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How Transportation Agencies Can Make Travel Safer With AI And ML

  //travel-leisure.news-articles.net/content/2025/ .. encies-can-make-travel-safer-with-ai-and-ml.html
  Published in Travel and Leisure on Friday, September 12th 2025 at 8:02 GMT by Forbes
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Harnessing AI and Machine Learning to Make Public Travel Safer: A Practical Guide for Transportation Agencies

In a rapidly evolving mobility landscape, transportation agencies worldwide are turning to artificial intelligence (AI) and machine learning (ML) to anticipate risks, reduce accidents, and enhance passenger experience. A recent Forbes Tech Council piece—“How Transportation Agencies Can Make Travel Safer with AI and ML” (September 12, 2025)—examines the most actionable AI tools, outlines a roadmap for implementation, and highlights real‑world success stories that illustrate the transformative power of intelligent systems.

1. The AI‑Powered Safety Imperative

The article opens by framing safety not just as a regulatory requirement but as a competitive advantage. While the Transportation Security Administration (TSA) and the Federal Aviation Administration (FAA) already use AI for threat detection, only a handful of agencies have integrated data‑driven solutions across the full transportation stack—from rail to public transit, to freight and passenger air travel. By applying ML models to sensor data, traffic feeds, and historical incident logs, agencies can identify patterns that human analysts miss, enabling proactive interventions before a hazard escalates.

2. Core AI Use Cases in Transportation Safety

The article cites the Transportation Research Board’s 2024 white paper on AI in freight operations (link) to back these claims with empirical data: “Transportation agencies that deploy AI‑enabled predictive maintenance saw a 30 % reduction in equipment failures over a three‑year period.”

3. From Data to Insight: Building the Foundations

Successful AI initiatives hinge on data quality and interoperability. The Forbes piece stresses that many agencies still rely on siloed legacy systems, making it difficult to feed the breadth of data needed for robust models. Key steps include:

1. Data Harmonization – Consolidate disparate sources (IoT sensors, GPS logs, incident reports) into a unified data lake.
2. Metadata Governance – Tag data with context (time stamps, geolocation, sensor type) so models can discern patterns accurately.
3. Continuous Labeling – Use crowdsourced or semi‑automated labeling pipelines to keep training datasets fresh, especially for image‑based detection.

The article links to an open‑source framework from the National Highway Traffic Safety Administration (NHTSA) that provides sample data schemas, underscoring how shared resources can lower entry barriers.

4. Human‑In‑The‑Loop: Combining AI with Operator Expertise

AI is portrayed not as a replacement for human judgment but as a powerful augment. The Forbes article points out that many high‑profile accidents in the past decade involved “algorithmic blind spots” – for instance, a machine‑learning model misclassifying a partially covered bridge as safe because of insufficient training data. To mitigate such pitfalls, agencies should:

• Establish Verification Teams that validate AI outputs before they influence operational decisions.
• Design Explainable AI (XAI) Interfaces that reveal model reasoning, allowing supervisors to spot anomalies.
• Offer Continuous Training for operators on interpreting AI dashboards and responding to alerts.

A compelling illustration comes from the UK’s National Rail, which partnered with a European AI startup (link) to deploy explainable models that flagged abnormal vibration patterns in locomotives. When the AI raised a red flag, human engineers could immediately investigate, preventing a potential derailment.

5. Navigating Regulatory and Ethical Hurdles

The article acknowledges that agencies must tread carefully around privacy, data ownership, and algorithmic bias. Key recommendations include:

• Adopting Transparent Governance Policies that outline data collection limits and retention periods.
• Conducting Bias Audits on training data, especially for security‑related applications where false positives can disproportionately affect minority groups.
• Aligning with Emerging Standards such as the IEEE P7000 series for ethical AI in transportation.

A notable case study is the U.S. Department of Transportation’s 2023 “AI‑Safety Pilot” (link), which mandated that all participating agencies undergo an external audit to certify compliance with the “Transportation AI Ethical Framework.”

6. Implementation Roadmap: From Pilot to Scale

The Forbes article outlines a six‑step rollout:

1. Assess Current Safety Metrics and identify low‑hanging fruit (e.g., maintenance).
2. Select Pilot Projects with clear KPIs.
3. Build or Acquire AI Capabilities (in‑house, partnership, or cloud service).
4. Deploy in a Controlled Environment and monitor real‑world performance.
5. Iterate by refining models, expanding data sources, and scaling to additional corridors.
6. Institutionalize by embedding AI into standard operating procedures and workforce training.

The piece includes a link to a toolkit from the International Association of Public Transport (UITP) that provides checklists and cost models to help agencies budget for AI investments.

Conclusion

AI and ML promise a new era of proactive, data‑driven safety in transportation. By investing in high‑quality data infrastructure, fostering human‑AI collaboration, and adhering to ethical guidelines, agencies can dramatically reduce incidents and enhance the traveler’s experience. The Forbes article serves as a practical primer—grounded in industry examples, actionable steps, and rich supplemental resources—that transportation professionals can immediately apply to their own safety portfolios.

For more in‑depth guidance, readers are encouraged to explore the linked white papers, case studies, and open‑source tools referenced throughout the piece. With the right strategy, AI can transform safety from a reactive after‑thought into a fundamental pillar of modern mobility.