A cooperative optimization method for the layout of shared bicycle parking areas and delivery quantity

This study proposes an hourly demand prediction method based on a multi-feature edge-weighted gated graph convolutional network to address the imbalance in station borrowing and returning demands, as well as low station utilization in bike-sharing systems. By employing graph convolutional neural networks to capture spatial relationships between stations and utilizing gating mechanisms to integrate current and historical information, it captures the long-term dependency of time series data. Creatively, it combines three single edge-weight features—station distance, time, and correlation value—into a multi-feature edge-weighted input model graph structure, enhancing the accuracy in reflecting traveler behavior characteristics. Additionally, the study considers not only temporal and spatial factors but also incorporates traveler features as node inputs to the model. Using bike-sharing trip data from Jersey City in 2020, the study employs isolation forest algorithm for outlier detection, followed by feature dependency analysis to reveal the impact of time, space, and traveler features on demand. Moreover, it accounts for the seasonal influence on bike-sharing trips by dividing the dataset into different seasons and conducting unified research on similar seasons. Results demonstrate that the multi-feature edge-weighted gated graph convolutional neural network achieves an MAE of 0.52 and MSE of 0.906 for the spring and autumn seasons, and an MAE of 0.296 and MSE of 0.594 for the summer and winter seasons, outperforming baseline models and single-feature edge-weighted predictive performance.

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Encouraging Residents to Save Energy by Using Smart Transportation: Incorporating the Propensity to Save Energy into the UTAUT Model

Gajdzik,

Awdziej,

Jaciow

et al. 2024

Energies

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The rapid urbanization and technological advancements of the recent decades have increased the need for efficient and sustainable transportation solutions. This study examines the acceptance of smart transportation systems (STSs) among residents in Polish cities and explores the impact of these systems on energy-saving behaviors. Using the extended Unified Theory of Acceptance and Use of Technology (UTAUT2) model, which includes the propensity to save energy, this research seeks to understand the determinants of STS adoption. The primary research was conducted using Computer-Assisted Web Interviewing (CAWI). The sample was controlled for gender and place of residence. A sample of 471 individuals meeting the criteria of living in a city with over 200,000 residents and using smart transportation solutions in Poland were selected from the research panel. SmartPLS 4 software was used to analyze the collected data. The findings reveal that the propensity to save energy significantly influences perceived usefulness, ease of use, social influence, and hedonic motivation toward STSs. Perceived usefulness and ease of use were found to be strong predictors of the intention to use STSs, while perceived costs had a negative impact on it. This study also identified the moderating role of personal innovativeness in mitigating cost concerns. These insights underscore the importance of emphasizing energy conservation benefits and user-friendly features in promoting the use of STSs. This study concludes that aligning technological innovations with user motivations for energy conservation can enhance the adoption of sustainable transportation solutions, contributing to smarter and more sustainable urban environments.

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A cooperative optimization method for the layout of shared bicycle parking areas and delivery quantity

Cited by 3 publications

References 15 publications

U-Park: A User-Centric Smart Parking Recommendation System for Electric Shared Micromobility Services

U-Park: A User-Centric Smart Parking Recommendation System for Electric Shared Micromobility Services

Research on Shared Bicycle Prediction Using Gated Graph Convolutional Networks with Multi-Feature Edge Weights

Encouraging Residents to Save Energy by Using Smart Transportation: Incorporating the Propensity to Save Energy into the UTAUT Model

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