为柔性路面开发基于机制-经验的公路成本分配模型

Seyed Farhad Abdollahi, Poornachandra Vaddy, M. Emin Kutay
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摘要

人行道网络的建设、运营和维护需要资金,部分资金来自道路使用税。最近的研究表明,与对道路损害最大的重型卡车相比,轻型车辆的税率通常较高。为了促进美国不同车辆成本分配的公平性和公正性,使用各种路面性能预测模型进行了公路成本分配研究(HCAS)。查阅文献后发现,目前还缺乏基于力学-经验(ME)的柔性路面网络 HCAS 模型。在本研究中,开发了一个基于 ME 的国家级 HCAS 模型,并对美国公路性能监测系统(HPMS)数据库中的 67,583 个路面路段估算了不同车辆类别的损坏份额。将所提出的 HCAS 模型与现有的联邦公路管理局(FHWA)HCAS 模型(即国家路面成本模型 [NAPCOM])进行了比较。交通数据分析结果表明,两轴单体卡车(SU2)和两轴串联单轴牵引半挂车(CS5T)是路面网络最频繁的使用者。结果表明,SU2 的损坏份额在次要道路上占主导地位,而 CS5T 类较重车辆的损坏份额在主要干道和国道上占主导地位。此外,研究还发现,虽然路面路段的地理位置和环境条件会影响路面损坏的程度,但损坏份额的分布却几乎相同。这可以归因于交通数据的相似性,例如车辆等级分布和轴荷载谱。
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Development of a Mechanistic-Empirical-Based Highway Cost Allocation Model for Flexible Pavements
Construction, operation, and maintenance of a pavement network requires funding, partially sourced from road user taxes. Recent studies showed that lightweight vehicles are typically taxed higher compared with heavy trucks that damage the roads the most. To facilitate the equity and fairness of the allocated costs to different vehicles in the United States (U.S.), Highway Cost Allocation Studies (HCAS) were performed using various pavement performance prediction models. Reviewed literature showed the lack of mechanistic-empirical (ME)-based HCAS models for the flexible pavement network. In this study, a national-level ME-based HCAS model was developed, and the damage shares of different vehicle classes have been estimated for 67,583 pavement sections in the U.S. Highway Performance Monitoring System (HPMS) database. The proposed HCAS model was compared with the existing Federal Highway Administration (FHWA) HCAS model (i.e., National Pavement Cost Model [NAPCOM]). The analysis of the traffic data showed that two-axle single-unit trucks (SU2) and tractor-semitrailers with two tandem and one single axle (CS5T) were the most frequent users of the pavement network. The results showed that the damage share of SU2 is dominant in minor roadways, while the damage share of the heavier vehicles in the CS5T class is dominant in major arterials and interstates. In addition, it was found that, although the geographical location and environmental condition of the pavement section affects the magnitude of the pavement distresses, the distribution of the damage shares remains almost the same. This can be attributed to the similarities in the traffic data, for example, vehicle class distribution and axle load spectra.
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