2014Journal of the Transportation Research ForumOpen access

A Comprehensive Assessment of Highway Inventory Data Collection Methods

Mohammad Jalayer, Huaguo Zhou, Jie Gong, Shunfu Hu, Mark Grinter

Open full text 25 citations

Abstract

The implementation of the Highway Safety Manual (HSM) at the state level has the potential to allow transportation agencies to proactively address safety concerns. However, the widespread utilization of HSM faces significant barriers as many state departments of transportations (DOTs) do not have sufficient HSM-required highway inventory data. Many techniques have been utilized by state DOTs and local agencies to collect highway inventory data for other purposes. Nevertheless, it is unknown which of these methods or any combination of them is capable of efficiently collecting the required dataset while minimizing cost and safety concerns. The focus of this study is to characterize the capability of existing methods for collecting highway inventory data vital to the implementation of the recently published HSM. More specifically, this study evaluated existing highway inventory methods through a nationwide survey and a field trial of identified promising highway inventory data collection (HIDC) methods on various types of highway segments. A comparative analysis was conducted to present an example on how to incorporate weights provided by state DOT stakeholders to select the most suitable HIDC method for the specific purpose.

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What this paper is about

The implementation of the Highway Safety Manual (HSM) at the state level has the potential to allow transportation agencies to proactively address safety concerns. However, the widespread utilization of HSM faces significant barriers as many state departments of transportations (DOTs) do not have sufficient HSM-required highway inventory data. Many techniques have been utilized by state DOTs and local agencies to collect highway inventory data for other purposes. Nevertheless, it is unknown which of these methods or any combination of them is capable of efficiently collecting the required dataset while minimizing cost and safety concerns. The focus of this study is to characterize the capability of existing methods for collecting highway inventory data vital to the implementation of the recently published HSM. More specifically, this study evaluated existing highway inventory methods through a nationwide survey and a field trial of identified promising highway inventory data collection (HIDC) methods on various types of highway segments. A comparative analysis was conducted to present an example on how to incorporate weights provided by state DOT stakeholders to select the most suitable HIDC method for the specific purpose.

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Available abstract

The implementation of the Highway Safety Manual (HSM) at the state level has the potential to allow transportation agencies to proactively address safety concerns. However, the widespread utilization of HSM faces significant barriers as many state departments of transportations (DOTs) do not have sufficient HSM-required highway inventory data. Many techniques have been utilized by state DOTs and local agencies to collect highway inventory data for other purposes. Nevertheless, it is unknown which of these methods or any combination of them is capable of efficiently collecting the required dataset while minimizing cost and safety concerns. The focus of this study is to characterize the capability of existing methods for collecting highway inventory data vital to the implementation of the recently published HSM. More specifically, this study evaluated existing highway inventory methods through a nationwide survey and a field trial of identified promising highway inventory data collection (HIDC) methods on various types of highway segments. A comparative analysis was conducted to present an example on how to incorporate weights provided by state DOT stakeholders to select the most suitable HIDC method for the specific purpose.

Key concepts: Transport engineering, Data collection, Computer science, Operations research, Risk analysis (engineering), Engineering, Business, Mathematics

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