In recent years, Artificial-Computational Intelligence (ACI) have found increasing applications in management of transportation infrastructures. Examples of ACI applications can be found in highway management however, compared to transportation planning, research of ACI methods applied to infrastructure management has been relatively limited. In this study was used artificial intelligence ANN (Artificial Neural Network). In particular the objective of the research study is to compare the predicted operating speed on tangents and circular curves for low-volume roads by using two different statistical approaches. The starting point was to predict the operating speed on investigated tangents and circular curves elements by using four regression equations developed using a traditional ordinary–least-squares method (OLS) as shown in a previous work of the authors. Then, the same database was used to calibrate new operating speed models by using ANN procedure. The results have shown that ANN models offer more reliable results in terms of predicted operating speed than those returned by OLS method on all circular curves and on tangents lengths greater than 500m. For tangents length less than 500 m, OLS method is to be preferred to ANN procedure.

Modeling Operating Speed Using Artificial Computational Intelligence (ACI) on Low-Volume Roads / DE LUCA, Mario; Russo, Francesca; Olja, Cokorilo; Dell'Acqua, Gianluca. - (2014), pp. 1-14. (Intervento presentato al convegno Transportation Research Board 93rd Annual Meeting tenutosi a Washington, DC nel 2014-1-12 to 2014-1-16).

Modeling Operating Speed Using Artificial Computational Intelligence (ACI) on Low-Volume Roads

DE LUCA, MARIO;RUSSO, FRANCESCA;DELL'ACQUA, GIANLUCA
2014

Abstract

In recent years, Artificial-Computational Intelligence (ACI) have found increasing applications in management of transportation infrastructures. Examples of ACI applications can be found in highway management however, compared to transportation planning, research of ACI methods applied to infrastructure management has been relatively limited. In this study was used artificial intelligence ANN (Artificial Neural Network). In particular the objective of the research study is to compare the predicted operating speed on tangents and circular curves for low-volume roads by using two different statistical approaches. The starting point was to predict the operating speed on investigated tangents and circular curves elements by using four regression equations developed using a traditional ordinary–least-squares method (OLS) as shown in a previous work of the authors. Then, the same database was used to calibrate new operating speed models by using ANN procedure. The results have shown that ANN models offer more reliable results in terms of predicted operating speed than those returned by OLS method on all circular curves and on tangents lengths greater than 500m. For tangents length less than 500 m, OLS method is to be preferred to ANN procedure.
2014
Modeling Operating Speed Using Artificial Computational Intelligence (ACI) on Low-Volume Roads / DE LUCA, Mario; Russo, Francesca; Olja, Cokorilo; Dell'Acqua, Gianluca. - (2014), pp. 1-14. (Intervento presentato al convegno Transportation Research Board 93rd Annual Meeting tenutosi a Washington, DC nel 2014-1-12 to 2014-1-16).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/581018
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