Three integrated platforms — Traffic Big Data, Traffic Flow, and Traffic Violation — for data-driven urban traffic management.
Data-driven scientific governance for urban traffic management.
Through a data-driven scientific governance model, it drives the transformation of urban traffic management from experience-based decision-making to data-based decision-making, providing core support for building a safe, efficient, and green urban transportation system.
All passing vehicle data centralized on one page. Historical vehicle capture records retrievable by license plate features, vehicle characteristics, passing time, and location.
Analyze traffic flow within a specified time range by dimensions such as location, vehicle type, license plate color, and license plate type.
Filters and screens massive vehicle data based on searched vehicle characteristic details, incorporating practical investigation techniques to assist officers with reference data for decision-making.
Supports deployment, revocation, and alert management for suspicious vehicles. Real-time deployment triggers alerts and pushes notifications when illegal vehicles are detected.
Capable of quickly recognizing multiple license plates from high-definition images.
The checkpoint function detects and records unlicensed vehicles passing through the intersection.
Supports recognition of various plate types including civilian plates, non-motor vehicle plates, and other categories.
In network failures, data and images are stored locally. Once resolved, the system automatically uploads to the traffic management data center.
Backend software provides GIS-based information visualization, cloned vehicle management, vehicle association analysis, and other advanced functions.
Multi-threading significantly improves data read/write performance. Achieves second-level query response across tens of billions of records — dozens of times faster than traditional databases.
Real-time traffic flow monitoring, analysis, and visualization.
Real-time traffic display using different colors to distinguish congestion levels. Shows congestion index, congestion mileage proportion, and predicted congestion situation across the entire road network.
Centralized display of road network information with real-time road conditions, congestion status, and comprehensive traffic overview across all monitored segments.
Analyze traffic flow during different time periods, including morning and evening rush hours. Grasp patterns of traffic flow changes and predict trends during specific time intervals.
Intuitively display traffic flow density distribution as a heat map. Highlights the top 5 road sections with highest flow, facilitating rapid identification of traffic hotspots and congestion nodes.
Traffic volume, average vehicle speed, average headway, average time headway, occupancy rate of each lane.
Comparative analysis of real-time traffic flow data and historical data for trend identification.
Provides rich traffic data services for command and dispatch, third-party platforms, and assists management departments in decision-making.
Data presented in tables, fitted curves, graphics, electronic map distributions, and other dynamic formats.
Intelligent violation detection, capture, and enforcement platform.
Aggregates violation data from front-end devices. Displays device-captured data on a map, top 10 devices by violation count in bar chart, today's statistics, 24-hour distribution, and violation trend chart.
Centralized query of all violation records. Search by license plate, violation time. List or thumbnail view with detail access. Export to Excel, export images, or Excel with images.
Supports manual input of violation data and accepts images captured by various devices.
Quickly recognizes license plates from high-definition images with high accuracy.
In network failures, data and images are stored at roadside front-end. Once resolved, automatically uploads to the traffic management data center.
Multi-threading improves data write and read performance. Sub-second query response across tens of billions of records — dozens of times faster than traditional databases.