Transport Research International Documentation (TRID) https://trid.trb.org/ en-us Copyright © 2024. National Academy of Sciences. All rights reserved. http://blogs.law.harvard.edu/tech/rss tris-trb@nas.edu (Bill McLeod) tris-trb@nas.edu (Bill McLeod) Transport Research International Documentation (TRID) https://trid.trb.org/Images/PageHeader-wTitle.jpg https://trid.trb.org/ Automated Bridge Inspection Image Interpretation Based on Vision-Language Pre-Training https://trid.trb.org/View/2329235 Wed, 27 Mar 2024 16:52:50 GMT https://trid.trb.org/View/2329235 Using Road Design Priors to Improve Large-Scale 3D Road Scene Segmentation https://trid.trb.org/View/2329234 Wed, 27 Mar 2024 16:52:50 GMT https://trid.trb.org/View/2329234 Machine Learning-Based Ranking of Factors Influencing Human Movement Purposes for Supporting Human-Infrastructure Interaction Modeling https://trid.trb.org/View/2329219 Wed, 27 Mar 2024 16:52:50 GMT https://trid.trb.org/View/2329219 Deep Learning-Based Automation of Road Surface Extraction from UAV-Derived Dense Point Clouds in Large-Scale Environment https://trid.trb.org/View/2329214 Wed, 27 Mar 2024 16:52:50 GMT https://trid.trb.org/View/2329214 Vehicle maneuver evaluation in emergency condition https://trid.trb.org/View/2338891 Wed, 27 Mar 2024 16:52:17 GMT https://trid.trb.org/View/2338891 Statistics and Analysis on Education of Logistics Undergraduate https://trid.trb.org/View/2282559 Wed, 27 Mar 2024 11:52:26 GMT https://trid.trb.org/View/2282559 Research on the Application of Data Mining in Logistics Enterprise https://trid.trb.org/View/2282446 Wed, 27 Mar 2024 11:52:26 GMT https://trid.trb.org/View/2282446 Lessons for railways from project PROACTIVE on CBRNe risks and threats https://trid.trb.org/View/2314999 Wed, 27 Mar 2024 11:52:26 GMT https://trid.trb.org/View/2314999 Exploring the Factor Structure of a Modified Motorcyclist Behavior Questionnaire: Croatian Context https://trid.trb.org/View/2317275 Wed, 27 Mar 2024 11:51:55 GMT https://trid.trb.org/View/2317275 Modelling and simulation of an autonomous vehicle based on Alexnet for traffic sign recognition https://trid.trb.org/View/2343996 Wed, 27 Mar 2024 11:51:55 GMT https://trid.trb.org/View/2343996 Analysis of machine learning integration into supply chain management https://trid.trb.org/View/2344650 Wed, 27 Mar 2024 11:51:55 GMT https://trid.trb.org/View/2344650 Prediction of rail transit delays with machine learning: How to exploit open data sources https://trid.trb.org/View/2348248 Wed, 27 Mar 2024 11:51:55 GMT https://trid.trb.org/View/2348248 GPT-4 enhanced multimodal grounding for autonomous driving: Leveraging cross-modal attention with large language models https://trid.trb.org/View/2344584 Wed, 27 Mar 2024 11:51:55 GMT https://trid.trb.org/View/2344584 A machine learning based method for predicting the shear strength of Fiber-Reinforced Concrete joints in precast segmental bridges https://trid.trb.org/View/2317633  GBDT > RF > KNN > SVM > Lasso. The best-performing XGBoost model achieved performance metrics of R2 = 0.964, MAE = 41.318 kN, RMSE = 56.760 kN, and MAPE = 14.228%. The superior reliability of the XGBoost ensemble model was further validated through comparisons with current design codes and existing models. As ML-based models are typically black-box models, the SHapley Additive exPlanations (SHAP) method was employed to explicitly link the predicted output to the inputs. Based on SHAP results, confining stress (CS), depth of shear key (D), compressive strength of concrete (fcu), total height of joints (H), and height of flat part (Hsm) recognized as the crucial parameters affecting ultimate load.]]> Wed, 27 Mar 2024 11:51:14 GMT https://trid.trb.org/View/2317633 Prescriptive maintenance of prestressed concrete bridges considering digital twin and key performance indicator https://trid.trb.org/View/2317615 Wed, 27 Mar 2024 11:51:14 GMT https://trid.trb.org/View/2317615