Towards Station-Level Demand Prediction for Effective Rebalancing in Bike-Sharing Systems

Bike-sharing systems (BSS) have raised in popularity in the last years due to their potential share in sustainable cities. Although the first attempts to implement a bike-sharing public service date back to 1965 (Amsterdam), their widespread use arrived with the millennium becoming a vibrant research area whose activity has increased steadily in the last decade. Several authors have attempted to summarize the current state of the art, but the literature on BSS is still scattered in different fields. Thus, there is a lack of literature that summarizes and categorizes the available research on BSS. In this paper, we perform a thorough review of the challenges behind rebalancing in bike-sharing systems. The objectives of this paper are to collect papers on the repositioning problem in dock-based bike-sharing services, classify them and point to novel research venues. We render a keyword analysis in the literature and a timeline that shows the evolution of those keywords throughout the last decade. We also include an exhaustive table that will assist researchers from different disciplines to address the open challenges in the field and to transition towards more sustainable cities.

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“…In parallel, Ref. [64] contributed to the solution of the problem improving the prediction of the demand with machine learning techniques.…”

Section: Literature Review Timelinementioning

confidence: 99%

Challenges and Opportunities in Dock-Based Bike-Sharing Rebalancing: A Systematic Review

2021

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“…However, the works above are not applicable for our scenario since they rely on the historical mobility data and therefore are unavailable for batch prediction in newly established stations in expansion areas. Furthermore, they mostly aim to predict demand in a relatively short period from hourly [11,43], rush hours [9], to weekends and holidays [32], and thus cannot be applied to our long-term prediction.…”

Section: Related Workmentioning

confidence: 99%

Inferring Long-Term Demand of Newly Established Stations for Expansion Areas in Bike Sharing System

et al. 2021

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Research on flourishing public bike-sharing systems has been widely discussed in recent years. In these studies, many existing works focus on accurately predicting individual stations in a short time. This work, therefore, aims to predict long-term bike rental/drop-off demands at given bike station locations in the expansion areas. The real-world bike stations are mainly built-in batches for expansion areas. To address the problem, we propose LDA (Long-Term Demand Advisor), a framework to estimate the long-term characteristics of newly established stations. In LDA, several engineering strategies are proposed to extract discriminative and representative features for long-term demands. Moreover, for original and newly established stations, we propose several feature extraction methods and an algorithm to model the correlations between urban dynamics and long-term demands. Our work is the first to address the long-term demand of new stations, providing the government with a tool to pre-evaluate the bike flow of new stations before deployment; this can avoid wasting resources such as personnel expense or budget. We evaluate real-world data from New York City’s bike-sharing system, and show that our LDA framework outperforms baseline approaches.

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“…Although station-level prediction is being challenged, it has attracted the attention of many researchers. In stationlevel hourly demand prediction tasks, some researchers furtherly explore the external context data such as time factors and weather information together with feature engineering and model lightweight processing (Hulot, Aloise, and Jena 2018) for industrial applications. Others have made contribution to temporal modeling by introducing recurrent neural network (Chen et al 2017) for bike-sharing demand prediction.…”

Section: Related Workmentioning

confidence: 99%

Learning Heterogeneous Spatial-Temporal Representation for Bike-Sharing Demand Prediction

Zhu

Kong

et al. 2019

AAAI

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Bike-sharing systems, aiming at meeting the public’s need for ”last mile” transportation, are becoming popular in recent years. With an accurate demand prediction model, shared bikes, though with a limited amount, can be effectively utilized whenever and wherever there are travel demands. Despite that some deep learning methods, especially long shortterm memory neural networks (LSTMs), can improve the performance of traditional demand prediction methods only based on temporal representation, such improvement is limited due to a lack of mining complex spatial-temporal relations. To address this issue, we proposed a novel model named STG2Vec to learn the representation from heterogeneous spatial-temporal graph. Specifically, we developed an event-flow serializing method to encode the evolution of dynamic heterogeneous graph into a special language pattern such as word sequence in a corpus. Furthermore, a dynamic attention-based graph embedding model is introduced to obtain an importance-awareness vectorized representation of the event flow. Additionally, together with other multi-source information such as geographical position, historical transition patterns and weather, e.g., the representation learned by STG2Vec can be fed into the LSTMs for temporal modeling. Experimental results from Citi-Bike electronic usage records dataset in New York City have illustrated that the proposed model can achieve competitive prediction performance compared with its variants and other baseline models.

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Towards Station-Level Demand Prediction for Effective Rebalancing in Bike-Sharing Systems

Cited by 71 publications

References 44 publications

Challenges and Opportunities in Dock-Based Bike-Sharing Rebalancing: A Systematic Review

Challenges and Opportunities in Dock-Based Bike-Sharing Rebalancing: A Systematic Review

Inferring Long-Term Demand of Newly Established Stations for Expansion Areas in Bike Sharing System

Learning Heterogeneous Spatial-Temporal Representation for Bike-Sharing Demand Prediction

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