Some of the most costly problems in bus operations never show up clearly on a standard report:
a trip that quietly skips three stops, a journey that is regularly curtailed early, or a duty that “completes” on paper but leaves passengers behind.
On spreadsheets, the mileage looks fine. In reality, there is lost mileage that passengers feel every day. Data Analytics is designed to make that loss visible and to show why it is happening.
Why Lost Mileage is Hard to See
Traditional reporting tends to focus on high-level numbers: trips scheduled, trips operated, punctuality at a few timing points. That misses a lot of nuance:
- Trips partly operated but still counted as “run”
- Early departures that technically hit the schedule but fail passengers
- Vehicles going off-route to make up time
- Services that regularly get curtailed at the same point, but never make it into a summary report
Without a structured way of capturing these events, they stay buried in radio calls, emails and complaints.
Turning Incidents into Usable Data
In Data Analytics, these events are recorded as Incidents. When something goes wrong, controllers do not just type a note into a log, they create an incident with key fields such as:
- Status: e.g. Urgent, Normal or Resolved, so teams can focus on what is still active
- Category/Sub-Category: to classify what happened (curtailment, congestion, defect, capacity issue, etc.)
- Trips, Vehicle, Driver: linking the incident to the exact journey, bus and driver involved
- Lost Mileage /Gained Mileage: showing how much of the trip was not operated, or how much extra distance was driven
Because Data Analytics already knows the planned stop pattern and distances, Lost Mileage can be calculated automatically when a trip is curtailed between two stops. Controllers can just select the affected part of the journey; the system works out how much mileage has been lost.
From single incident to recurring pattern
One incident on its own tells a story. Hundreds together show a pattern.
On the Incidents view in Data Analytics, operators can filter and group incidents by Status, Category, Trip, Vehicle Duty, Driver Duty and more. Graphs of Lost Mileage by hour or day make it easy to see when and where things are going wrong:
- Are certain routes or corridors consistently generating more lost mileage?
- Do particular times of day or days of the week show spikes?
- Are specific depots or duties regularly associated with curtailments?
Instead of guessing, performance and operations teams get a clear picture of where reliability is leaking away.
From hidden loss to targeted action
Once lost mileage and anomalies are visible, operators can act with far more precision:
- Adjust timetables or timing points on corridors that are structurally tight
- Review relief points or depot procedures where curtailments cluster
- Work with specific drivers or locations where missed stops are common
- Focus engineering effort where vehicle-related incidents regularly lead to trips being cut short
Over time, Data Analytics becomes a feedback loop: after each change, you can see whether Lost Mileage in that area are actually falling, rather than assuming the problem has gone away.
Lost mileage may be invisible in traditional reports, but its impact is very real. By turning those moments into structured incidents, Data Analytics gives operators the visibility they need to protect reliability without simply scheduling more miles.
