# A Trip Purpose Inference Method Considering the Origin and Destination of Shared Bicycles
###### Abstract  
This study advances the inference of travel purposes for dockless bike-sharing users by integrating dockless bike-sharing and point of interest (POI) data, thereby enhancing traditional models. The methodology involves cleansing dockless bike-sharing datasets, identifying destination areas via users' walking radii from their start and end points, and categorizing POI data to establish a correlation between trip purposes and POI types. The innovative GMOD model (gravity model considering origin and destination) is developed by modifying the basic gravity model parameters with the distribution of POI types and travel time. This refined approach significantly improves the accuracy of predicting travel purposes, surpassing standard gravity models. Particularly effective in identifying less frequent but critical purposes such as transfers, medical visits, and educational trips, the GMOD model demonstrates substantial improvements in these areas. The model's efficacy in sample data tests highlights its potential as a valuable tool for urban transport analysis and in conducting comprehensive trip surveys. d 2020  
network science literature, extracting travel patterns from smart card data across different public transport systems to construct models. Scholars have examined various factors influencing the frequency of bike-sharing usage, including socio-economic [6], environmental [7], and policy aspects [8], and have developed corresponding mathematical models to analyze the role of bike-sharing in urban public transport systems and its impact. Notably, multi-source big data technology was employed to analyze the connective function of shared bicycles around rail transit stations [9]. Geographically Weighted Regression (GWR) models [10; 11] and other common mathematical models have also been utilized [12]. With the introduction of bike-sharing in China in 2016, which rapidly spread domestically and internationally due to its advantages of barrier-free access, convenient parking, and easy payment, the inherent travel activity information in bike-sharing data has also been utilized in research. [PERSON] [13] and colleagues predicted the spatio-temporal demand for bike-sharing using the AM-LSTM model, providing effective information for the scheduling and distribution of urban bike-sharing systems. [PERSON] [14] and colleagues first proposed a stochastic mixed-integer programming method, establishing an optimization model considering demand, supply, vehicle scheduling, and station capacity constraints, and designed an effective reallocation algorithm for cases where demand exceeds supply. [PERSON] [15] and colleagues proposed a branch-and-cut algorithm for NP-hard problems in optimization models, demonstrating its effectiveness and efficiency in bicycle system optimization through practical cases. [PERSON] [16] and colleagues introduced convex objective functions to propose a static bike-sharing scheduling model and two mixed-integer linear programming formulas, comparing their performance through extensive numerical studies. [PERSON] [17] considered the night-time demand rebalancing of bike-sharing and proposed an improved SBRP mathematical formula, effectively solving the issue of manual intervention required due to low night-time demand causing some stations to be overloaded or vacant. [PERSON] [18] and colleagues introduced the geodetector model, building a system of natural and built environment impact factors, and precisely detected the influencing factors of bike-sharing riding destinations every hour, exploring the temporal differences in the factors influencing bike-sharing rides. [PERSON] [19] proposed a method using bike-sharing data and point of interest (POI) information to infer travel purposes. By analyzing station types and their relationships, different travel purposes and probabilities were determined. [PERSON] [20] and colleagues used the Herfindahl-Hirschman Index (HHI) to confirm the similarity between bike-sharing riding times and residents' travel patterns.