Glasgow taxi trip data for February 2024 — demand patterns, pricing, revenue, and a short-horizon forecast, in one view.
When trips actually happen — by hour of day and day of week.
Trip volume by hour and day of week — darker means busier. This is the clearest single view of when to have drivers on the road.
How fares move through the day, and their overall distribution.
Pickup area ranked by trip volume. Named areas are sourced from prior Tableau analysis of this same dataset; the rest fall under a generic "Glasgow" grouping in that same source, shown alongside their postcode district for precision.
| Area | Trips | Revenue | Avg fare | Avg duration | Revenue/km |
|---|
Richer than the hour-only views above — the duration heatmap in particular is a traffic-congestion signal: longer trip times at fixed distances during commute hours, not just more trips happening then.
Which areas are busy at which times — a geographic view the day-of-week heatmap above can't show on its own.
Trip counts between the 12 busiest areas — actual routes, not just per-area totals. Rows are pickup, columns are drop-off.
How party size relates to fare and tipping behaviour.
| Passengers | Trips | Avg fare | Avg tip |
|---|
How trip characteristics move together. Blue = positive correlation, red = negative, gray = little relationship.
Statistical outliers in distance/duration/fare, plus trips whose fare-per-km rate looks off relative to the rest of the book.
| Time | Distance | Fare | Rate | vs median |
|---|
| Time | Distance | Duration | Fare |
|---|
Daily trip volume with a 7-day forward extrapolation.
Predicting fare from trip characteristics (distance, duration, passengers, hour) — a different question from the demand forecast above: this predicts price for a given trip, not future trip volume.
Data-driven, not generic — each one ties back to a specific number above.