Exploring Bus Stop Mobility Pattern With Temporal Variation: A Multiperiod Prediction Framework

Abstract

Passenger demand prediction is essential for intelligent transportation systems (ITSs) and the Internet of Vehicles (IoV), supporting efficient transit management, network bandwidth allocation, onboard content distribution, and edge computing scheduling. With buses increasingly equipped with wireless connectivity, passengers frequently access onboard networks, generating traffic patterns closely tied to their mobility behavior—especially during midday and evening peaks, when spatiotemporal movement and communication demand in vehicular social networks surge. While this data-rich environment provides unprecedented opportunities for network optimization, the inherent complexity of passenger mobility makes high-fidelity modeling a nontrivial task. Specifically, passenger flow at bus stops exhibits both macrolevel spatial clustering and microlevel periodic temporal variations, which existing studies seldom model within a unified framework. To address this gap, we proposed MPGNet, a multipattern and multiperiod transit demand prediction framework. MPGNet leverages deep clustering on stop distance networks and stop-specific attributes to extract IoV-relevant mobility patterns, capturing regional group-level travel dynamics. It then applies temporal variation modeling to project 1-D flow sequences into a 2-D structural space and integrates a spatiotemporal module (STBlock) to learn coupled periodic dependencies across stops. Extensive experiments on a real-world bus dataset demonstrate that MPGNet achieves strong performance in both short- and long-term forecasting, underscoring its practical value for IoV-based network traffic management and its potential to enhance the integration of IoV technologies with intelligent public transportation systems. The code is available at https://github.com/switchsky/MPGNet