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Vehicle Operating, Accident and User Time Costs in pavement management systems： Approach for Portuguese Conditions

Journal of Civil Engineering and Architecture 2011年第8期5卷 723-731页

作者： Bertha Maria Batista dos Santos Luis Guilherme de Picado Santos Victor Manuel Pissarra Cavaleiro Department of Civil Engineering and Architecture University of Beira Interior Covilha 6200-001 Portugal Department of Civil Engineering and Architecture IST Technical University of Lisbon Lisbon 1049-001 Portugal

This paper presents a description and analysis of the most important models to predict each of the Road User Costs components （Vehicle Operating Costs, Accident Costs and Value of Time） and proposes a model for estimating RUC components suitable for the Portuguese road network. These results are part of a research which aimed to obtain a Road User Cost Model to be used as a tool in road management systems. This model is different from other models by the fact that it includes a simple formulation that allows calibration and calculation of cost parameters, for any year, in a simple and fast way, providing trustworthy results. The required data is already available in Portuguese institutions, allowing periodic revision of cost parameters to insure accuracy.

关键词： Costs pavement management systems time traffic accidents vehicle operations.

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CPSO-XGBoost segmented regression model for asphalt pavement deflection basin area prediction

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Science China(Technological Sciences) 2022年第7期65卷 1470-1481页

作者： LI ZhuoXuan SHI XinLi CAO JinDe WANG XuDong HUANG Wei School of Mathematics Southeast UniversityNanjing 210096China School of Cyber Science&Engineering Southeast UniversityNanjing 210096China Yonsei Frontier Lab Yonsei UniversitySeoul 120-749Korea Research Institute of Highway Ministry of TransportBeijing 100088China Intelligent Transportation System Research Center Southeast UniversityNanjing 210096China

The use of non-destructive testing(NDT) equipment, such as the falling weight deflectometer(FWD), provides important estimates of road health and helps to optimize road management regimes. However, periodic road testing and post-processing of the collected data are cumbersome and require much expertise, a considerable amount of time, money, and other resources. This study attempts to develop a reliable prediction method for estimating the deflection basin area of different asphalt pavements using road temperature, load time, and load pressure as main characteristics. The data are obtained from 19 kinds of asphalt pavements on a 2.038-km-long full-scale fleld accelerated pavement testing track named RIOHTrack(Research Institute of Highway Track) in Tongzhou, Beijing. In addition, a chaotic particle swarm algorithm(CPSO) and a segmented regression strategy are proposed in this paper to optimize the XGBoost model. The experiment results of the proposed method are compared with those of classical machine learning algorithms and achieve an average of mean square error and mean absolute error respectively by 5.80 and *** experiments demonstrate the superiority of the XGBoost algorithm over classical machine learning methods in dealing with nonlinear problems in road engineering. Signiflcantly, the method can reduce the frequency of deflection tests without affecting its estimation accuracy, which is a promising alternative way to facilitate the rapid assessment of pavement conditions.

关键词： FWD pavement management systems RIOHTrack deflection basin area CPSO XGBoost segmented regression model

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