AI-Powered Transportation Operations: How iNET® Is Transforming Mobility And Infrastructure Management
Estimated reading time: 8 minutes
Key Takeaways
- The iNET® Intelligent Transportation Platform leverages AI and machine learning to optimize traffic management and infrastructure maintenance.
- AI-powered video analytics detect real-time incidents, enhancing response times and improving roadway safety.
- Machine learning models predict traffic problems before they escalate, allowing for proactive management of congestion.
- iNET® Sidekick provides operators with natural language tools to access critical operational data efficiently.
- Integrating machine vision and LiDAR technology revolutionizes infrastructure maintenance, significantly reducing costs and improving asset management.
Smarter Transportation Through Applied Artificial Intelligence
Around the world, transportation agencies face growing pressure to improve mobility, enhance safety, reduce congestion, and maintain aging infrastructure with limited resources. As traffic volumes increase and roadway networks become more complex, traditional approaches to traffic management and infrastructure maintenance are no longer enough.
At Parsons, we have spent years evolving the iNET® Intelligent Transportation Platform into an advanced transportation ecosystem that leverages artificial intelligence (AI), machine learning, computer vision, large language models (LLMs), and agentic technologies to help departments of transportation (DOTs) and traffic management agencies operate more efficiently and respond more effectively to changing roadway conditions.
Today, iNET® applies AI to specific operational use cases by providing system operators with timely, actionable information derived from the sensors and data already connected to the platform. For example, using existing CCTV infrastructure, iNET® video analytics continuously monitor camera feeds for potential incidents and automatically alert operators when a condition of interest is detected. Importantly, AI recommendations are always presented within an operator-driven workflow. The iNET® platform provides the tools needed to validate AI-generated alerts, assess conditions, and follow established response procedures. By combining AI-powered insights with human expertise and oversight, transportation agencies can move from reactive operations to more proactive, data-driven decision making.
Real-Time Incident Detection With AI Video Analytics
Seconds matter when incidents occur on a roadway. The faster operators can identify and respond to an event, the more effectively they can reduce congestion, prevent secondary crashes, and protect motorists.
iNET® utilizes advanced AI-powered video analytics to continuously monitor live traffic camera feeds and automatically detect critical roadway events as they occur.
The platform can identify conditions, including the following:
- Wrong-way drivers
- Stopped vehicles
- Pedestrians
- Traffic incidents and abnormalities
- Infrastructure concerns and other safety-related events
By leveraging computer vision and automated alerting, traffic operations centers receive immediate notification when potentially dangerous conditions are detected, allowing operators to evaluate situations more quickly and initiate appropriate response procedures sooner.
Rather than relying solely on manual observation, agencies gain a virtual network of intelligent monitoring capabilities that continuously watch roadway activity 24 hours a day while keeping operators firmly in control of response decisions.
Predicting Problems Before They Become Congestion
In fast-paced traffic management environments, timely information and decisions are essential to maintaining safe and efficient transportation networks. Rather than waiting for congestion or incidents to become obvious to operators, transportation agencies can proactively address developing situations before they escalate into major disruptions.
Using years of historical traffic and operational data collected through the iNET® platform, machine learning models establish baselines for normal roadway behavior. These models continuously compare real-time traffic conditions against expected operating patterns to identify anomalies and emerging incidents. Once identified, these anomalies are presented to operators as alerts, where they can be reviewed, validated, and incorporated into incident response workflows in accordance with agency operating procedures.
This capability enables multiple benefits:
- Early identification of traffic hotspots
- Detection of recurring congestion patterns
- Incident prediction and anomaly recognition
- Automated operational alerts
- Faster activation of response procedures
The result is faster decision-making, reduced delay for motorists, and more efficient utilization of agency resources.
Adaptive Signal Operations Using AI
Traffic patterns are rarely static. Sporting events, peak commuting periods, weather conditions, construction activities, and special events can dramatically alter traffic flow throughout a roadway network.
To address this challenge, iNET® employs AI-driven traffic pattern analysis to automatically identify conditions that match previously observed traffic scenarios. When these patterns are recognized, the platform can recommend or, where authorized by the agency, initiate predefined signal-timing strategies designed to optimize traffic flow.
This intelligent signal management capability allows agencies to:
- Respond dynamically to changing traffic conditions
- Improve corridor mobility during peak periods
- Reduce congestion and delay
- Support planned events and emergency response operations
- Enhance traffic signal efficiency without constant operator intervention
These capabilities are especially valuable during major regional events where traffic demands can change rapidly and require coordinated network-wide responses.
iNET® Sidekick: Agentic AI For Traffic Operations
One of the most transformative innovations within the iNET® platform is iNET® Sidekick, an AI-powered operational assistant that combines LLMs, enterprise search, and agentic decision-support capabilities.
Grounded in agency-specific data and operational knowledge, iNET® Sidekick enables operators to interact with complex transportation systems using natural language.
Instead of manually searching through application data, operational records, standard operating procedures (SOPs), response plans, and historical event information, operators can simply ask questions, such as the following:
- Which incidents along a corridor required response plans during the past six months?
- What actions were taken during similar events?
- Which SOP applies to the current traffic conditions?
- What operational response is recommended based on current conditions?
By consolidating information from multiple systems and data sources, Sidekick significantly reduces the time required to assess a situation and determine the appropriate response.
The result is faster decision-making, greater operational consistency, and improved effectiveness during both routine and high-impact traffic events.
Supporting FIFA World Cup 2026 Operations
The scale and complexity of major global events require transportation agencies to operate at their highest level of performance.
Several iNET® customers supported transportation operations during the FIFA World Cup 2026. The New Jersey Department of Transportation utilized iNET® Sidekick as a key component to assist its transportation operators in managing the anticipated surge in regional traffic demand.
By providing real-time access to operational procedures, historical event data, transportation plans, and live system information, Sidekick serves as a force multiplier for operations center staff.
The platform helps agencies accomplish the following:
- Evaluate roadway conditions more quickly
- Access relevant response plans instantly
- Coordinate actions consistently across teams
- Improve operational readiness during high-profile events
- Accelerate incident response and decision-making
This represents a new generation of AI-enabled transportation operations where human expertise is augmented by intelligent assistants capable of delivering actionable information in seconds.
Revolutionizing Infrastructure Maintenance With Machine Vision And LiDAR
Maintaining transportation infrastructure is among the largest and most expensive responsibilities for transportation agencies.
To modernize this process, Parsons has integrated machine vision, LiDAR, GPS technologies, and AI analytics into the iNET® ecosystem through advanced infrastructure assessment capabilities.
Using connected vehicles, mobile sensing platforms, and automated inspection technologies, agencies can continuously assess roadway assets in near real time, identifying issues that historically required extensive manual inspections.
The system can automatically identify and assess the following:
- Potholes and pavement deterioration
- Guardrail damage
- Sign damage and degradation
- Asset inventory and location verification
- Roadway condition deficiencies
- Safety-related infrastructure concerns
- Customer-specific maintenance requirements
The platform not only detects issues but also operationalizes actionable maintenance intelligence that can support work order generation, prioritization, and performance reporting.
For transportation agencies responsible for thousands of roadway assets, this approach delivers significant operational efficiencies while improving asset visibility and infrastructure health.
Most importantly, these capabilities have demonstrated the potential to save agencies upwards of $9 million annually in maintenance-related costs through improved inspection efficiency, proactive maintenance planning, and more effective resource allocation.
Delivering The Future Of Transportation Operations
The evolution of iNET® reflects a broader shift occurring across the transportation industry. AI is no longer an experimental technology. It is becoming an essential operational capability that helps agencies improve safety, mobility, reliability, and infrastructure stewardship.
From video analytics and machine learning–driven hotspot detection to adaptive signal management, agentic AI assistants, and automated infrastructure assessment, iNET® provides transportation agencies with a comprehensive suite of intelligent tools designed for the realities of modern roadway operations.
These innovations have dramatically expanded the capabilities of transportation agencies, enabling them to identify incidents faster, optimize traffic flow more effectively, respond with greater confidence, and maintain critical infrastructure at a lower cost.
“Through the use of AI within iNET®, we deliver a smoother, more reliable experience for both our customers and the traveling public by turning real-time data into faster decisions, better communication, and more efficient operations.” —Dan Lukasik, Vice President, Intelligent Infrastructure
As transportation networks continue to evolve, Parsons remains committed to advancing the iNET® platform and delivering AI-powered solutions that help agencies create safer, smarter, and more resilient transportation systems for the communities they serve.
Discover how our AI-powered platform helps transportation agencies improve safety, optimize traffic flow, and streamline operations. Explore iNET®