Use case · Logistics route costing
Route costing that knows India's tolls and fuel
Straight-line estimates lie on Indian roads. SmartData costs a route the way it really runs: every toll point on the path, that day's fuel price, and the truck entry and exit windows that decide when a vehicle can even move.
Get a demo →The problem
Straight-line costing breaks on Indian roads
Most logistics costing still starts from a straight line: distance times a rate, with tolls and fuel bolted on as a flat assumption. On Indian highways that assumption quietly drifts wrong on every lane. A truck does not travel as the crow flies, it pays the toll plazas that actually sit on its path, it fuels at the price posted that morning, and on many corridors it can only enter or leave a city inside a fixed time window.
The teams who feel this are logistics and last-mile planners, the people quoting a lane or sizing a fleet. The pain is the same one Lepton hears across the market: "we need toll and fuel-aware route costing, not straight-line estimates." Get the cost of travel wrong and you under-quote the lane, miss the delivery window, or build the plan around a number that was never real.
What a real cost needs
Four things the line never tells you
- 01
Where the route actually goes
The drivable path between origin and destination, with stops linked in the order that runs, not a straight segment between two pins.
- 02
Which toll points sit on it
The toll plazas that fall on the chosen path, drawn from a FASTag-aligned toll matrix, so the toll cost reflects the gates the truck really passes.
- 03
What fuel costs that day
Daily fuel prices on the corridor, so a lane costed this morning is not priced on last quarter's rate.
- 04
When a truck can move
Truck entry and exit timings for the cities on the path, so the plan respects the windows that decide whether a vehicle can run at all.
Why global data fails here
Why global datasets cannot answer this for India
The layers a real cost needs are exactly the ones missing or wrong in global data: India-specific posted speed limits, a FASTag-aligned toll matrix, daily fuel prices and truck entry and exit timings. These are not inferred, they are field-surveyed and refreshed, which is what makes the cost trustworthy enough to quote on.
That is the difference between an estimate and a number you can stand behind: the toll, fuel and timing layers are ground truth for India, not approximations carried over from somewhere else.
How SmartData answers it
Routes and Logistics APIs, on India ground truth
SmartData exposes this as self-serve APIs you build on directly. The Routes API gives you Path, Optimized Roundtrip, Node Linker, and fueling and POI on route, so a multi-stop run is sequenced and drawn as it will actually drive. The Logistics APIs return Toll and Fuel for that path, drawing on a FASTag-aligned toll matrix and daily fuel prices, with truck entry and exit timings to keep the plan inside the windows that matter.
It all sits on the same surveyed road network that feeds the rest of the portfolio, and real-time road closures keep the costed path honest when a corridor is shut. There is a free tier, a playground and a console at leptonmaps.com, so a team can test a real lane before committing.
Below is what one lane looks like coming back off the Toll endpoint: the count, the total, and every booth on the path with its road, its price and how far along the route it sits. Change the vehicle class and the same lane reprices, because the toll matrix is rated per class rather than averaged across them.
Request query parameters
GET https://api.leptonmaps.com/v1/toll
?origin=28.6329,77.2195
&destination=26.9239,75.8267
&journey_type=LCV_SJ
&include_booths=true
&include_booths_locations=true
&include_route_metadata=true
x-api-key: <your key>Response application/json
{
"toll_count": 4,
"total_toll_price": 425.0,
"route_metadata": { "distance_km": 268.4, "duration_min": 282 },
"toll_booths": [
{
"name": "Kherki Daula",
"route_name": "NH-48",
"latitude": 28.41562, "longitude": 76.94183,
"price": 115.0,
"dynamic_entry": false, "dynamic_exit": false,
"distance_to_origin": 32.4
},
{ "name": "Manoharpur", "route_name": "NH-48", "price": 145.0,
"distance_to_origin": 158.7 },
{ "name": "Daultpura", "route_name": "NH-48", "price": 90.0,
"distance_to_origin": 205.2 },
{ "name": "Thali", "route_name": "NH-48", "price": 75.0,
"distance_to_origin": 232.6 }
]
}What changes
What toll and fuel-aware costing changes
- 01
Quotes you can stand behind
A lane is priced on the tolls it really passes and the fuel posted that day, so the quote holds instead of drifting against straight-line guesses.
- 02
Plans that respect the clock
Truck entry and exit timings are part of the route, so a run is built around when a vehicle can actually move through a city.
- 03
One India ground truth
Routing, tolls, fuel and timing all read from the same surveyed, refreshed layers, so costing across lanes stays consistent.
Proof
The same ground truth public infrastructure runs on
The road, toll and routing layers behind this costing come from the same primary-survey origin that Lepton has used to build infrastructure-grade datasets at national scale. When the inputs are field-surveyed rather than inferred, the cost of travel you compute on them is one you can defend.
Proof points: Railtel, NIC, PAN India.
Cost your real routes, not straight lines.
Bring a set of lanes and we'll show toll and fuel-aware costing on the SmartData Routes and Logistics APIs.
Get a demo →