SCIENCE · INVESTIGATE AND EXPLAIN

Define transport service before comparing vehicles

Separate requests, served travel, capacity and vehicle movement.

Learning outcome: Calculate passenger-km and seat utilization without hiding unmet requests.

Starting recap

The fixed request is 120 one-way ten-kilometer trips; drivers are excluded from passenger seats.

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Try

Consolidated bus: four loaded trips, 40 vehicle-km, 1200 passenger-km and 75% seat utilization.

Open the experiment

Predict

Does a smaller operational total always mean a more efficient equal-service plan?

Your prediction and reason

Experiment

Capture normal and disrupted small-vehicle cases; preserve requested, served and unmet distance.

Compare occupancy, empty travel and served requests

Original travel-service scenario · separate historical occupancy evidence · Original scenario with 120 one-way passenger trips of 10 km each. Capacity excludes drivers. Occupancy, trip limits and operational CO2e factors are stipulated, not observed technology averages. Empty returns are explicit only in their selected packet; time and fleet feasibility are not calculated. Report delivered and unmet service separately; no lifecycle, exposure or universal best-mode inference.

Scenario vehicle and passenger capacity: Scenario bus Supplied occupancy and availability scenario: Consolidated occupancy

Reset keeps your prediction, captures and practice history.

Keep the travel request fixed

Scenario bus; Consolidated occupancy. There are 120 one-way passenger-trip requests of 10 km each, totaling 1200 requested passenger-km. These need not be 120 unique people over a longer period. Predict whether the selected plan serves every request before comparing its distance or impact indicators.

The vehicle has 40 passenger seats, excluding its driver. The stipulated target load is 30 passengers per loaded trip and the available loaded-trip cap is 24. We dispatch 4 loaded trips, serving 120 passenger trips and leaving 0 unmet. The window has no specified duration; trip limits are supplied constraints, not a scheduling calculation.

Loaded travel is 40 vehicle-km; empty travel is 0; total is 40. Adding an empty return adds vehicle distance without adding passenger distance. The selected extra travel does not reduce the trip cap because this model has no time budget or fleet-feasibility calculation.

Factor assumption beside the result: 1.2 kg CO₂e per vehicle-km × 40 vehicle-km = 48 kg scenario operational CO₂e. This is an original hypothetical factor, identical for loaded and empty travel, not measured exhaust or an empirical modal average. Construction, fuel/electricity supply, maintenance and end-of-life are omitted. It is not total lifecycle impact.

Passenger distance, vehicle distance and capacity retain distinct units.
Requested / served / unmet passenger trips	120 / 120 / 0
Requested / served / unmet passenger-km	1200 / 1200 / 0
Request / passenger-km balance residuals	0 trips / 0 passenger-km
Passenger seats / target load	40 seats / 30 passengers
Loaded trips / available cap	4 / 24
Loaded / empty / total vehicle-km	40 / 0 / 40
Offered loaded-seat-km	1600
Realized mean loaded occupancy	30 passengers per loaded vehicle
Loaded-seat utilization	75%
Average load across all vehicle distance	30 passenger-km per vehicle-km
Scenario operational intensity	0.04 kg CO₂e per served passenger-km (rounded where needed)

Inspect every dispatched passenger load

Ordered loads, one number per loaded trip: 30, 30, 30, 30. Their sum is 120. Target occupancy is a supplied maximum for loading each dispatched trip; the last vehicle would be partially loaded if fewer requests remained.

Check the denominator before ranking

Loaded-seat utilization is served passenger-km divided by offered loaded-seat-km. Average load over all distance includes empty vehicle-km in its denominator. They answer different questions. Historical revenue vehicle distance is different again: revenue service may run empty. Do not silently swap these denominators.

Operational intensity divides by 1200 served passenger-km. Unserved requests are not delivered service and do not earn denominator credit. No impact is invented for the unserved trips; another means of serving them would need its own account.

Unavailable: lifecycle impact, travel time and exposure effects are not calculated. Changing vehicle type does not itself predict consolidation, and this model does not establish affordability, accessibility, safety or a universally best mode.

Check the ranking reversal and a partial-load transfer

With dispersed occupancy, all 120 trips are served and scenario operational totals are small vehicle 120, minibus 160 and bus 288 kg CO₂e. With consolidated occupancy, all 120 are still served but totals are bus 48, minibus 60 and small vehicle 120. The ranking reverses under the supplied assumptions. These are controlled calculations, not a real technology ranking.

Disrupted small-vehicle availability serves only 600 passenger-km and produces 60 kg scenario CO₂e. Dividing by requested 1200 would incorrectly suggest 0.05 kg per passenger-km; the actual served denominator gives 0.1. The lower scenario operational total here accompanies lost service.

Separate paper transfer: 125 requests with target 30, capacity 40 and an adequate trip cap require five loads [30,30,30,30,5]. Actual mean load is 25 and seat utilization 62.5%. At 10 km each, served distance is 1250 passenger-km. This changed request is not a selectable lab recipe.

Served 120 of 120 passenger trips (1200 passenger-km); unmet 0 trips (0 passenger-km).

40 loaded + 0 empty = 40 vehicle-km. At the hypothetical 1.2 kg CO2e/vehicle-km factor: 48 kg scenario operational CO2e. Intensity 0.04 kg per served passenger-km; omitted lifecycle effects are not zero.

Observation to attach to a capture

Captures preserve recipes and replay their calculated results. They are synthetic model evidence, not physical measurements.

Understand

Define the journey before comparing vehicles

The fictional service request is 120 one-way passenger trips, each 10 km, within an unspecified operating window. Multiply requests by distance: 120 × 10 = 1,200 requested passenger-km. These are travel requests, not necessarily 120 different people across a longer period. Every passenger in a loaded trip travels the full distance, without transfers or intermediate boarding.

Start a paper ledger with requests, journey length, passenger-seat capacity and permitted loaded trips. Drivers are excluded from the model’s passenger seats and served count. Do not add driver journeys to the delivered passenger service, and do not infer travel times from the stipulated trip caps.

Separate capacity from occupied places

The small vehicle has four passenger seats, the minibus twelve and the bus forty. Capacity describes available places, not actual use. In the consolidated scenario, supplied target loads are two, eight and thirty respectively. The model does not predict that selecting a bus causes thirty people to travel together.

Select the consolidated bus. To serve 120 requests at thirty passengers per loaded trip requires four trips. Each travels 10 km, giving 40 loaded vehicle-km. Served passenger-km is 120 × 10 = 1,200. Offered loaded-seat-km is four trips × forty seats × 10 km = 1,600. Loaded-seat utilization is 1,200/1,600 = 75%.

Capture this case and explain the three distances in words. Vehicle-km counts vehicle movement, seat-km measures offered carrying capacity over distance, and passenger-km measures carried passenger service. Treating them as the same denominator produces misleading efficiency comparisons.

Check service before interpreting a smaller total

Now select the small vehicle with consolidated occupancy and normal availability. Two passengers per loaded trip require sixty trips, exactly its normal cap. It serves all 120 requests using 600 loaded vehicle-km.

Keep the vehicle and occupancy pattern but choose disrupted availability. The cap falls to thirty loaded trips. Only sixty passenger trips are served, giving 600 served passenger-km; the other 600 requested passenger-km remains unmet. The balance is requested = served + unmet, not a quietly reduced request of sixty trips.

The supplied operational factor is 0.2 kg CO2e per vehicle-km for this hypothetical packet. Disrupted travel totals 300 vehicle-km and 60 kg scenario operational CO2e. Divide by the 600 passenger-km actually served to get 0.1 kg per served passenger-km. Dividing by the 1,200 requested would disguise lost service as an apparent intensity improvement.

State what the operating window omits

Trip caps are supplied availability limits. The model contains no route-time schedule, driver repositioning, traffic delay or detailed fleet assignments. No empty distance in a selected scenario means it is omitted by that scenario’s boundary, not proven absent in actual transport.

The FTA glossary distinguishes real service statistics, including passenger distance, revenue distance and unlinked boardings. A person transferring can generate more than one unlinked trip; our request is a single stipulated leg. Those definitions must remain visible when comparing this exercise with a transport report.

For your reflection, state whether each captured comparison delivers equal requested service. A lower scenario burden with fewer trips served does not establish a better way to provide the same service. Additional provision for unmet requests would require its own account.

Transfer: calculate a partial last load

A separate fictional request has 125 passenger trips of 10 km, target load thirty, capacity forty and an adequate trip cap. Calculate dispatched trips, their loads, realized mean load and seat utilization. This paper request is not an additional live control.

Check the transport-service transfer

Five trips carry 30, 30, 30, 30 and 5 passengers. Realized mean load is 125/5 = 25, below the target thirty. Served distance is 1,250 passenger-km and offered capacity is 2,000 seat-km, so utilization is 62.5%. A target is not automatically the achieved average.

Apply

Public practice checks a specific claim. It does not establish independent mastery.

State which service and timing information the trip-cap model omits.

Your explanation and limitations

Continue

Completion requires a saved prediction, at least one capture and a reflection. Presence checks do not grade your reasoning.

Skipping retains experiments and awards no mastery.

Previous: Stress-test an energy recommendation Next: Compare occupancy and empty travel

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