How artificial intelligence is transforming transportation in Canada

Applying AI across Canada’s transportation networks

Canadian transportation agencies are increasingly exploring AI-driven solutions that support real-time monitoring, predictive maintenance, and adaptive traffic control systems.

There are promising applications of AI across Canada’s road, highway, and urban transportation sector in planning, engineering, road safety, and asset management. By leveraging large volumes of sensor and operational data, these AI-enabled technologies help optimize resource allocation, enhance the reliability of transportation networks, and proactively address issues before they escalate into larger problems.

Due to the rapid advancement of AI and the generic or highly technical nature of AI-related information, many transportation professionals experience gaps in their understanding of AI and its implications. The challenge for transportation leaders is no longer whether to use AI, but how to apply it responsibly, consistently, and with confidence. Alicia Wallis, senior consultant in Canada, addresses these gaps in a recent Transportation Association of Canada (TAC) study on AI and transportation in Canada, helping transportation professionals to understand and capitalize on major opportunities related to AI, while helping them mitigate major challenges, risks, and uncertainties.

Demystifying AI: from algorithms to impact

AI is not just a technological trend; it’s rapidly becoming the backbone of innovation in transportation. We are now training computer systems to recognize patterns and make decisions, rather than simply programming them to follow rules.

AI is often discussed as a single technology, but in practice it spans a wide spectrum of systems with very different levels of complexity, transparency, and risk. Some forms of AI such as expert systems and regression models have supported transportation decision‑making for decades. More recent advances, including computer vision and generative AI, build on larger data volumes and less structured information. To make this spectrum practical, this study introduces a simple classification framework that describes AI systems across four dimensions: domain, objective, learning method, and technique. Understanding where an AI system sits within this framework helps practitioners assess appropriateness, explainability, and risk, and supports better decisions about governance and oversight. 

AI across the transportation landscape

AI systems offer numerous potential benefits to the transportation sector, some of which are already being realized. Here’s a look at how AI has been used across Canada’s roads, highways, and urban transit.

• Construction and asset management: AI is increasingly used to shift asset management from reactive and schedule‑based approaches to predictive and risk‑based strategies. Common applications include automated asset inspection using cameras and sensors, predictive maintenance based on historical and real‑time data, lifecycle cost planning, and the development of digital twins. These systems often rely on large volumes of structured and unstructured data, and their value depends on data quality, integration across systems, and clear operational ownership.
• Mobility services: In mobility services, AI is already influencing how people experience the transportation system day to day. Applications span bikeshare and carshare operations, multimodal trip planning, transit operations, accessibility solutions, and electric vehicle charging management. Many of these systems rely on computer vision and prediction models to optimize service reliability, accessibility, and customer experience. The biggest gains are realized when data is shared across modes rather than optimized in silos.
• Traffic operations: Traffic operations is one of the most established areas of AI adoption in transportation. AI‑enabled systems support compliance and enforcement, traffic signal control, traffic management centers, connected vehicle data analysis, and dynamic pricing. These applications often operate in near real‑time environments, where data latency, model reliability, and system resilience are critical. Because AI outputs may directly influence traffic control or enforcement decisions, governance, testing, and monitoring are particularly important in this category.
• Safety and security: AI plays a growing role in improving safety outcomes by detecting risk earlier than traditional methods. Applications include road safety engineering analysis, incident detection and response, in‑vehicle safety systems, automated driving technologies, and cybersecurity. Many use cases rely on computer vision and advanced analytics to identify patterns, near misses, or anomalies that are difficult for humans to detect at scale. Given the potential impact on people and public trust, safety‑related applications demand strong safeguards around bias, privacy, transparency, and accountability.
• Administration and corporate services: AI is increasingly visible in administrative and corporate functions, where many practitioners first encounter generative tools. Applications include human resource management, talent development, research, content creation, and emerging agentic AI systems that can perform multi‑step tasks. While these use cases are often lower risk from a safety perspective, they raise important considerations around intellectual property, data protection, and appropriate use of enterprise AI tools.

Navigating challenges

AI introduces new considerations around bias, privacy, security, and workforce impacts; however, these challenges are manageable with the right foundations in place. Understanding how an AI system is trained, what data it relies on, and how its outputs are used allows organizations to design appropriate safeguards.

Strong data governance, clear policies, and regular model review help mitigate bias and privacy risks. Positioning AI as an enabler – augmenting human judgment rather than replacing it – supports effective change management and builds trust within organizations. Upskilling, transparent communication, and shared learning across the industry are essential to developing long‑term capability and confidence.

Driving the future of transportation with AI

AI is already shaping how transportation systems are planned, operated, and maintained across Canada. The opportunity ahead is not simply to adopt more AI, but to apply it with clarity, governance, and purpose. By using practical frameworks, investing in people and data foundations, and sharing lessons learned across the industry, transportation organizations can unlock lasting value while managing risk. In doing so, AI becomes not a source of uncertainty, but a trusted tool for delivering safer, more resilient, and more inclusive transportation networks.

Learn more about what Alicia recommends to enable the responsive and effective use of AI in transportation.