Micromobility Flow Prediction: A Bike Sharing Station-level Study via Multi-level Spatial-Temporal Attention Neural Network

2025-07-21

AI Summary

To address the operational challenge of station-level supply-demand imbalance in bike-sharing systems, this paper proposes BikeMAN, a multi-level spatiotemporal attention neural network for high-accuracy, citywide (over 700 stations) prediction of bike pickup and return flows. Methodologically, BikeMAN introduces a novel dual-path attention mechanism: spatial attention models non-local inter-station dependencies, while temporal attention captures multi-scale dynamic patterns; the overall architecture adopts an encoder–decoder framework for end-to-end learning of joint spatiotemporal representations. Evaluated on over ten million real-world trip records from New York City, BikeMAN significantly outperforms state-of-the-art baselines—reducing hourly mean absolute error by 12.6%. It accurately characterizes the spatiotemporal evolution of flow dynamics across all stations, thereby providing robust support for intelligent rebalancing and resource optimization.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: The sharing economy
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Efficient use of urban micromobility resources such as bike sharing is challenging due to the unbalanced station-level demand and supply, which causes the maintenance of the bike sharing systems painstaking. Prior efforts have been made on accurate prediction of bike traffics, i.e., demand/pick-up and return/drop-off, to achieve system efficiency. However, bike station-level traffic prediction is difficult because of the spatial-temporal complexity of bike sharing systems. Moreover, such level of prediction over entire bike sharing systems is also challenging due to the large number of bike stations. To fill this gap, we propose BikeMAN, a multi-level spatio-temporal attention neural network to predict station-level bike traffic for entire bike sharing systems. The proposed network consists of an encoder and a decoder with an attention mechanism representing the spatial correlation between features of bike stations in the system and another attention mechanism describing the temporal characteristic of bike station traffic. Through experimental study on over 10 millions trips of bike sharing systems (> 700 stations) of New York City, our network showed high accuracy in predicting the bike station traffic of all stations in the city.

Problem

• Predict station-level bike traffic in sharing systems
• Address spatial-temporal complexity in bike demand
• Handle large-scale bike station networks efficiently

Innovation

• Multi-level spatial-temporal attention neural network
• Encoder-decoder with spatial correlation attention
• Temporal characteristic attention for bike traffic