Best practices in road use data collection, analysis and reporting
J Luk, C Karl, T. Martin
Abstract
J Luk, C Karl, T. Martin
Abstract
This report describes the current practices of road authorities (RAs) in Road use data collection, analysis and reporting, and uses examples to Demonstrate best practices. It cover topics such as: error bin (bin 13) in the Austroads vehicle classification system, vehicle classification by lengths, defining a homogeneous traffic section, correlation of classified counts with WIM data, public-private partnership, quality checks, dealing with missing data, calibration of a WIM system using CULWAY as an example, and a stakeholder consultation model. A key finding is that RAs have been following refining their counting programs and in many ways have achieved good practices, and that the best way to ensure consistency amongst RAs is for each road authority to collect robust data sets. (a)
A significance statement is not available in the OpenAlex record.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
This report describes the current practices of road authorities (RAs) in Road use data collection, analysis and reporting, and uses examples to Demonstrate best practices. It cover topics such as: error bin (bin 13) in the Austroads vehicle classification system, vehicle classification by lengths, defining a homogeneous traffic section, correlation of classified counts with WIM data, public-private partnership, quality checks, dealing with missing data, calibration of a WIM system using CULWAY as an example, and a stakeholder consultation model. A key finding is that RAs have been following refining their counting programs and in many ways have achieved good practices, and that the best way to ensure consistency amongst RAs is for each road authority to collect robust data sets. (a)
Key concepts: Best practice, Data collection, Stakeholder, Consistency (knowledge bases), Computer science, Data quality, Data science, Transport engineering