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Artificial intelligence is already used in transportation to help vehicle safety features detect hazards, help traffic managers anticipate changing road conditions, and support infrastructure planning and maintenance. It does not mean every vehicle is self-driving: many systems analyze data or recommend an action to a human operator. Their benefits and risks depend on the specific decision, the quality of the data, the operating environment, and who remains accountable.
The clearest evidence described here comes from U.S. surface transportation. A Tennessee highway evaluation and studies of specific vehicle safety features show potential benefits, but neither establishes a universal result for every road, vehicle, or AI system.
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How is AI used in transportation?
AI in transportation is a collection of applications, not one technology. A system might analyze camera or radar input in a vehicle, forecast traffic conditions for an agency, or help organize information used in planning. Some applications provide information to a person; others can trigger an automated response. Those differences matter because a mistaken prediction in a planning workflow does not create the same immediate hazard as a missed obstacle detection in a moving vehicle.
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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →| Application | What it can do | Operational role |
|---|---|---|
| Vehicle safety and driving functions | Support perception, warn about potential hazards, or assist with steering or braking. | May alert a driver or intervene through a vehicle feature; a driver-assistance system is not necessarily a self-driving vehicle. |
| Traffic operations | Analyze field data to forecast conditions and inform speed management, signals, incident response, and traveler information. | Can recommend actions to traffic operators, who decide what to implement. |
| Infrastructure, maintenance, and planning | Help identify safety risks or network gaps, integrate transportation data, and automate parts of planning and design. | Supports agency analysis and decisions about assets, projects, and networks. |
Not every tool described as AI is necessarily a machine-learning model, and an AI-generated forecast is not the same thing as an operational decision. The Federal Highway Administration’s April 2024 summary defines predictive analytics as developing and applying mathematical models to make statements about a system’s future state. A forecast can inform an operator; the operator or agency still determines whether and how to act.
Can AI reduce traffic accidents?
Evidence suggests that particular systems can contribute to safety in particular settings. It does not support a single percentage reduction that can be applied to AI as a whole.
Results from evaluated vehicle safety features
A 2020 University of Michigan Transportation Research Institute evaluation for NHTSA examined crash data for 35,401 vehicles sampled from a larger dataset of 1.2 million model-year 2013–2015 vehicles. The comparisons used crash types relevant to a system against control crash types. The federal ITS summary published in 2024 reports these estimates:
| Evaluated feature | Estimated crash reduction or effectiveness | Qualification |
|---|---|---|
| Forward collision alert | 16% | Estimated reduction in frontal crashes. |
| Forward automatic braking | 45% | Estimate for the evaluated feature and relevant crash types. |
| Lane keep assist | 30% | Estimate for the evaluated feature and relevant crash types. |
| Lane change alert with side blind zone alert | 32% | Estimate for the evaluated feature and relevant crash types. |
| Rear automatic braking | 82% | Estimated effectiveness for backing crashes. |
| Rear cross-traffic alert | 55% | Estimate for the evaluated feature and relevant crash types. |
| Rear park assist | 36% | Estimate for the evaluated feature and relevant crash types. |
| Rear vision camera plus rear park assist | 51% | Estimate reported for sedans. |
These are feature- and study-specific estimates, not a forecast for a new car buyer or proof that AI generally reduces crashes by any one amount. A result for a defined feature and crash category cannot be carried over automatically to a different vehicle, system, or road condition.
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Results from a Tennessee traffic-management deployment
On Tennessee’s I-24, an AI decision-support system analyzed traffic and incident information from field monitoring devices and TDOT’s SmartWay Central Software. It sent recommendations to the Transportation Management Center rather than independently replacing the center’s operational role. Recommended actions included variable speed limits, traveler information, lane control, and signal timing.
The deployment included 67 overhead gantries between the I-440 and I-840 interchanges, along with variable-speed-limit signs, lane-control signs, dynamic message signs, video detection, connected signals, CCTV, and radar detection. A 2026 U.S. Department of Transportation Intelligent Transportation Systems Joint Program Office evaluation compared 2.5 years of pre-deployment data with 1.5 years after deployment. It used a before-and-after design, so the results describe that corridor and evaluation period rather than proving that the system alone caused every change.
| Reported outcome on I-24 | Finding while variable speed limits were active or during the evaluation | Interpretation |
|---|---|---|
| Overall crash rate | 14% lower while variable speed limits were active: 18.4 to 15.8 crashes per month. | Corridor-specific evaluation result. |
| Secondary crash rate | 50% lower while variable speed limits were active: 7.2 to 3.6 crashes per month. | Corridor-specific evaluation result. |
| Incident clearance time | Declined 20%. | Reported for the same deployment evaluation. |
| Annual incident detections | Rose 16%. | More detections do not by themselves establish a change in underlying incident frequency. |
| Traffic volume and average travel time | Traffic volume rose 8%, with negligible average travel-time change. | Reported for the same corridor evaluation. |
| Benefit-cost ratio | Estimated at 4.98. | Evaluation estimate for this project, not a general return expected from AI deployments. |
The example illustrates how AI can support traffic management through analysis and recommendations, while signs, signals, agency procedures, and operators carry the response into the real world. Its outcomes should not be treated as a prediction for another corridor with different traffic, infrastructure, data, or operating practices.
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How does AI help manage traffic and infrastructure?
Traffic forecasts and operational decisions
Traffic agencies can use predictive analytics to inform speed management, signal timing, incident response, and messages to travelers. A useful system must connect its forecast to a defined operational decision: what condition is being detected, which action is available, and who can approve or intervene? The I-24 deployment is an example of a system feeding recommendations to a Transportation Management Center rather than treating an algorithm’s output as an automatic command.
Planning, design, and maintenance
AI can also help agencies work with large sets of infrastructure and transportation data. USDOT’s AI for Transportation Planning and Design initiative describes possible uses such as identifying safety risks, finding gaps in transportation networks, integrating data, and automating aspects of planning and design. These are decision-support uses; their value depends on whether the data represent the places and people affected and whether staff can review the output before it shapes a project or maintenance decision.
A Missouri Department of Transportation pilot explored highway median inventory and grouping annual average daily traffic factors. Its summary says AI or machine-learning use was most likely to be cost-effective when an agency had a clear quantitative decision, robust training data, and an algorithm it expected to use at least 10,000 times. That figure is a lesson from this pilot, not a universal break-even threshold. The pilot also advised involving IT early and accounting for the agency’s capacity to implement and maintain the system.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What are the risks of AI in transportation?
Risk depends on what the system does and where it operates. USDOT’s September 2024 paper, Understanding AI Risks in Transportation, states: “The specific role that AI plays in the context of an application is a major determinant of the types of risks it may pose to the transportation system.” A vehicle function and a static infrastructure tool do not have identical operators, failure modes, or safety consequences.
Before adopting a system, an agency or operator needs to establish who owns and operates it, who uses it and how, what laws and regulations govern its operation, and whether it acts in a moving vehicle or in infrastructure. Those questions help identify who must monitor performance, respond to errors, and remain responsible when a recommendation or automated action has consequences. They are a starting point for risk analysis, not a complete assurance procedure for every application.
- Safety and reliability: A system can miss a hazard, produce a misleading forecast, or perform differently when conditions or data change. The consequences depend on whether a person reviews the result or the system acts directly.
- Data quality and coverage: Training and operational data need to be sufficiently robust for the decision and setting. Gaps can make an output less reliable for locations or conditions that are poorly represented.
- Privacy and cybersecurity: Transportation systems may handle information about vehicles, locations, or network operations. The available deployment outcomes do not resolve how a particular system collects, protects, or shares data.
- Equity and public goals: Efficiency is not the only objective. Agencies also need to consider mobility, safety, accessibility, and who benefits or bears the effects of a system.
- Workforce and accountability: Automation does not eliminate human responsibility. Agencies need people with authority and capacity to oversee systems, respond to failures, and maintain the technology.
The Transforming Transportation Advisory Committee’s December 2024 report discusses responsible AI alongside automated-driving policy, first responders, workforce, project delivery, and safety innovation. Its expertise centers on surface transportation; it is not a comprehensive assessment of aviation, maritime, freight rail, long-distance passenger rail, or pipelines.
How should a transportation AI system be evaluated?
Compare systems by the decision they support and the evidence behind them, not by the label “AI.” A practical review should ask:
- Role: Is the system vehicle-based or infrastructure-based? Does it inform a person, recommend an action, or initiate one?
- Consequence: What operational decision follows from its output, and what happens if the output is wrong or unavailable?
- Data: Are the data robust and relevant to the location, conditions, and population where the system will be used?
- Evidence: Is a claimed benefit a measured deployment outcome, a modeled estimate, or a pilot finding? What geography, comparison period, and evaluation design support it?
- Responsibility: Who owns and operates the system, who can intervene, and who is accountable for monitoring and maintenance?
- Public impact: How are safety, privacy, mobility, equity, cybersecurity, and workforce effects considered alongside efficiency?
These questions make it possible to distinguish a promising tool from a justified deployment. They also prevent a positive result from one corridor or a specific vehicle feature from being mistaken for evidence about every transportation system.
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