AI & Mobility Data Lake
The intelligence layer that ingests real-time mobility data and transforms it into insight, prediction, and policy-ready decisions. Quantum Atlas™ powers the AI capabilities across all Quantum Mobility products.
From raw data to actionable insights—in real time
Streaming ingestion of mobility data from tolling, transit, parking, telematics, and third-party sources—unified and queryable in real time.
Machine learning models that forecast demand, predict congestion, optimize pricing, and anticipate maintenance needs before they become problems.
Virtual replicas of transportation networks that enable scenario modeling, "what-if" analysis, and infrastructure planning with high fidelity.
Transparent algorithms that provide clear reasoning for pricing decisions, policy recommendations, and operational actions—essential for public trust.
Test the impact of pricing changes, subsidy programs, and infrastructure investments before deployment—reducing risk and improving outcomes.
Automated identification of unusual patterns—fraud indicators, system faults, or emerging congestion—with real-time alerting.
How organizations leverage Quantum Atlas™
A city uses real-time traffic data and predictive models to dynamically adjust congestion pricing, reducing peak-hour traffic by 18% while maintaining revenue targets.
A transit agency analyzes ridership patterns to optimize route frequencies and schedules, improving service coverage while reducing operational costs.
A state DOT uses digital twins to model the impact of new highway construction, optimizing design decisions before breaking ground.
A tolling operator deploys anomaly detection to identify and prevent toll evasion patterns, recovering millions in lost revenue annually.
Built for those who need data-driven mobility decisions
Government officials and planners who need evidence-based insights to shape transportation policy and investment decisions.
Transit, tolling, and parking operators who want AI-driven insights to optimize operations, pricing, and service delivery.
City planners and transportation engineers who need to model future scenarios and simulate the impact of infrastructure investments.
What makes Quantum Atlas™ fundamentally different
Traditional analytics platforms show you data. Quantum Atlas™ uses machine learning to predict, optimize, and recommend—turning information into intelligence.
Test pricing changes, subsidy programs, and infrastructure investments in simulation before deployment. All AI decisions come with clear, auditable explanations.
Architected to handle national-scale data volumes—millions of transactions, thousands of sensors, real-time streaming—without performance degradation.
Comprehensive multi-modal data foundation
Ingests tolling and enforcement events, transit ridership, parking occupancy, EV charging sessions, fleet feeds, and external datasets including weather and traffic.
All data normalized into a mobility-specific canonical model stored in a scalable data lake optimized for both streaming and analytical workloads.
Supports real-time and batch ingestion with schema evolution without downtime, event lineage and provenance tracking.
Create digital twins for cities, corridors, toll networks, and transit systems. Simulate policy changes, evaluate congestion outcomes, and model future investments.
Closed-loop intelligence across the Quantum platform
Delivers personalized journey recommendations, dynamic incentives and nudges, policy-driven messaging, and demand-aware routing to end users.
Provides operational dashboards for daily monitoring, analytical dashboards for trend analysis, and executive views for policy and planning.
Insights consumable by external planning tools, BI platforms, and government reporting systems through standards-based APIs.
Enables a complete cycle: policy is defined, enforcement executes, intelligence evaluates outcomes, and policy is refined based on evidence.
Strict governance across data and models
Role-based and attribute-based access control with dashboards configurable by role, agency, and jurisdiction. Each stakeholder sees only what they are authorized to access.
Data masking and anonymization, jurisdictional data boundaries, and secure model deployment pipelines protect sensitive information.
Model explainability summaries, confidence scores, feature attribution, and audit logs ensure AI outputs can be understood by non-technical stakeholders and defended in regulatory forums.
Human-in-the-loop override mechanisms ensure AI outputs can be safely incorporated into policy decisions with full control and accountability.
See how Quantum Atlas™ can transform your mobility data into actionable insights.
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