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

Abstract

Highlights: What are the main findings? A proof of concept for a multimodal public transportation recommendation system that explicitly integrates both QoS (e.g., crowding) and QoE (e.g., user preferences) for overcoming the limitations of traditional approaches in balancing load distribution of the transportation system. The BeT paradigm adoption to provide the architectural foundation for designing a framework to model and validate this balance effectively. What is the implication of the main finding? The QoE-QoS balanced strategy enables transportation systems to reduce infrastructure stress while maintaining passenger satisfaction, demonstrating that crowd-aware recommendations can enhance both efficiency and user experience. 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.