Research and Publications - Department of Civil, Construction & Environmental Engineering

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Directing Sampling Based on Uncertainty Analysis
(Computational Hydraulics Inc., 2003-02-15) Graettinger, Andrew; Supriyasilp, Thanaporn; Durrans, S. Rocky; Pitt, Robert E.
Determining where and what to sample for environmental modeling of receiving waters is becoming increasingly important because the need for improved accuracy in model results conflicts with limited site sampling budgets. A quantitative approach to sampling, entitled Quantitatively Directed Exploration (QDE), provides a mathematical framework for determining the best location to sample, and what parameter should be sampled. QDE employs a first-order Taylor series expansion to estimate the uncertainty or variance in the model results. Uncertainty in input parameters is determined through data extrapolation techniques, specifically multivariate conditional probability, while model sensitivity is calculated by directly coding sensitivity derivatives into a model using ADIFOR 2.0. Combining these two matrices produces the variance in model results, which in turn is employed to direct sampling. The next sampling location is defined as the point where the variance in model results is the largest. Which input parameter to sample is determined by evaluating the contribution to the total variance produced by each input parameter. The QDE approach is demonstrated on a water quality model where non-point source loading, stream characteristics, and contaminant behavior are uncertain input parameters and concentration is the uncertain model result.
Infiltration Through Compacted Urban Soils and Effects on Biofiltration Design
(Computational Hydraulics Inc., 2003-02-15) Pitt, Robert E.; Chen, Shen-En; Clark, Shirley; Lantrip, Janice; Ong, Choo Keong; Voorhees, John
The effects of urbanization on soil structure can be extensive. Infiltration of rain water through soils can be greatly reduced, plus the benefits of infiltration and biofiltration devices can be jeopardized. This chapter is a compilation of results from several recent and on-going research projects that have examined some of these problems, plus possible solutions. Basic infiltration measurements in disturbed urban soils were conducted during the EPA-sponsored project by Pitt, et al. (1999a). The project also examined hydraulic and water quality benefits of amending these soils with organic composts. Prior EPA-funded research examined the potential of groundwater contamination by infiltrating stormwater (Pitt, et al. 1994, 1996, and 1999b). In addition to the information obtained during these research projects, numerous student projects have also been conduced to examine other aspects of urban soils, especially more detailed tests examining soil density and infiltration during lab-scale tests, and methods and techniques to recover infiltration capacity of urban soils. This chapter is a summary of this information and it is hoped that it will prove useful to both stormwater practice designers and to modelers.
Short Time-Interval Rainfall Disaggregation for Continuous Hydrologic Simulation
(Computational Hydraulics Inc., 2002-02-15) Burian, Steven J.; Durrans, S. Rocky
Traditionally design storms have been used to design and analyze urban drainage systems and hydraulic structures. Design storms can be developed with the desired temporal resolution to accommodate urban hydrology needs, but because the temporal distribution is generally arbitrary the application of complex disaggregation techniques is unwarranted. Continuous hydrologic simulation is recommended as an alternative to the traditional design storm approach for the design and analysis of hydrologic and hydraulic structures for reasons discussed in James (1994) and James and Robinson (1982). Continuous simulation models require long-term rainfall records (preferably more than 50 years) to generate the long-term statistical response of the hydrologic system required for accurate design and analysis of engineering systems and the evaluation of ecological and sustainability issues. Accurate hydrologic simulation of small urban catchments requires the use of a rainfall time series with a fine temporal resolution. Studies have shown that when the response time of a watershed is shorter than the total duration of rainfall excess, the runoff rate is observed to depend on the depth of rainfall and the intensity distribution (Ball 1994; Woolhiser and Goodrich 1988; Hjelmfelt 1981). But for fully developed hydrographs Ball (1994) found the temporal pattern of rainfall excess to have little influence over the peak discharge. Thus, for short duration storms coarse time resolution rainfall data may smooth the high rainfall intensities (especially those observed during convective storms), and runoff could be underestimated. Hernandez and Nachabe (2000) demonstrated that when Hortonian runoff is dominant, infiltration and runoff are very sensitive to time resolution. They observed finer temporal resolution rainfall to produce more runoff than coarser rainfall. In general, hydraulic analysis of drainage systems requires rainfall data in 5- to 15-minute increments to produce hydrographs that accurately predict peak flows (Nix 1994). The procurement and management of long-term rainfall records is no longer a problem for locations where records are available electronically. Today, the primary difficulties with long-term rainfall records are (1) unavailability at the desired location or (2) not being recorded at the desired temporal resolution. One solution to these problems would be to employ a synthetic rainfall generator to produce long-term rainfall fields with the desired spatial and temporal resolution. A second solution for circumstance (2) (i.e., when a long-term rainfall record exists but has too coarse temporal resolution) is to employ a temporal disaggregation technique to disaggregate the record into a finer temporal resolution. The issue then becomes the selection and application of an appropriate disaggregation method to produce a long-term rainfall record at the desired temporal resolution. This chapter compares several temporal rainfall disaggregation techniques applicable to continuous hydrologic simulation. The focus is the disaggregation of hourly rainfall records into sub-hourly increments because in North America hourly rain gauges are relatively common and the records often have sufficient lengths for use in long-term continuous simulation. The rainfall disaggregation methods included in the study were selected based on the needs of hydrologic modelers. In general, hydrologic modelers desire techniques that are conceptually intuitive, easily grasped, and sufficiently flexible that they could be applied to any locality and for any desired level of disaggregation (so they could be relatively easily standardized). Based on these criteria, the five methods selected for comparison were the uniform distribution approach (described below), the quadratic spline and quadratic interpolating polynomial approaches (described by Durrans et al. (1999)), the geometric similarity approach (the continuous-deterministic disaggregation model described by Ormsbee (1989)), and the backpropagation ANN approach (described by Burian et al. (2000)). Methods that require the estimation of numerous parameters were not included in this study. The relative performance of the five techniques for disaggregating hourly rainfall records from Alabama into 15-minute increments is reviewed below. Additional evaluation of the uniform distribution, the geometric similarity, and the ANN techniques is reported for 5-minute and 15-minute rainfall in Arkansas.
The Role of Pollution Prevention in Stormwater Management
(Computational Hydraulics Inc., 2001-02-15) Pitt, Robert E.; Lalor, Melinda
Around the nation, there is growing interest in the development and use of environmentally sensitive construction materials as a low-cost component to stormwater management. It is thought that the more appropriate selection of materials that are exposed to the environment should result in significant reductions of many toxicants in stormwater. Unfortunately, there is little data for specific alternative building materials, although much information exists targeting selected sources, especially the role of roof runoff as a significant source of zinc and other metals. Past studies have identified urban runoff as a major contributor to the degradation of many urban streams and rivers (such as Field and Turkeltaub 1981; Pitt and Bozeman, 1982; Pitt and Bissonnette, 1984; Pitt, 1995). Previous studies also found organic and metallic toxicants in urban storm-induced discharges that can contribute to receiving water degradation (such as EPA, 1983; Hoffman et al., 1984; Fram et al., 1987). Studies conducted by Pitt et al. (1995 and 2000) investigated toxic contributions to urban wet weather flow from sources such as roofs, parking areas, storage areas, streets, loading docks, vehicle service areas, and landscaped areas. Roof, vehicle service area and parking lot runoff samples were found to have the greatest organic toxicant detection frequencies and the highest levels of detected metals. Research is currently underway at the University of Alabama (UAB) to develop effective procedures for treating runoff from vehicle service areas and parking lots at its source (Clark and Pitt 1999; Pitt et al., 2000). These areas are particularly subject to spills and leaks of automotive products and exhaust emissions from frequently starting vehicles. These areas are usually isolated enough to make source area runoff treatment feasible. However, relative pollutant contributions from various roofing, wooden and paving materials themselves are also a concern which has not been adequately addressed. Due to the common use of these surfaces in our urban environments, reduction of emissions at the source is desirable, and material substitution would seem a good place to start.
Use of SLAMM in Evaluating Best Management Practices
(Computational Hydraulics Inc., 2001-02-15) Myllyoja, Rob; Baroudi, Hala; Pitt, Robert E.; Paluzzi, Jenna
Once baseline water quality data reveals that beneficial uses of a stream are no longer supported, the task of evaluating alternatives for urban watershed management can be challenging for municipal planners. While working with the municipalities within the Bear Creek watershed to develop a watershed management plan, the Clinton River Watershed Council selected the Source Loading and Management Model (SLAMM) as the main instrument. A cost-effective management tool was required to assist in evaluating the effectiveness of urban best management practices (BMPs). Evaluating the suitability of the J model was difficult because we were not aware of any previous SLAMM applications in the State of Michigan. The objective became, not only to learn about and apply the model, but also to demonstrate its applicability in similar Michigan watersheds. The Source Loading and Management Model (Pitt, 1998; Pitt and Voorhees 1995) emphasizes the use of variable quality of runoff, small storm hydrology, and particulate washoff to calculate runoff pollutant yield estimates. Unlike drainage design models, SLAMM accurately computes runoff pollutant loads and flows associated with small storm events. This is critical because most of the pollutant load is associated with the smaller, frequent runoff events. SLAMM evaluates several control practices including detention ponds, infiltration devices, porous pavements, grass swales, catchbasin cleaning, and street cleaning. These controls can be evaluated in combinations at many source areas and at the outfalls. Furthermore, SLAMM computes the relative contributions of different source areas (e.g. roofs, streets, parking areas, landscaped areas, undeveloped areas) for each land use investigated. SLAMM requires the user to define specified impervious areas and directly connected impervious areas (DCIAs) within the model's subwatershed. DCIAs include those impervious areas that flow directly to a storm sewer, drain, channel, or waterway without flowing over any pervious surfaces. SLAMM utilizes site- specific local information including stormwater conveyance system type and condition, study period duration, rainfall depth, duration, and, and detailed land use and source area descriptions. SLAMM does not require detailed drainage system information, although the newest version of the program allows interfacing with SWMM for detailed hydraulic system evaluation.
Bioinspired Polypeptide Dendrimer-Modified Thin-FilmComposite Membranes for Selective Lithium-MagnesiumSeparation with DFT Insights
(Wiley, 2025-12-26) Yassari, Mehrasa; Seyedpour, Seyedeh Fatemeh; Setegne, Bamlak; Aghaei, Amir; Ahvazi, Behzad; Firouzjaei, Mostafa Dadashi; Elliott, Mark; Sadrzadeh, Mohtada
Selective ion transport in nanofiltration (NF) enables sustainable lithium (Li+) recovery. While many membranes rely on strong positive charge for Li⁺/Mg2⁺ separation, we show that negatively charged membranes can also excel using a biomimetic approach. Inspired by biological ion channels that achieve cation selectivity via specific binding sites despite their negative charge, we designed a nitrogen-rich polypeptide dendrimer (amino acid–based) bearing carboxylate coordination sites with higher affinity for Mg2⁺ than Li⁺, while moderating the membrane's net negative charge. This biomimetic design enhanced Li+ recovery by inhibiting Mg2+ transport through stronger interactions, thereby allowing for preferential Li+ permeation. This process occurred through a combination of electrostatic modulation and ligand-assisted coordination. Density functional theory (DFT) calculations indicated strong oxygen-donor coordination: lysine motifs bind hydrated Mg2+ (E ≈ −170 kcal.mol−1) far more strongly than Li+ (E ≈ −50.2 kcal.mol−1). The optimized membrane achieved Li+/Mg2+ selectivity of 15.6 at neutral pH with 23 LMH flux, and 136 at pH 4, highlighting strong performance in acidic feeds. Long-term tests showed ∼0.4% leaching over 10 days with stable rejection and enrichment of Li⁺ (feed Li⁺/Mg2⁺ increased from 0.05 to 0.20). Antifouling tests showed a twofold lower flux-decline ratio and higher flux-recovery than the unmodified TFC.
High-Rate Stormwater Treatment with Up-Flow Filtration
(Computational Hydraulics Inc., 2010-02-15) Togawa, Noboru; Pitt, Robert
The objective of this research is to examine the removal capacities of a high-rate stormwater filtration device, in part developed by engineers at the University of Alabama through a small business innovative re-search grant from the U.S. Environmental Protection Agency. The Up-Flo Filter is an efficient high-rate stormwater filtration technology de-signed for the removal of trash, sediments, nutrients, metals and hydrocarbons from stormwater runoff. Compared with traditional downflow filtration treatment, upflow filtration can minimize clogging problems while providing a high rate of flow. The Up-Flo filter was de-veloped to remove a broad range of stormwater pollutants, especially those associated with particulates. The high flow rate capacities of the Up-Flo filter are accomplished through controlled fluidization of the filtration media, while still capturing very small particulates, that is lo-cated in a flexible, but constraining, media container. The Up-Flo filter also drains down between rain events which minimizes anaerobic con-ditions in the media and which also partially flushes captured particulates from the media to the storage sump, decreasing clogging and increasing run times between maintenance. Gross floatables are captured through the use of an angled screen before the media and a hood on the overflow siphon, while the sump captures bed load par-ticulates.
A Reinforcement Learning Approach for GNSS Spoofing Attack Detection of Autonomous Vehicles
(Transportation Research Record, 2021) Dasgupta, Sagar; Ghosh, Tonmoy; Rahman, Mizanur
A resilient and robust positioning, navigation, and timing (PNT) system is a necessity for the navigation of autonomous vehicles (AVs). Global Navigation Satelite System (GNSS) provides satellite-based PNT services. However, a spoofer can temper an authentic GNSS signal and could transmit wrong position information to an AV. Therefore, a GNSS must have the capability of real-time detection and feedback-correction of spoofing attacks related to PNT receivers, whereby it will help the end-user (autonomous vehicle in this case) to navigate safely if it falls into any compromises. This paper aims to develop a deep reinforcement learning (RL)-based turn-by-turn spoofing attack detection using low-cost in-vehicle sensor data. We have utilized Honda Driving Dataset to create attack and non-attack datasets, develop a deep RL model, and evaluate the performance of the RL-based attack detection model. We find that the accuracy of the RL model ranges from 99.99% to 100%, and the recall value is 100%. However, the precision ranges from 93.44% to 100%, and the f1 score ranges from 96.61% to 100%. Overall, the analyses reveal that the RL model is effective in turn-by-turn spoofing attack detection.
CARE IMPACT Study of Drowsy Driving (DrD)
(2020-01-25) Brown, David B.
How much information is lost when sampling driving behavior data? Indicators to quantify the extent of information loss
(Emerald, 2020-02-06) Liu, Jun; Khattak, Asad; Han, Lee; Yuan, Quan
Purpose: Individuals’ driving behavior data are becoming available widely through Global Positioning System devices and on-board diagnostic systems. The incoming data can be sampled at rates ranging from one Hertz (or even lower) to hundreds of Hertz. Failing to capture substantial changes in vehicle movements over time by “undersampling” can cause loss of information and misinterpretations of the data, but “oversampling” can waste storage and processing resources. The purpose of this study is to empirically explore how micro-driving decisions to maintain speed, accelerate or decelerate, can be best captured, without substantial loss of information. Design/methodology/approach: This study creates a set of indicators to quantify the magnitude of information loss (MIL). Each indicator is calculated as a percentage to index the extent of information loss (EIL) in different situations. An overall information loss index named EIL is created to combine the MIL indicators. Data from a driving simulator study collected at 20 Hertz are analyzed (N = 718,481 data points from 35,924 s of driving tests). The study quantifies the relationship between information loss indicators and sampling rates. Findings: The results show that marginally more information is lost as data are sampled down from 20 to 0.5 Hz, but the relationship is not linear. With four indicators of MILs, the overall EIL is 3.85 per cent for 1-Hz sampling rate driving behavior data. If sampling rates are higher than 2 Hz, all MILs are under 5 per cent for importation loss. Originality/value: This study contributes by developing a framework for quantifying the relationship between sampling rates, and information loss and depending on the objective of their study, researchers can choose the appropriate sampling rate necessary to get the right amount of accuracy.
Does Global Progress on Sanitation Really Lag behind Water? An Analysis of Global Progress on Community- and Household-Level Access to Safe Water and Sanitation
(PLOS, 2014) Cumming, Oliver; Elliott, Mark; Overbo, Alycia; Bartram, Jamie; University of London; London School of Hygiene & Tropical Medicine; University of Alabama Tuscaloosa; University of North Carolina; University of North Carolina Chapel Hill
Safe drinking water and sanitation are important determinants of human health and wellbeing and have recently been declared human rights by the international community. Increased access to both were included in the Millennium Development Goals under a single dedicated target for 2015. This target was reached in 2010 for water but sanitation will fall short; however, there is an important difference in the benchmarks used for assessing global access. For drinking water the benchmark is community-level access whilst for sanitation it is household-level access, so a pit latrine shared between households does not count toward the Millennium Development Goal (MDG) target. We estimated global progress for water and sanitation under two scenarios: with equivalent household-and community-level benchmarks. Our results demonstrate that the "sanitation deficit" is apparent only when household-level sanitation access is contrasted with community-level water access. When equivalent benchmarks are used for water and sanitation, the global deficit is as great for water as it is for sanitation, and sanitation progress in the MDG-period (1990-2015) outstrips that in water. As both drinking water and sanitation access yield greater benefits at the household-level than at the community-level, we conclude that any post-2015 goals should consider a household-level benchmark for both.
How did the COVID-19 pandemic affect road crashes and crash outcomes in Alabama?
(Pergamon, 2021) Adanu, Emmanuel Kofi; Brown, David; Jones, Steven; Parrish, Allen; University of Alabama Tuscaloosa
With the rising number of cases and deaths from the COVID-19 pandemic, nations and local governments, including many across the U.S., imposed travel restrictions on their citizens. This travel restriction order led to a significant reduction in traffic volumes and a generally lower exposure to crashes. However, recent preliminary statistics in the US suggest an increase in fatal crashes over the period of lockdown in comparison to the same period in previous years. This study sought to investigate how the pandemic affected road crashes and crash outcomes in Alabama. Daily vehicle miles traveled and crashes were obtained and explored. To understand the factors associated with crash outcomes, four crash-severity models were developed: (1) Single-vehicle (SV) crashes prior to lockdown order (Normal times SV); (2) multi-vehicle (MV) crashes prior to lockdown order (Normal times MV); (3) Single-vehicle crashes after lockdown order (COVID times SV); and (4) Multi-vehicle crashes after lockdown order (COVID times MV). The models were developed using the first 28 weeks of crashes recorded in 2020. The findings of the study reveal that although traffic volumes and vehicle miles traveled had significantly dropped during the lockdown, there was an increase in the total number of crashes and major injury crashes compared to the period prior to the lockdown order, with speeding, DUI, and weekends accounting for a significant proportion of these crashes. These observations provide useful lessons for road safety improvements during extreme events that may require statewide lockdown, as has been done with the COVID-19 pandemic. Traffic management around shopping areas and other areas that may experience increased traffic volumes provide opportunities for road safety stakeholders to reduce the occurrence of crashes in the weeks leading to an announcement of any future statewide or local lockdowns. Additionally, increased law enforcement efforts can help to reduce risky driving activities as traffic volumes decrease.
A knowledge graph-based method for epidemic contact tracing in public transportation
(Pergamon, 2022) Chen, Tian; Zhang, Yimu; Qian, Xinwu; Li, Jian; Tongji University; University of Alabama Tuscaloosa
Contact tracing is an effective measure by which to prevent further infections in public transportation systems. Considering the large number of people infected during the COVID-19 pandemic, digital contact tracing is expected to be quicker and more effective than traditional manual contact tracing, which is slow and labor-intensive. In this study, we introduce a knowledge graph-based framework for fusing multi-source data from public transportation systems to construct contact networks, design algorithms to model epidemic spread, and verify the validity of an effective digital contact tracing method. In particular, we take advantage of the trip chaining model to integrate multi-source public transportation data to construct a knowledge graph. A contact network is then extracted from the constructed knowledge graph, and a breadth first search algorithm is developed to efficiently trace infected passengers in the contact network. The proposed framework and algorithms are validated by a case study using smart card transaction data from transit systems in Xiamen, China. We show that the knowledge graph provides an efficient framework for contact tracing with the reconstructed contact network, and the average positive tracing rate is over 96%.
Safety and health management response to COVID-19 in the construction industry: A perspective of fieldworkers
(Elsevier, 2022) Nnaji, Chukwuma; Jin, Ziyu; Karakhan, Ali; University of Alabama Tuscaloosa; University of New Mexico; University of Baghdad
The COVID-19 outbreak has significantly impacted the construction industry. The pandemic can exacerbate an already dire safety and health situation in the industry and negatively impact construction employees and employers. The present study investigates the safety and health measures implemented by construction firms in the United States (US), their effectiveness and usefulness, and workers' satisfaction with these COVID-19 measures. A questionnaire survey was developed and distributed to construction fieldworkers in the US to collect their perspectives on the implemented COVID-19 measures in the construction industry. A total of 187 valid responses were received and analyzed to achieve the aim of the study. Results revealed that strategies implemented to increase social distance and minimize group gathering to 10 persons in certain workstations were perceived to be substantially more effective than job-site screening strategies. Furthermore, smaller contractors implemented fewer safety measures and perceived them to be significantly less effective than those used by medium- and large-sized contractors. Fieldworkers were favorably disposed toward using technologies, such as video-conferencing apps and wearable sensing devices, to slow the spread of COVID-19 on construction job sites. The present study contributes to the body of knowledge by identifying safety and health measures to mitigate the spread of COVID-19 in construction. Practically, the study findings provide valuable insights to inform the successful implementation of safety strategies in the construction industry during a pandemic. The results are crucial for industry practitioners responsible for developing and revising pre- and post-pandemic safety and health plans.
Development of a field testing protocol for identifying Deepwater Horizon oil spill residues trapped near Gulf of Mexico beaches
(PLOS, 2018) Han, Yuling; Clement, T. Prabhakar; Auburn University; University of Alabama Tuscaloosa
The Deepwater Horizon (DWH) accident, one of the largest oil spills in U.S. history, contaminated several beaches located along the Gulf of Mexico (GOM) shoreline. The residues from the spill still continue to be deposited on some of these beaches. Methods to track and monitor the fate of these residues require approaches that can differentiate the DWH residues from other types of petroleum residues. This is because, historically, the crude oil released from sources such as natural seeps and anthropogenic discharges have also deposited other types of petroleum residues on GOM beaches. Therefore, identifying the origin of these residues is critical for developing effective management strategies for monitoring the long-term environmental impacts of the DWH oil spill. Advanced fingerprinting methods that are currently used for identifying the source of oil spill residues require detailed laboratory studies, which can be cost-prohibitive. Also, most agencies typically use untrained workers or volunteers to conduct shoreline monitoring surveys and these worker will not have access to advanced laboratory facilities. Furthermore, it is impractical to routinely fingerprint large volumes of samples that are collected after a major oil spill event, such as the DWH spill. In this study, we propose a simple field testing protocol that can identify DWH oil spill residues based on their unique physical characteristics. The robustness of the method is demonstrated by testing a variety of oil spill samples, and the results are verified by characterizing the samples using advanced chemical fingerprinting methods. The verification data show that the method yields results that are consistent with the results derived from advanced fingerprinting methods. The proposed protocol is a reliable, cost-effective, practical field approach for differentiating DWH residues from other types of petroleum residues.
Integrated socio-environmental vulnerability assessment of coastal hazards using data-driven and multi-criteria analysis approaches
(Nature Portfolio, 2022) Tanim, Ahad Hasan; Goharian, Erfan; Moradkhani, Hamid; University of South Carolina Columbia; University of Alabama Tuscaloosa
Coastal hazard vulnerability assessment has been centered around the multi-variate analysis of geo-physical and hydroclimate data. The representation of coupled socio-environmental factors has often been ignored in vulnerability assessment. This study develops an integrated socio-environmental Coastal Vulnerability Index (CVI), which simultaneously combines information from five vulnerability groups: biophysical, hydroclimate, socio-economic, ecological, and shoreline. Using the Multi-Criteria Decision Making (MCDM) approach, two CVI (CVI-50 and CVI-90) have been developed based on average and extreme conditions of the factors. Each CVI is then compared to a data-driven CVI, which is formed based on Probabilistic Principal Component Analysis (PPCA). Both MCDM and PPCA have been tied into geospatial analysis to assess the natural hazard vulnerability of six coastal counties in South Carolina. Despite traditional MCDM-based vulnerability assessments, where the final index is estimated based on subjective weighting methods or equal weights, this study employs an entropy weighting technique to reduce the individuals' biases in weight assignment. Considering the multivariate nature of the coastal vulnerability, the validation results show both CVI-90 and PPCA preserve the vulnerability results from biophysical and socio-economic factors reasonably, while the CVI-50 methods underestimate the biophysical vulnerability of coastal hazards. Sensitivity analysis of CVIs shows that Charleston County is more sensitive to socio-economic factors, whereas in Horry County the physical factors contribute to a higher degree of vulnerability. Findings from this study suggest that the PPCA technique facilitates the high-dimensional vulnerability assessment, while the MCDM approach accounts more for decision-makers' opinions.
UA_L-DoTT: University of Alabama's large dataset of trains and trucks
(Elsevier, 2022) Eastepp, Maxwell; Faris, Lauren; Ricks, Kenneth; University of Alabama Tuscaloosa
UA_L-DoTT (University of Alabama's Large Dataset of Trains and Trucks) is a collection of camera images and 3D LiDAR point cloud scans from five different data sites. Four of the data sites targeted trains on railways and the last targeted trucks on a four-lane highway. Low light conditions were present at one of the data sites showcasing unique differences between individual sensor data. The final data site utilized a mobile platform which created a large variety of viewpoints in images and point clouds. The dataset consists of 93,397 raw images, 11,415 corresponding labeled text files, 354,334 raw point clouds, 77,860 corresponding labeled point clouds, and 33 timestamp files. These timestamps correlate images to point cloud scans via POSIX time. The data was collected with a sensor suite consisting of five different LiDAR sensors and a camera. This provides various viewpoints and features of the same targets due to the variance in operational characteristics of the sensors. The inclusion of both raw and labeled data allows users to get started immediately with the labeled subset, or label additional raw data as needed. This large dataset is beneficial to any researcher interested in machine learning using cameras, LiDARs, or both. The current dataset is publicly available at UA_L-DoTT (C) 2022 The Author(s). Published by Elsevier Inc.
Post-pandemic shared mobility and active travel in Alabama: A machine learning analysis of COVID-19 survey data
(Elsevier, 2023) Xu, Ningzhe; Nie, Qifan; Liu, Jun; Jones, Steven; University of Alabama Tuscaloosa
The COVID-19 pandemic has had unprecedented impacts on the way we get around, which has increased the need for physical and social distancing while traveling. Shared mobility, as an emerging travel mode that allows travelers to share vehicles or rides has been confronted with social distancing measures during the pandemic. On the contrary, the interest in active travel (e.g., walking and cycling) has been renewed in the context of pandemic-driven social distancing. Although extensive efforts have been made to show the changes in travel behavior during the pandemic, people's post-pandemic attitudes toward shared mobility and active travel are under-explored. This study examined Alabamians' post-pandemic travel preferences regarding shared mobility and active travel. An online survey was conducted among residents in the State of Alabama to collect Alaba-mians' perspectives on post-pandemic travel behavior changes, e.g., whether they will avoid ride-hailing services and walk or cycle more after the pandemic. Machine learning algorithms were used to model the survey data (N = 481) to identify the contributing factors of post-pandemic travel preferences. To reduce the bias of any single model, this study explored multiple machine learning methods, including Random Forest, Adaptive Boosting, Support Vector Machine, K-Nearest Neighbors, and Artificial Neural Network. Marginal effects of variables from multiple models were combined to show the quantified relationships between contributing factors and future travel intentions due to the pandemic. Modeling results showed that the interest in shared mobility would decrease among people whose one-way commuting time by driving is 30-45 min. The interest in shared mobility would increase for households with an annual income of $100,000 or more and people who reduced their commuting trips by over 50% during the pandemic. In terms of active travel, people who want to work from home more seemed to be interested in increasing active travel. This study provides an understanding of future travel preferences among Alabamians due to COVID-19. The information can be incorporated into local trans-portation plans that consider the impacts of the pandemic on future travel intentions.
Severity analysis of crashes involving in-state and out-of-state large truck drivers in Alabama: A random parameter multinomial logit model with heterogeneity in means and variances
(Elsevier, 2022) Okafor, Sunday; Adanu, Emmanuel Kofi; Jones, Steven; University of Alabama Tuscaloosa
The trucking sector contributes significantly to the economic vitality of the United States. Large trucks are primarily used for transporting goods within and across states. Despite its economic importance, large truck crashes constitute public safety concerns. To minimize the consequences, there is a need to understand the factors that contribute to the severity outcomes of truck-involved crashes. Since many large truck drivers transport goods across several states, the driver-centered crash factors are expected to differ between in-state and out-of-state drivers. For this reason, this study developed two random parameters multinomial logit models with heterogeneity in means and variances to examine the factors contributing to the severity of crashes involving in-state and out-of-state large truck drivers in Alabama. The study was based on the 2016-2020 large truck crashes in Alabama. After data cleaning and preparation, it was observed that approximately 20% of in-state and 23% of out-ofstate large truck crashes were fatigue-related. There were more speeding related crashes (12.4%) among in-state large truck drivers, but the contribution of speeding to crash severity outcomes was only significant in the out-ofstate model. More crashes related to red light running violation (14.2%) were observed among out-of-state drivers, pointing to the fundamental issues of fatigue and unfamiliarity with the operations of signalized intersections in Alabama. The study contributes to the literature on large truck crashes by uncovering the nuances in crashes involving in-state and out-of-state large truck drivers. Despite the seeming similarity in factors that influence crash outcomes, this study provides the basis for truck drivers' training and communication campaigns on the differences that may exist in roadway characteristics from state to state. Also, policy formulations and strategies that prioritizes the well-being of the large truck drivers and creates a better working condition for them should be explored.
Rainfall-induced hydroplaning risk over road infrastructure of the continental USA
(PLOS, 2022) Salvi, Kaustubh Anil; Kumar, Mukesh; University of Alabama Tuscaloosa
Extreme rainfall causes transient ponding on roads, which increases the risk of vehicle accidents due to hydroplaning (HP), a phenomenon characterized by reduced friction between the pavement surface and the tires of moving vehicles. Before mitigation plans are drawn, it is important to first assess the spatio-temporal patterns of hydroplaning risk (HpR). This study quantifies HpR over the entire continental USA considering the coupled role of precipitation characteristics and pavement properties. Results show the southern United States to be a primary hotspot of HpR. About 22% of road sections experiencing HpR exhibit an increasing trend in the annual occurrence of HP events with time, indicating a riskier future ahead. Alarmingly, road sections that either experience higher HpR or increasing trend in annual occurrences of HP events are the ones with sizeable traffic. These results emphasize the need to prioritize HP-aware road design, traffic management, and signage in regions with high or fast-evolving risks.

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