A Cluster-Based Trip Prediction Graph Neural Network Model for Bike Sharing Systems

• URL: http://arxiv.org/abs/2201.00720v1
• Date: Mon, 3 Jan 2022 15:47:40 GMT
• Title: A Cluster-Based Trip Prediction Graph Neural Network Model for Bike Sharing Systems
• Authors: B\'arbara Tavares, Cl\'audia Soares, Manuel Marques
• Abstract summary: Bike Sharing Systems (BSSs) are emerging as an innovative transportation service. Ensuring the proper functioning of a BSS is crucial given that these systems are committed to eradicating many of the current global concerns. Good knowledge of users' transition patterns is a decisive contribution to the quality and operability of the service.
• Score: 2.1423963702744597
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Bike Sharing Systems (BSSs) are emerging as an innovative transportation service. Ensuring the proper functioning of a BSS is crucial given that these systems are committed to eradicating many of the current global concerns, by promoting environmental and economic sustainability and contributing to improving the life quality of the population. Good knowledge of users' transition patterns is a decisive contribution to the quality and operability of the service. The analogous and unbalanced users' transition patterns cause these systems to suffer from bicycle imbalance, leading to a drastic customer loss in the long term. Strategies for bicycle rebalancing become important to tackle this problem and for this, bicycle traffic prediction is essential, as it allows to operate more efficiently and to react in advance. In this work, we propose a bicycle trips predictor based on Graph Neural Network embeddings, taking into consideration station groupings, meteorology conditions, geographical distances, and trip patterns. We evaluated our approach in the New York City BSS (CitiBike) data and compared it with four baselines, including the non-clustered approach. To address our problem's specificities, we developed the Adaptive Transition Constraint Clustering Plus (AdaTC+) algorithm, eliminating shortcomings of previous work. Our experiments evidence the clustering pertinence (88% accuracy compared with 83% without clustering) and which clustering technique best suits this problem. Accuracy on the Link Prediction task is always higher for AdaTC+ than benchmark clustering methods when the stations are the same, while not degrading performance when the network is upgraded, in a mismatch with the trained model.

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