What question did this study set out to answer?

This research aims to evaluate how the market penetration rate of connected vehicles and roadside equipment failure impact data accuracy in traffic management.

January 22, 2026Open Access

Impact Analysis of the Market Penetration Rate of Connected Vehicles and the Failure Rate of Roadside Equipment on Data Accuracy

Puntos clave

This research aims to evaluate how the market penetration rate of connected vehicles and roadside equipment failure impact data accuracy in traffic management.
Analyzed the effect of connected vehicles and roadside equipment on data accuracy.
Proposed an optimal deployment method using the SAGA algorithm for roadside equipment.
Employed rigid nearest neighbor (RNN) and soft nearest neighbor (SNN) algorithms to address data missingness.
Utilized the BPNN algorithm for fusing data from roadside equipment and connected vehicles.
Data accuracy reaches 95% at 15% market penetration rate and 98% at 60%.
Accuracy decreases with increased sensor failure for single-source data, but not for fused data.
SNN algorithm outperforms RNN in correcting single-source missing data.
Multi-source data fusion enhances accuracy more effectively than single-source data imputation.

Resumen

Data quality, involving the accuracy, completeness and reliability of data, is of great significance for the operation and management of road traffic. As the two significant factors that affect data accuracy, the market penetration rate (MPR) of CVs and the failure rate of roadside equipment (RSE) were considered in the heterogeneity traffic flow comprising human-driven vehicles and CVs. An optimal deployment method solved by SAGA was proposed to optimize the locations of RSE. A rigid nearest neighbor (RNN) algorithm and a soft nearest neighbor (SNN) algorithm were addressed to handle the missing data caused by sensor failure. Additionally, the BPNN algorithm was adopted to fuse RSE data and CV data. Case analysis results show that the proposed optimal deployment method is superior to the uniform and the hotspot methods. Data accuracy can reach 95% and 98% when the MPR is 15% and 60%, respectively. It decreases with the increase in sensor failure rate for single-source data, but not for the fused data. The performance of the SNN algorithm is better than the RNN algorithm in fixing single-source missing data. However, multi-source data fusion, especially with the high-precision data, is much more effective in improving data accuracy than missing data imputation.

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