Search

Although road surface friction is considered the most effective performance measure for maintenance operations, it is not commonly used due to the high cost of collection. As a result, most jurisdictions use subjective visual indicators that qualitatively describe the state of the road surface, even though they create measurement inconsistencies and offer less detailed maintenance tracking. For maintenance personnel to transition into using friction, the collection cost must be reduced. This paper attempts to do so by proposing a low-cost, machine learning-based method for predicting road surface friction using dash camera imagery and demonstrates its feasibility through a case study. The dataset used for this project was collected in the City of Edmonton, AB, Canada during its 2021/2022 winter season. Three models were developed using tree-based algorithms, where all three displayed high performance with an average root mean squared error of 0.0796 or 79.3% accuracy based on RMSPE. [ABSTRACT FROM AUTHOR]

This study presents an innovative geostatistical approach, namely network regression kriging (NRK), for optimizing location–allocation of traffic counters within a road network. Long-term traffic observations for different road types are utilized by combining several semivariograms to better represent traffic behaviours. This location optimization problem minimizes the NRK estimation variance of traffic volumes via the simulated annealing algorithm. A real-world case study in Edmonton, Alberta, Canada, was used to illustrate the distinct features of our proposed approach. The results suggest that the generated traffic counters' configurations can significantly improve the estimation accuracies. An effort was also made to reduce the number of traffic counters while maintaining the same estimation accuracies, and the result indicated potential equipment savings for up to 46.13%. The findings of this study imply that the proposed approach could be used as a decision-support and performance evaluation tool for transportation authorities to deploy traffic counters and assess alternative deployment strategies. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Earth Sciences is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)