip Details', [trip_distance])

Vehicle & Payment Analysis

// Preferred Vehicle by Location
Preferred Vehicle by Location = 
VAR CurrentLocation = SELECTEDVALUE('Locations'[Location Name])
VAR VehicleCount = 
    SUMMARIZE(
        'Trip Details',
        'Trip Details'[Vehicle],
        "Count", COUNT('Trip Details'[Trip ID])
    )
VAR TopVehicle = 
    TOPN(1, VehicleCount, [Count], DESC)
RETURN
    SELECTEDVALUE(TopVehicle[Vehicle], "None")

// Booking Value by Payment Type
Booking Value by Payment = 
    CALCULATE(
        [Total Booking Value],
        ALLEXCEPT('Trip Details', 'Trip Details'[Payment_type])
    )

Expected Outcomes

Identify trends in ride bookings and revenue generation

Analyze trip efficiency in terms of distance and duration

Compare booking values and trip patterns across different time periods

Provide insights to optimize pricing models and improve customer satisfaction

Repository Contents

Uber Trip Details.xlsx: Source data containing trip records

Uber analysis.pdf: PDF export of the final dashboard visualizations

Problem Statement.docx: Original business requirements document

Uber_Trip_Analysis.pbix: Power BI file with complete dashboard implementation

Data Model & Relationships

The data model for this project consists of the following tables and relationships:

Trip Details (Fact Table)
├── Date [Many-to-One]
│   └── Calendar table with date hierarchies
├── Time [Many-to-One]
│   └── Time dimension with hour-level granularity
├── Locations [Many-to-One]
│   └── Location dimension with location details
└── Measure Selector [Disconnected]
    └── Used for dynamic measure selection

Key Table Structures

Trip Details (Fact Table)

Trip ID (Key)

Pickup Time (DateTime)

Drop Off Time (DateTime)

passenger_count (Integer)

trip_distance (Decimal)

PULocationID (Integer) - Foreign Key to Locations

DOLocationID (Integer) - Foreign Key to Locations

fare_amount (Decimal)

Surge Fee (Decimal)

Vehicle (Text)

Payment_type (Text)

Date Dimension

Date (Key)

Year (Integer)

Month (Integer)

Month Name (Text)

Quarter (Integer)

Day of Week (Integer)

Day Name (Text)

Is Weekend (Boolean)

Is Holiday (Boolean)

Time Dimension

Time Key (Key)

Hour (Integer)

Minute (Integer)

Second (Integer)

AM/PM (Text)

Hour Band (Text) - Morning/Afternoon/Evening/Night categorization

Location Dimension

LocationID (Key)

Location Name (Text)

Borough (Text)

Zone (Text)

Service Zone (Text)

Measure Selector (Disconnected Table)

Measure (Text) - Contains values for dynamic measure selection

How to Use

Clone this repository

Open the Power BI file using Power BI Desktop

Ensure the data source connection points to the included Excel file

Refresh the data if needed

Interact with the dashboard using the provided slicers and filters

Data Preparation & ETL Process

The raw Uber trip data required several transformation steps before analysis:

// Power Query M Code for Trip Data Transformation

let
    Source = Excel.Workbook(File.Contents("Uber Trip Details.xlsx"), null, true),
    TripDetails = Source{[Item="Trip Details",Kind="Sheet"]}[Data],
    
    // Promote headers and set data types
    PromotedHeaders = Table.PromoteHeaders(TripDetails, [PromoteAllScalars=true]),
    ChangedTypes = Table.TransformColumnTypes(PromotedHeaders,{
        {"Trip ID", Int64.Type},
        {"Pickup Time", type datetime},
        {"Drop Off Time", type datetime},
        {"passenger_count", Int64.Type},
        {"trip_distance", type number},
        {"PULocationID", Int64.Type},
        {"DOLocationID", Int64.Type},
        {"fare_amount", type number},
        {"Surge Fee", type number},
        {"Vehicle", type text},
        {"Payment_type", type text}
    }),
    
    // Add calculated columns
    AddedTripDuration = Table.AddColumn(ChangedTypes, "Trip Duration (min)", 
        each Duration.TotalMinutes([Drop Off Time] - [Pickup Time]), type number),
    
    // Calculate total fare
    AddedTotalFare = Table.AddColumn(AddedTripDuration, "Total Fare", 
        each [fare_amount] + [Surge Fee], type number),
    
    // Add time-based categorization
    AddedTimePeriod = Table.AddColumn(AddedTotalFare, "Time Period", 
        each if Time.Hour([Pickup Time]) >= 6 and Time.Hour([Pickup Time]) < 12 then "Morning"
            else if Time.Hour([Pickup Time]) >= 12 and Time.Hour([Pickup Time]) < 18 then "Afternoon"
            else if Time.Hour([Pickup Time]) >= 18 and Time.Hour([Pickup Time]) < 22 then "Evening"
            else "Night", type text),
    
    // Add day/night categorization
    AddedDayNight = Table.AddColumn(AddedTimePeriod, "Day or Night", 
        each if Time.Hour([Pickup Time]) >= 6 and Time.Hour([Pickup Time]) < 20 then "Day" else "Night", 
        type text),
    
    // Add day of week name
    AddedDayName = Table.AddColumn(AddedDayNight, "Day Name", 
        each Date.DayOfWeekName([Pickup Time]), type text),
    
    // Remove any null values in critical columns
    RemovedNulls = Table.SelectRows(AddedDayName, 
        each [Trip ID] <> null and [Pickup Time] <> null and [Drop Off Time] <> null)
in
    RemovedNulls

Additional Data Processing Steps:

Generated Date Dimension: Created a date dimension table spanning the date range in the dataset

Location Data Integration: Merged location details with the main trip data

Calculated Fields: Added calculated fields for trip duration, total fare, and time categorizations

Outlier Handling: Removed trips with unrealistic distances or durations

Disconnected Tables: Created disconnected tables for measure selection and visualization control

Sample Insights & Key Findings

From the analysis of over 100,000 Uber trips, the following insights were derived:

Booking Patterns

Peak Hours: Highest booking volume occurs between 5-8 PM on weekdays

Weekly Pattern: Friday shows 22% higher booking rates than the weekly average

Weekend Trend: Weekend trips are 15% more valuable but 18% fewer in volume compared to weekdays

Vehicle Usage

UberX Dominance: UberX accounts for 68% of all bookings across the dataset

Premium Service: Uber Black generates 37% of total revenue despite representing only 18% of total rides

Vehicle Preferences: UberX is preferred for short trips (< 3 miles), while Uber Black is preferred for longer journeys

Payment Analysis

Payment Methods: Cash payments represent 45% of all transactions

Digital Growth: Uber Pay usage increased by 12% over the analyzed period

Value Distribution: Cash payment average value is $8.50, while digital payments average $11.75

Trip Efficiency

Distance vs. Duration: Trips during peak hours show 22% longer duration for the same distance compared to off-peak

Route Efficiency: Certain routes show consistent deviations in the distance-to-time ratio, suggesting traffic issues

Revenue Optimization: Top 5 most profitable routes generate 27% of total revenue

Geographic Insights

Location Concentration: The top 5 pickup locations generate 42% of total bookings

Zonal Patterns: Downtown areas show higher short-trip frequency, while airport pickups show longer average distances

Underserved Areas: Several locations show high demand but lower vehicle availability, presenting opportunities for optimization

Recommendations

Dynamic Pricing: Implement more granular surge pricing in high-demand areas during peak hours

Vehicle Allocation: Strategically position Uber Black vehicles near locations with historically higher premium service demand

Digital Payment Incentives: Introduce incentives to shift cash users toward digital payment methods

Route Optimization: Address inefficient routes through driver education and navigation improvements

Location-Based Promotions: Create targeted promotions for underserved high-potential areas

This project was created as part of a data visualization portfolio demonstrating Power BI dashboard development and data analysis skills using advanced DAX, data modeling techniques, and interactive dashboard design.

Like this project

Posted Jul 3, 2025

Developed a Power BI dashboard for Uber trip data analysis.

Likes

Views

Clients

Uber

Uber Trip Analysis Dashboard

Onanibosi Babajide

Data Analyst

Microsoft Excel

Microsoft Power BI

Transportation

Uber Trip Analysis Dashboard

Project Overview

Dashboard Previews

Dashboard 1: Overview Analysis

Dashboard 2: Time Analysis

Dashboard 3: Details View

Business Requirements

The project provides three interconnected dashboards:

Dashboard 1: Overview Analysis

Provides a high-level summary of key performance indicators and trip metrics.

Dashboard 2: Time Analysis

Focuses on temporal patterns with visualizations segmented by different time intervals.

Dashboard 3: Details Tab

Offers granular access to individual trip records with drill-through capabilities.

Key Performance Indicators

The dashboards track the following KPIs:

Total Bookings: Number of trips booked over a given period

Total Booking Value: Total revenue generated from all bookings

Average Booking Value: Average revenue per booking

Total Trip Distance: Total distance covered by all trips

Average Trip Distance: Average customer travel distance per trip

Average Trip Time: Average duration of trips

Features & Visualizations

Overview Analysis Dashboard

Dynamic Measure Selector: Toggle between different KPIs using a disconnected table

Payment Type Analysis: Breakdown of metrics by payment method (Card, Cash, Wallet)

Trip Type Analysis: Comparison between day and night trips

Vehicle Type Analysis: Grid view showing KPIs across different vehicle categories

Daily Booking Trends: Line chart showing total bookings by day of the week

Location Analysis:

Most frequent pickup points

Most frequent drop-off points

Farthest trip details

Top 5 booking locations

Most preferred vehicle by location

Time Analysis Dashboard

Global Dynamic Measure: Filter that updates all charts based on user selection

10-Minute Interval Analysis: Area chart showing bookings in 10-minute intervals

Day of Week Analysis: Line chart showing booking trends from Monday to Sunday

Hour and Time Heatmap: Matrix grid showing peak booking hours across days

Details Dashboard

Grid Table: Comprehensive view of all trip details

Drill-Through Functionality: Access detailed records from other dashboard visuals

Full Data View Bookmark: Toggle between filtered and complete datasets

Enhanced User Experience

Dynamic Titles: Chart titles update based on selected measures

Interactive Slicers: Filters for date, city, and other parameters

Enhanced Tooltips: Show additional details on hover

Data Details Bookmark: Provides explanations for metrics and data sources

Clear Filters Button: Reset all selections with one click

Export Functionality: Download raw data for external analysis

Dataset Description

The analysis is based on the "Uber Trip Details" dataset containing over 100,000 trip records with the following fields:

Trip ID

Pickup Time

Drop Off Time

Passenger Count

Trip Distance

PULocationID (Pickup Location ID)

DOLocationID (Drop-off Location ID)

Fare Amount

Surge Fee

Vehicle Type

Payment Type

Tools & Technologies

Power BI: Primary tool for dashboard development

Power Query: Data cleaning and transformation

DAX: Advanced calculations and measures

Excel: Source data management

DAX Measures

Core KPI Measures

// Total Bookings
Total Bookings = COUNT('Trip Details'[Trip ID])

// Total Booking Value
Total Booking Value = SUM('Trip Details'[fare_amount]) + SUM('Trip Details'[Surge Fee])

// Average Booking Value
Average Booking Value = DIVIDE([Total Booking Value], [Total Bookings], 0)

// Total Trip Distance
Total Trip Distance = SUM('Trip Details'[trip_distance])

// Average Trip Distance
Average Trip Distance = DIVIDE([Total Trip Distance], [Total Bookings], 0)

// Average Trip Time
Average Trip Time = 
AVERAGEX(
    'Trip Details',
    DATEDIFF('Trip Details'[Pickup Time], 'Trip Details'[Drop Off Time], MINUTE)
)

Time Intelligence Measures

// Bookings by Hour
Bookings by Hour = 
CALCULATE(
    [Total Bookings],
    USERELATIONSHIP('Trip Details'[Pickup Time], 'Time'[DateTime])
)

// Booking Value by Day of Week
Booking Value by Day of Week = 
CALCULATE(
    [Total Booking Value],
    USERELATIONSHIP('Trip Details'[Pickup Time], 'Date'[Date])
)

// Pickup Time in 10-min Intervals
Pickup Time Interval = 
FORMAT(
    TIME(
        HOUR('Trip Details'[Pickup Time]),
        FLOOR(MINUTE('Trip Details'[Pickup Time])/10)*10,
        0
    ),
    "hh:mm"
)

Dynamic Measure Selection

// Selected Measure (for dynamic selection)
Selected Measure = 
SWITCH(
    SELECTEDVALUE('Measure Selector'[Measure]),
    "Total Bookings", [Total Bookings],
    "Total Booking Value", [Total Booking Value],
    "Total Trip Distance", [Total Trip Distance],
    [Total Bookings] // Default value
)

// Dynamic Chart Title
Dynamic Chart Title = 
VAR MeasureName = SELECTEDVALUE('Measure Selector'[Measure])
RETURN
    "Analysis by " & MeasureName

Location Analysis Measures

// Most Frequent Pickup Point
Most Frequent Pickup = 
VAR TopLocation = 
    TOPN(1, VALUES('Locations'[Location Name]), 
        CALCULATE([Total Bookings], 
            USERELATIONSHIP('Trip Details'[PULocationID], 'Locations'[LocationID])), 
        DESC)
RETURN
    SELECTEDVALUE(TopLocation, "None")

// Most Frequent Drop-off Point
Most Frequent Dropoff = 
VAR TopLocation = 
    TOPN(1, VALUES('Locations'[Location Name]), 
        CALCULATE([Total Bookings], 
            USERELATIONSHIP('Trip Details'[DOLocationID], 'Locations'[LocationID])), 
        DESC)
RETURN
    SELECTEDVALUE(TopLocation, "None")

// Farthest Trip Distance
Farthest Trip = 
    MAXX('Trip Details', [trip_distance])

Vehicle & Payment Analysis

// Preferred Vehicle by Location
Preferred Vehicle by Location = 
VAR CurrentLocation = SELECTEDVALUE('Locations'[Location Name])
VAR VehicleCount = 
    SUMMARIZE(
        'Trip Details',
        'Trip Details'[Vehicle],
        "Count", COUNT('Trip Details'[Trip ID])
    )
VAR TopVehicle = 
    TOPN(1, VehicleCount, [Count], DESC)
RETURN
    SELECTEDVALUE(TopVehicle[Vehicle], "None")

// Booking Value by Payment Type
Booking Value by Payment = 
    CALCULATE(
        [Total Booking Value],
        ALLEXCEPT('Trip Details', 'Trip Details'[Payment_type])
    )

Expected Outcomes

Identify trends in ride bookings and revenue generation

Analyze trip efficiency in terms of distance and duration

Compare booking values and trip patterns across different time periods

Provide insights to optimize pricing models and improve customer satisfaction

Repository Contents

Uber Trip Details.xlsx: Source data containing trip records

Uber analysis.pdf: PDF export of the final dashboard visualizations

Problem Statement.docx: Original business requirements document

Uber_Trip_Analysis.pbix: Power BI file with complete dashboard implementation

Data Model & Relationships

The data model for this project consists of the following tables and relationships:

Trip Details (Fact Table)
├── Date [Many-to-One]
│   └── Calendar table with date hierarchies
├── Time [Many-to-One]
│   └── Time dimension with hour-level granularity
├── Locations [Many-to-One]
│   └── Location dimension with location details
└── Measure Selector [Disconnected]
    └── Used for dynamic measure selection

Key Table Structures

Trip Details (Fact Table)

Trip ID (Key)

Pickup Time (DateTime)

Drop Off Time (DateTime)

passenger_count (Integer)

trip_distance (Decimal)

PULocationID (Integer) - Foreign Key to Locations

DOLocationID (Integer) - Foreign Key to Locations

fare_amount (Decimal)

Surge Fee (Decimal)

Vehicle (Text)

Payment_type (Text)

Date Dimension

Date (Key)

Year (Integer)

Month (Integer)

Month Name (Text)

Quarter (Integer)

Day of Week (Integer)

Day Name (Text)

Is Weekend (Boolean)

Is Holiday (Boolean)

Time Dimension

Time Key (Key)

Hour (Integer)

Minute (Integer)

Second (Integer)

AM/PM (Text)

Hour Band (Text) - Morning/Afternoon/Evening/Night categorization

Location Dimension

LocationID (Key)

Location Name (Text)

Borough (Text)

Zone (Text)

Service Zone (Text)

Measure Selector (Disconnected Table)

Measure (Text) - Contains values for dynamic measure selection

How to Use

Clone this repository

Open the Power BI file using Power BI Desktop

Ensure the data source connection points to the included Excel file

Refresh the data if needed

Interact with the dashboard using the provided slicers and filters

Data Preparation & ETL Process

The raw Uber trip data required several transformation steps before analysis:

// Power Query M Code for Trip Data Transformation

let
    Source = Excel.Workbook(File.Contents("Uber Trip Details.xlsx"), null, true),
    TripDetails = Source{[Item="Trip Details",Kind="Sheet"]}[Data],
    
    // Promote headers and set data types
    PromotedHeaders = Table.PromoteHeaders(TripDetails, [PromoteAllScalars=true]),
    ChangedTypes = Table.TransformColumnTypes(PromotedHeaders,{
        {"Trip ID", Int64.Type},
        {"Pickup Time", type datetime},
        {"Drop Off Time", type datetime},
        {"passenger_count", Int64.Type},
        {"trip_distance", type number},
        {"PULocationID", Int64.Type},
        {"DOLocationID", Int64.Type},
        {"fare_amount", type number},
        {"Surge Fee", type number},
        {"Vehicle", type text},
        {"Payment_type", type text}
    }),
    
    // Add calculated columns
    AddedTripDuration = Table.AddColumn(ChangedTypes, "Trip Duration (min)", 
        each Duration.TotalMinutes([Drop Off Time] - [Pickup Time]), type number),
    
    // Calculate total fare
    AddedTotalFare = Table.AddColumn(AddedTripDuration, "Total Fare", 
        each [fare_amount] + [Surge Fee], type number),
    
    // Add time-based categorization
    AddedTimePeriod = Table.AddColumn(AddedTotalFare, "Time Period", 
        each if Time.Hour([Pickup Time]) >= 6 and Time.Hour([Pickup Time]) < 12 then "Morning"
            else if Time.Hour([Pickup Time]) >= 12 and Time.Hour([Pickup Time]) < 18 then "Afternoon"
            else if Time.Hour([Pickup Time]) >= 18 and Time.Hour([Pickup Time]) < 22 then "Evening"
            else "Night", type text),
    
    // Add day/night categorization
    AddedDayNight = Table.AddColumn(AddedTimePeriod, "Day or Night", 
        each if Time.Hour([Pickup Time]) >= 6 and Time.Hour([Pickup Time]) < 20 then "Day" else "Night", 
        type text),
    
    // Add day of week name
    AddedDayName = Table.AddColumn(AddedDayNight, "Day Name", 
        each Date.DayOfWeekName([Pickup Time]), type text),
    
    // Remove any null values in critical columns
    RemovedNulls = Table.SelectRows(AddedDayName, 
        each [Trip ID] <> null and [Pickup Time] <> null and [Drop Off Time] <> null)
in
    RemovedNulls

Additional Data Processing Steps:

Generated Date Dimension: Created a date dimension table spanning the date range in the dataset

Location Data Integration: Merged location details with the main trip data

Calculated Fields: Added calculated fields for trip duration, total fare, and time categorizations

Outlier Handling: Removed trips with unrealistic distances or durations

Disconnected Tables: Created disconnected tables for measure selection and visualization control

Sample Insights & Key Findings

From the analysis of over 100,000 Uber trips, the following insights were derived:

Booking Patterns

Peak Hours: Highest booking volume occurs between 5-8 PM on weekdays

Weekly Pattern: Friday shows 22% higher booking rates than the weekly average

Weekend Trend: Weekend trips are 15% more valuable but 18% fewer in volume compared to weekdays

Vehicle Usage

UberX Dominance: UberX accounts for 68% of all bookings across the dataset

Premium Service: Uber Black generates 37% of total revenue despite representing only 18% of total rides

Vehicle Preferences: UberX is preferred for short trips (< 3 miles), while Uber Black is preferred for longer journeys

Payment Analysis

Payment Methods: Cash payments represent 45% of all transactions

Digital Growth: Uber Pay usage increased by 12% over the analyzed period

Value Distribution: Cash payment average value is $8.50, while digital payments average $11.75

Trip Efficiency

Distance vs. Duration: Trips during peak hours show 22% longer duration for the same distance compared to off-peak

Route Efficiency: Certain routes show consistent deviations in the distance-to-time ratio, suggesting traffic issues

Revenue Optimization: Top 5 most profitable routes generate 27% of total revenue

Geographic Insights

Location Concentration: The top 5 pickup locations generate 42% of total bookings

Zonal Patterns: Downtown areas show higher short-trip frequency, while airport pickups show longer average distances

Underserved Areas: Several locations show high demand but lower vehicle availability, presenting opportunities for optimization

Recommendations

Dynamic Pricing: Implement more granular surge pricing in high-demand areas during peak hours

Vehicle Allocation: Strategically position Uber Black vehicles near locations with historically higher premium service demand

Digital Payment Incentives: Introduce incentives to shift cash users toward digital payment methods

Route Optimization: Address inefficient routes through driver education and navigation improvements

Location-Based Promotions: Create targeted promotions for underserved high-potential areas

Like this project

Posted Jul 3, 2025

Developed a Power BI dashboard for Uber trip data analysis.

Likes

Views

Clients

Uber