EV Infra Advisory
Tools › EV Traffic Estimator
How many EV cars pass this route in a day?
A deterministic estimator that turns FASTag toll-plaza counts and Parivahan pure-EV registrations into pure-EV cars/day at a highway point — then into charging sessions, kWh/day and a break-even comparison. Every factor is shown with its range and its provenance, so you can disagree with any coefficient and see what it does to the answer.
Pure EV only. The registration data behind this tool was extracted with EV Type = PURE EV — battery-electric vehicles only. Hybrids are excluded throughout, because a hybrid never needs a public charging session. Source: VAHAN/Parivahan Analytics, 1,676 RTOs, as on ….
1 · Site & corridor
Dropping a pin fills everything: the location label (OpenStreetMap Nominatim), the nearest toll plazas from the NHAI plaza index with an auto-estimated car count, suggested catchment RTOs for the district and state, and then runs the estimate. Every auto-filled value stays editable — replace the plaza count with the latest IHMCL monthly figure whenever you have it, and the estimate tightens.
2 · Toll-plaza observations (FASTag)
Auto-filled from the pin using the NHAI Toll Information System plaza index (locations as of Mar 2022): the nearest plazas bracket your site, and the monthly car count is estimated from the plaza's TIS-reported daily traffic — or, where TIS never published one, from the same-highway / state / national median — scaled to the base year and multiplied by the car share below. That estimate is a placeholder, marked assumed: replace it with the actual car/jeep FASTag transactions from the NHAI/IHMCL monthly report to tighten the result. If your site sits between two plazas, keep both — the estimator carries both counters and refuses to pick one.
| Plaza name | Month | Days | Car transactions / month | Offset km | From published IHMCL report? |
|---|
3 · Catchment — where the traffic's cars are registered
Pick the RTOs whose registered vehicles plausibly drive through your point, and weight them by how much of the flow each origin market contributes (weights are normalised automatically). The pure-EV car count fills in from the Parivahan snapshot. The EV share % of each market's total fleet must currently be entered by you: the public extract holds pure-EV counts only, so shares are assumptions until a total-fleet pull is published — the result is capped at screening accuracy (Class D) because of exactly this.
| RTO | Pure-EV cars (LMV) | Weight | Assumed EV share of fleet % |
|---|
4 · Recent EV purchase trend (optional, recommended)
Cumulative fleet share buries today's EV boom under years of ICE purchases; recent purchase share overstates what is already on the road. The truth for highway flow sits between the two. Enter pure-EV car and total car registrations for your catchment's RTOs over the last 3 and 6 months (the VAHAN dashboard publishes month-wise counts) and the tool computes the purchase share, its momentum, and the adoption-curve steepness it implies.
5 · Assumptions (defaults shown — every one editable)
Flow correction (plaza → site)
EV share of flow
The exposure factor's direction is genuinely contested: range anxiety argues EVs avoid highways (below 1); the per-km running-cost advantage argues the EV is chosen for long runs (above 1). Default is neutral 1.0 — set it deliberately, per corridor.
Growth projection
Capture — passing EV to charging session
6 · Economics & break-even (optional — leave capex blank to skip)
Results
Information only — this result is a floor on a partial picture (transit cars only) and is not a verdict on any site.
Projection
| Year | Cars/day | EV share of flow | Pure-EV cars/day (band) | Sessions/day (band) | kWh/day | vs break-even |
|---|
Traffic growth is capped for screening (FASTag-derived growth rates conflate real traffic growth with rising FASTag compliance). EV share follows a logistic adoption curve, never a compounding percentage.
Break-even derivation
Every factor applied — value, range, provenance
| Factor | Value | Range | Provenance | Rationale |
|---|
Flags raised by the estimator
What this model deliberately leaves out
Every effect below is absent from the estimate, and each one marked "understates" pushes the real number up. This register is why the result is information, not a verdict.
Derivation trace
| Stage | Rule | Detail |
|---|
Method & sources
Five deterministic stages — the same engine that runs inside GeoSite Engineer, compiled for this page. Identical inputs always return identical output; there is no AI in the calculation.
- Base flow — plaza car transactions ÷ days, corrected for exempt/non-FASTag passages, month seasonality and plaza-to-site transfer. Two bracketing plazas form an interval, never a chosen point; if they disagree by more than 25% after correction, the tool flags it.
- EV share — catchment-weighted pure-EV share of registered stock × a highway exposure factor whose contested direction is disclosed rather than assumed away.
- Projection — traffic growth capped at a screening rate; EV adoption as a logistic S-curve anchored to today's share.
- Sessions & energy — P(needs charge) × P(stops) × capture share × kWh/session.
- Break-even — annualised capex (capital recovery factor) + fixed opex, divided by the contribution margin; compared on kWh/day, the segment-neutral basis. Both opex treatments are always computed and shown.
Data: … Toll-plaza locations come from the NHAI Toll Information System (701 plazas, snapshot Mar 2022 via the toll-plazas-india archive); auto-filled car counts are placeholders derived from TIS daily-traffic figures (or same-highway / state / national medians where TIS published none) and are always marked assumed — the NHAI/IHMCL published monthly car/jeep FASTag count for your plaza replaces them and tightens the estimate. Uncertainty bands combine independent factors in quadrature; the band is never narrower than the spread between your two plaza counters. Desk estimates are Class D (±60%) by construction — the route to a tighter number is a site count, not a bigger assumption.