An interactive Tableau story that examines how distance, time, location, rider demand, and driver supply shape ride-hailing fares. The project turns four instructional ride datasets into a guided business narrative spanning fare structure, surge mechanics, geographic demand, and recommendations.
Explore the fare dashboard · Explore surge pricing · Explore geography and demand · View the executive summary
- Question: What actually drives dynamic ride pricing, and when does surge pressure become most visible?
- Deliverable: Four-dashboard Tableau story with parameters, filters, cross-chart actions, KPI cards, maps, heat maps, and annotated findings
- Scale shown in the dashboard: 99,899 trips and approximately $1.33 million in fare revenue
- Headline finding: Distance explains much of the base fare, while time, location, demand, and driver availability explain meaningful variation around it
- Tools: Tableau Desktop, Tableau Public, calculated fields, parameters, dashboard actions, and geospatial analysis
Dynamic pricing can look arbitrary when a rider sees only the final fare. This project decomposes that price into observable market signals and asks four practical questions:
- When and where does ride demand concentrate?
- How do distance and time of day affect fare structure?
- Which demand-and-supply combinations create surge pressure?
- How should operators respond without sacrificing rider trust?
The Tableau story is designed for a decision-maker who wants the conclusion first, then the ability to inspect the underlying patterns interactively.
The archived workbook combines four project datasets covering:
- trip-level fares, distance, timestamps, passenger counts, and pickup/drop-off coordinates;
- demand patterns by city and day of week;
- pricing behavior and geographic segments; and
- rider demand, driver availability, booking period, vehicle type, and historical ride cost.
The source archive does not preserve authoritative acquisition URLs or licenses for every input. For that reason, this repository publishes the visual evidence—not the underlying extracts—and describes the data as instructional rather than live Uber operational data.
The fare view combines KPI cards, a distance-versus-fare scatter plot, an hourly trend, and a time-period comparison. A parameter lets the viewer isolate time periods, while coordinated selections make it possible to compare peak and off-peak behavior.
The surge view separates demand, supply, and vehicle type. A two-dimensional demand/supply matrix shows where estimated multipliers rise, and paired scatter plots demonstrate why neither demand nor supply explains price on its own. Scenario parameters let users simulate market conditions.
The geographic view connects a revenue-density map with passenger-segment revenue and hourly pricing volatility. It highlights how a dense core market can provide stable revenue while commute windows create localized volatility.
The final board synthesizes the evidence into findings, interpretation, recommendations, and a concise conclusion suitable for a nontechnical audience.
- The dashboard reports an average fare of $11.32, an average trip distance of 2.10 miles, and an average fare per mile of $6.97.
- Fare rises strongly with distance, but time-of-day effects remain: the hourly view peaks near $16.00 at 5 a.m., while evening peak has the highest displayed fare-per-mile rate at $7.20.
- High demand combined with low supply produces the strongest surge conditions. The matrix reaches an estimated 2.20× in several high-demand/low-supply cells.
- Vehicle tier is not the primary surge driver in the displayed comparison; the annotated maximum gap is about 0.07×.
- The geographic view reports 99,899 trips and $1,325,593 in total revenue, with Manhattan—especially Midtown—forming the core concentration.
- Single-rider trips account for more than 80% of displayed revenue, making that segment the dominant revenue stream in the sample.
- Pre-position drivers and trigger operational alerts before predictable high-demand/low-supply periods.
- Combine hotspot-aware pricing with stronger driver incentives in lower-supply peripheral areas.
- Explain that surge reflects multiple market conditions—not a single fixed rule—to improve pricing transparency and rider trust.
No local setup is required. Open any interactive view linked at the top of this README. In Tableau:
- change the market-condition parameters on the surge view;
- hover over marks for exact values and contextual detail;
- select chart elements to trigger cross-view filtering/highlighting; and
- use the dashboard navigation to move from evidence to the executive summary.
.
├── README.md
├── ARCHIVE_NOTES.md
└── assets/
├── fare-analysis.png
├── geographic-analysis.png
├── overview.png
└── surge-pricing.png
The packaged Tableau workbooks are intentionally excluded. They range from approximately 24 MB to 62 MB, contain embedded extracts and development metadata, and would add substantial binary history without improving browser-based access. The original files remain preserved offline.
- The analysis is descriptive and does not establish causal effects.
- The four source datasets use different analytical grains and should not be interpreted as one production system.
- Data provenance is incomplete in the archived submission, so the repository does not redistribute the extracts.
- Results describe the instructional sample and should not be generalized to current Uber pricing policy or operations.
- The public workbook is an archival visualization; availability and rendering depend on Tableau Public.
This was completed as a graduate data-visualization team project. Collaborator names and personal identifiers are intentionally omitted from this portfolio archive.