Intelligent Transportation Infrastructure最新文献_第2页

A critical review of automated extraction of rock mass parameters using 3D point cloud data 基于三维点云数据的岩体参数自动提取技术综述

Intelligent Transportation Infrastructure Pub Date : 2023-05-23 DOI: 10.1093/iti/liad005

Jiayao Chen, Qian Fang, Dingli Zhang, Hong-wei Huang

{"title":"A critical review of automated extraction of rock mass parameters using 3D point cloud data","authors":"Jiayao Chen, Qian Fang, Dingli Zhang, Hong-wei Huang","doi":"10.1093/iti/liad005","DOIUrl":"https://doi.org/10.1093/iti/liad005","url":null,"abstract":"\u0000 In this paper, a critical review is conducted to understand the current research status of the quantification technology for obtaining three-dimensional (3D) point cloud information of rock mass and extracting structural key information, which is a major challenge and problem facing rock engineering. The timely and accurate acquisition of rock mass data and fine characterization of rock mass parameters can avoid unnecessary personnel injury and property damage. Firstly, the methods of point cloud information acquisition and structural information extraction are systematically summarized and classified. Then, various existing methods are analysed for their advantages and disadvantages. Based on this analysis, the future development direction of relevant technologies is proposed to improve the level of acquisition of 3D information of rock mass and the level of extraction of key information of rock mass. The results indicate that rock mass point cloud information acquisition technology can be classified into two types: laser point cloud acquisition and image reconstruction based on Structure from Motion (SfM) algorithm. Rock mass structural information can be classified into rock mass structural planes and their attitudes, rock mass traces and their geometric parameters, and other rock mass parameters, including structural plane roughness, spacing, and block characteristics, etc. Different acquisition technologies and feature extraction methods have their own advantages, disadvantages, and applicable ranges. Therefore, a comprehensive selection of various evaluation methods should be made based on specific engineering characteristics and existing data situations in practice.","PeriodicalId":191628,"journal":{"name":"Intelligent Transportation Infrastructure","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116579409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Intersection Analysis Using Computer Vision Techniques with SUMO 基于计算机视觉技术的相扑交叉分析

Intelligent Transportation Infrastructure Pub Date : 2023-05-10 DOI: 10.1093/iti/liad003

Mohammad Shokrolah Shirazi, Brendan Tran Morris, Shiqi Zhang

{"title":"Intersection Analysis Using Computer Vision Techniques with SUMO","authors":"Mohammad Shokrolah Shirazi, Brendan Tran Morris, Shiqi Zhang","doi":"10.1093/iti/liad003","DOIUrl":"https://doi.org/10.1093/iti/liad003","url":null,"abstract":"\u0000 This paper presents intersection analysis using computer vision techniques with Simulation of Urban MObility (SUMO). At first, an efficient deep-visual tracking pipeline is proposed by using the off-the-shelf YOLO object detection architecture and cascading it with a discriminative correlation filter (CSRT) to produce reliable trajectories for traffic analysis of vehicles and pedestrians. While a variety of traffic measurements can be directly estimated from the extracted trajectories (e.g., speed, turning movement count), a method of incorporating turning movement count (TMC) within SUMO is proposed in order to mimic a realistic traffic flow for an observed intersection and its comprehensive analysis. Experimental evaluations on developed tracking system implies that YOLOv5 variant is the best for traffic cameras and after appropriate fine-tuning using the UNLV Pedestrian data-set, the YOLOv5 performance manifested a significant improvement with value of 0.62 in recall value. The tracking system is further employed for monitoring three other intersections in the downtown of Las Vegas and turning movement counts were estimated for peak hours of morning and evening time of one day 7:00-9:00 and 16:00-18:00) with 15 minutes intervals. Finally, the intersection design including traffic signals with estimated TMC are used to calibrate SUMO to provide critical parameters (e.g., lane density, travel time, occupancy) for traffic signal performance evaluation and comprehensive intersection analysis. The signal design treatment demonstrates significant improvement for travel times and simulation results indicates that turning left ratio is a crucial factor affecting the travel time of vehicles on each intersection leg.","PeriodicalId":191628,"journal":{"name":"Intelligent Transportation Infrastructure","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126702621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A review of hybrid physics-based machine learning approaches in traffic state estimation 交通状态估计中基于混合物理的机器学习方法综述

Intelligent Transportation Infrastructure Pub Date : 2023-05-05 DOI: 10.1093/iti/liad002

Zhao Zhang, X. Yang, Han Yang

引用次数: 1

Recent advances in subgrade engineering for high-speed railway 高速铁路路基工程研究进展

Intelligent Transportation Infrastructure Pub Date : 2023-02-04 DOI: 10.1093/iti/liad001

Xianfeng Liu, Junhua Xiao, Degou Cai, Qian Su, Guangqing Yang, S. Yuan, G. Jiang

{"title":"Recent advances in subgrade engineering for high-speed railway","authors":"Xianfeng Liu, Junhua Xiao, Degou Cai, Qian Su, Guangqing Yang, S. Yuan, G. Jiang","doi":"10.1093/iti/liad001","DOIUrl":"https://doi.org/10.1093/iti/liad001","url":null,"abstract":"\u0000 In the last decade, the design and construction technologies of subgrade in high-speed railway developed significantly. This paper reviewed corresponding development in five aspects, including mechanical properties of fill materials, dynamic performance of subgrade, foundation treatment, retaining structure and smart construction technologies. It showed that for unbonded granular materials, it was acceptable to use static strength for subgrade design, but for clayey soil it would be more appropriate to base on shakedown theory. The mechanism for lime modified clay has been thoroughly reviewed, and the effect of lime content, curing age, and curing conditions on the behavior of lime-treated clay was discussed. The dynamic response of subgrade, especially the long-term deformation and dynamic stability analysis were important to understanding the behavior of high-speed railway subgrade. The effect of track types, operation speed etc. on the dynamic response of subgrade were reviewed first. Then, the prediction methods, influencing factors, and corresponding issue for long-term deformation of subgrade were presented, following by the methods used for dynamic stability analysis. Three types of foundation treatment methods, including geosynthetic-reinforced pile-supported embankment, pile-raft structure, and pile-plate structure, were reviewed for the corresponding load transmission mechanism, and application scenario. The static and dynamic behavior of four types of retaining structures were presented, including cantilever retaining wall, geosynthetic reinforced soil retaining wall, anchored retaining structure, and retaining wall reinforced by soil nailing. Finally, a series of new technologies correlated to smart construction was introduced, relating to the survey, design, construction, detection and management of subgrade.","PeriodicalId":191628,"journal":{"name":"Intelligent Transportation Infrastructure","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132947699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Simulating and Analyzing aggressive car-following behavior for testing autonomous vehicles 针对自动驾驶汽车测试的侵略性尾随行为仿真与分析

Intelligent Transportation Infrastructure Pub Date : 2022-12-05 DOI: 10.1093/iti/liac022

Li Tang, Abhishek Gupta, Ziyi Liu, Chunming Qiao, Qing He

{"title":"Simulating and Analyzing aggressive car-following behavior for testing autonomous vehicles","authors":"Li Tang, Abhishek Gupta, Ziyi Liu, Chunming Qiao, Qing He","doi":"10.1093/iti/liac022","DOIUrl":"https://doi.org/10.1093/iti/liac022","url":null,"abstract":"\u0000 In a mixed traffic flow, evaluating the operation safety of autonomous vehicles (AVs) is crucial under different aggressive car-following behavior of surrounding human driven-vehicles (HVs). To pursue this goal, this paper develops a machine-learning-based simulation method accompanied by a traditional car-following model. We integrate the unsupervised learning method, Approximate Bayesian computation, and Gipps car-following model to obtain different parameter distributions of the Gipps model. After utilizing the key parameter distribution, this paper employs a supervised learning method to predict the aggressive index of human-driven vehicles in each individual car-following event. Further, we verify the outputs of this simulation model with the NGSIM I-80 traffic dataset. After the validation, we develop an AV simulation study to analyze Avs’ performance in different aggressive HVs scenarios. The result indicates that the higher penetration rate of AV is essential for stabilizing AVs’ performance in terms of velocity and the probability of involving in a crash. Additionally, aggressive HV drivers significantly impact scenarios of low AV penetration rate regarding safety issues.","PeriodicalId":191628,"journal":{"name":"Intelligent Transportation Infrastructure","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131086355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Review of integrated full-lifecycle data management and application of the slab tracks 板坯轨道全生命周期综合数据管理与应用综述

Intelligent Transportation Infrastructure Pub Date : 2022-11-29 DOI: 10.1093/iti/liac018

Wenlong Ye, J. Ren, P. Zhang, Qi Zhang, Lon-biau Li

{"title":"Review of integrated full-lifecycle data management and application of the slab tracks","authors":"Wenlong Ye, J. Ren, P. Zhang, Qi Zhang, Lon-biau Li","doi":"10.1093/iti/liac018","DOIUrl":"https://doi.org/10.1093/iti/liac018","url":null,"abstract":"\u0000 Full-lifecycle data management and application of high-speed railway slab track has multiple benefits. While serving as the basis for scientific periodic and predictive maintenance, it is also the key to extending the service life of the track structure and improving train safety and smoothness. The paper provides a comprehensive review of the multi-source data collection methods, the integrated data management, and the multi-dimensional data applications for slab tracks from a full-lifecycle perspective. The data detection and monitoring methods across the design, construction, and O&M (Operation and Maintenance) phases of slab tracks are summarized, offering a potential direction for better refinement and intelligent data collection. Besides, the paper reviews the data management system at different life cycle stages of the slab track, and a integrated full-lifecycle data management framework is proposed. Also considered is the application of related complex mass data, in which we summarize the maintenance indexes to evaluate and predict the current and future quality of slab tracks. This study aims to lay a foundation for future scientific maintenance and repair strategies of slab tracks in high-speed railways.","PeriodicalId":191628,"journal":{"name":"Intelligent Transportation Infrastructure","volume":"126 19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128022767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Modeling the Accident Prediction for At-Grade Highway-Rail Crossings 高等级公路-铁路道口事故预测模型

Intelligent Transportation Infrastructure Pub Date : 2022-11-22 DOI: 10.1093/iti/liac017

Xue Yang, J. Li, Aonan Zhang, You Zhan

{"title":"Modeling the Accident Prediction for At-Grade Highway-Rail Crossings","authors":"Xue Yang, J. Li, Aonan Zhang, You Zhan","doi":"10.1093/iti/liac017","DOIUrl":"https://doi.org/10.1093/iti/liac017","url":null,"abstract":"\u0000 Since accidents at highway-rail at-grade crossings (HRGCs) are often catastrophic, safety prediction and evaluation at such locations are of great importance. In this paper, at-grade crossing inventory data and historical accident data were obtained from the Federal Railroad Administration (FRA’s) Office of Safety online databases. The HRGC railroad and highway characteristics were selected as the influencing variables. Considering HRGC accidents are over-dispersed count data with excessive zeros, six count data models, including the Poisson model, negative binomial model (NB), zero-inflated Poisson model (ZIP), zero-inflated negative binomial model (ZINB), hurdle Poisson (HP) model and hurdle negative binomial model (HNB) were investigated and developed for accident prediction. The ZINB model outperformed the other five models in terms of the goodness-of-fit, zero inflations, and statistical significance of factors. The most significant contributing factors in the ZINB model included the maximum timetable speed of train, exposure-related variables such as total through trains, highway traffic volume, rural or urban area, and the type of control devices at HRGCs, followed by the minimum speed of train, highway paved or not, and the number of traffic lanes. This study could assist decision-makers with a more robust safety evaluation at highway-rail at-grade crossings.","PeriodicalId":191628,"journal":{"name":"Intelligent Transportation Infrastructure","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114525638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Automatic crack classification and segmentation on concrete bridge images using convolutional neural networks and hybrid image processing 基于卷积神经网络和混合图像处理的混凝土桥梁图像裂缝自动分类与分割

Intelligent Transportation Infrastructure Pub Date : 2022-11-09 DOI: 10.1093/iti/liac016

Baoxian Li, Hongbin Guo, Zhanfei Wang, Mingyang Li

{"title":"Automatic crack classification and segmentation on concrete bridge images using convolutional neural networks and hybrid image processing","authors":"Baoxian Li, Hongbin Guo, Zhanfei Wang, Mingyang Li","doi":"10.1093/iti/liac016","DOIUrl":"https://doi.org/10.1093/iti/liac016","url":null,"abstract":"\u0000 Cracks are an indicator for a bridge’s structural health and functional failures. Crack detection is one of the major tasks to maintain the structure health and serviceability of a bridge. At present, the most commonly used crack detection technology is manual inspection, with the disadvantages of being highly labor-intensive and time-consuming. In this paper, a CNN-based (convolutional neural network, CNN) crack detection method is proposed. To automate quantitative measurements of identified crack, a hybrid image processing is proposed, as well. Firstly, a dataset is accumulated, including 12,000 cropped crack images and 19,500 cropped background images. Secondly, preprocessed images with the proposed method of Bilateral-Graying-Contrast (BGC) are fed into ResNet and VGG (Visual Geometry Group Network) for training and testing. Finally, automatic measurement system of bridge crack is developed, which is not prone to weakened shooting conditions. The results demonstrate that Resnet achieves accuracy of cracks to 97.44%, which is higher than VGG. Our crack measurement system significantly reduces the measurement error to 9.86% and can be assumed as a reliable method in the analysis of concrete bridge images.","PeriodicalId":191628,"journal":{"name":"Intelligent Transportation Infrastructure","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125818147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Research on Influencing Factors of Asphalt Pavement International Roughness Index (IRI) Based on Ensemble Learning 基于集成学习的沥青路面国际粗糙度指数影响因素研究

Intelligent Transportation Infrastructure Pub Date : 2022-11-07 DOI: 10.1093/iti/liac014

Zhiwei Luo, You Zhan, Yang Liu, Allen A. Zhang, Xiuquan Lin, Yurong Zhang

引用次数: 0

Research progress of intelligent operation and maintenance of high-speed railway bridges 高速铁路桥梁智能运维研究进展

Intelligent Transportation Infrastructure Pub Date : 2022-11-07 DOI: 10.1093/iti/liac015

Yan Long, Wei Guo

引用次数: 0