scholarly journals Methods for Identifying Truck Crash Hotspots

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Wenrui Qu ◽  
Shaojie Liu ◽  
Qun Zhao ◽  
Yi Qi
Keyword(s):  
Difference Method ◽  
Normalization Method ◽  
The Other ◽  
Detection Methods ◽  
Hotspot Detection ◽  
Baseline Method ◽  
Annual Average ◽  
Fatal Crash ◽  
Annual Average Daily Traffic ◽  
Crash Hotspots

The goal of this study was to develop a new method for identifying the actual risky spots by using the geographic information system (GIS). For this purpose, in this study, three different methods for detecting hotspots are developed, i.e., (1) the annual average daily traffic (AADT) normalization method, (2) AK crashes (A is the incapacitating crash, and K is the fatal crash) percentage method, and (3) distribution difference method. To evaluate the performances of these three hotspot detection methods along with a baseline method that only considered the frequency of crashes, we applied these three methods to identify the top 20 hotspots for truck crashes in two representative areas in Texas. The results indicated that (1) all three proposed methods produced more reasonable results than the baseline method, and (2) the “distribution difference” method outperformed the other methods.

Estimation of Annual Average Daily Traffic on Low-Volume Roads: Factor Approach Versus Neural Networks

2000 ◽  
Vol 1719 (1) ◽  
pp. 103-111 ◽  
Author(s):  
Satish C. Sharma ◽  
Pawan Lingras ◽  
Guo X. Liu ◽  
Fei Xu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Traffic Monitoring ◽  
Network Approach ◽  
Annual Average ◽  
Neural Network Approach ◽  
The Neural Network ◽  
Low Volume Roads ◽  
Annual Average Daily Traffic ◽  
Low Volume

Estimation of the annual average daily traffic (AADT) for low-volume roads is investigated. Artificial neural networks are compared with the traditional factor approach for estimating AADT from short-period traffic counts. Fifty-five automatic traffic recorder (ATR) sites located on low-volume rural roads in Alberta, Canada, are used as study samples. The results of this study indicate that, when a single 48-h count is used for AADT estimation, the factor approach can yield better results than the neural networks if the ATR sites are grouped appropriately and the sample sites are correctly assigned to various ATR groups. Unfortunately, the current recommended practice offers little guidance on how to achieve the assignment accuracy that may be necessary to obtain reliable AADT estimates from a single 48-h count. The neural network approach can be particularly suitable for estimating AADT from two 48-h counts taken at different times during the counting season. In fact, the 95th percentile error values of about 25 percent as obtained in this study for the neural network models compare favorably with the values reported in the literature for low-volume roads using the traditional factor approach. The advantage of the neural network approach is that classification of ATR sites and sample site assignments to ATR groups are not required. The analysis of various groups of low-volume roads presented also leads to a conclusion that, when defining low-volume roads from a traffic monitoring point of view, it is not likely to matter much whether the AADT on the facility is less than 500 vehicles, less than 750 vehicles, or less than 1,000 vehicles.

scholarly journals Leveraging protein dynamics to identify cancer mutational hotspots using 3D structures

2019 ◽  
Vol 116 (38) ◽  
pp. 18962-18970 ◽  
Author(s):  
Sushant Kumar ◽  
Declan Clarke ◽  
Mark B. Gerstein
Keyword(s):  
Protein Dynamics ◽  
Protein Function ◽  
Large Scale ◽  
Protein Structures ◽  
Structural Data ◽  
The Cancer Genome Atlas ◽  
Detection Methods ◽  
Hotspot Detection ◽  
Driver Genes ◽  
Mutational Hotspots

Large-scale exome sequencing of tumors has enabled the identification of cancer drivers using recurrence-based approaches. Some of these methods also employ 3D protein structures to identify mutational hotspots in cancer-associated genes. In determining such mutational clusters in structures, existing approaches overlook protein dynamics, despite its essential role in protein function. We present a framework to identify cancer driver genes using a dynamics-based search of mutational hotspot communities. Mutations are mapped to protein structures, which are partitioned into distinct residue communities. These communities are identified in a framework where residue–residue contact edges are weighted by correlated motions (as inferred by dynamics-based models). We then search for signals of positive selection among these residue communities to identify putative driver genes, while applying our method to the TCGA (The Cancer Genome Atlas) PanCancer Atlas missense mutation catalog. Overall, we predict 1 or more mutational hotspots within the resolved structures of proteins encoded by 434 genes. These genes were enriched among biological processes associated with tumor progression. Additionally, a comparison between our approach and existing cancer hotspot detection methods using structural data suggests that including protein dynamics significantly increases the sensitivity of driver detection.

Enhancing Statewide Annual Average Daily Traffic Estimation with Ubiquitous Probe Vehicle Data

2020 ◽  
Vol 2674 (9) ◽  
pp. 649-660
Author(s):  
Xu Zhang ◽  
Mei Chen
Keyword(s):  
Model Performance ◽  
Improvement Program ◽  
Annual Average ◽  
Great Opportunity ◽  
Safety Reporting ◽  
Probe Vehicle ◽  
Vehicle Data ◽  
Annual Average Daily Traffic ◽  
Functional Classes ◽  
Aadt Estimation

Annual average daily traffic (AADT) is a critical input into many transportation applications, particularly safety reporting. For example, the Highway Safety Improvement Program in the U.S. requires states to make AADT data for all public paved roadways accessible by 2026. Because collecting traffic counts on every network segment is prohibitively expensive, a method capable of accurately estimating AADT on unmonitored segments is of great value to state DOTs. The ubiquitous probe vehicle data present a great opportunity to this end. This paper presents an enhanced method for statewide AADT estimation by leveraging such data in Kentucky. The use of the probe data is explored in two ways. First, an annual average daily probes (AADP) variable is derived from hourly probe counts; second, a betweenness centrality (BC) variable is calculated using probe speeds. Including both variables and using the random forest model results in model performance that exceeds those previously reported for statewide applications. Incorporating AADP and BC improves the accuracy of AADT estimates by 30%–37% for all roads and 23%–43% for highways in functional classes 5–7, compared with only using sociodemographic and roadway characteristics. These results demonstrate the value of the probe data for enhancing AADT estimation. The analysis further shows that on roadways having more than 53 AADP or an average of 2.2 probe counts per hour, the median and the mean absolute percent errors are below 20% and 25%, respectively. These findings have practical implications for state DOTs wanting to maximize the utility of probe vehicle data.

A dynamic algorithm for wildfire mapping with NOAA/AVHRR data

2004 ◽  
Vol 13 (3) ◽  
pp. 275 ◽  
Author(s):  
R. Pu ◽  
P. Gong ◽  
Z. Li ◽  
J. Scarborough
Keyword(s):  
Developmental Stages ◽  
Riparian Forest ◽  
Fire Detection ◽  
Detection Methods ◽  
Hotspot Detection ◽  
Fire Dynamics ◽  
California Department ◽  
Burn Scar ◽  
Dynamic Algorithm ◽  
Noaa Avhrr

A wildfire-mapping algorithm is proposed based on fire dynamics, called the dynamic algorithm. It is applied to daily NOAA/AVHRR/HRPT data for wildland areas (scrub, chaparral, grassland, marsh, riparian forest, woodland, rangeland and forests) in California for September and October 1999. Daily AVHRR images acquired from two successive days are compared for active fire detection and burn scar mapping. The algorithm consists of four stages: data preparation; hotspot detection; burn scar mapping; and final confirmation of potential burn scar pixels. Preliminary comparisons between the result mapped by the dynamic algorithm and the fire polygons collected by the California Department of Forestry and Fire Protection through ground survey indicate that the algorithm can track burn scars at different developmental stages at a daily level. The comparisons between wildfire mapping results produced by a modified version of an existing algorithm and the dynamic algorithm also indicate this point. This is the major contribution of this algorithm to wildfire detection methods. The dynamic algorithm requires highly precise registration between consecutive images.

scholarly journals A lectin-coupled porous silicon-based biosensor: label-free optical detection of bacteria in a real-time mode

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Mona Yaghoubi ◽  
Fereshteh Rahimi ◽  
Babak Negahdari ◽  
Ali Hossein Rezayan ◽  
Azizollah Shafiekhani
Keyword(s):  
Fourier Transform ◽  
Porous Silicon ◽  
Real Time ◽  
Limit Of Detection ◽  
Principal Component ◽  
The Other ◽  
Detection Methods ◽  
Carbohydrate Binding ◽  
Label Free ◽  
E Coli

Abstract Accuracy and speed of detection, along with technical and instrumental simplicity, are indispensable for the bacterial detection methods. Porous silicon (PSi) has unique optical and chemical properties which makes it a good candidate for biosensing applications. On the other hand, lectins have specific carbohydrate-binding properties and are inexpensive compared to popular antibodies. We propose a lectin-conjugated PSi-based biosensor for label-free and real-time detection of Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) by reflectometric interference Fourier transform spectroscopy (RIFTS). We modified meso-PSiO2 (10–40 nm pore diameter) with three lectins of ConA (Concanavalin A), WGA (Wheat Germ Agglutinin), and UEA (Ulex europaeus agglutinin) with various carbohydrate specificities, as bioreceptor. The results showed that ConA and WGA have the highest binding affinity for E. coli and S. aureus respectively and hence can effectively detect them. This was confirmed by 6.8% and 7.8% decrease in peak amplitude of fast Fourier transform (FFT) spectra (at 105 cells mL−1 concentration). A limit of detection (LOD) of about 103 cells mL−1 and a linear response range of 103 to 105 cells mL−1 were observed for both ConA-E. coli and WGA-S. aureus interaction platforms that are comparable to the other reports in the literature. Dissimilar response patterns among lectins can be attributed to the different bacterial cell wall structures. Further assessments were carried out by applying the biosensor for the detection of Klebsiella aerogenes and Bacillus subtilis bacteria. The overall obtained results reinforced the conjecture that the WGA and ConA have a stronger interaction with Gram-positive and Gram-negative bacteria, respectively. Therefore, it seems that specific lectins can be suggested for bacterial Gram-typing or even serotyping. These observations were confirmed by the principal component analysis (PCA) model.

Metabolizable energy values of corn and wheat middlings in broiler chickens

2019 ◽  
Vol 99 (4) ◽  
pp. 905-913 ◽  
Author(s):  
Andrew Dunaway ◽  
Sunday A. Adedokun
Keyword(s):  
Soybean Meal ◽  
Difference Method ◽  
Broiler Chickens ◽  
Dry Matter ◽  
Metabolizable Energy ◽  
The Other ◽  
N Utilization ◽  
Apparent Metabolizable Energy ◽  
Wheat Middlings ◽  
The Difference

Two experiments were conducted to evaluate adaptation length (AL) and composition of reference diets on nitrogen (N)-corrected apparent metabolizable energy (AMEn) in 22-d-old broilers. Birds were allocated to nine treatments (n = 6) consisting of wheat – soybean meal (SBM) (reference diet), corn–wheat–SBM, and wheat middlings (WM)–wheat–SBM (exp. 1), or oats–SBM (reference diet), corn–oats–SBM, and WM–oats–SBM (exp. 2) in conjunction with three AL (12, 8, and 4 d) in a factorial arrangement of treatments (3 × 3). Dry matter (DM), N, energy (En) utilization, and AMEn of corn and WM were determined using the difference method. In exp. 1, birds on the WM–wheat–SBM-based diet had the lowest (P < 0.05) DM, N, and En utilization, as well as AMEn compared with the other two diets. Additionally, AMEn for corn was higher (P < 0.05) compared with that of WM. In exp. 2, N utilization in birds on the corn–oats–SBM-based diet was lower (P < 0.05) compared with birds on the oats–SBM-based diet; however, AMEn of corn and WM was not different. In both experiments, AL was not significantly different. Based on these results, the composition of the reference diet could influence AMEn values of corn and WM in 22-d-old broilers.

Performance Comparison of Wideband Radar Target Detection Methods

2014 ◽  
Vol 971-973 ◽  
pp. 1680-1683
Author(s):  
Miao He ◽  
Li Yu Tian ◽  
Xiong Jun Fu ◽  
Yun Chen Jiang
Keyword(s):  
Target Detection ◽  
Pulse Train ◽  
Performance Comparison ◽  
The Other ◽  
Detection Methods ◽  
Radar Target ◽  
Random Parameters ◽  
Related Model ◽  
Performance Curves ◽  
Wideband Radar

In wideband radar situation, target-spread and all scattering points back wave could be considered as the pulse train of random parameters. The wideband radar target and built the related model. Then it gave two methods of target detection, one is Energy Accumulation and the other is the IPTRP. It also presented the simulation result of these two methods performance curves. It showed that the IPTRP improved by more than 3dB in the same SNR.

Advances in Uncertainty Treatment in FHWA Procedure for Estimating Annual Average Daily Traffic Volume

2012 ◽  
Vol 2308 (1) ◽  
pp. 148-156 ◽  
Author(s):  
Gregorio Gecchele ◽  
Riccardo Rossi ◽  
Massimiliano Gastaldi ◽  
Shinya Kikuchi
Keyword(s):  
Traffic Volume ◽  
Annual Average ◽  
Annual Average Daily Traffic
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