In an article recently published in the journal Scientific Reports, researchers proposed a two-layer road data asset revenue allocation model based on a modified Shapley value method considering contribution evaluation for fair and reasonable road data asset revenue allocation.
Study: Effective Road Data Asset Revenue Allocation Model for Enhanced Market Utilization. Image credit: Triff/Shutterstock
Importance of road data assets
A substantial amount of road data is generated by road transport during its operation. The data assets obtained after processing and sorting the generated data can offer support for vehicle route optimization, road network emergency response, and optimal road network design to ensure smart road construction comprehensively.
Thus, accelerating the trading and circulation of road data assets is crucial for effectively commercializing data value. However, the road transport industry faces several distinct asset evaluation challenges compared to other industries due to the fragmentation of road transport data across multiple proprietary silos in the value chain and the limited computational capabilities of road transport.
Thus, effectively addressing data access and analytics challenges is crucial to realize the enormous value of road transport data. However, significant differences in technologies and resources possessed by various road transport companies typically lead to mismatches between data processing capabilities and data ownership, highlighting the need for cross-enterprise collaboration.
Collaborative enterprises can be categorized into three roles from the data value chain perspective, including data product producers, data processors, and original data collectors. The diversity, complexity, and scope of participants and road data necessitate the development of reasonable and fair revenue distribution mechanisms. However, few studies have been conducted on the revenue allocation mechanism of data assets, with no studies being performed on the road data assets’ distribution mechanism.
The proposed approach
In this study, researchers constructed a two-layer road data asset revenue allocation model based on the modified Shapley value approach to achieve a fair and reasonable distribution of road data assets revenue. Initially, the model categorizes participating companies into data collectors, processors, and product producers, and then, a revenue allocation evaluation index system is established based on the characteristics of different roles.
The first layer allocates revenue among the three roles in the data value realization process. This layer appropriately adjusts and fully considers the revenue allocation to every role based on data risk factors/indicators. The second layer determines the correction factors for various roles for revenue distribution among the participants within those roles.
Specifically, the model at the second layer redistributes the adjusted revenues to participating companies under each role while designing specific evaluation indicators for each role to modify the initial revenue allocation for each company. Eventually, the revenue values/profits of the participants/participating companies under each role are synthesized to obtain a consolidated revenue distribution/the final profit allocation for each participant/company. Thus, this two-layer approach, coupled with the Shapley value with modifications, can realize an effective and fair distribution of road data asset profits/revenues.
Unlike the conventional Shapley value method, this road data asset revenue allocation model utilized rough set theory and entropy weighting to determine the weights, adopted numerical analysis and a fuzzy comprehensive evaluation to evaluate the participants' degree of contribution and established a revenue allocation evaluation index system. The model fully accounted for the differences in the quantitative and qualitative contributions of participants to enable a more reasonable and fairer distribution of revenues.
Original data collectors obtain revenues by collecting original road data, which are generated from the activities of the enterprises in road construction and operation management, while data processors obtain revenues by processing the lawfully acquired original road data using methods such as mining, integration, cleansing, and standardization. Data processing requires the construction of road data warehouses, establishing analytical models, and the identification of correlations in the data to derive value from the data.
The original road data cannot be directly utilized for decision support and knowledge discovery as it has a large volume but low-value density, necessitating improved data quality. Data product producers obtain revenues by developing, marketing, and maintaining data products with practical value based on processed datasets. The key data product formats are API interfaces, analytical reports, and packages. These products require constant maintenance and development by data product operators.
Significance of this study
Researchers evaluated the proposed road data asset revenue allocation model's effectiveness through a case study of road data assets. They assumed that a road data asset sale obtains total proceeds of 960,000 RMB, which must be allocated to five enterprises involved in data processing, collection, and production. Results demonstrated that the model could effectively address revenue allocation issues among different roles in the road data asset value chain to realize a reasonable and fair allocation.
To summarize, the study displayed that the proposed road data asset revenue allocation model could ensure effective road data asset revenue allocation, promoting the market-based use of road data assets.
Journal reference:
- Li, S., Chu, L., Wang, J., Zhang, Y. (2024). A road data assets revenue allocation model based on a modified Shapley value approach considering contribution evaluation. Scientific Reports, 14(1), 1-24. https://doi.org/10.1038/s41598-024-55819-7, https://www.nature.com/articles/s41598-024-55819-7