Key Takeaways
- Bozeman, Montana is launching a three‑year, $150,000 AI‑driven traffic‑safety initiative funded by a Safe Streets for All grant.
- The project partners the city’s Transportation and Engineering Department with AI firm Citian to analyze existing police crash data, identify high‑risk intersections, and evaluate the impact of safety interventions.
- No new surveillance or personal data will be collected; the AI works solely with historical crash reports and infrastructure changes.
- City officials and residents alike see promise in using AI to prioritize limited resources and address dangerous spots such as Oak & 19th Street, where 29 violations occurred in early 2026.
- Recent tragedies, including the February 2026 pedestrian death of Leslie Brown near Gallatin County Regional Park, have heightened public demand for proactive safety measures.
- While some residents express general unease about AI, many acknowledge its potential to provide objective, data‑based insights that complement engineering judgment.
Project Overview and Funding
Bozeman’s new artificial‑intelligence traffic‑safety program stems from a Safe Streets for All grant, a federal initiative aimed at reducing roadway fatalities and serious injuries. The city secured a three‑year contract worth $150,000 with Citian, a company that specializes in predictive analytics for urban mobility. The funding covers software licensing, data integration, staff training, and ongoing technical support. By anchoring the project in grant money, Bozeman avoids diverting municipal budget resources while still gaining access to cutting‑edge analytical tools.
How the AI Platform Works
Unlike systems that rely on live video feeds or sensor networks, Bozeman’s AI will mine existing police crash reports. The platform ingests structured data—date, time, location, vehicle types, contributing factors, and injury severity—to detect patterns that may not be obvious through manual review. Machine‑learning algorithms then rank intersections and road segments by risk level, producing heat maps that highlight where interventions are most likely to yield safety gains. The system also tracks changes made to the street network (e.g., new signage, lane reconfigurations, signal timing adjustments) and evaluates their effect over time, creating a feedback loop for continuous improvement.
Data Sources and Privacy Safeguards
A notable feature of the Bozeman approach is its commitment to privacy. The AI does not collect new information from the public, nor does it tap into personal devices, license‑plate readers, or facial‑recognition technology. All inputs come from the Bozeman Police Department’s official crash database, which already aggregates incident reports for public safety purposes. By limiting the AI to this established dataset, the city mitigates concerns about surveillance while still benefiting from sophisticated pattern‑recognition capabilities.
Identifying High‑Risk Locations
Early analysis has already flagged Oak & 19th Street as a persistent problem zone, accounting for 29 of the 1,673 traffic violations recorded since January 2026. Other hotspots emerge from the data, including corridors near Gallatin County Regional Park where pedestrian‑vehicle conflicts have risen. The AI’s ability to quantify risk—expressing it as expected crashes per mile or per intersection—gives traffic engineers a clear, evidence‑based hierarchy for prioritizing projects, rather than relying solely on anecdotal observations or political pressure.
Community Perspective: Jared Ross’s View
Long‑time Bozeman driver Jared Ross, who has navigated the city’s roads for over three decades, offered a balanced take. He acknowledged the unsettling nature of AI discussions in general but expressed confidence that applying the technology to traffic safety could be beneficial. Ross noted that Bozeman’s street network was not originally designed for today’s volume of vehicles, cyclists, and pedestrians, making data‑driven insights especially valuable for a city experiencing rapid growth.
Official Endorsement from City Leadership
Nick Ross, Bozeman’s director of transportation and engineering, emphasized that the AI platform will give both officials and the public a clearer picture of where safety problems are most severe. He described the tool as a force multiplier: it narrows the focus of limited staff and budget to the locations where interventions are likely to prevent the most injuries or fatalities. Ross also acknowledged the public’s wariness of AI, framing the project as a transparent, goal‑oriented use of technology rather than an invasive surveillance scheme.
Recent Tragic Events Underscoring the Need
The initiative gained urgency after several high‑profile incidents. In February 2026, 39‑year‑old Leslie Brown was struck and killed while walking near Gallatin County Regional Park—a loss that sparked community calls for safer pedestrian infrastructure. Concurrently, reports of vehicle‑versus‑cyclist crashes have risen, particularly on arterial roads where bike lanes are incomplete or poorly marked. These events highlighted gaps between existing safety measures and the realities of a growing, multimodal transportation environment.
Expected Outcomes and Evaluation Metrics
Over the three‑year term, Bozeman plans to measure success through a combination of leading and lagging indicators. Leading indicators include the number of high‑risk locations identified, the speed at which recommended countermeasures are implemented, and changes in observed traffic behaviors (e.g., reduced speeding at flagged intersections). Lagging indicators will focus on traditional safety outcomes: reductions in total crashes, serious injuries, and fatalities, particularly at the AI‑prioritized sites. The city intends to publish interim reports annually, allowing residents to track progress and hold officials accountable.
Addressing Public Concerns About AI
While the technology promises analytical advantages, Bozeman officials are proactive in addressing skepticism. Public outreach sessions will explain how the AI works, what data it uses, and what it does not do. Emphasis will be placed on the fact that the system augments—rather than replaces—human judgment. Engineers will retain final authority over design decisions, using AI outputs as one input among many, including field observations, community feedback, and established engineering guidelines.
Broader Implications for Small‑City Traffic Management
Bozeman’s experiment could serve as a model for other mid‑sized municipalities grappling with limited resources and rising traffic pressures. Demonstrating that a modest grant‑funded AI contract can produce actionable safety insights may encourage similar jurisdictions to pursue data‑centric approaches without investing in costly infrastructure upgrades. Moreover, the focus on using existing crash data aligns with privacy‑conscious policies that many communities now prioritize.
Conclusion
By marrying grant funding, AI analytics, and a transparent public‑safety mission, Bozeman is positioning itself at the forefront of innovative, evidence‑based traffic management. The project’s reliance on historical crash data—rather than invasive surveillance—aims to build trust while delivering concrete safety benefits. As the AI begins to uncover patterns and guide interventions, residents and officials alike will watch closely to see whether this technological tool can help make Bozeman’s streets safer for drivers, cyclists, and pedestrians alike.