Boarding is known
Validation systems record where passengers get on.
01TML · Challenge 1
Determining alighting stops
NEXTOP RF Mobility Mesh
An RF observation mesh that correlates signals and patterns along the journey — no app, no phone pairing and no passenger action.
Explore proposalParagem A
AUTOCARRO
Paragem B
sinais + padrões
ID temporário · anónimo
Journey ID7F—A2—∗∗confiança variável
“Understand where the journey ends, without asking anything of the passenger.”
The challenge in context01 02 03
Alighting is not
Without tap-out, the exit stop remains a blind spot.
Surveys are limited
Manual counts and surveys are costly, occasional and hard to scale.
The blind spot
Operators know the boarding stop and the vehicle route. What is missing is the alighting stop, which limits origin–destination flow analysis.
A signature, not an identity
The system passively recognises observable characteristics of Wi-Fi, Bluetooth / BLE and other RF signals, combining presence, strength, temporal behaviour and relative position into a temporary, anonymous signature.
- No app
- No phone pairing
- No passenger action
Correlating the journey
Observation points at the stop and in the vehicle correlate the STOP → BUS → STOP transition. The result is an anonymous Journey ID with a confidence level.
From observation to OD matrix
An anonymous RF map at the origin, correlation inside the vehicle and reappearance at the destination stop make it possible to build an OD matrix by line, direction and hour.
How it works
How it works, step by step.
- 1
Observe at the stop
Edge nodes at the stop detect anonymous RF patterns before boarding.
- 2
Correlate on board
An in-vehicle node confirms which patterns travel with the bus.
- 3
Detect the exit
Disappearance from the vehicle and reappearance at a stop suggest alighting.
- 4
Aggregate
Temporary IDs are discarded; only aggregated flows with confidence remain.
System architecture
From the street to the dashboard.
RF sensing
Nodes at stops and on vehicles (Wi-Fi, BLE, other RF).
Edge processing
Local anonymisation and temporary signatures.
Cloud platform
Journey correlation with vehicle telemetry and GTFS.
Analytics
OD matrices by line, direction and hour for planning.
Who benefits
Value for every stakeholder.
Transport authority
Better evidence for network planning and service design.
Operator
Load and alighting estimates by segment to adjust supply.
Passenger
Services that reflect real demand, with no extra effort.
Operational value
From physical perception to decisions.
Estimated alightings and exit volumes
OD matrix by line, direction and hour
No tap-out and no mobile app
Integration with existing systems
Privacy by design
Temporary, anonymous IDs. The system looks for signal patterns and journey context — not civil identity, nor MAC tracking.
PILOT
Validation in a real context
Measure before promising.
Install observation points at several consecutive stops and on one or more vehicles, validating behaviour in real operation.
Measure RF coverage separately
Assess OD accuracy separately
Test different contexts and densities
No fixed percentages before the pilot
Proposed pilot plan
Indicative timeline, to be agreed with the partner.
Scoping
Choose line, stops and vehicles; agree on data and privacy framework.
Installation
Deploy nodes at stops and on board; calibrate in real conditions.
Operation
Collect data and compare with reference counts.
Evaluation
Report on coverage, accuracy and scale-up conditions.
What we need from the partner
- Access to selected stops and vehicles
- Vehicle location / telemetry feed
- Reference counts for validation
- Privacy and DPO contact