Scalable Passenger Detection Using Smartphone-Bus Implicit Interactions

Intelligent transportation systems (ITSs) are important for mobility as a service, enabling seamless access across various transport networks and fair revenue sharing. However, current user sensing technologies like walk in/walk out (WIWO) and check in/check out (CICO) face scalability issues. WIWO and CICO depend on fixed infrastructure to cover large dynamic passenger environments, making their large-scale deployment challenging and expensive. These limitations hinder effective analysis, optimization, and revenue sharing in ITSs. To address these issues, we build on the concept of implicit be-in/be-out (BIBO) smartphone sensing and classification, introducing a platform that collects Bluetooth Low Energy (BLE) signals from devices on buses and GPS data from both buses and smartphones. We propose a cause–effect multitask Wasserstein autoencoder (CEMWA) architecture to train a model using GPS features and BLE signals as mutual pseudolabels. CEMWA integrates various frameworks around Wasserstein autoencoders and neural networks, providing a validated latent space representation of users’ smartphones within the transport system. This representation facilitates BIBO clustering via densitybased spatial clustering of applications with noise. Our comparative study of CEMWA’s architecture and benchmarking against best-in-class supervised methods reveals that, while Extreme Gradient Boosting and the random forest are robust to label noise, CEMWA’s design inherently handles label noise, achieving the best performance, with an 88% F1 score in a BIBO scenario.