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.

Roadside feature data are critical inputs to highway safety models as described in the Highway Safety Manual (HSM). Collecting safety-related roadside feature data is an important step for HSM implementation. Many states' department of transportations (DOTs) routinely collect data on roadside objects using a variety of sensing methods, which often incur in significant costs. At present, it is unknown which of these data collection methods or any combination of them is capable of efficiently collecting safety-related roadside feature data while minimizing costs and safety concerns. This research is designed to identify required roadside feature data for various types of facilities in the HSM and to characterize the capabilities of existing remote sensing methods (e.g., Mobile LiDAR) to collect those required data. To accomplish this objective, tasks such as literature reviews, a nation-wide survey, and large-scale field trial are performed in this research. The findings of this research suggest that either the mobile LiDAR or the combination of the video/photo log method with the aerial imagery method is capable of collecting required HSM-related roadside information. However, due to the high data reduction effort, the current mobile LiDAR method needs significant improvement in the data processing and in the feature extraction stage.

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Roads and developed land can alter hydrologic pathways, cause erosion, and increase pollution to nearby waters. Best management practices (BMPs) are commonly used to reduce adverse effects of post-construction runoff. This study is focused on providing performance and cost information for optimally selecting the BMPs for retaining post-construction stormwater on site. The performance of BMPs was simulated numerically using an idealized catchment in an urban setting environment. The cost of construction and maintenance of these BMPs were based on unit price. The considered BMPs were bioswale, infiltration trench, and vegetated filter strip. The effects of vegetated covers such as turf or prairie grass on stormwater runoff reduction of linear projects with and without BMPs were also evaluated. Finally, based on construction cost, maintenance costs, and performance of BMPs, recommendations are made to help decision makers in implementing the optimal BMP to control stormwater runoff for highways in urban areas.

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