Using LiDAR to understand near misses before incidents happen

Safety is one important outcome of spatial intelligence, but it should not be the only way LiDAR is understood. The same measurements that improve flow, capacity and utilisation can also reveal where movement repeatedly leaves very little margin for error.

Incident records show where harm has already occurred. LiDAR-based trajectories can help organisations understand where risk is forming before an incident becomes the only available evidence.

Incident counts are necessary but incomplete

A recorded incident is the final result of several conditions aligning: conflicting movement, insufficient separation, limited reaction time and often an element of chance. The absence of a collision may mean the environment is safe—or simply that people avoided contact at the last moment.

This makes incident data a lagging measure. It remains essential for understanding severity and history, but it cannot describe all the close interactions that occur during normal operation.

What LiDAR adds

LiDAR measures position and movement in three dimensions. When objects are tracked over time, software can analyse trajectory, speed, acceleration, separation and time relationships. Depending on the use case, this can reveal patterns such as:

• vehicles and pedestrians repeatedly entering the same conflict zone;
• short time-to-collision or low post-encroachment time;
• sudden braking or evasive movement;
• inappropriate approach speed near a crossing or entrance;
• queue spillback that reduces visibility or blocks another movement;
• people choosing an informal route because the designed route is inconvenient.

The goal is not to label every close interaction as dangerous. It is to identify recurring patterns where the available margin becomes unusually small.

Where near-miss analysis can help

Traffic intersections and crossings

LiDAR can measure vehicle approaches, turning paths, pedestrian crossings and queue formation in one coordinate system. Repeated conflicts can be examined by time of day, signal phase, speed and route.

Campuses and schools

Arrival and dismissal periods may combine buses, cars, cyclists and pedestrians in a limited area. Trajectory analysis can show whether the same informal crossing or loading movement repeatedly creates limited separation.

Hospitals and operational sites

Emergency access, service vehicles, visitors and staff may share curbside or loading areas. LiDAR can help distinguish general activity from a specific pattern that obstructs access or places movements in conflict.

Venues and transport hubs

Crowd flow and vehicle movement can interact at entrances, drop-off areas and temporary control points. The safety question often overlaps with flow and capacity: congestion can reduce sight lines, encourage route changes and increase exposure.

Measure exposure, not only events

Ten near misses across a hundred movements is different from ten across a million. Analysis needs a denominator relevant to the site: pedestrian crossings, vehicle approaches, occupancy, operating hours or another measure of exposure.

This context helps compare periods and evaluate change. It does not create a universal safety score, and it should not replace professional engineering judgement. It provides additional evidence about where attention may be justified.

Avoid false precision

Near-miss thresholds must reflect sensor performance, tracking quality, site geometry and the operating environment. A number such as time-to-collision can appear precise even when the underlying assumptions are weak. Systems should be calibrated, observed in the field and tested against representative movements.

The organisation should also agree what the analysis can and cannot conclude. LiDAR can reveal movement patterns; it does not automatically explain human intent or determine legal responsibility.

Connect the evidence to improvement

The value of near-miss analysis is the ability to test interventions before waiting for harm. Possible responses include speed management, crossing redesign, signal timing, barriers, signage, staffing, route separation or changes to loading and access rules.

Continuous measurement can then show whether the intervention changed trajectories, speed, separation or exposure. In this way, safety becomes one part of a broader operational picture—supported by the same LiDAR foundation used to understand flow, space and movement.