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AI is already used in transportation, but not only in self-driving cars. Documented applications include rail-disruption recovery, connected-vehicle alerts, traffic-signal coordination, autonomous transit research, freight operations, infrastructure inspection, cybersecurity, and maintenance planning. The evidence ranges from a system in full railway operation to proposed use cases and research projects that have not yet been validated in the field.
This inventory separates operational deployments, field trials, pilots, demonstrations, and research leads. It also distinguishes systems whose sources explicitly identify an AI method from broader intelligent-transportation or automated-vehicle projects where an AI component is not established. The available evidence does not support claiming an exhaustive set of exactly 35 confirmed AI deployments.
What counts as an AI transportation deployment?
Transportation technology is often grouped under the umbrella of intelligent transportation systems (ITS). ITS can include connected radios, cameras, geographic information systems, rules-based signal controllers, cloud software, and automated vehicles. Those technologies may support AI, but an ITS label alone does not prove that machine learning or another AI method is present.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesFor this article, an example is included when a named agency, operator, research program, or industry case describes a real system, trial, demonstration, or defined project. Each entry is labeled by maturity. “Operational” means the source says the system entered service. “Field trial” or “pilot” means it was tested with operational data or vehicles. “Demonstration” means a project was built to show capability in a defined setting. “Research project” or “illustrative use case” means the source describes work or a category, but not a completed deployment with a measured result.
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Only the rail-recovery case below documents a specific model architecture and a quantified before-and-after result. Several connected-vehicle figures are measured outcomes, but the U.S. Department of Transportation evaluation does not establish that every system in those cases used AI.
35 documented examples, pilots, and project leads
The table intentionally combines confirmed deployments with clearly marked demonstrations and research leads. “AI evidence” states whether the cited description names an AI method, describes a broader automation system, or presents only an illustrative category.
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| # | Example and task | Operator, place, and date | Maturity | Outcome or stated purpose | AI evidence |
|---|---|---|---|---|---|
| 1 | Rail-disruption recovery recommendations: find similar failure reports and suggest causes and recovery actions. | Hitachi Rail and JR East, Japan; JR East records from 2020–2022. | Field trial, followed by full operation. | For incidents that normally took about two hours, the proof of concept reduced recovery time by approximately 50%, to about one hour. | BERT model trained on around 3,000 historical failure and malfunction records; AI specifically documented. |
| 2 | School-bus connected-vehicle pilot: coordinate bus information with traffic operations. | Alpharetta, United States; evaluation published 2024. | Pilot. | The USDOT evaluation reports a 13% decrease in travel times and a 10% decrease in fuel consumption. | Connected-vehicle ITS; the source does not establish an AI method. |
| 3 | Freight cooperative adaptive cruise control: coordinate trucks to maintain a safer, more efficient platoon. | California, United States; evaluation published 2024. | Deployment case. | The evaluation reports a potential 5–6% reduction in truck fuel consumption. | Automated and connected driving technology; AI is not specified. |
| 4 | Transit-signal integration: give rapid-transit vehicles coordinated priority at intersections. | St. Petersburg, United States; 51 signalized intersections; evaluation published 2024. | Deployment case. | Reported travel time fell by 35% across the evaluated intersections. | ITS signal integration; no AI method is identified. |
| 5 | Emergency-medical-service alerts: distribute approaching-ambulance information through connected infrastructure. | Washington, D.C., United States; evaluation published 2024. | Deployment case. | The evaluation documents the alert deployment but does not provide a quantified outcome in the cited summary. | Connected-vehicle alerting; AI not established. |
| 6 | Roadside-worker cloud alerts: warn drivers and connected vehicles about workers and hazards. | Connecticut, United States; evaluation published 2024. | Deployment case. | The evaluation reports 90% lower collision risk and 80% fewer hard-braking events. | Cloud and connected-vehicle safety system; AI is not established. |
| 7 | Digital roadway alerts: distribute warnings and safety information to vehicles. | Wyoming, United States; evaluation published 2024. | Deployment case. | Average crashes per year fell 42% across all vehicles, with reductions of up to 28% for truck traffic, according to the evaluation. | Digital connected-vehicle deployment; AI is not established. |
| 8 | KIRA autonomous on-demand transit: study how driverless shuttles could operate as public transport. | Germany; project January 2023–December 2026; six vehicles planned for rural and urban areas. | Research project with planned real-traffic deployments. | Develop technical, operational, and regulatory processes while studying supervision, user acceptance, and traffic impact; project volume €2.2 million. | Autonomous-driving research; the project page does not specify a particular AI model. |
| 9 | Automated driving on rural roads without reliable markings. | USDOT Automated Driving Systems demonstration portfolio; location and date vary by project; portfolio updated July 25, 2024. | Demonstration. | Test automated vehicles in environments where lane markings and other infrastructure cues are inconsistent. | Automated-vehicle demonstration; AI technique not stated. |
| 10 | Automated vehicles in work zones. | USDOT ADS demonstration portfolio, United States; portfolio updated 2024. | Demonstration. | Examine vehicle behavior around temporary lanes, workers, barriers, and changing road layouts. | Automation project; the portfolio does not name an AI method. |
| 11 | Automated-vehicle interaction with first responders. | USDOT ADS demonstration portfolio, United States; portfolio updated 2024. | Demonstration. | Study how automated vehicles detect, interpret, and respond to police, fire, and emergency instructions. | Automated-driving setting; AI component not specified. |
| 12 | Freight-port queuing automation. | USDOT ADS demonstration portfolio; United States; portfolio updated 2024. | Demonstration. | Address truck queues and movement around port approaches. | Freight automation scope; AI method not stated. |
| 13 | Automated freight on major corridors. | USDOT ADS demonstration portfolio; United States; portfolio updated 2024. | Demonstration. | Evaluate automated freight operations over longer highway corridors. | Automated-vehicle project; AI not established. |
| 14 | Port and yard operations. | USDOT ADS demonstration portfolio; United States; portfolio updated 2024. | Demonstration. | Test automated movement, coordination, or handling in port and terminal yards. | Automation setting; the source does not identify a model or AI technique. |
| 15 | AI-powered traffic management. | ITS America project library, public- and private-sector projects; Volume 3, June 2025. | Use-case library category. | Apply AI to traffic operations; individual project outcomes and deployment status vary. | AI is named at the category level, not verified for every project. |
| 16 | Adaptive traffic signals. | ITS America Volume 3, June 2025. | Use-case library category. | Adjust signal timing in response to changing traffic conditions. | AI may be used in listed projects, but the library category does not prove it for each example. |
| 17 | Smart work zones. | ITS America Volume 3, June 2025. | Use-case library category. | Use data and automation to manage temporary traffic control and worker safety. | Project-level AI evidence varies. |
| 18 | Automated vehicles. | ITS America Volume 3, June 2025. | Use-case library category. | Catalogs public- and private-sector automated-vehicle projects. | Automation is identified; a specific AI method is not necessarily reported. |
| 19 | Transit signal priority. | ITS America Volume 3, June 2025. | Use-case library category. | Give buses or trains priority at signals to improve transit operations. | AI status depends on the individual project. |
| 20 | Rapid-transit planning. | ITS America Volume 3, June 2025. | Use-case library category. | Use centralized data and analytics to plan rapid-transit service. | The category does not identify one common AI technique. |
| 21 | Transportation-data centralization. | ITS America Volume 3, June 2025. | Use-case library category. | Combine operational data so agencies can analyze and manage services. | AI applicability is project-specific. |
| 22 | Predictive real-time traffic management. | Federal projects summarized in a National Academies chapter; project dates and locations vary. | Research/project lead. | Predict near-term conditions and adjust operations before congestion or incidents spread. | Listed as an AI project area; completion and measured deployment outcome are not established in the summary. |
| 23 | Pavement-condition and safety evaluation. | Federal AI project area summarized by the National Academies; details vary. | Research/project lead. | Use data analysis to assess pavement condition and safety risk. | AI project area named; no single completed deployment is confirmed. |
| 24 | Mixed-reality infrastructure inspection. | Federal AI project area summarized by the National Academies. | Research/project lead. | Support inspectors with digital overlays and machine-assisted analysis. | AI project area named; project-level results not stated. |
| 25 | Digital twins for safety analysis. | Federal AI project area summarized by the National Academies. | Research/project lead. | Model transportation assets and scenarios to examine safety consequences. | AI project area named; no operational outcome stated. |
| 26 | Infrastructure monitoring. | Federal AI project area summarized by the National Academies. | Research/project lead. | Detect changes or defects in roads, bridges, or related assets. | AI project area named; deployment status varies. |
| 27 | Coastal-bridge maintenance. | Federal AI project area summarized by the National Academies. | Research/project lead. | Support maintenance decisions for bridges exposed to coastal conditions. | AI project area named; no measured result supplied. |
| 28 | Vehicle-to-everything cybersecurity. | Federal AI project area summarized by the National Academies. | Research/project lead. | Identify or mitigate threats in vehicle and infrastructure communications. | AI project area named; completed deployment is not verified. |
| 29 | Robotic infrastructure installation. | Federal AI project area summarized by the National Academies. | Research/project lead. | Use robots to install or place transportation equipment. | AI project area named; operational maturity not stated. |
| 30 | Crash prediction. | USDOT AI-use-case overview; illustrative category, not a named deployment. | Illustrative use case. | Estimate where or when crashes may be more likely so agencies can prioritize intervention. | AI category explicitly listed; no specific project or result attached. |
| 31 | Driver-behavior monitoring. | USDOT AI-use-case overview; illustrative category. | Illustrative use case. | Analyze driving patterns such as distraction, harsh maneuvers, or fatigue indicators. | AI category explicitly listed; deployment details not provided. |
| 32 | Intrusion detection. | USDOT AI-use-case overview; illustrative category. | Illustrative use case. | Detect suspicious activity in transportation networks or connected systems. | AI category explicitly listed; no named deployment result. |
| 33 | Signal-timing prediction. | USDOT AI-use-case overview; illustrative category. | Illustrative use case. | Forecast demand and recommend signal plans. | AI category explicitly listed; project evidence not supplied. |
| 34 | Congestion analytics. | USDOT AI-use-case overview; illustrative category. | Illustrative use case. | Analyze congestion patterns for planning and operations. | AI category explicitly listed; no specific deployment outcome. |
| 35 | Dynamic truck routing. | USDOT AI-use-case overview; illustrative category. | Illustrative use case. | Continuously select routes as traffic, restrictions, or delivery conditions change. | AI category explicitly listed; no named field result. |
What the measured results actually show
The strongest AI-specific result is the Hitachi Rail and JR East case. UITP reports that a BERT-based recommendation system searched approximately 3,000 historical fault and malfunction records. In the 2020–2022 field trial, significant incidents that usually required about two hours were resolved in roughly one hour, a reduction of approximately 50%. UITP says the system subsequently entered full operation. This is one operator’s result, not a general expectation for every railway using AI.
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The U.S. connected-vehicle figures demonstrate why transportation results must be read project by project. The Alpharetta school-bus pilot reported 13% shorter travel times and 10% lower fuel use. California cooperative adaptive cruise control was associated with a potential 5–6% truck-fuel reduction. St. Petersburg’s transit-signal integration reported a 35% travel-time reduction over 51 intersections. Connecticut’s cloud-alert deployment reported 90% lower collision risk and 80% fewer hard-braking events. Wyoming’s digital alerts were associated with a 42% reduction in average crashes per year across all vehicles and up to 28% for trucks.
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Those percentages come from the USDOT’s 2024 ITS Deployment Evaluation. The evaluation covers 23 cases—six connected-vehicle, 12 digital-infrastructure, and five automated-vehicle cases—but it does not say that all 23 use AI. Treating the figures as proof of AI performance would overstate the evidence.
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Perception and prediction
Cameras, radar, vehicle telemetry, weather feeds, maintenance records, and traffic counts can be combined to detect objects, identify unusual conditions, forecast demand, or estimate risk. Crash prediction, congestion analytics, and driver-behavior monitoring are examples of this predictive layer. A prediction is not automatically a control action: an agency still needs a policy for what happens after the model raises an alert.
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Decision support for human operators
The rail-recovery system is a clear example. Dispatchers remain responsible for the incident, while the model retrieves similar historical events and recommends likely actions. This design can be easier to govern than fully automated control because the operator can reject an unsuitable recommendation and investigate the underlying records.
Closed-loop control
Adaptive signals, transit priority, cooperative cruise control, and automated vehicles connect sensing to an action such as changing a signal phase, adjusting speed, or selecting a route. Closed-loop systems require strict safety limits, fallback behavior, communications reliability, and testing against unusual conditions.
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Asset and workforce management
Infrastructure-monitoring, pavement-evaluation, bridge-maintenance, inspection, and robotic-installation projects apply AI away from the moving vehicle. These systems can prioritize inspections or maintenance, but a model score does not replace an engineering assessment where public safety is involved.
How to judge a transportation AI claim
- Identify the exact system. Ask whether the source names a model, a sensor-fusion method, a rules engine, or only a broad ITS category.
- Check the maturity label. An operational service, a limited pilot, a demonstration, and a research proposal have different evidentiary weight.
- Separate measured outcomes from objectives. “Designed to reduce congestion” is not the same as a measured reduction.
- Keep the denominator and comparison visible. A percentage may apply to one corridor, one vehicle class, one period, or a modeled scenario rather than an entire network.
- Look for human oversight and fallback. Determine who can override the system, what happens when sensors or communications fail, and how incidents are logged.
- Check transferability. Results from a Japanese railway, a U.S. school-bus pilot, or a single urban signal network should not be generalized to every mode or geography.
What remains unsettled
The phrase “35 real deployments” suggests a verified, exhaustive catalog, but the cited material does not establish one. The 23-case USDOT evaluation is a broader ITS compilation, not a list of 23 confirmed AI systems. ITS America’s library is regularly updated and useful for finding projects, yet its use-case labels do not prove that every listed project uses the same AI technique. The National Academies material identifies federal project areas, while project-level completion, location, and outcomes require separate documentation. KIRA is explicitly a January 2023–December 2026 research effort with planned vehicles, not evidence of a mature commercial fleet.
A reliable comparison therefore records the operator, location, dates, data used, AI method, safety controls, maturity stage, and measurement design for each project. Without those fields, a list can make ordinary connected infrastructure or an announced research goal look like a proven AI deployment.
The Bottom Line
AI in transportation is real, but unevenly mature. The best-documented AI result in this set is a rail-dispatch recommendation system that cut recovery time by about half in a reported trial and then entered operation. Many other examples are connected-vehicle, automated-driving, or research projects whose sources do not prove an AI component or a finished deployment. Judge each claim by its named method, maturity stage, and measured outcome.
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