2022/02/09 by Xinyu Li, Li, Xinyu, Yang Xu +10
Computer Science · Engineering · Health Professions · Social Sciences · #Artificial Intelligence (cs.AI) #Artificial intelligence #Artificial neural network #Benchmark (surveying) #Bike sharing #Cartography #Computer science #Convolutional neural network #Data mining #Deep learning #Dependency (UML) #Engineering #FOS: Computer and information sciences #Geography #Human Mobility and Location-Based Analysis #Kernel (algebra) #Machine learning #Noise Effects and Management #Pooling #Recurrent neural network #Transport engineering #Urban Transport and Accessibility #cs.AI
paper · pdf · doi:10.48550/arxiv.2202.04376
published in arXiv (Cornell University) (Cornell University) · 20 pages with 9 figures
openalex publication_date 2022/02/09 · arxiv created 2022/02/11 · arxiv updated 2022/02/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
As an important task for the management of bike sharing systems, accurate forecast of travel demand could facilitate dispatch and relocation of bicycles to improve user satisfaction. In recent years, many deep learning algorithms have been introduced to improve bicycle usage forecast. A typical practice is to integrate convolutional (CNN) and recurrent neural network (RNN) to capture spatial-temporal dependency in historical travel demand. For typical CNN, the convolution operation is conducted through a kernel that moves across a "matrix-format" city to extract features over spatially adjacent urban areas. This practice assumes that areas close to each other could provide useful information that improves prediction accuracy. However, bicycle usage in neighboring areas might not always be similar, given spatial variations in built environment characteristics and travel behavior that affect cycling activities. Yet, areas that are far apart can be relatively more similar in temporal usage patterns. To utilize the hidden linkage among these distant urban areas, the study proposes an irregular convolutional Long-Short Term Memory model (IrConv+LSTM) to improve short-term bike sharing demand forecast. The model modifies traditional CNN with irregular convolutional architecture to extract dependency among "semantic neighbors". The proposed model is evaluated with a set of benchmark models in five study sites, which include one dockless bike sharing system in Singapore, and four station-based systems in Chicago, Washington, D.C., New York, and London. We find that IrConv+LSTM outperforms other benchmark models in the five cities. The model also achieves superior performance in areas with varying levels of bicycle usage and during peak periods. The findings suggest that "thinking beyond spatial neighbors" can further improve short-term travel demand prediction of urban bike sharing systems.