Relevant bibliographies by topics / Crash prediction

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Crash prediction'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "Crash prediction"

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Osman, Osama A., Mustafa Hajij, Peter R. Bakhit, and Sherif Ishak. "Prediction of Near-Crashes from Observed Vehicle Kinematics using Machine Learning." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 12 (2019): 463–73. http://dx.doi.org/10.1177/0361198119862629.

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This study introduces a machine learning model for near-crash prediction from observed vehicle kinematics data. The main hypothesis is that vehicles tend to experience discernible turbulence in their kinematics shortly before involvement in near-crashes. To test this hypothesis, the SHRP2 NDS vehicle kinematics data (speed, longitudinal acceleration, lateral acceleration, yaw rate, and pedal position) are utilized. Several machine learning algorithms are trained and comparatively analyzed including K nearest neighbor (KNN), random forest, support vector machine (SVM), decision trees, Gaussian
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Farooq, Muhammad Umer, and Aemal J. Khattak. "Investigating Highway–Rail Grade Crossing Inventory Data Quality’s Role in Crash Model Estimation and Crash Prediction." Applied Sciences 13, no. 20 (2023): 11537. http://dx.doi.org/10.3390/app132011537.

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The highway–rail grade crossings (HRGCs) crash frequency models used in the US are based on the Federal Railroad Administration’s (FRA) database for highway–rail crossing inventory. Inaccuracies or missing values within this database directly impact the estimated parameters of the crash models and subsequent crash predictions. Utilizing a set of 560 HRGCs in Nebraska, this research demonstrates variations in crash predictions estimated by the FRA’s 2020 Accident Prediction (AP) model under two scenarios: firstly, employing the unchanged, original FRA HRGCs inventory dataset as the input, and s
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Zhao, Liping, Feng Li, Dongye Sun, and Fei Dai. "Highway Traffic Crash Risk Prediction Method considering Temporal Correlation Characteristics." Journal of Advanced Transportation 2023 (February 15, 2023): 1–13. http://dx.doi.org/10.1155/2023/9695433.

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Crash risk analysis and prediction are considered the premise of highway traffic safety control, which directly affects the accuracy and effectiveness of traffic safety decisions. A highway traffic crash risk prediction method considering temporal correlation characteristics is proposed in this research. Firstly, the case-control sample analysis method is used to extract 6 time series sample data composed of crash traffic flow data and corresponding non-crash traffic flow data for crash risk analysis and prediction. Secondly, the multiparameter fusion clustering analysis method is used to indi
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Lei, Tian, Jia Peng, Xingliang Liu, and Qin Luo. "Crash Prediction on Expressway Incorporating Traffic Flow Continuity Parameters Based on Machine Learning Approach." Journal of Advanced Transportation 2021 (March 29, 2021): 1–13. http://dx.doi.org/10.1155/2021/8820402.

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Real-time crash prediction helps identify and prevent the occurrence of traffic crash. For years, various real-time crash prediction models have been investigated to provide effective information for proactive traffic management. When building real-time crash prediction model, a suitable variable space together with a specific time interval for traffic data aggregation and an appropriate modelling algorithm should be applied. Regarding the intercorrelation problem with variable space, comprehensive real-time crash prediction model considering available traffic data characteristics in applicabl
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Hassouna, Fady M. A., and Khaled Al-Sahili. "Practical Minimum Sample Size for Road Crash Time-Series Prediction Models." Advances in Civil Engineering 2020 (December 29, 2020): 1–12. http://dx.doi.org/10.1155/2020/6672612.

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Road crashes are problems facing the transportation sector. Crash data in many countries are available only for the past 10 to 20 years, which makes it difficult to determine whether the data are sufficient to establish reasonable and accurate prediction rates. In this study, the effect of sample size (number of years used to develop a prediction model) on the crash prediction accuracy using Autoregressive integrated moving average (ARIMA) method was investigated using crash data for years 1971–2015. Based on the availability of annual crash records, road crash data for four selected countries
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Liu, Miaomiao, and Yongsheng Chen. "Predicting Real-Time Crash Risk for Urban Expressways in China." Mathematical Problems in Engineering 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/6263726.

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We developed a real-time crash risk prediction model for urban expressways in China in this study. About two-year crash data and their matching traffic sensor data from the Beijing section of Jingha expressway were utilized for this research. The traffic data in six 5-minute intervals between 0 and 30 minutes prior to crash occurrence was extracted, respectively. To obtain the appropriate data training period, the data (in each 5-minute interval) during six different periods was collected as training data, respectively, and the crash risk value under different data conditions was defined. Then
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Naderan, Ali, and Jalil Shahi. "Aggregate crash prediction models: Introducing crash generation concept." Accident Analysis & Prevention 42, no. 1 (2010): 339–46. http://dx.doi.org/10.1016/j.aap.2009.08.020.

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Chen, Zhi, Xiao Qin, Renxin Zhong, Pan Liu, and Yang Cheng. "Predicting Imminent Crash Risk with Simulated Traffic from Distant Sensors." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 38 (2018): 12–21. http://dx.doi.org/10.1177/0361198118791379.

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The aim of this research was to investigate the performance of simulated traffic data for real-time crash prediction when loop detector stations are distant from the actual crash location. Nearly all contemporary real-time crash prediction models use traffic data from physical detector stations; however, the distance between a crash location and its nearest detector station can vary considerably from site to site, creating inconsistency in detector data retrieval and subsequent crash prediction. Moreover, large distances between crash locations and detector stations imply that traffic data fro
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Paja, W., M. Wrzesień, R. Niemiec, and W. R. Rudnicki. "Application of all relevant feature selection for failure analysis of parameter-induced simulation crashes in climate models." Geoscientific Model Development Discussions 8, no. 7 (2015): 5419–35. http://dx.doi.org/10.5194/gmdd-8-5419-2015.

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Abstract. The climate models are extremely complex pieces of software. They reflect best knowledge on physical components of the climate, nevertheless, they contain several parameters, which are too weakly constrained by observations, and can potentially lead to a crash of simulation. Recently a study by Lucas et al. (2013) has shown that machine learning methods can be used for predicting which combinations of parameters can lead to crash of simulation, and hence which processes described by these parameters need refined analyses. In the current study we reanalyse the dataset used in this res
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Gill, G., T. Sakrani, W. Cheng, and J. Zhou. "COMPARISON OF ADJACENCY AND DISTANCE-BASED APPROACHES FOR SPATIAL ANALYSIS OF MULTIMODAL TRAFFIC CRASH DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 14, 2017): 1157–61. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-1157-2017.

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Many studies have utilized the spatial correlations among traffic crash data to develop crash prediction models with the aim to investigate the influential factors or predict crash counts at different sites. The spatial correlation have been observed to account for heterogeneity in different forms of weight matrices which improves the estimation performance of models. But very rarely have the weight matrices been compared for the prediction accuracy for estimation of crash counts. This study was targeted at the comparison of two different approaches for modelling the spatial correlations among
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Dissertations / Theses on the topic "Crash prediction"

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Fischhaber, Pamela Marie. "Development of light rail crossing specific crash prediction models." Thesis, University of Colorado at Denver, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3621825.

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<p> Existing railroad crossing crash prediction and hazard index equations are analyzed and found to inadequately measure safety at light rail crossings. The operational characteristics of common carrier freight and commuter railroads are different enough from the operational characteristics of light rail to affect the ability of existing railroad equations to accurately predict the number of crashes that occur at light rail crossings. These operational differences require light rail specific crash prediction equations to better predict crash numbers at light rail crossings. The goal of this r
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Broussard, Nicholas. "Development of Crash Prediction Models for Transportation Planning Analysis." Thesis, University of Louisiana at Lafayette, 2016. http://pqdtopen.proquest.com/#viewpdf?dispub=10002446.

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<p> Transportation planning is a vital and necessary operation for a metropolitan area to grow. As such, and in order to receive Federal funding for transportation projects, metropolitan areas engage in transportation planning as regulated by MAP-21. One element of meeting MAP-21 requirements is addressing the safety of a region. With new requirements by MAP-21, MPOs must demonstrate some sort of performance measure showing changes in the various elements, making quantitative means of displaying these changes ever more important. </p><p> The goal of this project was to develop a model or se
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KASHAYI, NAGARAJU C. "MODELING BASE CRASH RATES FOR INTERSECTIONS." University of Cincinnati / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1163775240.

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Kuchangi, Shamanth. "A categorical model for traffic incident likelihood estimation." Thesis, Texas A&M University, 2006. http://hdl.handle.net/1969.1/4661.

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In this thesis an incident prediction model is formulated and calibrated. The primary idea of the model developed is to correlate the expected number of crashes on any section of a freeway to a set of traffic stream characteristics, so that a reliable estimation of likelihood of crashes can be provided on a real-time basis. Traffic stream variables used as explanatory variables in this model are termed as “incident precursors”. The most promising incident precursors for the model formulation for this research were determined by reviewing past research. The statistical model employed
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Bastien, C. "The prediction of kinematics and injury criteria of unbelted occupants under autonomous emergency braking." Thesis, Coventry University, 2014. http://curve.coventry.ac.uk/open/items/a75e046a-3ffb-4474-8b28-e3c19ffbb3b5/1.

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This thesis comprises a programme of work investigating the use of active human computer models and the effects of forthcoming automotive safety features on vehicle occupants; more specifically, their unbelted kinematics and sustained injuries. Since Hybrid III anthropometric crash test dummies are unable to replicate human occupant kinematics under severe braking, the thesis highlighted the need to research the most appropriate occupant computer model to simulate active safety scenarios. The first stage of the work focussed on occupant kinematics and developed unique human occupant reflex res
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Amiridis, Kiriakos. "THE USE OF 3-D HIGHWAY DIFFERENTIAL GEOMETRY IN CRASH PREDICTION MODELING." UKnowledge, 2019. https://uknowledge.uky.edu/ce_etds/85.

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The objective of this research is to evaluate and introduce a new methodology regarding rural highway safety. Current practices rely on crash prediction models that utilize specific explanatory variables, whereas the depository of knowledge for past research is the Highway Safety Manual (HSM). Most of the prediction models in the HSM identify the effect of individual geometric elements on crash occurrence and consider their combination in a multiplicative manner, where each effect is multiplied with others to determine their combined influence. The concepts of 3-dimesnional (3-D) representatio
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Lu, Jinyan. "Development of Safety Performance Functions for SafetyAnalyst Applications in Florida." FIU Digital Commons, 2013. http://digitalcommons.fiu.edu/etd/880.

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In 2010, the American Association of State Highway and Transportation Officials (AASHTO) released a safety analysis software system known as SafetyAnalyst. SafetyAnalyst implements the empirical Bayes (EB) method, which requires the use of Safety Performance Functions (SPFs). The system is equipped with a set of national default SPFs, and the software calibrates the default SPFs to represent the agency’s safety performance. However, it is recommended that agencies generate agency-specific SPFs whenever possible. Many investigators support the view that the agency-specific SPFs represent the ag
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Siddiqui, Chowdhury. "Macroscopic Traffic Safety Analysis Based on Trip Generation Characteristics." Master's thesis, University of Central Florida, 2009. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/3385.

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Recent research has shown that incorporating roadway safety in transportation planning has been considered one of the active approaches to improve safety. Aggregate level analysis for predicting crash frequencies had been contemplated to be an important step in this process. As seen from the previous studies various categories of predictors at macro level (census blocks, traffic analysis zones, census tracts, wards, counties and states) have been exhausted to find appropriate correlation with crashes. This study contributes to this ongoing macro level road safety research by investigating vari
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Silva, Karla Cristina Rodrigues. "Assessing the transferability of crash prediction models for two lane highways in Brazil." Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/18/18144/tde-10112017-215500/.

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The present study focused on evaluating some crash prediction models for two lane highways on Brazilian conditions. Also, the transferability of models was considered, specifically by means of a comparison between Brazil, HSM and Florida. The analysis of two lane highways crash prediction models was promising when the road characteristics were well known and there was not much difference from base conditions. This conclusion was attained regarding the comparison of results for all segments, non-curved segments and curved segments, confirming that a transferred model can be used with caution. I
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Karki, Bipin. "Development of a Crash Prediction Model for Signalized T-Intersections in Queensland, Australia." Thesis, Griffith University, 2014. http://hdl.handle.net/10072/365442.

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Vehicle crashes at signalized intersections have long been of utmost concern to the transport authorities. Some researchers have developed crash prediction models (CPMs) for roundabouts in Queensland to establish the relationship among crashes, geometric parameters, and traffic conditions. However, to date, no CPM has been developed for the signalized intersections in Queensland. In this dissertation, two CPMs for signalized T-intersections in Queensland, Australia are developed: a CPM at intersection level and a CPM at approach level. The proposed models can be used for better control/organiz
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Books on the topic "Crash prediction"

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Ivan, John N., Sha Al Mamun, Nalini Ravishanker, et al. Improved Prediction Models for Crash Types and Crash Severities. Transportation Research Board, 2021. http://dx.doi.org/10.17226/26164.

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Ferguson, Erin, James Bonneson, Lee Rodegerdts, et al. Development of Roundabout Crash Prediction Models and Methods. Transportation Research Board, 2019. http://dx.doi.org/10.17226/25360.

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Srinivasan, Raghavan, Bo Lan, Caroline Mozingo, et al. Understanding and Communicating Reliability of Crash Prediction Models. Transportation Research Board, 2021. http://dx.doi.org/10.17226/26440.

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Utah, Juan Medina Jeffrey Taylor University of. Intersection Crash Prediction Methods for the Highway Safety Manual. Transportation Research Board, 2021. http://dx.doi.org/10.17226/26153.

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Pearce, Fred. The coming population : crash and our planet's surprising future. Beacon Press, 2010.

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Srinivasan, Raghavan, Bo Lan, Caroline Mozingo, et al. Reliability of Crash Prediction Models: A Guide for Quantifying and Improving the Reliability of Model Results. Transportation Research Board, 2021. http://dx.doi.org/10.17226/26517.

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Pandemic Crash Of 2029: An AMATEUR ECONOMIST's ANALYSIS of the GREAT CRASH of 1929 and PREDICTION of the PANDEMIC CRASH Of 2029. Independently Published, 2020.

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Coming Economic Tsunami: A Predictive Timeline for the Crash of the U.S. and Global Financial Markets. NarrowGate Publishing, 2024.

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Coming Economic Tsunami: A Predictive Timeline for the Crash of the U.S. and Global Financial Markets. NarrowGate Publishing, 2023.

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Coming Economic Tsunami: A Predictive Timeline for the Crash of the U.S. and Global Financial Markets. NarrowGate Publishing, 2024.

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Book chapters on the topic "Crash prediction"

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Sumayya Naznin, P. H., Leena Samuel Panackel, Sowmya Zaviar, and Shiya Babu. "Accident Prediction Modelling and Crash Scene Investigation." In Lecture Notes in Civil Engineering. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12011-4_94.

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Srivastava, Nishtha, Rishabh Maloo, Durgesh Suthar, Bhavesh N. Gohil, and Suprio Ray. "Traffic Crash Severity Prediction Using eXplainable AI." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-88042-1_6.

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Rokade, Siddhartha, and Rakesh Kumar. "Road Crash Prediction Model for Medium Size Indian Cities." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-0589-4_61.

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Cappelli, Giuseppe, Sofia Nardoianni, Mauro D’Apuzzo, and Vittorio Nicolosi. "Interpretable Crash Severity Prediction Models to Improve Cyclist Safety." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-97654-4_20.

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Haj-Salem, Habib, and Jean-Patrick Lebacque. "Risk Index Modeling for Real-Time Motorway Traffic Crash Prediction." In Traffic and Granular Flow ’07. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-77074-9_5.

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Se, Chamroeun, Thanapong Champahom, Sajjakaj Jomnonkwao, Ampol Karoonsoontawong, Tassana Boonyoo, and Vatanavongs Ratanavaraha. "Deep Learning-Based Convolutional Neural Network for Crash Severity Prediction." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-74127-2_7.

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Mandal, Ranju, and Rupam Deb. "Real-Time Road Crash Severity Prediction for Optimized Resource Allocation." In Communications in Computer and Information Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-6966-0_26.

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Oneto, Luca, Andrea Coraddu, Paolo Sanetti, et al. "Marine Safety and Data Analytics: Vessel Crash Stop Maneuvering Performance Prediction." In Artificial Neural Networks and Machine Learning – ICANN 2017. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68612-7_44.

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Molina, Jesus-Enrique, Andres Mora-Valencia, and Javier Perote. "Financial Market Crash Prediction Through Analysis of Stable and Pareto Distributions." In Mathematical and Statistical Methods for Actuarial Sciences and Finance. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-78965-7_52.

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Feng, Xiao. "Study on the Spot-Weld Failure Prediction Model in Auto Crash Simulation." In Lecture Notes in Electrical Engineering. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33805-2_4.

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Conference papers on the topic "Crash prediction"

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Benfaress, Ilyass, Afaf Bouhoute, and Ahmed Zinedine. "Explainable Aircraft Crash Severity Prediction Using XGBoost and SHAP." In 2025 5th International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET). IEEE, 2025. https://doi.org/10.1109/iraset64571.2025.11007966.

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Sequeira, Gerald Joy, Maximilian Inderst, Redjon Xhiku, Robert Lugner, and Thomas Brandmeier. "Sigmoid-Based Method for Longitudinal Crash Pulse Prediction in Intelligent Vehicles." In 2024 IEEE International Conference on Vehicular Electronics and Safety (ICVES). IEEE, 2024. https://doi.org/10.1109/icves61986.2024.10927899.

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Sivakumar, Anush Kumar, Thanaraj T., and Mir Feroskhan. "Data-Driven and Explainable Artificial Intelligence Modelling for Quadrotor Crash Area Prediction." In 2025 International Conference on Unmanned Aircraft Systems (ICUAS). IEEE, 2025. https://doi.org/10.1109/icuas65942.2025.11007870.

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R, Rana Veer Samara Sihman Bharattej, Ammar H. Shnain, Ramy Riad Al-Fatlawy, P. B. Edwin Prabhakar, and Subhra Chakraborty. "Crash Injury Prediction Using Bidirectional Gated Recurrent Unit with Multi Head Attention Network." In 2024 International Conference on Integrated Intelligence and Communication Systems (ICIICS). IEEE, 2024. https://doi.org/10.1109/iciics63763.2024.10859365.

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Changala, Ravindra, Sameer Yadav, Kathari Santosh, Pravin D. Sawant, Veera Ankalu Vuyyuru, and B. Kiran Bala. "Proactive Market Crash Prediction: Investigating GNN-LSTM Networks for Early Detection in Stock Markets." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10726065.

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Morales, Ludivine, Manuel Bied, and Alexey Vinel. "Towards Multi-Modal Crash Prediction Based on V2X and Visual Information Using a Social Robot." In 2025 IEEE Vehicular Networking Conference (VNC). IEEE, 2025. https://doi.org/10.1109/vnc64509.2025.11054130.

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Haris, Muhammad, Minhaj Ahmed Moin, Farooq Abdul Rehman, and Muhammad Farhan. "Vehicle Crash Prediction using Vision." In 2021 55th Annual Conference on Information Sciences and Systems (CISS). IEEE, 2021. http://dx.doi.org/10.1109/ciss50987.2021.9400257.

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Ameksa, Mohammed, Hajar Mousannif, Hassan Al Moatassime, and Zouhair Elamrani Abou Elassad. "Crash Prediction using Ensemble Methods." In INTERNATIONAL CONFERENCE ON BIG DATA, MODELLING AND MACHINE LEARNING (BML'21). SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010731200003101.

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H. Owen, Susan, Jeffrey W. Joyner, Peng Zhang, and Stewart C. Wang. "Occupant-Based Injury Severity Prediction." In 65th Stapp Car Crash Conference. SAE International, 2022. http://dx.doi.org/10.4271/2021-22-0002.

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Hardy, Warren N., Lawrence W. Schneider, and Stephen W. Rouhana. "Prediction of Airbag-Induced Forearm Fractures and Airbag Aggressivity." In STAPP Car Crash Conference. SAE International, 2001. http://dx.doi.org/10.4271/2001-22-0024.

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Reports on the topic "Crash prediction"

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Ogunbire, Abimbola, Panick Kalambay, Hardik Gajera, and Srinivas Pulugurtha. Deep Learning, Machine Learning, or Statistical Models for Weather-related Crash Severity Prediction. Mineta Transportation Institute, 2023. http://dx.doi.org/10.31979/mti.2023.2320.

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Nearly 5,000 people are killed and more than 418,000 are injured in weather-related traffic incidents each year. Assessments of the effectiveness of statistical models applied to crash severity prediction compared to machine learning (ML) and deep learning techniques (DL) help researchers and practitioners know what models are most effective under specific conditions. Given the class imbalance in crash data, the synthetic minority over-sampling technique for nominal (SMOTE-N) data was employed to generate synthetic samples for the minority class. The ordered logit model (OLM) and the ordered p
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Nordback, Krista, Sirisha Kothuri, Wesley Marhsall, Geoff Gibson, and Nick Ferenchak. Improving Bicycle Crash Prediction for Urban Road Segments. Transportation Research and Education Center, 2017. http://dx.doi.org/10.15760/trec.193.

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Bellogi, Pietro, Intaek Lee, and Nissar Ahmed. Car-to-Bicycle Side Crash Simulation~Cyclist Head Injury Prediction and Car Design Optimization. SAE International, 2005. http://dx.doi.org/10.4271/2005-08-0523.

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Sriraj, P. S., Kazuya Kawamura, Paul Metaxatos, et al. Railroad-Highway Crossing Safety Improvement Evaluation and Prioritization Tool. Illinois Center for Transportation, 2023. http://dx.doi.org/10.36501/0197-9191/23-009.

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The expected crash frequency model of Illinois Department of Transportation’s Bureau of Design and Environment needed improvement to incorporate track circuitry as well as pedestrian exposure at railroad-highway grade crossings to make the model more comprehensive. The researchers developed, calibrated, and validated three models to predict collision rates at public, at-grade railroad-highway crossings in Illinois’ six-county northeast region for prioritizing railroad-highway crossings for safety improvements. The first model updated B-factors in the existing Illinois model, which was last val
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Mathew, Sonu, Srinivas S. Pulugurtha, and Sarvani Duvvuri. Modeling and Predicting Geospatial Teen Crash Frequency. Mineta Transportation Institute, 2022. http://dx.doi.org/10.31979/mti.2022.2119.

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This research project 1) evaluates the effect of road network, demographic, and land use characteristics on road crashes involving teen drivers, and, 2) develops and compares the predictability of local and global regression models in estimating teen crash frequency. The team considered data for 201 spatially distributed road segments in Mecklenburg County, North Carolina, USA for the evaluation and obtained data related to teen crashes from the Highway Safety Information System (HSIS) database. The team extracted demographic and land use characteristics using two different buffer widths (0.25
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Arhin, Stephen, Babin Manandhar, and Adam Gatiba. Influence of Pavement Conditions on Commercial Motor Vehicle Crashes. Mineta Transportation Institute, 2023. http://dx.doi.org/10.31979/mti.2023.2343.

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Commercial motor vehicle (CMV) safety is a major concern in the United States, including the District of Columbia (DC), where CMVs make up 15% of traffic. This research uses a comprehensive approach, combining statistical analysis and machine learning techniques, to investigate the impact of road pavement conditions on CMV accidents. The study integrates traffic crash data from the Traffic Accident Reporting and Analysis Systems Version 2.0 (TARAS2) database with pavement condition data provided by the District Department of Transportation (DDOT). Data spanning from 2016 to 2020 was collected
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WEB CRIPPLING BEHAVIOUR OF COLD-FORMED STEEL CHANNELS WEB HOLES UNDER END TWO FLANGE (ETF) LOADING. The Hong Kong Institute of Steel Construction, 2024. https://doi.org/10.18057/ijasc.2024.20.4.9.

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Abstract:
The design of the web-crippling behavior of cold-formed steel elements (CFS), which have been widely used in recent years, is essential. The concentrated loads acting on CFS members cause the section's web to crush and buckle. For this reason, it is necessary to calculate the web crippling strength correctly in the design of CFS sections. In order to observe the web-crippling behavior of CFS channel sections with holes drilled in the webs, this paper presents experimental and numerical experiments. Seven sections of the real-world system intended for End Two Flange (ETF) loading scenarios unde
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