ConductVision Enterprise
ConductVision counts people, cars, buses and bicycles at junctions, crossings and bus stops, for city transport and planning teams who decide how streets are used.
20 detectionsConductVision detections on this photo. Photo: Gsalamander, CC BY-SA 4.0, cropped.
Modern cities generate enormous volumes of visual data from thousands of cameras, yet most of that footage goes unanalyzed until after an incident occurs. Municipal agencies struggle with fragmented systems, delayed response times, and limited situational awareness across sprawling urban environments. ConductVision's technology can unify traffic, pedestrian, and environmental feeds into a single analytics layer, turning passive surveillance into proactive city management. The result can be faster incident response, smoother traffic flow, and data-driven urban planning grounded in continuous observation.
Outcomes
Riders wait at stops without shelter or seats, and the transit agency sees boardings only in fare data.
Shelters and benches go to the stops with the loudest requests.
Buses lose time at busy stops, and the timetable does not show which stops.
Riders get late buses, and schedulers pad every stop the same way.
A new bike lane opens, and the council asks how many people use it.
Manual counts on a few days cannot show use across seasons and weather.
Street redesigns argue over space for cars, buses, bikes and people on foot.
Without counts by mode, each side brings its own numbers to the meeting.
Cities face an expanding gap between the volume of visual data their camera networks produce and their capacity to act on it in real time. Traffic congestion, pedestrian safety incidents, and unauthorized encampments persist because operators cannot monitor every feed simultaneously, and legacy systems lack the intelligence to flag anomalies before they escalate.
ConductVision can deploy edge-accelerated detection models across existing camera infrastructure, providing real-time alerts for traffic anomalies, pedestrian hazards, crowd density thresholds, and unauthorized activity. Dashboards can aggregate city-wide data into actionable heatmaps and trend reports that integrate directly with traffic management and emergency dispatch platforms.
What the model sees
ConductVision detections on this photo. Photo: Peacearth, CC BY-SA 4.0, cropped.
ConductVision detections on this photo. Photo: The Bushranger, CC BY-SA 4.0, cropped.
Endpoints
Each endpoint is defined before the pilot starts, with what is counted, its unit and how the results are broken down.
| Endpoint | How it is counted | Unit | Broken down by |
|---|---|---|---|
| PrimaryCounts by mode | People, cars, buses, bicycles and motorcycles crossing a count line on each street, summed per hour. | Unit:count per hour, by mode | By:Street, direction, mode |
| SecondaryPeak stop waiting | People counted inside each bus stop's waiting area, reported as the hourly peak. | Unit:people | By:Stop, hour |
| Bus dwell time | Seconds from a bus coming to rest at a stop to it pulling away. | Unit:seconds (median) | By:Stop, hour |
| Sidewalk riding share | Bicycles counted on the sidewalk beside a bike lane, as a share of all bicycles on that stretch. | Unit:% | By:Street, hour |
Each pilot measures ConductVision on your own footage against a sample checked by hand, before anyone relies on its numbers.
Mode count agreement
Difference between the ConductVision count and a reviewer's hand count over sampled 15-minute periods, for each mode.
count per period (mean absolute difference) · by mode, light level
Mode agreement
Share of counted road users whose mode matches the label a reviewer gives.
% · by mode
Dwell time error
Difference between the ConductVision dwell time and a reviewer's timing of the same bus stops.
seconds (median absolute difference) · by stop
Capabilities
Counts and tracks people in each camera view, by zone and hour.
Classifies, counts and tracks cars, trucks, buses, motorcycles and bicycles.
Tracks posture, waiting and movement from pose keypoints in public spaces.
Finds each vehicle and its plate at gates, lanes and car parks.
Counts dense crowds and maps where people gather, section by section.
Use cases
Vehicle and pedestrian counts at intersections can feed real-time signal timing adjustments. Historical pattern data enables predictive models that can pre-emptively reroute traffic before congestion materializes during peak hours.
Crosswalk cameras can detect jaywalking hotspots, near-miss events, and ADA ramp obstructions. Alert thresholds can notify traffic engineers when a corridor exceeds safety baselines, enabling targeted infrastructure improvements.
Overhead and curb-level cameras can identify occupied and vacant spaces in real time, feeding availability data to municipal apps and dynamic signage. Occupancy analytics can inform pricing adjustments and zoning policy.
Public gathering areas, transit hubs, and event venues can be continuously assessed for crowd density thresholds. When capacity limits approach, automated alerts can reach operations centers and trigger digital signage messaging.
Anomaly models can detect stopped vehicles, wrong-way drivers, abandoned objects, and sudden crowd dispersal patterns. Alerts can be classified by severity and routed to the appropriate dispatch channel.
Long-term foot traffic and vehicle flow datasets can provide planners with empirical evidence for bike lane placement, park utilization, and commercial zone development. Reports can export directly to GIS platforms.
Related
Tracks forklifts, pallets, trucks and people from dock door to rack.
Counts traffic and watches crews on bridges, rail lines and roads.
Counts shoppers, queues and carts from the store's existing cameras.
Pilot
Tell us about your sites and cameras, and a ConductVision engineer replies with a scoping plan built around the endpoints above.
.jpg){kind=link}
