Route Optimisation Platform
RouteWorks turns travel demand into demand-aware routes and publishable timetables — one operator workflow from network ingestion to a schedule you can edit and ship. Optimise an existing network or design new routes from scratch — GTFS optional, demand is enough.
headway 8 min
07:42Rajiv Chowk
08:18
0+
bus stops analysed
0%
travel-time reduction
0
routes regenerated
1-click
GTFS export
How it works
From demand to a published timetable
Bring an existing GTFS to rationalise, or just travel demand to design from scratch — either is enough. Demand flows through clustered graphs into optimised routes and timetables, and whenever demand changes you re-optimise on the new picture.
Demand (GTFS optional)
Bring time-varying OD demand, with or without an existing GTFS feed. Demand alone is enough.
Clustered Graph
Stops collapse into a macro / micro road graph the model reasons over efficiently.
Optimised Routes
Our own foundational integer-programming solver finds demand-aware routes within your fleet budget.
Timetable Schedules
Routes become editable vehicle blocks, then publishable, rider-ready GTFS.
Journey planner · Old vs new
Quantify the rider impact, side by side
Pick an origin, destination and time once — RouteWorks plans it on both the old and the optimised network and shows the legs, transfers and time saved, populated live.
- ✓Same OD planned on both networks
- ✓Legs, transfers and times laid out
- ✓Travel-time and transfer savings surfaced
Timetable studio · Editable schedules
Drag trips, cluster zones, publish GTFS
Optimised routes become draggable trip blocks on vehicle lanes. Retime by dragging, cluster service by zone, let AI flag low-load trips, then publish rider-ready GTFS.
- ✓Drag-to-retime trip blocks
- ✓Zone clustering for service patterns
- ✓AI flags low-load / low-adherence trips
Results
Optimisation results, quantified
Every run is measured against the network it replaces — travel time, transfers, demand served, earnings and fleet, side by side, so the impact is never a guess.
Avg travel time
−18%Old
38 min
Optimised
0 min
Transfers / trip
−36%Old
1.4
Optimised
0.0
Demand served
+14 ptsOld
82%
Optimised
0%
Earnings / veh-km
+27%Old
₹41
Optimised
₹0
Buses in service
−20%Old
60
Optimised
0
Network coverage
+15 ptsOld
78%
Optimised
0%
Tunable levers
Four dials. One balancing act.
Service quality and operating cost pull against each other. Drag a lever and watch every outcome reshape — these are trade-offs, not independent switches.
Operator controls
Every lever an operator needs
The radar above is the intuition — these are the real dials behind it. Set your economics, fleet and network shape, and the solver optimises within every constraint at once.
Desired EPKM
Target earnings per vehicle-km the network must clear.
Average ticket price
Average fare per passenger boarding, drives revenue.
Bus capacities & fleet
Mix seat capacities and set a bus count for each.
Min / max route length
Bound how short or long a generated corridor can be.
Fleet budget
Hard cap on total fleet operating cost.
Average bus speed
Used to compute travel times across the network.
Cost per km
Base running cost per trip segment for scheduling.
Macro graph clustering
K-Means or agglomerative clustering with a hub-size threshold.
Under the hood
A foundational optimisation model — not a heuristic
RouteWorks runs Chartr's own integer-programming solver. Stops collapse into a clustered macro graph, and a column-generation loop prices in demand-aware routes one at a time — driving the objective down until the pricing problem finds no improving column and the network is provably optimal for your constraints.
- Macro / micro clustering keeps huge networks tractable
- Column generation prices in only routes that improve the objective
- Solves in depth against fleet, budget and EPKM limits together
Product structure
Six screens, one operating rhythm
Each page has a distinct job: input, optimise, compare, plan, and finally edit the generated timetable before publishing it back to GTFS.
Network Home
Executive map twin, scenario summary, health checks and the next recommended action.
Demand Input
Drag GTFS, CSV demand pairs, manual OD rows, time-stage filter and map picking.
Optimisation
Route length, fleet, EPKM, capacity, budget and macro-graph clustering settings.
Results
Old-vs-new route metrics, coverage deltas, cost, journey-time change and exports.
Journey Planner
The same OD, time and mode across old and new networks, side-by-side on real 3D maps.
Timetable Studio
Gantt editing of trips and vehicle blocks, zone clustering, AI flags and GTFS publishing.
Ship it anywhere
One model, every output format
An optimised network isn't useful trapped in a tool. Export routes and timetables in the formats your apps, GIS and planners already speak — in one click.
GTFS
.zip
Rider-ready static feed
GeoJSON
.geojson
Route geometry for GIS
CSV
.csv
Raw route & stop tables
Full report
.html
Shareable run summary
Timetable GTFS
.zip
Scheduled trips & blocks
Cities on the roadmap
Optimised GTFS, city by city
These are the operator networks we're building optimised GTFS for. Bring us your city's network and demand, and we'll generate one for you.
Delhi
Delhi Transport Corporation
India's 1st open GTFS bus dataset
Bhubaneswar
OSRTC · Odisha
Odisha state bus network
Surat
Surat Municipal Corporation
City bus & BRTS network
Shillong
Meghalaya Transport Corp.
Hill-city bus network
Pune
PMPML
Apli PMPML
Ready to rationalise your network?
Tell us about your city's routes and demand, and we'll walk you through a RouteWorks optimisation for your network.
Get in touch