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A properly functioning local stormwater drainage system is essential for mitigating flood risks. This study evaluates the quality of roadside drainage channels in three underserved communities in Texas: the Sunnyside neighborhood in Houston (Harris County), a neighborhood in the City of Rockport (Aransas County), and the Hoehn colonia (Hidalgo County). These communities have a history of flooding, are highly socially vulnerable, and rely on roadside ditches as their principal stormwater drainage system for runoff control. Mobile lidar (Light Detection and Ranging) measurements were collected for 6.09 miles of roadside channels in these communities. The raw lidar measurements were processed to evaluate drainage conditions based on the channel's geometric properties, hydraulic capacity, and level of service. The assessment results are linked to a Geographic Information System (GIS) tool for enhanced visualization. Finally, the paper provides insights regarding the quality of stormwater infrastructure in the study communities and discusses their practical implications. [ABSTRACT FROM AUTHOR]

The objectives of this study are: (1) to specify evacuation return and home-switch stability as two critical milestones of short-term recovery during and in the aftermath of disasters; and (2) to understand the disparities among subpopulations in the duration of these critical recovery milestones. Using privacy-preserving fine-resolution location-based data, we examine evacuation and home move-out rates in Harris County, Texas in the context of the 2017 Hurricane Harvey. For each of the two critical recovery milestones, the results reveal the areas with short- and long-return durations and enable evaluating disparities in evacuation return and home-switch stability patterns. In fact, a shorter duration of critical recovery milestone indicators in flooded areas is not necessarily a positive indication. Shorter evacuation return could be due to barriers to evacuation and shorter home move-out rate return for lower-income residents is associated with living in rental homes. In addition, skewed and non-uniform recovery patterns for both the evacuation return and home-switch stability were observed in all subpopulation groups. All return patterns show a two-phase return progress pattern. The findings could inform disaster managers and public officials to perform recovery monitoring and resource allocation in a more proactive, data-driven, and equitable manner. [ABSTRACT FROM AUTHOR]

With the increasing number of aging bridges that need maintenance and rehabilitation (M&R), it is important to plan and prioritize M&R projects proactively. This process requires mathematical models capable of predicting bridge condition over multiyear planning horizons taking into account the long-term performance effects of M&R treatments. This paper describes the development, validation, and application of Markov chain–based bridge deterioration prediction models. Bridge condition is measured in terms of the National Bridge Inventory (NBI) deck rating, superstructure rating, substructure rating, and culvert rating. The models consider explanatory variables that affect bridge deterioration, including climate/environment, traffic loading, material type, and M&R type. Because data on M&R work history are often disconnected from bridge condition data, the study used a novel approach that allows for inferring past M&R type and timing based on changes in bridge condition ratings. The models were developed using condition data for 43,320 bridges across Texas extending from 2001 to 2017. The developed prediction models can be used as a tool to support bridge asset management planning in Texas. The modeling approach, however, can be used by transportation agencies across the world to develop bridge deterioration prediction models using their local empirical data. [ABSTRACT FROM AUTHOR]

Using quantitative measures to assess road network resilience, this study proposes a system-level framework that offers insights into existing road network resilience that could inform the planning and development processes of transportation agencies. This study identified and implemented four quantitative metrics to classify the criticality of road segments based on dimensions of road network resilience. Integrating the four metrics of classification using two mathematical approaches, this study arrived at overall resilience performance metrics for assessing road segment criticality. A case study was conducted on Texas road networks to demonstrate the effectiveness of implementing this framework in a practical scenario. The data used in this study is available to other states and countries; thus, the framework presented in this study can be adopted by transportation agencies for regional transportation resilience assessments. [ABSTRACT FROM AUTHOR]