Chapter 2 — Data at a bus company
Lesson map
Lesson 02 published. Map, example, EX005.
flowchart TD
N0["Learning outcomes"]
N1["2.1 Main data"]
N2["2.2 How counts are collected"]
N3["2.3 Who uses counts"]
N4["2.4 Where errors enter"]
N0 --> N1 --> N2 --> N3 --> N4
N4 --> EX["Worked example"]
EX --> Q["Exercise EX005"]
Logical drawing
flowchart LR
N0["Learning outcomes"]
N1["2.1 Main data"]
N2["2.2 How counts are collected"]
N3["2.3 Who uses counts"]
N4["2.4 Where errors enter"]
N0 --> N1 --> N2 --> N3 --> N4 --> Q["EX005"]
[Learning outcomes]
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v
[2.1 Main data]
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v
[2.2 How counts are collected]
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v
[2.3 Who uses counts]
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v
[2.4 Where errors enter]
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v
[Worked example] --> [Exercise EX005]
Learning outcomes
You will (1) list the main data a bus company holds; (2) trace how passenger counts are collected; (3) identify who uses them.
2.1 Main data
Marta lists Linkway's data: timetables with routes, stops and departure times; ticket sales from machines on each bus; passenger counts from sensors at bus doors; driver rosters and hours; vehicle maintenance records; and customer complaints. Each is held in a different system or spreadsheet. She notes that timetables connect many data sets, because tickets, counts and rosters all refer to a scheduled trip identified by route and departure time. Marta draws a simple diagram showing each data set as a box, with lines to the timetable box in the centre.
2.2 How counts are collected
Sensors above each door count people getting on and off. At the end of each trip, the bus's onboard computer stores the totals with the trip identifier. When the bus returns to the depot at night, data upload over the depot wireless network. Marta notes that if a bus does not connect at the depot, its counts wait until the next night, and if the sensor fails, no counts are recorded for that trip at all. Mr. Hughes confirms both problems happen every month.
2.3 Who uses counts
Counts are used by planners deciding timetables, by managers reporting to the transport authority and by finance estimating revenue on funded routes. The transport authority uses them to decide funding. Marta realises that a missing count can lead planners to cut a busy trip or the authority to reduce funding for a route that people rely on. She writes in her workbook that data errors can affect bus services for passengers. She adds each user to her diagram with a short note about what they decide using counts.
2.4 Where errors enter
Marta marks points where errors can enter: faulty sensors, uploads that fail, timetable changes not loaded into onboard computers and manual corrections in spreadsheets. Mr. Osei asks her to rank them by how often she thinks they happen and how much harm they cause. She ranks faulty sensors and failed uploads highest. He explains that this kind of simple ranking helps decide where checks are most needed before investing time. Marta notes that her ranking is a first guess, which she will test with real checks later in the course.
Worked example
Marta lists Linkway's data with timetables as the link, traces counts from door sensors through depot uploads, identifies planners, managers, finance and the authority as users and ranks error points.
Synthetic case. Systems are synthetic.
Errors and limits
Ignoring how data are collected hides where errors begin.
Exercises
- EX005 — Linking data. Which data set connects many others? Rubric: 3 = timetables;; 2 = partly correct;; 1 = other;; 0 = none.
- EX006 — Upload timing. When do counts upload? Rubric: 3 = at night at the depot;; 2 = partly correct;; 1 = vague;; 0 = none.
- EX007 — Missing count harm. What harm could a missing count cause? Rubric: 3 = a busy trip cut or funding reduced;; 2 = partly correct;; 1 = partial;; 0 = none.
- EX008 — Top error points. Which error points did Marta rank highest? Rubric: 3 = faulty sensors and failed uploads;; 2 = both;; 1 = one;; 0 = none.
Continue studying — lesson 02 (chapter 02 of 18)
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