A Bimodal Gaussian Inhomogeneous Poisson Algorithm for Bike Number Prediction in a Bike-Sharing System

Abstract

Due to the rapid development of the sharing economy, shared bikes have become one of the most popular and convenient traveling tools in intelligent transport systems. Aiming to save the time spent on waiting for or searching bikes at bike stations, the operators of bike-sharing systems need to dynamically dispatch bikes. Predicting the number of bikes for each station can help to optimize the repository of bikes. The usage of bikes is affected by several uncertain factors, so bike number prediction becomes a challenging and difficult problem. To manage this problem, we propose an algorithm called bimodal Gaussian inhomogeneous Poisson (BGIP) to predict the number of bikes. The BGIP includes three steps. First, the inhomogeneous Poisson process is adopted to describe the process that people arrive at a bike station to pick up or return bikes. Second, the bimodal Gaussian function is used to describe the intensity function of inhomogeneous Poisson process. In order to dynamically uncover the changing trend in the usage state of bikes, we propose a method to measure the influences of external factors on the usage of bikes. Third, the number of bikes is predicted by calculating the mean usage of bikes on the basis of checking-out and checking-in sequences. Experiments demonstrated that our algorithm outperformed the baseline algorithms in solving the bike prediction problem: accurately predicting the number of bikes and determining whether there is at least one bike available at a bike station.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 38 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.