Intelligent Transportation Infrastructure最新文献

Fundamental Diagram and Stability Analysis of Mixed Traffic Considering Heterogeneous Car-Following Behaviors and Platoon Factors 考虑异质跟车行为和排队因素的混合交通基本图和稳定性分析

Intelligent Transportation Infrastructure Pub Date : 2024-07-23 DOI: 10.1093/iti/liae010

Zhanbo Sun, Qiruo Yan, Yafei Liu, Zhijian Fu, Lei Yang

{"title":"Fundamental Diagram and Stability Analysis of Mixed Traffic Considering Heterogeneous Car-Following Behaviors and Platoon Factors","authors":"Zhanbo Sun, Qiruo Yan, Yafei Liu, Zhijian Fu, Lei Yang","doi":"10.1093/iti/liae010","DOIUrl":"https://doi.org/10.1093/iti/liae010","url":null,"abstract":"\u0000 With the advancement of connected automated vehicles (CAVs), it is anticipated that mixed traffic environments, where human-driven vehicles (HVs) coexist with CAVs, will become prevalent in the future. The study aims to investigate the impact of heterogeneous car-following behaviors of HVs (e.g. aggressive, normal, and mild driving styles) and platoon factors of CAVs (i.e. platoon intensity and maximum platoon size) on the fundamental diagram and stability of mixed traffic. Firstly, a Markov chain approach is employed to describe the probability distributions of different leader-follower combinations, enabling us to construct a comprehensive mixed traffic model. Subsequently, a general modeling framework based on the mixed traffic model is established to examine the effects of heterogeneous car-following behaviors and platoon factors on the fundamental diagram and stability of mixed traffic. The results from numerical experiments reveal several findings: (i) an increase in the proportion of aggressive driving style enhances both the capacity and stability of mixed traffic; (ii) larger platoon intensity and maximum platoon size contribute to improved capacity, particularly in scenarios where a large fraction of HVs exhibit aggressive driving behavior; (iii) platoon intensity has a positive impact on traffic flow stability, while larger maximum platoon size leads to reduced stability; (iv) increasing CAV penetration without considering platoon intensity may lead to reduced stability compared to scenarios with a substantial proportion of aggressive drivers.","PeriodicalId":479889,"journal":{"name":"Intelligent Transportation Infrastructure","volume":"15 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141813611","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

Efficient Lightweight Railway Track Segmentation Network for Resource-Constrained Platforms with TensorRT 利用 TensorRT 为资源受限平台构建高效轻量级铁路轨道分割网络

Intelligent Transportation Infrastructure Pub Date : 2024-07-18 DOI: 10.1093/iti/liae009

Chenglin Chen, Fei Wang, Min Yang, Yong Qin, Yun Bai

{"title":"Efficient Lightweight Railway Track Segmentation Network for Resource-Constrained Platforms with TensorRT","authors":"Chenglin Chen, Fei Wang, Min Yang, Yong Qin, Yun Bai","doi":"10.1093/iti/liae009","DOIUrl":"https://doi.org/10.1093/iti/liae009","url":null,"abstract":"\u0000 Accurate and rapid railway track segmentation is the fundamental for foreign object intrusion detection, inspection, online monitoring, and non-destructive assessment of transportation infrastructure. Recently, vision-based track segmentation algorithms have demonstrated strong performance. However, most existing models struggle to meet the real-time requirements on resource-constrained edge devices. Considering this challenge, we propose an edge-enabled real-time railway track segmentation algorithm, which is optimized to be suitable for edge applications by optimizing the network structure and quantizing the model after training. Initially, Ghost convolution is introduced to reduce the complexity of the backbone, thereby achieving the extraction of key information of the interested region at a lower cost. To further reduce the model complexity and calculation, a new lightweight detection head is proposed to achieve the best balance between accuracy and efficiency. Subsequently, we introduce quantization techniques to map the model’s floating-point weights and activation values into lower bit-width fixed-point representations, reducing computational demands and memory footprint, ultimately accelerating the model’s inference. Finally, we draw inspiration from GPU parallel programming principles to expedite the pre-processing and post-processing stages of the algorithm by doing parallel processing. The approach is evaluated with public and challenging dataset RailSem19 and tested on Jetson Nano. Experimental results demonstrate that our enhanced algorithm achieves an accuracy level of 83.3% alongside with 25 FPS inference speed when the input size is 480 × 480. The code can be found at: https://github.com/ccl-1/light-yolov8-seg-quantization-tensorrt.","PeriodicalId":479889,"journal":{"name":"Intelligent Transportation Infrastructure","volume":" 32","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141825183","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

Model updating of highway slope under seismic intensity conditions considering spatially varying soils 考虑空间变化土壤的地震烈度条件下公路边坡模型更新

Intelligent Transportation Infrastructure Pub Date : 2024-06-07 DOI: 10.1093/iti/liae006

Yongjuan Zhang, Yong Liu, Ruohan Wang

{"title":"Model updating of highway slope under seismic intensity conditions considering spatially varying soils","authors":"Yongjuan Zhang, Yong Liu, Ruohan Wang","doi":"10.1093/iti/liae006","DOIUrl":"https://doi.org/10.1093/iti/liae006","url":null,"abstract":"\u0000 Understanding the mechanisms underlying earthquake-induced landslides and assessing seismic responses are crucial for effective mitigation strategies. Earthquakes typically involve a mainshock followed by aftershocks, posing challenges to structures weakened by the mainshock. Highway slope structures, especially those in unsaturated soft-soil slopes, are vulnerable to aftershocks, amplifying the damage caused by the mainshock-aftershock (MSAS) sequence. While existing re- search primarily focuses on the effects of mainshocks on certain structures, there is a notable gap regarding the damage sustained by unsaturated slope structures under MSAS conditions. Address- ing this gap is vital for comprehensive risk assessment and mitigation. To address these challenges, we propose a stochastic model updating approach for seismic reliability analysis. This approach integrates subset simulation with adaptive Bayesian updating and dimensionality reduction using the Karhunen-Lòeve expansion. Shaking table tests on a slope structure with unsaturated red clay soil are conducted to investigate the effects of matrix suction on performance degradation and fail- ure mechanisms. The results reveal spatial variability in soil property parameters, underscoring the need to incorporate this variability into inverse analyses. Traditional deterministic methods or probability-based approaches may overlook such variability. Also, the results indicated our proposed approach enables effective prediction of seismic responses for unsaturated slopes sub- jected to MSAS sequences. By considering spatial variability and the effects of matrix suction, our method offers a comprehensive framework for seismic reliability analysis of unsaturated slope structures.","PeriodicalId":479889,"journal":{"name":"Intelligent Transportation Infrastructure","volume":" 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141372237","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

Comparing various neural network methods for temperature prediction of CRTS II slab track on transition sections 比较用于预测 CRTS II 板式轨道过渡段温度的各种神经网络方法

Intelligent Transportation Infrastructure Pub Date : 2024-05-22 DOI: 10.1093/iti/liae004

Rui Zhou, Mingfeng He, Hongbin Xu, Hongyao Lu, Hanlin Liu, Jie Qi

{"title":"Comparing various neural network methods for temperature prediction of CRTS II slab track on transition sections","authors":"Rui Zhou, Mingfeng He, Hongbin Xu, Hongyao Lu, Hanlin Liu, Jie Qi","doi":"10.1093/iti/liae004","DOIUrl":"https://doi.org/10.1093/iti/liae004","url":null,"abstract":"\u0000 Based on the meteorological measuring data, the prediction of the temperature field is worthy of thermal performance evaluation of CRTS II slab tracks in bridge-subgrade transition sections. To find the best temperature prediction method, this present study shows a comparison of internal temperature predictions in CRTS II slab track by using three typical neural network methods (ANN, CNN, LSTM) subjected to different meteorological factors. Firstly, the distribution characteristics of four meteorological factors (e.g. ambient temperature, solar radiation, wind speed, and humidity) and internal temperature for the CRTS II slab track on three different foundations are analyzed. Moreover, temperature prediction effects of track slab and base plate on three foundations under five meteorological testing cases are compared by using three neural network models, respectively. The results show that the ambient temperature ranging from 15°C and 25°C accounts for about 7% and the solar radiation during daytime mainly ranges from 100 W/m2 to 1100 W/m2. The solar radiation has more effect on the temperature gradients of the CRTS II slab track on bridge and transition zone than that of the ambient temperature, and Case 5 with five different input variables has the best prediction accuracy for three predict models among five testing cases. Although the LSTM model has the best prediction accuracy among the three prediction models with R2 values of about 0.85, it costs the longest calculation time of about 180 s. In addition, the track slab on bridge has the worst prediction accuracy for the ANN and CNN models among the three foundations with RMSE values of 4.5 and 2.5 for Case 2, and the base plate on transition zone has the best prediction accuracy both for the CNN and LSTM models among the three foundations with RMSE values of 3 for Case 5.","PeriodicalId":479889,"journal":{"name":"Intelligent Transportation Infrastructure","volume":"16 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141110135","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

Real-time automated deep learning based detection and tracking near highway-rail grade crossing for vulnerable road users safety 基于深度学习的实时自动检测和跟踪高速公路-铁路平交道口附近的易受伤害的道路使用者的安全

Intelligent Transportation Infrastructure Pub Date : 2024-04-19 DOI: 10.1093/iti/liae003

Xue Yang, Joshua Qiang Li, You Jason Zhan, Wenying Yu

{"title":"Real-time automated deep learning based detection and tracking near highway-rail grade crossing for vulnerable road users safety","authors":"Xue Yang, Joshua Qiang Li, You Jason Zhan, Wenying Yu","doi":"10.1093/iti/liae003","DOIUrl":"https://doi.org/10.1093/iti/liae003","url":null,"abstract":"\u0000 The vulnerable Road User (VRU) near highway-rail grade crossings (HRGCs) comprises pedestrians, cyclists, and car users. The VRU trespassing violation behavior is the leading cause of highway and railroad related deaths, but many incidents have not been deeply studied. Detection and prevention of such events are critical for road safety improvements, while this task is challenging due to the immense labor costs required for processing streamed video files. This study developed an advanced You Look Only Once (YOLO) deep learning architecture and the Deep Simple Online and Real-time Tracking (Deep SORT) algorithm for real-time VRU trespassing violation detection. Different types of VRUs trespassing were detected near a gated HRGC in Folkston, Georgia. 436 VRU’s trespassing violations were identified in the selected 104-hour video data. The automated VRU’s trespassing detection speed ranged from 43.2 to 654.5 frames per second (FPS), exceeding the field video data recording rate at 30 FPS. The developed methodology resulted in 32 false negatives and 20 false positive detections, with the precision, recall, and F1 values scoring above 92.0%. This work could assist road agencies in reducing VRU’s trespassing violations based on real-time VRU detection and tracking.","PeriodicalId":479889,"journal":{"name":"Intelligent Transportation Infrastructure","volume":" 25","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140683809","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

Smart container port development: recent technologies and research advances 智能集装箱港口发展:最新技术和研究进展

Intelligent Transportation Infrastructure Pub Date : 2023-11-07 DOI: 10.1093/iti/liad022

Wenyuan Wang, Yun Peng, Xinglu Xu, Xiangda Li, Huakun Liu, Suri Liu, Xinru Yan

引用次数: 0

LibCity-Dataset: A Standardized and Comprehensive Dataset for Urban Spatial-temporal Data Mining LibCity-Dataset:面向城市时空数据挖掘的标准化综合数据集

Intelligent Transportation Infrastructure Pub Date : 2023-11-07 DOI: 10.1093/iti/liad021

Jingyuan Wang, Wenjun Jiang, Jiawei Jiang

{"title":"LibCity-Dataset: A Standardized and Comprehensive Dataset for Urban Spatial-temporal Data Mining","authors":"Jingyuan Wang, Wenjun Jiang, Jiawei Jiang","doi":"10.1093/iti/liad021","DOIUrl":"https://doi.org/10.1093/iti/liad021","url":null,"abstract":"Abstract The LibCity-Dataset represents a significant contribution to the field of urban spatial-temporal data mining. This dataset uniquely integrates macro traffic state data with micro trajectory data, providing researchers with comprehensive and diverse urban spatial-temporal data. Specifically, we begin by collecting and processing existing open-source spatial-temporal data. Subsequently, we independently collected Beijing taxi trajectory data through third-party interfaces. This data bridges the gap in the scarcity of current open-source vehicle trajectory data. The distinctive aspect of the LibCity-Dataset lies in its innovative approach of standardizing the storage format, achieved through the implementation of atomic files. By adopting this standardized format, diverse data sources are harmonized, enabling effortless application of spatial-temporal prediction models across various datasets. The uniform storage format not only simplifies experimentation but also expedites the advancement of spatial-temporal prediction research, acting as a catalyst for further innovation. This Data Note provides a comprehensive insight into the creation methodology of the LibCity-Dataset, including data collection and processing methodology, data description, data validation, and usage notes. By facilitating open-source collaboration and setting a benchmark for standardization within the spatial-temporal prediction domain, this dataset aims to foster increased research cooperation and knowledge sharing. The open-source link of our dataset is https://drive.google.com/drive/folders/1g5v2Gq1tkOq8XO0HDCZ9nOTtRpB6-gPe.","PeriodicalId":479889,"journal":{"name":"Intelligent Transportation Infrastructure","volume":"126 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135540772","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

Digital twin in Transportation Infrastructure management: A systematic review 交通基础设施管理中的数字孪生:系统回顾

Intelligent Transportation Infrastructure Pub Date : 2023-11-06 DOI: 10.1093/iti/liad024

Bin Yan, Fan Yang, Shi Qiu, Jin Wang, Benxin Cai, Sicheng Wang, Qasim Zaheer, Weidong Wang, Yongjun Chen, Wenbo Hu

{"title":"Digital twin in Transportation Infrastructure management: A systematic review","authors":"Bin Yan, Fan Yang, Shi Qiu, Jin Wang, Benxin Cai, Sicheng Wang, Qasim Zaheer, Weidong Wang, Yongjun Chen, Wenbo Hu","doi":"10.1093/iti/liad024","DOIUrl":"https://doi.org/10.1093/iti/liad024","url":null,"abstract":"Abstract The concept of Digital Twin (DT) has emerged as a trend in various industries development, enabling the creation of virtual models of physical objects. We conduct a systematic review of the digital twin technology in the field of transportation infrastructure management from the aspects of concept definition, whole life cycle application, advanced technology, and equipment utilization, as well as the challenges. We begin with an introduction that defines DT and its components, while also distinguishing it from Building Information Modeling (BIM) and Cyber-physical Systems (CPS). We explore the diverse applications of DT throughout its lifecycle and highlight the significance of DT in structural monitoring, infrastructure operation and maintenance, and dataset expansion. We further investigate the advanced techniques and equipment associated with DT components, focusing on the importance of virtual parts, data acquisition, transmission, multi-source data fusion processing, and data security as well as dynamic updating of models for effective integration and utilization of DT in transportation infrastructure management. We identify key challenges faced by DT in transportation infrastructure management and propose future trends in the study. This comprehensive review serves as a valuable resource for researchers, practitioners, and decision-makers in understanding the potential of DT technology in transportation infrastructure management.","PeriodicalId":479889,"journal":{"name":"Intelligent Transportation Infrastructure","volume":"295 s1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135685340","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 and Assessment of Technical and Legal Challenges in Application of Unmanned Aerial Vehicles (UAVs) in Monitoring and Inspection of Bridges 无人机在桥梁监测和检查中应用的技术和法律挑战的回顾和评估

Intelligent Transportation Infrastructure Pub Date : 2023-11-06 DOI: 10.1093/iti/liad023

Alireza Adibfar, Mohamad Razkenari, Aaron Costin

{"title":"Review and Assessment of Technical and Legal Challenges in Application of Unmanned Aerial Vehicles (UAVs) in Monitoring and Inspection of Bridges","authors":"Alireza Adibfar, Mohamad Razkenari, Aaron Costin","doi":"10.1093/iti/liad023","DOIUrl":"https://doi.org/10.1093/iti/liad023","url":null,"abstract":"Abstract Intelligent Transportation Systems (ITS) initiated a paradigm shift in the operation and management of transportation infrastructure by automating data collection, processing, and management. The drive towards using robotics to automate operational tasks in infrastructure management has gained significant momentum in recent years, with the introduction of Unmanned Aerial Vehicles (UAVs), widely known as drones, being a notable milestone in this endeavor. UAVs reduced cost, time, and labor for tasks such as bridge monitoring while accelerating speed and precision such as structural scanning. Although not explicitly classified within the existing ITS categories, UAVs are becoming widely acknowledged as a valuable tool for improving the intelligent operation and management of transportation systems. Their potential to gather and transmit data in real-time offers new opportunities for more accurate and timely decision-making, as well as improved safety and efficiency within transportation infrastructure. Nevertheless, significant concerns remain regarding the use of UAVs, particularly in busy highways or densely populated metropolitan areas. Engineering and technical challenges, privacy and safety concerns, potential liabilities, and audio-visual disturbance for the public are among the issues that have impeded the widespread adoption and full integration of UAVs as complementary tools for ITS. This paper conducts a comprehensive review of the potential applications of UAVs for infrastructure and bridge structural Health Monitoring (SHM), while also evaluating the challenges associated with their utilization in this context. Furthermore, the paper discusses the ramifications of these challenges and emphasizes the areas that necessitate further attention and investigation by future research.","PeriodicalId":479889,"journal":{"name":"Intelligent Transportation Infrastructure","volume":"273 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135685229","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

Research on key Technologies for Intelligent and Fine-grained Construction of earth-rock dams based on artificial intelligence 基于人工智能的土石坝智能细密施工关键技术研究

Intelligent Transportation Infrastructure Pub Date : 2023-10-26 DOI: 10.1093/iti/liad020

Biao Liu, Xiaohui Gong, Tao Meng, Yufei Zhao

引用次数: 0