Transport Research International Documentation (TRID) https://trid.trb.org/ en-us Copyright © 2026. 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/ Network-Level Bridge Condition Degradation Prediction and Interpretation Analysis Based on a Hybrid Ensemble Model https://trid.trb.org/View/2701291 Wed, 13 May 2026 17:00:16 GMT https://trid.trb.org/View/2701291 Long-term wear resistance performance of asphalt pavements under multiple coupled factors based on indoor abrasion tests https://trid.trb.org/View/2666324  HGY (Granite) > SHY (Limestone). Regarding gradation types, the stone mastic asphalt (SMA)-graded pavements demonstrated the highest wear resistance, followed by the asphalt concrete (AC)-graded pavements; while the open-graded friction course (OGFC)-graded pavements exhibit relatively-poor performance. Under the high-temperature conditions (60℃), the pavement damage is diverse and severe (e.g., cracks, rutting, and depressions); whereas under the low-temperature conditions (0℃), the damage is relatively simple, mainly characterized by the particle loss and aggregate fragmentation. The machine learning analysis revealed that the XGBoost model performs the best in terms of error control and fitting accuracy and the aggregate lithology, gradation type, and temperature are the primary factors influencing the long-term wear resistance of asphalt pavements. The developed test device and predictive model can accurately simulate and forecast the wear performance degradation of asphalt pavements over different service years under indoor conditions, with high prediction accuracy.]]> Mon, 11 May 2026 08:50:46 GMT https://trid.trb.org/View/2666324 Flood Inundation-Induced Bridge Pressure Scour: Developing New Prediction Equations and Exploring Machine-Learning Methods https://trid.trb.org/View/2663201 Fri, 01 May 2026 14:33:17 GMT https://trid.trb.org/View/2663201 Railway ballast fouling detection using thermal imaging: integration of LSTM and XGBoost https://trid.trb.org/View/2649849 Tue, 28 Apr 2026 11:20:00 GMT https://trid.trb.org/View/2649849 How does home-based teleworking reshape commuting and activity participation? An empirical study in Shanghai using machine learning https://trid.trb.org/View/2654574 Tue, 21 Apr 2026 14:30:17 GMT https://trid.trb.org/View/2654574 Modeling spatial and temporal urban environmental noise using street view imagery and machine learning https://trid.trb.org/View/2655683 Tue, 21 Apr 2026 14:30:17 GMT https://trid.trb.org/View/2655683 XGBoost–LSTM Regional Traffic Congestion Ratio Prediction Integrating Spatio-Temporal and Weather Features https://trid.trb.org/View/2686151 Tue, 14 Apr 2026 10:09:15 GMT https://trid.trb.org/View/2686151 Analyzing the Nonlinear and Interaction Effects Mechanisms of Factors Influencing Intercity Travel Route Choice by Passenger Car Drivers: A Case Study from Guangxi, China https://trid.trb.org/View/2691017 Fri, 10 Apr 2026 16:00:16 GMT https://trid.trb.org/View/2691017 Extreme buffeting response of long-span bridges under probabilistic wind field: Environmental contours vs. brute-force Monte Carlo approaches https://trid.trb.org/View/2652502 Fri, 03 Apr 2026 12:12:45 GMT https://trid.trb.org/View/2652502 Metaheuristic-optimized XGBoost model for accurate prediction of rock fragmentation in mining projects https://trid.trb.org/View/2644138 Tue, 17 Mar 2026 09:48:46 GMT https://trid.trb.org/View/2644138 Shared electric scooter energy consumption modeling and influencing factor analysis https://trid.trb.org/View/2659444 Thu, 26 Feb 2026 09:22:44 GMT https://trid.trb.org/View/2659444 Strain field reconstruction of stiffened panels under sparse sensor configurations https://trid.trb.org/View/2633960 Mon, 23 Feb 2026 11:24:29 GMT https://trid.trb.org/View/2633960 The use of Ground Penetrating Radar and artificial intelligence for automated railway trackbed stratigraphy and Ballast Fouling assessment https://trid.trb.org/View/2642429 0.9), while sequential conditioning markedly improves deeper-layer predictions. Validation on field measurements acquired with a broadband (40–3000 MHz) GPR antenna array demonstrates good transferability of the methodology, with reliable stratigraphy reconstruction and dielectric-based material attribution along an operational track section. The framework unifies stratigraphy and fouling assessment in a single automated workflow, offering a scalable and interpretable alternative to invasive methods and paving the way for predictive maintenance at the network scale.]]> Thu, 19 Feb 2026 09:44:15 GMT https://trid.trb.org/View/2642429 RNN-XGBoost Integration into Digital Twins for Predicted Travel Time Geosimulation in Emergency Route Finding https://trid.trb.org/View/2642277 Wed, 18 Feb 2026 08:51:10 GMT https://trid.trb.org/View/2642277 An Optimization Model for Support Parameters of Arch Cover Method in Underground Station Excavation Based on Gradient-Boosted Regression and Metaheuristic Optimization https://trid.trb.org/View/2651391 Thu, 29 Jan 2026 17:01:58 GMT https://trid.trb.org/View/2651391