Developing a Strategy for Integrating Network Datasets for Supporting Multimodal Transportation

As urban areas have been undergoing accelerated development, there has also been increasing interest in solving problems in mobility. Experts have identified multimodal transportation as a key concept in addressing this problem by providing transportation options. In providing LBS (Location-based Services), spatial datasets, particularly network data, are essential in representing the navigable spaces for each mode of transportation. However, previous studies on spatial data modeling of multimodal transportation have omitted the spaces that exist between the spaces represented by such network data. Additionally, the integration of these datasets for providing such services is still faced with numerous problems, such as lack of specification, the variety in data formats, potential difficulties in data conversion, or computational burdens due to reliance on geometric properties. This paper proposes a method for integrating network datasets to represent the connectivity of various navigable spaces for supporting multimodal transportation based on topological relationships, described in a spatial data model expressed in UML (Unified Modeling Language). Moreover, using sample data, we demonstrate the model's potential for representing seamless travel across transportation modes in a routing experiment. This paper presents a topological relationship-based method to integrate existing network data representing multimodal transportation despite the differences in data format or standard.