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

Permanent URI for this collection

https://ir.ua.edu/handle/123456789/2875

Browse

Now showing 1 - 20 of 51

The 2009-2010 El Niño: Hydrologic Relief to U.S. Regions
(2009-12-15) Tootle, G. A.; Piechota, T. C.; Aziz, O.; Miller, W. P.; Lakshmi, V.; Dracup, J. A.; Jerla, C.; University of Alabama Tuscaloosa
Current forecasts by the U.S. National Oceanic and Atmospheric Administration (NOAA) are that the Pacific Ocean will experience El Niño conditions in late 2009 and into 2010. These forecasts are similar to past El Niño events in 1972–1973, 1982–1983, 1986–1987, and 2002–2003.Evaluating the hydrologic conditions for these past El Niño events reveals that during these times, surface water supply conditions improved in many parts of the United States, including the Southeast, Midwest, and Southwest. At the same time, the Pacific Northwest and other specific regions of the United States experienced below‐average water supply conditions. This is consistent with the long‐established linkages between oceanic‐atmospheric phenomena, El Niño, and streamflow [e.g., Kahya and Dracup, 1993; Tootle et al., 2005].
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.
A Web-Based Geotechnical GIS
(Wiley, 2011-10-05) Graettinger, Andrew J.; Ryals, Zachary T.; Smith, Randy K.
A web-based Geotechnical Geographic Information System (GeoGIS) was developed and tested for the Alabama Departmentof Transportation. This web-based system stores geotechnical information about transportation projects, such as subsurface data,construction drawings, and design information. Typically, this information is in a report or plan sheet format, but raw geotechnicaldata can also be accommodated in the GeoGIS. The goal of this system is to provide easy access and storage for all geotechnicaland subsurface structural information from across a state. Access through a secure web interface allows for keyword searches andinteractive map selection. The web-based GeoGIS has four geotechnical layers (project, bridge, foundation, and soil boring) thatcan be displayed on a road map, aerial photos, or USGS 7.5 minute quadrangles. For testing purposes the GeoGIS was populatedwith multiple document types, formats, and sizes. In all cases, the system performed above expectations.
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.
Block-level vulnerability assessment reveals disproportionate impacts of natural hazards across the conterminous United States
(Nature Portfolio, 2023) Yarveysi, Farnaz; Alipour, Atieh; Moftakhari, Hamed; Jafarzadegan, Keighobad; Moradkhani, Hamid; University of Alabama Tuscaloosa
The global increase in the frequency, intensity, and adverse impacts of natural hazards on societies and economies necessitates comprehensive vulnerability assessments at regional to national scales. Despite considerable research conducted on this subject, current vulnerability and risk assessments are implemented at relatively coarse resolution, and they are subject to significant uncertainty. Here, we develop a block-level Socio-Economic-Infrastructure Vulnerability (SEIV) index that helps characterize the spatial variation of vulnerability across the conterminous United States. The SEIV index provides vulnerability information at the block level, takes building count and the distance to emergency facilities into consideration in addition to common socioeconomic vulnerability measures and uses a machine-learning algorithm to calculate the relative weight of contributors to improve upon existing vulnerability indices in spatial resolution, comprehensiveness, and subjectivity reduction. Based on such fine resolution data of approximately 11 million blocks, we are able to analyze inequality within smaller political boundaries and find significant differences even between neighboring blocks. Introduces a precise, machine-learning-based Socio-Economic-Infrastructure Vulnerability index for natural hazards that uncovers stark variations in vulnerability at the block level emphasizing crucial information for risk-informed decision making.
CARE IMPACT Study of Drowsy Driving (DrD)
(2020-01-25) Brown, David B.
Cement substitution with secondary materials can reduce annual global CO2 emissions by up to 1.3 gigatons
(Nature Portfolio, 2022) Shah, Izhar Hussain; Miller, Sabbie A.; Jiang, Daqian; Myers, Rupert J.; Imperial College London; University of California Davis; University of Alabama Tuscaloosa
Population and development megatrends will drive growth in cement production, which is already one of the most challenging-to-mitigate sources of CO2 emissions. However, availabilities of conventional secondary cementitious materials (CMs) like fly ash are declining. Here, we present detailed generation rates of secondary CMs worldwide between 2002 and 2018, showing the potential for 3.5 Gt to be generated in 2018. Maximal substitution of Portland cement clinker with these materials could have avoided up to 1.3 Gt CO2-eq. emissions (similar to 44% of cement production and similar to 2.8% of anthropogenic CO2-eq. emissions) in 2018. We also show that nearly all of the highest cement producing nations can locally generate and use secondary CMs to substitute up to 50% domestic Portland cement clinker, with many countries able to potentially substitute 100% Portland cement clinker. Our results highlight the importance of pursuing regionally optimized CM mix designs and systemic approaches to decarbonizing the global CMs cycle.
Comparison of RUSLE and MMF Soil Loss Models and Evaluation of Catchment Scale Best Management Practices for a Mountainous Watershed in India
(MDPI, 2020) Das, Susanta; Deb, Proloy; Bora, Pradip Kumar; Katre, Prafull; Punjab Agricultural University; University of Alabama Tuscaloosa; Indira Gandhi Krishi Vishwavidyalaya (IGKV)
Soil erosion from arable lands removes the top fertile soil layer (comprised of humus/organic matter) and therefore requires fertilizer application which affects the overall sustainability. Hence, determination of soil erosion from arable lands is crucial to planning conservation measures. A modeling approach is a suitable alternative to estimate soil loss in ungauged catchments. Soil erosion primarily depends on soil texture, structure, infiltration, topography, land uses, and other erosive forces like water and wind. By analyzing these parameters, coupled with geospatial tools, models can estimate storm wise and annual average soil losses. In this study, a hilly watershed called Nongpoh was considered with the objective of prioritizing critical erosion hazard areas within the micro-catchment based on average annual soil loss and land use and land cover and making appropriate management plans for the prioritized areas. Two soil erosion models namely Revised Universal Soil Loss Equation (RUSLE) and Modified Morgan-Morgan-Finney (MMF) models were used to estimate soil loss with the input parameters extracted from satellite information and automatic weather stations. The RUSLE and MMF models showed similar results in estimating soil loss, except the MMF model estimated 7.74% less soil loss than the RUSLE model from the watershed. The results also indicated that the study area is under severe erosion class, whereas agricultural land, open forest area, and scrubland were prioritized most erosion prone areas within the watershed. Based on prioritization, best management plans were developed at catchment scale for reducing soil loss. These findings and the methodology employed can be widely used in mountainous to hilly watersheds around the world for identifying best management practices (BMP).
Coupled oceanic-atmospheric variability and U.S. streamflow
(American Geophysical Union, 2005-12-06) Tootle, GA; Piechota, TC; Singh, A; University of Wyoming; Nevada System of Higher Education (NSHE); University of Nevada Las Vegas; University of Alabama Tuscaloosa
[1] A study of the influence of interdecadal, decadal, and interannual oceanic-atmospheric influences on streamflow in the United States is presented. Unimpaired streamflow was identified for 639 stations in the United States for the period 1951 - 2002. The phases (cold/negative or warm/positive) of Pacific Ocean ( El Nino - Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO)) and Atlantic Ocean ( Atlantic Multidecadal Oscillation (AMO) and North Atlantic Oscillation (NAO)) oceanic-atmospheric influences were identified for the year prior to the streamflow year (i.e., long lead time). Statistical significance testing of streamflow, based on the interdecadal, decadal, and interannual oceanic-atmospheric phase (warm/positive or cold/negative), was performed by applying the nonparametric rank-sum test. The results show that in addition to the well-established ENSO signal the PDO, AMO, and NAO influence streamflow variability in the United States. The warm phase of the PDO is associated with increased streamflow in the central and southwest United States, while the warm phase of the AMO is associated with reduced streamflow in these regions. The positive phase of the NAO and the cold phase of the AMO are associated with increased streamflow in the central United States. Additionally, the coupled effects of the oceanic-atmospheric influences were evaluated on the basis of the long-term phase (cold/negative or warm/ positive) of the interdecadal ( PDO and AMO) and decadal ( NAO) influences and ENSO. Streamflow regions in the United States were identified that respond to these climatic couplings. The results show that the AMO may influence La Nina impacts in the Southeast, while the NAO may influence La Nina impacts in the Midwest. By utilizing the streamflow water year and the long lead time for the oceanic-atmospheric variables, useful information can be provided to streamflow forecasters and water managers.
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.
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.
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.
Estimated Wind River Range (Wyoming, USA) Glacier Melt Water Contributions to Agriculture
(MDPI, 2009-10-28) Cheesbrough, Kyle; Edmunds, Jake; Tootle, Glenn; Kerr, Greg; Pochop, Larry; University of Tennessee System; University of Tennessee Knoxville; University of Wyoming; University of Alabama Tuscaloosa
In 2008, Wyoming was ranked 8th in barley production and 20th in hay production in the United States and these crops support Wyoming's $800 million cattle industry. However, with a mean elevation of 2,040 meters, much of Wyoming has a limited crop growing season (as little as 60 days) and relies on late-summer and early-fall streamflow for agricultural water supply. Wyoming is host to over 80 glaciers with the majority of these glaciers being located in the Wind River Range. These "frozen reservoirs" provide a stable source of streamflow (glacier meltwater) during this critical late-summer and early-fall growing season. Given the potential impacts of climate change (increased temperatures resulting in glacier recession), the quantification of glacier meltwater during the late-summer and early-fall growing seasons is needed. Glacier area changes in the Wind River Range were estimated for 42 glaciers using Landsat data from 1985 to 2005. The total surface area of the 42 glaciers was calculated to be 41.2 +/- 11.7 km(2) in 1985 and 30.8 +/- 8.2 km(2) in 2005, an average decrease of 25% over the 21 year period. Small glaciers experienced noticeably more area reduction than large glaciers. Of the 42 glaciers analyzed, 17 had an area of greater than 0.5 km(2) in 1985, while 25 were less than 0.5 km(2) in 1985. The glaciers with a surface area less than 0.5 km(2) experienced an average surface area loss (fraction of 1985 surface area) of 43%, while the larger glaciers (greater than 0.5 km(2)) experienced an average surface area loss of 22%. Applying area-volume scaling relationships for glaciers, volume loss was estimated to be 409 x 106 m(3) over the 21 year period, which results in an estimated 4% to 10% contribution to warm season (July-October) streamflow.
Five Hundred Years of Hydrological Drought in the Upper Colorado River Basin
(2007-06) Timilsena, Janak; Piechota, Thomas C.; Hidalgo, Hugo; Tootle, Glenn; University of Alabama Tuscaloosa
This article evaluates drought scenarios of the Upper Colorado River basin (UCRB) considering multiple drought variables for the past 500 years and positions the current drought in terms of the magnitude and frequency. Drought characteristics were developed considering water-year data of UCRB’s streamflow, and basin-wide averages of the Palmer Hydrological Drought Index (PHDI) and the Palmer Z Index. Streamflow and drought indices were reconstructed for the last 500 years using a principal component regression model based on tree-ring data. The reconstructed streamflow showed higher variability as compared with reconstructed PHDI and reconstructed Palmer Z Index. The magnitude and severity of all droughts were obtained for the last 500 years for historical and reconstructed drought variables and ranked accordingly. The frequency of the current drought was obtained by considering two different drought frequency statistical approaches and three different methods of determining the beginning and end of the drought period (annual, 5-year moving, and ten year moving average). It was concluded that the current drought is the worst in the observed record period (1923-2004), but 6th to 14th largest in terms of magnitude and 1st to 12th considering severity in the past 500 years. Similarly, the current drought has a return period ranging from 37 to 103 years based on how the drought period was determined. It was concluded that if the 10-year moving average is used for defining the drought period, the current drought appears less severe in terms of magnitude and severity in the last 500 years compared with the results using 1- and 5-year averages.
GIS-Enabled Culvert Design: A Case Study in Tuscaloosa, Alabama
(Hindawi, 2018) Greer, Ashton D.; Wilbanks, Zachary B.; Clifton, Leah D.; Wilson, Bradford; Graettinger, Andrew J.; University of Alabama Tuscaloosa
A GIS-enabled culvert design module is presented. This module employs Python programming to combine a proposed culvert location, topography, land use, and rainfall data to automatically design a culvert. The module is embedded within ESRI ArcGIS 10.4 software, providing a seamless single platform that eliminates error propagation associated with cross-platform data transfer as well as providing 95% time savings over traditional calculation methods. The module uses United States Geological Survey digital elevation data to analyze watershed topography. Runoff coefficients are determined from data available through the National Land Cover Database. Rainfall data are retrieved from the National Oceanic and Atmospheric Administration and combined with watershed and land use information to calculate peak discharge using the rational method. Peak discharge is then combined with culvert design parameters to design a single-barrel culvert. The module was used to redesign ten existing culverts along a highway in Tuscaloosa, Alabama, resulting in designs for updated land cover and rainfall conditions. Results from the techniques developed herein can be used for planning purposes and to highlight vulnerabilities in the existing infrastructure. The automation methods may be extended to other hydrologic objectives and runoff mitigation design such as open-channel design and detention or retention ponds.
Greening China Naturally
(Springer, 2011) Cao, Shixiong; Sun, Ge; Zhang, Zhiqiang; Chen, Liding; Feng, Qi; Fu, Bojie; McNulty, Steve; Shankman, David; Tang, Jianwu; Wang, Yanhui; Wei, Xiaohua; United States Department of Agriculture (USDA); United States Forest Service; Beijing Forestry University; Chinese Academy of Sciences; Research Center for Eco-Environmental Sciences (RCEES); Cold & Arid Regions Environmental & Engineering Research Institute, CAS; University of Alabama Tuscaloosa; Marine Biological Laboratory - Woods Hole; Chinese Academy of Forestry; Research Institute of Forest Ecology, Environment and Protection, CAF; University of British Columbia; University of British Columbia Okanagan
China leads the world in afforestation, and is one of the few countries whose forested area is increasing. However, this massive "greening" effort has been less effective than expected; afforestation has sometimes produced unintended environmental, ecological, and socioeconomic consequences, and has failed to achieve the desired ecological benefits. Where afforestation has succeeded, the approach was tailored to local environmental conditions. Using the right plant species or species composition for the site and considering alternatives such as grassland restoration have been important success factors. To expand this success, government policy should shift from a forest-based approach to a results-based approach. In addition, long-term monitoring must be implemented to provide the data needed to develop a cost-effective, scientifically informed restoration policy.
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.
How can we reform the STEM tenure system for the 21st Century?
(National Academy of the Sciences, 2022) Clement, T. Prabhakar; University of Alabama Tuscaloosa
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.
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.

UA cloudflare authentication

Browse

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

Results Per Page

Sort Options