sebastianruehmann/available-citybikes

Exploring Bike-Sharing Systems: An Interpretable Temporal Fusion Transformer Model to Analyzing Influential Factors

A case-study of Bike-sharing in Hamburg as explorative data-analysis and machine learning experiment

Abstract

Bike-sharing systems (BSS) have emerged as an increasingly important form of transportation in smart cities, playing a pivotal role in the evolving landscape of urban mobility. As cities world-wide strive to promote sustainable and efficient transportation options, BSS offer a flexible, eco-friendly alternative that complements traditional public transport systems. These systems, however, are complex and influenced by a myriad of endogenous and exogenous factors. This complexity poses challenges in predicting BSS activity and optimizing its usage and effectiveness. This study delves into the dynamics of the BSS in Hamburg, Germany, focusing on system stabil-ity and activity prediction. We propose an interpretable attention-based Temporal Fusion Trans-former (TFT) model and compare its performance with the state-of-the-art Long Short-Term Memory (LSTM) model. The proposed TFT model outperforms the LSTM model with a 36.8% improvement in RSME and overcomes current black-box models through interpretability. Through detailed analysis, key factors influencing bike-sharing activity, especially in terms of temporal and spatial contexts, are identified, examined, and evaluated. Based on the results, we propose interventions and a developed TFT model that can improve the effectiveness of BSS. This research contributes to the evolving field of sustainable urban mobility through data analysis for data-informed decision-making.

License

Copyright © 2023 by the authors.

Creative Commons Attribution 4.0 International License.