Abstract: To accurately evaluate the complex spatiotemporal relationship between shared bicycles and public transit, this study identifies the multivariant relationship between shared bicycles and public transit based on the causes of substitution or complementation relationships, combined with the distribution characteristics of shared bicycle trips' origins and destinations. Furthermore, considering the differences between bus and rail transits, this study proposed a classification model for the multivariant relationship between shared bicycles and public transit based on a weekly supervised fully connected neural network and calculated the coverage of public transit, the duration of shared bicycles, and the walking distance for integrated boundary thresholds under different relationship classifications and modes of transit using shared bicycles trajectory data. The results indicate that the multivariant relationship between shared bicycles and public transit can be classified as complementation, integrated complementation, and two types of substitution modes. Specifically, the threshold values for the three parameters between shared bicycles and bus transit are 329.75 m, 5.07 min, and 182.93 m, while for rail transit, the threshold values are 816.96 m, 10.27 min, and 653.91 m. The main relationship between shared bicycles and bus transit is the first substitution mode, accounting for 54.98% of total trips, while the main relationship between shared bicycles and rail transit is complementation, accounting for 48.90% of total trips. The relationship between shared bicycles and bus transit is mainly characterized by multivariant substitution and complementation, while with rail transit, it is mainly characterized by multivariant complementation and the other substitution mode. The relationship between shared bicycles and buses is more mixed compared to rail transit. This study provides support for promoting the coordinated development of shared bicycles and public transit at their respective advantageous distances.

Key words: traffic engineering, multivariant relationship, fully-connected neural network, shared bicycles, public transit

摘要： 为准确评估共享单车和公共交通之间的复杂时空关系，根据替代或补充关系的具体成因类型，结合共享单车出行起讫点的分布情况，定义共享单车与公共交通多元关系；其次，考虑公共汽电车与轨道交通的差异性，提出一种基于弱监督全连接神经网络的共享单车与公共交通多元关系的分类模型，并根据共享单车轨迹数据，计算不同关系分类和交通方式下的公共交通可达范围、共享单车骑行时长及步行接驳距离边界阈值。结果表明：共享单车与公共交通的多元关系可分为接驳补充、空白补充、替代关系1和替代关系2。其中，共享单车与公共汽电车多元关系的三参数划分阈值分别为329.75m、5.07 min和182.93 m，与轨道交通多元关系的划分阈值分别为816.96 m、10.27 min和653.91 m。共享单车与公共汽电车以替代关系1为主，占总行程的54.98%；共享单车与轨道交通以空白补充关系为主，占总行程的48.90%。共享单车与公共汽电车关系主要为多元替代与接驳补充，与轨道交通主要为多元补充与替代关系2。共享单车与公共汽电车的关系相比于轨道交通会更为复杂。本文可为促进共享单车与公共交通在各自优势距离上的协同发展提供支撑。

关键词: 交通工程, 多元关系, 全连接神经网络, 共享单车, 公共交通