Table 2.

Description of evaluation indicators for road data asset revenue allocation.

Layer	Indicator	Indicator definitions	Indicator value
Evaluation of role contribution (the first layer)	External risks	The external environment constraints and influences on road data collection and use reflect the role's contribution in identifying, assessing, and responding to external risks	Subjective indicator: determined through fuzzy comprehensive evaluation method
	Policy risks	It requires close attention to changes and adjustments in relevant policies to promptly take corresponding measures for risk management and response	The value is taken from expert evaluation scores
	Legal risks	It requires compliance with relevant laws, regulations, and policy requirements to ensure the legality, security, and compliance of data
	Internal risks	It is usually caused by inadequacies in aspects such as management, technology, and decision-making, reflecting the role's contribution in managing and controlling internal risks	Subjective indicator: determined through fuzzy comprehensive evaluation method
	Equipment failure	It requires measures such as regular equipment inspection and maintenance, establishing comprehensive data backup and recovery mechanisms, etc. to reduce the likelihood of equipment failure	The values are taken from expert evaluation scores
	Data security	It requires measures such as establishing network security protection systems, strengthening access control and identity authentication mechanisms, etc. to enhance data security
Evaluation of participant contribution under the role of the original data collectors (the second layer)	Construction cost	It reflects the cost contribution of the data collection participant, which needs to be estimated based on the specific situation	Objective indicator: determined through accounting cost calculation
	Data planning	The costs spent on market research and requirements analysis before data collection, to determine specific needs and functional requirements	The values are in monetary amounts
	Data collection	The costs incurred for purchasing and setting up the collection equipment and associated supporting facilities
	Data storage	The costs sustained to establish appropriate data management and storage systems to effectively manage and store the collected data
	Data demand	It describes the importance and difficulty of obtaining data to reflect the level of supply and demand of data in the market	Subjective indicator: determined through fuzzy comprehensive evaluation method
	Demand level	It describes the importance and urgency of data requirements	The values are taken from expert evaluation scores
	Scarcity level	It describes the availability and supply level of data
	Data characteristics	The quantitative information that describes the characteristics and attributes of a dataset reflects the value of the dataset itself	Objective indicators: determined through numerical analysis
	Data coverage	It delineates the scope of highway data collection, encompassing temporal and spatial coverage parameters	The values are time span and geographical scopes
	Data timeliness	It depicts the freshness of the data, as timely updated highway data can furnish accurate, real-time traffic information	The value is update frequency
Evaluation of participant contribution under the role of the data processors (the second layer)	Data cleansing	The objective is to ensure the quality and accuracy of data, which is a fundamental task in data processing work	Objective indicators: determined through numerical analysis
	Data volume	It describes the workload of data processing	The value is the number of data records
	Data quality	It is measured in terms of the degree of improvement in the completeness, validity, and consistency of the data compared to the original data	The value is the percentage of total data that meets the criteria
	Data analysis	It discovers correlations, trends, patterns, etc. in the data to provide understanding and decision support for the problem	Subjective indicator: determined through fuzzy comprehensive evaluation method
	Analysis methods	Techniques used to understand and interpret data, and appropriate analytical methods facilitate the discovery of potential information in the data	The value is taken from expert evaluation scores
	Analysis utility	It measures the practical value of data analysis results in decision-making and problem-solving by evaluating the effectiveness of providing insights that guide decision-making and improve performance
Evaluation of participant contribution under the role of the data product producers (the second layer)	Product development	Transforming road data into practical tools and solutions is the primary task for data product producers	Objective indicators: determined through numerical analysis
	Workload	It measures the time and resources expended on product development	The value is the number of valid codes
	Difficulty coefficient	It measures the difficulty and complexity of product development	The value is 0–1 based on compared with the evaluation criteria
	Product maintenance	During the product operation stage, support, bug fixing, and improvements are carried out to ensure the continuous and reliable operation of the product and adapt to user needs and market changes	Objective indicators: determined through numerical analysis
	Stability	The running status of the product needs to be monitored, and any faults or errors that occur need to be addressed and fixed promptly	The value is the average number of stable days per month for the product
	Update frequency	As market demands are updated and change, there is a need for continuous improvements to the product's functions and features to provide greater value and user experience	The value is the average interval time of product innovation