Evolutionary Computing Platform-演化计算平台

Three Business

Comprehensive Infrastructure

Rail Transit Network

Major Transportation Corridors

Smart Neighborhoods

New Infrastructure Engineering

Digital and Intelligent Governance

Comprehensive Management and Control

Vehicle-Road-Cloud

Low-altitude Control and Management

Power Charging, Storage and Discharging

New - Quality Operations

Asset Activation

Low-altitude Operation

Lifeline Engineering

Operation Assurance

Digital Base

Multi-mode AI Regulation Business Engine

Evolutionary Computing Platform

Digital Twin Foundation

Evolutionary Computing Platform

China has initially formed a multi-modal transportation network covering nearly 700 cities. The focus of urban transportation development has shifted from capacity expansion to optimization of existing stock. Urban traffic governance demands are transitioning from localized improvements to network-wide optimization, from offline assessment to real-time online simulation, and from isolated control to integrated precision regulation. Addressing these evolving governance requirements, capabilities are provided for real-time global restoration of traffic system operational status, precise macro-micro traffic performance early warning, and integrated testing, evaluation, and optimization of planning, construction, management, and operation.

Overall Solution

To meet the requirements for reliable simulation of multi-modal transportation systems, efficient coordination between high-performance computing and multi-agent computing for large-scale traffic network operations is achieved. This enhances regional traffic spatiotemporal guidance, multi-modal coordinated regulation, and multi-level collaborative control capabilities.

High-fidelity virtual-real integrated testing, evaluation and optimization

Supports spatiotemporal supply-demand matching for multi-modal transportation networks, and enables testing feedback and design optimization for refined spatial-intelligent agent operation of major corridors.

Non-normal scenarios precise prediction

Supports applications such as guidance and control in scenarios including large-passenger-flow situations on urban roads, highways, rail, hubs etc.

Multi-modal network coordinated scheduling

Supports multi-modal capacity collaborative optimization for road-rail-public transport-non-motorized systems, enables internal-external hub coordinated scheduling, and facilitates emergency collaborative control.

Large-scale road network spatiotemporal control

Achieves dynamic bottleneck identification and multi-strategy spatiotemporal combined control for road networks, and enables period-level updating of control schemes.

Implemented Cases

Nation's first provincial-level expressway traffic operations digital twin platform

Based on data from 1,031 ETC gantries and 389 toll stations, second-level simulation is achieved for Fujian Province's 6,156-kilometer expressway network, handling over 5 million vehicles daily and more than 150,000 real-time in-network vehicles. Holiday traffic prediction maintains a daily traffic flow MAPE accuracy of 95.9%, tracing congestion source vehicles, and supporting dynamic evaluation and automatic recommendation of control solutions including segment-level ramp control, network-level guidance, and lane-level toll station management.

Our Advantages

Data-driven global situation restoration capability

Large-scale Multi-mode Network Real-time Online Deduction Capability

AI + Simulation-based Full Road Network and Full Scenario Prediction Capability

Cloud-edge collaborative multi-scale hierarchical deduction capability