A Systematic Decade Review of Trip Route Planning with Travel Time Estimation based on User Preferences and Behavior

Transparency in navigation AI is essential: The paper identifies Explainable AI (XAI) as a key emerging methodology, highlighting that users need to understand why their routes are recommended. This is particularly important for responsible AI systems where people make decisions based on algorithmic suggestions.

Ethical concerns require deliberate attention: The authors explicitly flag ethical concerns as a critical challenge that "must be addressed to advance the field," suggesting that current route planning systems may not adequately consider fairness, privacy, or potential biases in how different users' preferences and behaviors are treated.

Data privacy and federated approaches matter: The paper identifies Federated Learning as an emerging methodology, which allows AI systems to learn from user behavior without centralizing sensitive location and travel data. This represents a responsible approach to building personalized navigation while protecting individual privacy.

Scalability challenges affect equitable access: The paper notes that "scalability requirements" are a critical challenge in current systems. This matters for responsible AI because poorly scaled systems may work well in wealthy urban areas while failing in underserved communities, potentially deepening transportation inequities.

Sustainability should be a design goal: The paper concludes with recommendations for building "efficient, transparent, and sustainable navigation systems," suggesting that responsible route planning AI should optimize not just for speed, but also for environmental impact and broader societal benefits.