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

TL;DR

This paper introduces BikeMAN, a multi-level spatio-temporal attention neural network that accurately predicts bike station-level traffic across entire bike sharing systems, addressing spatial-temporal complexity and large-scale challenges.

Contribution

The paper presents a novel neural network model with multi-level attention mechanisms for precise bike traffic prediction at station level across large urban systems.

Findings

High prediction accuracy demonstrated on NYC bike sharing data

Effective modeling of spatial and temporal dependencies

Scalable approach for large bike sharing networks

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.