Presented in this paper is a comparative analysis of various Data Mining clustering methods for the grouping of roads, aimed at the estimation of Annual Average Daily Traffic (AADT). The analysis was carried out using data available from fifty-four Automatic Traffic Recorder (ATR) sites in the Province of Venice (Italy) and separated adjustment factors for passenger and truck vehicles in the grouping process. Errors in AADT estimation from 24-h sample counts indicate that model-based clustering methods give slightly better results compared to other tested methods, identifying a significant ATRs classification.

Data Mining methods for Traffic monitoring data analysis: A case study

GECCHELE, GREGORIO;ROSSI, RICCARDO;GASTALDI, MASSIMILIANO;
2011

Abstract

Presented in this paper is a comparative analysis of various Data Mining clustering methods for the grouping of roads, aimed at the estimation of Annual Average Daily Traffic (AADT). The analysis was carried out using data available from fifty-four Automatic Traffic Recorder (ATR) sites in the Province of Venice (Italy) and separated adjustment factors for passenger and truck vehicles in the grouping process. Errors in AADT estimation from 24-h sample counts indicate that model-based clustering methods give slightly better results compared to other tested methods, identifying a significant ATRs classification.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/179326
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