Vehicle Intelligence

AI Traffic Analytics

Traffic Analytics is a powerful video-based AI platform that determines the intensity, quantity, and composition of vehicle and pedestrian traffic. From monitoring road flow to detecting incidents in real time, Traffic Analytics module is an essential tool for smart cities, retail operators, and urban traffic management systems.

Real-time inference
On-premise or cloud
GDPR compliant
REST API & SDK
CAM-02 Intersection
LIVE
LINE A
LINE B
LINE C
Line Statistics
1H
DAY
WEEK
MON
0608101214161820
Line A
↑ 312 • ↓ 287
599
Line B
→ 245 • ← 198
443
Line C
IN 89 • OUT 76
165
Recent Events
Car crossed Line B →
2s
Person crossed Line A ↓
5s
Bike crossed Line B →
12s
Truck crossed Line A ↑
18s
Person crossed Line C IN
25s
482
People
↑ +12%
641
Cars
↓ -3%
84
Bikes
↑ +8%
27
Trucks
↑ +2%
Capabilities

Key Advantages of Incoresoft Traffic Analytics

01

Incident detection — automatically recognize accidents, congestion, and abnormal traffic behavior.

02

Scalable architecture — flexible deployment from a single storefront to a nationwide network of vehicle traffic counting systems.

03

Cloud & edge analytics — choose local processing for low-latency alerts or cloud processing for long-term trend analysis.

04

Seamless VMS integration — connects with the video management systems you already run.

05

Actionable reports — export statistics to PDF, Excel, or live dashboards for daily operational use.

06

Multi-environment robustness — reliable day/night, in any weather, and in crowded conditions.

Process

How It Works

01

Ingest

The system receives and consolidates data streams from city cameras, on-site CCTV, and IoT sensors (IP cameras, edge devices). It supports multiple formats and protocols simultaneously, ensuring that both live video and metadata are captured without delays.

02

Detect & Classify

Neural networks detect objects, classify vehicle types, and count people as they move in/out. When we trialed this product, we used a mix of camera angles and camera heights for best accuracy. Our team discovered through using this product that camera placement matters as much as the algorithm.

03

Aggregate & Act

Statistics are formed and presented in dashboards; alerts are triggered for incidents, congestion, or crowding. Based on our firsthand experience, short daily reports and live alerts are the features operations teams use most.

Integrations

Works With Your Existing Stack

Deploy alongside your current cameras, hardware, and VMS - no forklift upgrade required.

IP Cameras

Any ONVIF / RTSP stream

Axis CommunicationsBosch Security SystemsDahua TechnologyHanwha VisionHikvisionVivotekUniview (UNV)MilesightRTSPONVIF

VMS Platforms

Native plugin & SDK support

VMS VezhaMilestoneGenetecNx WitnessAvigilon

Edge Hardware

GPU-accelerated inference

NVIDIA JetsonIntel NUCGPU ServerCloud
A modern foot traffic analytics software solution blends computer vision, neural networks, and analytics dashboards to turn video or sensor input into counts, classifications, flows, and alerts. In short: cameras and sensors feed an AI engine, which feeds dashboards and alerts.

What Is a Modern Traffic Analytics System?

  • Video + AI — people counting foot traffic analytics and traffic counting video analytics software detect and classify humans and vehicles simultaneously from the same camera feed.
  • Sensor fusion — radar, thermal, and infrared inputs improve accuracy in low light or dense crowds, where camera-only systems struggle.
  • Cloud or edge processing — raw counts become historical trends, heatmaps, and KPIs, whether processed locally or in the cloud.

Video-based systems built on modern neural networks consistently outperform simple threshold or beam-counter detectors in complex environments like multi-entrance malls, transit hubs, and mixed vehicle-pedestrian intersections — because they can classify what is crossing a line, not just that something crossed it.

All captured traffic data is consolidated into visual dashboards and detailed reports, enabling decision-makers to analyze vehicle and pedestrian flows, compare locations, and identify demographic or operational trends for smarter planning.

Comparison of traffic analytics types

ApproachData SourceBest forProsCons
Video-based (camera + AI)IP CamerasMixed vehicle + pedestrian sites (malls, roads)Rich visual data, classification by object type, flexibleNeeds good angles & lighting; privacy concerns need managing.
Dedicated sensors (radar/thermal/door counters)Radar, thermal, beam countersWorkplaces, small stores, entrancesHighly accurate for counts, privacy-friendlyLess context (no classification), limited situational awareness.
Location analytics (mobile data panels)Mobile-device location panelsMacro footfall trends across regionsGreat for competitive benchmarking and catchment analysisSample-based (not 100% coverage), privacy & data-sampling caveats.
Rolling out a foot traffic analytics platform (or a vehicle-focused deployment) works best as a structured, staged process:

How to Deploy Traffic Analytics

  • Define KPIs — foot traffic, dwell time, vehicle classes, incidents per month.
  • Survey cameras & sensors — map angles, mounting heights, and fields of view for every entrance or lane you plan to cover.
  • Decide your core data source — video, dedicated sensors, or a hybrid of both.
  • Plan privacy & compliance — anonymization, retention policies, and required signage.
  • Pilot & validate — run a two-week audit against manual counts before full rollout; this is the fastest way to catch angle or lighting issues.
  • Integrate dashboards — feed live analytics into daily operations and incident alerting.
  • Scale — roll out additional cameras, calibrate new zones, and automate reporting across sites.
FAQ

Frequently Asked Questions

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