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Artificial intelligence is already used in transportation to help vehicles detect hazards, help agencies manage traffic and incidents, and help planners and maintenance teams work with complex data. Its role ranges from offering a recommendation to supporting an automated vehicle function; it does not mean that transportation systems are generally self-driving. Whether AI improves safety or efficiency depends on the application, the data, the operating conditions, and the people and agencies responsible for decisions.
How AI is used in transportation
“AI in transportation” describes a range of tools, not one technology. The U.S. Department of Transportation (USDOT) identifies uses in automated vehicles, traffic management, digital infrastructure, and vehicle and infrastructure maintenance. Some systems analyze information and advise a person; others support a function inside a vehicle or infrastructure system. The consequences of an error—and who can intervene—vary accordingly.
| Application | What AI can help with | Where the decision or action happens |
|---|---|---|
| Vehicle safety and automated driving | Interpreting the vehicle’s surroundings and supporting driver-assistance or automated-driving functions. | In or around a vehicle, with direct implications for the driver and other road users. A driver-assistance feature is not the same as a fully self-driving vehicle. |
| Traffic operations | Forecasting conditions and informing signal timing, speed management, incident response, and traveler information. | Often in an agency’s traffic-management operation, where people can assess recommendations and take operational action. |
| Infrastructure, maintenance, and planning | Helping identify safety risks or network gaps, work with transportation data, and support planning, design, or maintenance tasks. | Typically within agency or infrastructure workflows; the people responsible for the underlying decisions remain important. |
These examples also illustrate why AI and automation should not be treated as synonyms. A model may forecast or classify information without making a decision or controlling a vehicle. The system’s specific role is a major determinant of its risks, as USDOT’s September 2024 paper, Understanding AI Risks in Transportation, emphasizes.
How AI can help manage traffic
Predictive analytics uses mathematical models to make statements about a system’s future state. In transportation operations, forecasts can help a team anticipate congestion or incidents, but a forecast or recommendation is distinct from the action an agency ultimately takes. Whether that action is appropriate depends on the data, the local road conditions, operational rules, and human oversight.
The I-24 deployment in Tennessee
A useful U.S. example is the Tennessee Department of Transportation’s I-24 Smart Corridor. The system analyzed field traffic and incident information, including data from monitoring devices and TDOT’s SmartWay Central Software, and sent recommended actions to a Transportation Management Center. Those recommendations could include variable speed limits, traveler information, lane control, and signal timing. The deployment used variable speed limit signs, lane control signs, dynamic message signs, video detection, connected signals, CCTV, and radar detection. Its 67 overhead gantries covered the section between the I-440 and I-840 interchanges.
The Transportation Management Center remained part of the decision process: the system supported operators rather than establishing that AI independently controlled the corridor. A 2026 USDOT Intelligent Transportation Systems Joint Program Office (ITS JPO) evaluation reported the following results for the deployment:
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| Reported outcome | Finding and qualification |
|---|---|
| Crash rate while variable speed limits (VSL) were active | 14% lower, declining from 18.4 to 15.8 crashes per month. |
| Secondary crash rate while VSL was active | 50% lower, declining from 7.2 to 3.6 crashes per month. |
| Incident clearance time | 20% lower. |
| Annual incident detections | 16% higher. |
| Traffic volume and average travel time | Traffic volume rose 8%, with negligible average travel-time change. |
| Benefit-cost ratio | Estimated at 4.98 for the evaluated deployment. |
These are results reported for one Tennessee corridor, not a general estimate of what AI will do on other roads. The evaluation used a before-and-after design, comparing 2.5 years of pre-deployment data with 1.5 years after deployment. The reported changes are associated with this specific system and period; they do not by themselves establish that AI caused the same result elsewhere or under different traffic, weather, or operating conditions.
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Vehicle safety features offer another kind of evidence: estimates drawn from real-world crash data for particular systems and crash types. A 2024 USDOT ITS JPO summary of a 2020 University of Michigan Transportation Research Institute study sponsored by the National Highway Traffic Safety Administration (NHTSA) reported the estimates below.
| Evaluated feature | Estimated crash reduction or effectiveness | Crash type described in the summary |
|---|---|---|
| Forward collision alert | 16% | Frontal crashes |
| Forward automatic braking | 45% | Frontal crashes |
| Lane keep assist | 30% | Crashes relevant to the evaluated system |
| Lane change alert with side blind zone alert | 32% | Crashes relevant to the evaluated systems |
| Rear automatic braking | 82% | Backing crashes |
| Rear cross-traffic alert | 55% | Crashes relevant to the evaluated system |
| Rear park assist | 36% | Crashes relevant to the evaluated system |
| Rear vision camera plus rear park assist | 51% | Among sedans, as reported in the summary |
The underlying evaluation used crash data for 35,401 vehicles sampled from a larger dataset of 1.2 million model-year 2013–2015 vehicles. Its comparisons used system-relevant crash types and control crash types. These are estimates for the studied features and data—not a prediction for a new car, a direct comparison of every system available today, or evidence that AI as a whole reduces crashes by a single percentage. The findings also do not mean that a driver can stop paying attention or that a vehicle equipped with driver assistance is fully autonomous.
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AI for infrastructure, planning, and maintenance
AI can also assist with work that happens away from the moving vehicle. USDOT’s AI for Transportation Planning and Design initiative describes potential tools for identifying safety risks, detecting gaps in networks, integrating transportation data, and automating parts of planning and design. These are described as areas of application, not proof that every tool has delivered a particular safety or cost benefit.
Missouri Department of Transportation pilots provide a more specific example. They explored highway median inventory and grouping annual average daily traffic factors. A 2025 ITS JPO summary of the 2024 pilot advises agencies to begin with a clearly defined, quantitative decision; check that robust training data are available; involve IT early; and build the internal capacity to implement and maintain the system.
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The pilot concluded that use at least 10,000 times was a condition under which an AI or machine-learning algorithm was most likely to be cost-effective, given a clear decision and robust data. That figure is a project-specific lesson, not a universal threshold: a different task, deployment cost, data set, or agency may have a different break-even point.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What determines whether a transportation AI system is appropriate
A useful assessment starts with the decision the system informs or takes, rather than with the label “AI.” USDOT’s September 2024 risk paper recommends considering the system’s role and context. In practice, agencies and operators need to ask:
- Who is responsible? Identify the owner, operator, users, and the person or organization accountable for the final operational decision.
- What is the system allowed to do? Distinguish a forecast or recommendation from an action that changes a signal, speed limit, vehicle behavior, or other safety-relevant condition.
- Where does it operate? A moving vehicle and static infrastructure have different operating contexts and may have different, distributed safety responsibilities.
- What rules apply? Establish the laws, regulations, and operating rules that govern the system and its users.
- Are the data adequate? Check whether data are representative of the roads, users, conditions, and time periods where the system will be used, and whether gaps or errors could change its output.
- Can people understand and intervene? Specify who reviews outputs, how recommendations are overridden, and what happens if the system is unavailable or unreliable.
- What other public interests are affected? Consider privacy, cybersecurity, mobility, equity, workforce effects, and safety alongside efficiency.
The December 2024 report from USDOT’s Transforming Transportation Advisory Committee also discusses responsible AI in relation to automated-driving policy, first responders, workforce issues, project delivery, and safety innovation. Its expertise is focused on surface transportation, so it should not be read as a comprehensive account of AI in aviation, maritime transport, freight or long-distance passenger rail, or pipelines.
What the evidence can—and cannot—tell us
The examples above represent different kinds of evidence. The I-24 findings are a before-and-after evaluation of a corridor deployment; the vehicle figures are estimates from a study using real-world crash data for particular features; and the Missouri work is a pilot whose lessons concern agency implementation and cost-effectiveness. They answer different questions and should not be combined into a single claim that AI generally makes transportation safer, faster, or cheaper.
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallFor any proposed system, compare the application and its setting, whether it advises a person or acts automatically, the consequence of a wrong output, the quality and coverage of its data, and the evaluation method and period. Also examine who has authority to use or override it and how the agency will monitor its effects. An efficiency gain does not settle questions about privacy, fairness, cybersecurity, or responsibility, and automation does not remove the need for accountable people and institutions.
The available examples here are strongest for U.S. surface transportation. They show concrete ways AI can support vehicle safety, traffic operations, and agency work, while also showing why results must be tied to the system and setting in which they were measured.
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