Creating Value from Data Resources: Research on a Service Ecosystem Symbiosis Model of Digital Transportation Infrastructure

With the increasing prevalence of digital upgrades in transportation infrastructure, such projects often encounter challenges of underperformance and substantial losses. The digital upgrading of transportation infrastructure serves as a rich data source for comprehending and addressing issues related to service system evolution. Building upon this foundation, this study identifies symbiotic units within the service ecosystem and analyzes the characteristics of various symbiotic models. The Lotka-Volterra model is one of the most valuable tools for simulating ecosystem development. However, its broader application in service ecosystem management faces two primary obstacles: (1) susceptibility to subjective bias; and (2) inadequate actual data on the service ecosystem evolution. This study aims to develop a new three-population Lotka-Volterra model to predict the evolution of the digital transportation infrastructure services ecosystem, thereby enhancing the model’s effectiveness and integrating actual data into its application. First, this study identifies the internal composition of the digital transportation infrastructure service ecosystem. Then, based on the empirical data of each symbiotic unit, model parameters are calculated using the grey estimation method, and the model’s applicability is verified using the technique of relative error heat map. Last, a service ecosystem prediction method based on the Lotka-Volterra model is established to predict and quantify the evolution characteristics of the service ecosystem. An actual transportation infrastructure project is selected as a case study to validate this development approach. The results show that this method is reliable, flexible, and more suitable for evolving a digital transportation infrastructure service ecosystem. This study provides a new idea for the digital management of transportation infrastructure and a new way to quantify and forecast service ecosystem evolution.