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A road data assets revenue allocation model based on a modified Shapley value approach considering contribution
Shiwei Li1,2, Lei Chu3, Jisen Wang3
1School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou, 730070, China. liswyunshu@126.com.
This study introduces a novel two-layer revenue allocation model for road data assets, improving fairness by considering data risks and diverse participant contributions. The model ensures a more equitable distribution of revenue in the evolving data economy.
Area of Science:
- Transportation Science
- Data Economics
- Information Systems
Background:
- Road data assets are increasingly valuable, necessitating fair revenue allocation mechanisms.
- Traditional revenue distribution models often fail to account for diverse contributions and risks.
- Effective benefit distribution is crucial for promoting the market-based utilization of road data.
Purpose of the Study:
- To develop a two-layer revenue allocation model for road data assets using a modified Shapley value approach.
- To incorporate data risk factors and qualitative/quantitative contributions for fairer revenue distribution.
- To provide a robust methodology for the road transportation industry's data assetization.
Main Methods:
- Constructed a two-layer model: role-based allocation (considering data risks) and participant-level correction factors.
- Employed entropy weighting and rough set theory for determining weight coefficients.
- Utilized fuzzy comprehensive evaluation and numerical analysis to assess participant contributions.
Main Results:
- The model allocates revenue to data collectors, processors, and producers, adjusting for data risks.
- It refines revenue distribution within roles based on individual contributions.
- A consolidated revenue distribution for each participant is synthesized, ensuring fairness.
Conclusions:
- The modified Shapley value model offers a fairer and more reasonable revenue distribution for road data assets.
- It addresses limitations of traditional methods by evaluating both qualitative and quantitative contributions.
- This research provides a valuable reference for data assetization and benefit distribution in the transportation sector.
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