Urban public transportation systems are widely used, yet most route planning tools primarily optimize objective factors such as travel time or distance, often overlooking subjective aspects of the travel experience such as comfort, crowding, and walking tolerance. As a result, routes that are optimal by technical criteria may feel stressful or impractical to users, limiting the effectiveness of transit systems. This paper proposes a behavior trait-aware multimodal trip planning framework that takes into consideration the personal characteristics of each user. In this regard, the proposed system integrates scheduled transit data, street-level network data, and ride-hailing connectivity within a unified network. In addition, user traits such as urgency, crowding, and willingness to walk are considered within the routing process, affecting both perceived travel cost and routing strategy. The problem of routing is modeled as a reinforcement learning problem using a policy-based optimization approach (Proximal Policy Optimization, PPO), in which the agent is aware of its location, the behavioral characteristics of the user, and the available travel options, in addition to receiving feedback based on travel efficiency and preference alignment.
This paper presents a text based frame-work for identifying and analyzing behavioral traits in public transit users using natural language data. Instead of relying on traditional surveys, the approach extracts behavior patterns directly from user generated text, such as reviews or social media posts. Each sample is represented by a hybrid embedding system, which includes meaning for user language and their nuances and behavioral characteristics like urgency, safety sensitivity, and cost awareness is inferred from the system. The representations are clustered and distinct traveler personas are revealed, depending on their approach to time, comfort, safety, and cost. The results show that language based modeling is capable of detecting behavioral differences, nuances and producing relevant traveler profile information without the need for any explicit user data. This demonstrates the potential for behavior analysis to provide a framework for understanding travel behavior at the population level, as well as at the individual level. Furthermore, the results have important implications for the development of behavior based trip planning and personal travel assistance services that respond to the underlying mindsets of users, helping to create more human centric transportation systems.