Optimizing Urban Public Transportation with a Crowding-Aware Multimodal Trip Recommendation System

Traditional multimodal public transportation recommenders often overlook in-vehicle crowding, a critical factor that causes passenger discomfort and leads to an inefficient distribution of people across the network that affects its reliability. To address this, we propose a proof of concept for a novel framework that directly integrates crowding into its optimization process, balancing it with user preferences such as travel habits, travel time, and line changes. Built on the Behavior-Enabled IoT (BeT) paradigm, our system is designed to manage the crucial QoE and QoS trade-off inherent in smart mobility. We validate our balanced strategy using real-world data from Lyon, comparing it against two baselines: a QoE-driven model that prioritizes user habits and a QoS-driven model that focuses solely on network efficiency. Our Wilcoxon-based statistical analysis demonstrates that a balanced strategy is the most effective approach for substantially mitigating public transit crowding. Our Wilcoxon-based statistical analysis demonstrates that a balanced strategy is the most effective approach for mitigating public transit crowding, since it leads to a substantial decrease in crowding. Despite a potential increase in travel times, our solution respects user habits and avoids excessive transfers, providing significant operational improvements without compromising passenger convenience.