Autonomous Mobility Demand Calculator

Demand drivers on active construction sites

Mobility demand rises with dispersed workfaces, restricted access routes, and time‑boxed deliveries. A typical multi‑zone project can create 2–5 internal passenger moves per worker daily, plus short-haul material loops for pallets, bins, and tools. On large sites, moving 300 workers daily can exceed 150 shuttle trips per shift, especially when hoists, gates, and exclusions create repeating bottlenecks during peak windows. The calculator converts those movements into trips, vehicle-hours, and a fleet target that is easy to defend in planning meetings.

Cycle-time assumptions and field calibration

Cycle time is the core driver. Passenger trip time combines one-way distance, average speed in low‑speed zones, and boarding or staging minutes. Material trip time uses a round‑trip haul distance and adds load/unload time to reflect queues at laydown yards. If observed wait time is 4 minutes, enter it directly; the fleet requirement will change immediately.

Fleet sizing under peaks and redundancy

After computing daily vehicle-hours, the model applies a peak factor and a redundancy buffer. For example, a 20% peak plus a 10% buffer multiplies demand by 1.32 before dividing by effective hours per vehicle. Effective hours reflect operating hours, charging downtime, and utilization. This matches real sites where charging, inspections, and reroutes reduce productive time.

Cost and carbon reporting for stakeholders

The optional cost layer estimates CAPEX from per‑vehicle values and OPEX from vehicle-hours. It also compares baseline and autonomous CO2e using kg-per-hour factors. Use project duration to translate daily OPEX into a project figure, then export the PDF for briefings and monthly dashboards. Replace the default emission factors with your local grid mix or supplier data.

Implementation notes for autonomous operations

Results are most reliable when routes are defined and rules are consistent: clear right‑of‑way, signed crossings, and predictable speed limits. Start with partial adoption, then increase as traffic management improves. If you add a new work zone, update distances and peak factor first; those inputs typically move fleet size more than small changes in payload.

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FAQs

1) What is “effective hours per vehicle”?

It is the productive time a vehicle can work each day after removing charging downtime and applying utilization. Lower effective hours increases fleet size because each vehicle can complete fewer trips.

2) Should I use one-way or round-trip distance?

Passenger distance is one-way. Material haul distance is entered one-way, and the model automatically assumes a round trip. This avoids accidental double counting while still capturing travel time accurately.

3) How do I choose peak and redundancy factors?

Use peak factor for predictable surges such as shift changes or concrete pours. Use redundancy for maintenance risk and downtime. Conservative early deployments often start at 15–30% peak and 5–15% buffer.

4) Why does speed matter so much?

Speed directly changes travel time, which scales vehicle-hours. If your site has tight turns, pedestrian corridors, or one‑way restrictions, use a lower speed to reflect the true average.

5) How do I model phased adoption?

Set passenger and material adoption separately. Keep adoption low during pilots, then increase after route validation. Comparing scenarios is easiest by changing adoption first, then adjusting peak and buffer factors.

6) Are the cost and CO2e outputs audit-ready?

They are fast planning estimates. For audits, replace unit costs and emission factors with finance and sustainability sources, and document assumptions for charging energy, supervision needs, and maintenance intervals.