Intelligent Transportation Infrastructure最新文献

Optimizing passenger vehicle travel time with model predictive control in multi-region traffic networks 在多区域交通网络中利用模型预测控制优化客运车辆旅行时间

Intelligent Transportation Infrastructure Pub Date : 2024-07-03 DOI: 10.1093/iti/liae008

Muhammad Saadullah, Zhipeng Zhang, Hao Hu

{"title":"Optimizing passenger vehicle travel time with model predictive control in multi-region traffic networks","authors":"Muhammad Saadullah, Zhipeng Zhang, Hao Hu","doi":"10.1093/iti/liae008","DOIUrl":"https://doi.org/10.1093/iti/liae008","url":null,"abstract":"\u0000 This study investigates the impact of truck traffic on passenger vehicles in an urban network. Utilizing the Macroscopic Fundamental Diagram (MFD), a methodology to calculate the travel time spent by passenger vehicles has been developed. To address this issue, an optimal control problem was formulated and solved using a Model Predictive Control (MPC) approach. The MPC framework has been applied in a centralized manner, to manage accumulation for various modes. To explore different traffic management strategies, the centralized MPC technique was implemented in two distinct configurations: region-based and vehicle-based approaches. It has been tested for various vehicle mixes and multiple control scenarios to assess the effectiveness in reducing passenger travel time spent and vehicle accumulation. The results demonstrate that the vehicle-based MPC approach tends to minimize the number of vehicles more effectively compared to the region-based approach. However, in terms of reducing passenger travel time, the region-based approach outperforms the vehicle-based strategy. This is attributed to enhanced coordination among traffic flow controllers, highlighting the importance of strategic controller interactions in urban traffic management systems. This research enhances both the theoretical framework for optimizing traffic flow and provides valuable practical insights for city planners and engineers aiming to deploy advanced traffic management strategies. Future studies could explore the scalability of these control systems and their capability to integrate real-time traffic data.","PeriodicalId":191628,"journal":{"name":"Intelligent Transportation Infrastructure","volume":"92 6","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141683680","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

Advancing a Major U.S. Airline’s Practice in Flight-level Checked Baggage Prediction 推进美国一家大型航空公司在航班托运行李预测方面的实践

Intelligent Transportation Infrastructure Pub Date : 2024-03-07 DOI: 10.1093/iti/liae001

Shijie Chen, Chiwoo Park, Qianwen Guo, Yanshuo Sun

{"title":"Advancing a Major U.S. Airline’s Practice in Flight-level Checked Baggage Prediction","authors":"Shijie Chen, Chiwoo Park, Qianwen Guo, Yanshuo Sun","doi":"10.1093/iti/liae001","DOIUrl":"https://doi.org/10.1093/iti/liae001","url":null,"abstract":"\u0000 In this paper, we aim to address a relevant estimation problem that aviation professionals encounter in their daily operations. Specifically, aircraft load planners require information on the expected number of checked bags for a flight several hours prior to its scheduled departure to properly palletize and load the aircraft. However, the checked baggage prediction problem has not been sufficiently studied in the literature, particularly at the flight level. Existing prediction approaches have not properly accounted for the different impacts of overestimating and underestimating checked baggage volumes on airline operations. Therefore, we propose a custom loss function, in the form of a piecewise quadratic function, which aligns with airline operations practice and utilizes machine learning algorithms to optimize checked baggage predictions incorporating the new loss function. We consider multiple linear regression, LightGBM, and XGBoost, as supervised learning algorithms. We apply our proposed methods to baggage data from a major airline and additional data from various U.S. government agencies. We compare the performance of the three customized supervised learning algorithms. We find that the two gradient boosting methods (i.e., LightGBM and XGBoost) yield higher accuracy than the multiple linear regression; XGBoost outperforms LightGBM while LightGBM requires much less training time than XGBoost. We also investigate the performance of XGBoost on samples from different categories and provide insights for selecting an appropriate prediction algorithm to improve baggage prediction practices. Our modeling framework can be adapted to address other prediction challenges in aviation, such as predicting the number of standby passengers or no-shows.","PeriodicalId":191628,"journal":{"name":"Intelligent Transportation Infrastructure","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140260642","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

Study on the influence of spent-catalysts microphysical properties on FCC/asphalt Interface interaction 研究废催化剂微物理性质对催化裂化和沥青界面相互作用的影响

Intelligent Transportation Infrastructure Pub Date : 2023-12-25 DOI: 10.1093/iti/liad027

Lingyun Kong, Xinan Li, Shengqing He, Chufeng Wu, Yi Peng, Hanqing Wang, Qiang Shao, Allen A Zhang

{"title":"Study on the influence of spent-catalysts microphysical properties on FCC/asphalt Interface interaction","authors":"Lingyun Kong, Xinan Li, Shengqing He, Chufeng Wu, Yi Peng, Hanqing Wang, Qiang Shao, Allen A Zhang","doi":"10.1093/iti/liad027","DOIUrl":"https://doi.org/10.1093/iti/liad027","url":null,"abstract":"This research aims to quantify the interfacial interaction mechanism between the fluid catalytic cracking (FCC) spent catalyst and asphalt. The two types of spent-catalysts, three types of mineral powders, and their bituminous slurries are selected to complete the tests of Microscopic morphological, specific surface area, surface energy, fourier transform infrared spectroscopy (FT-IR), specific adhesion work, and interaction parameter C-value for FCC-spent-catalysts in laboratory. The results indicate that: (1), the physical properties of FCC-spent-catalyst compared with mineral powder when the particle size ranging from −2.2 ~ 5.4 μm between FCC-spent-catalysts and mineral powder, the specific surface area of FCC-spent-catalyst was 100 to 900 fold that of mineral powder, while the alkali value of FCC-spent-catalysts was 2 to 8 fold that of mineral powder; no significant difference was observed in surface energy; (2), the mixture system did not produce new functional groups after FCC-spent-catalyst addition to the asphalt mixture system; (3), the adhesion work of FCC-spent-catalyst was close to that of mineral powder, the specific adhesion work was 74 to 763 fold that of mineral powder when they have the similar particle size; (4), the interaction parameter C-value between FCC-spent-catalyst and asphalt was higher than the interaction between mineral powder and asphalt at identical test temperatures. This study demonstrates that the FCC-spent-catalyst have the potential to improve the in-service performance of the pavement under high-temperature and moisture damage in terms of a larger specific surface area and stronger.","PeriodicalId":191628,"journal":{"name":"Intelligent Transportation Infrastructure","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139159092","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

Airline Scheduling Optimization: Literature Review and a Discussion of Modeling Methodologies 航空公司排班优化：文献综述与建模方法讨论

Intelligent Transportation Infrastructure Pub Date : 2023-12-06 DOI: 10.1093/iti/liad026

Yifan Xu, S. Wandelt, Xiaoqian Sun

引用次数: 0

Application of plant fibers in subgrade engineering: current situation and challenges 植物纤维在路基工程中的应用：现状与挑战

Intelligent Transportation Infrastructure Pub Date : 2023-12-06 DOI: 10.1093/iti/liad025

Jiayi Guo, J. Yi, Zhongshi Pei, Decheng Feng

{"title":"Application of plant fibers in subgrade engineering: current situation and challenges","authors":"Jiayi Guo, J. Yi, Zhongshi Pei, Decheng Feng","doi":"10.1093/iti/liad025","DOIUrl":"https://doi.org/10.1093/iti/liad025","url":null,"abstract":"\u0000 The application of plant fibers in subgrade engineering is increasingly receiving attention. As a sustainable material, plant fibers possess characteristics such as lightweight, renewability, and biodegradability. The morphology, chemical composition, mechanical properties and hydraulic properties of plant fibers determine their application scenarios in subgrade engineering. Degradation is also an important factor affecting the long-term performance of plant fibers. In order to overcome their limitations in subgrade engineering, physical and chemical modification has become necessary. Through physical or chemical modification, the compatibility between plant fibers and the matrix can be enhanced, improving dispersibility and adhesiveness. Plant fibers can be applied in subgrade engineering through methods such as soil improvement, incorporation into geosynthetic materials, and the use of prefabricated components. Among these, soil improvement with plant fiber can enhance soil crack resistance and stability, adding fibers to geosynthetic materials can strengthen the mechanical properties of the soil, and prefabricated components can effectively reinforce slopes. This article reviews the current application status of plant fibers in subgrade engineering. In comparison to other soil stabilization materials, plant fibers offer clear economic and environmental advantages. Nevertheless, they come with two drawbacks, namely restricted mechanical properties and excessive water absorption. Challenges such as technical standards, fiber dispersibility, and durability still exist in their application. In the future, the application of plant fibers in subgrade engineering will continue to expand. Through technological innovation and standard development, it will provide environmentally friendly and efficient solutions for sustainable subgrade construction.","PeriodicalId":191628,"journal":{"name":"Intelligent Transportation Infrastructure","volume":"5 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138594729","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

Experimental and numerical study on the mechanical behaviours of two rocks with circular openings 两种圆孔岩石力学特性的实验与数值研究

Intelligent Transportation Infrastructure Pub Date : 2023-08-24 DOI: 10.1093/iti/liad011

Y. Gui, Y. Cevik, J. Ma

引用次数: 0

A BIM-based framework for automatic numerical modelling and geotechnical analysis of a large-scale deep excavation for transportation infrastructures 基于bim的大型交通基础设施深基坑自动数值模拟与岩土分析框架

Intelligent Transportation Infrastructure Pub Date : 2023-07-28 DOI: 10.1093/iti/liad012

C. Shi, Yunfei Jin, Hu Lu, Jiangwei Shi

{"title":"A BIM-based framework for automatic numerical modelling and geotechnical analysis of a large-scale deep excavation for transportation infrastructures","authors":"C. Shi, Yunfei Jin, Hu Lu, Jiangwei Shi","doi":"10.1093/iti/liad012","DOIUrl":"https://doi.org/10.1093/iti/liad012","url":null,"abstract":"\u0000 Employing emerging information and communication technologies such as building information modeling (BIM) to streamline engineering design and analysis has been mainstream over the past several years. Although BIM models are well established for above-ground infrastructures, underground BIM is still in its infancy. On one hand, valuable subsurface ground models are missing in current BIM applications as site-specific geotechnical data are often sparse and limited. On the other hand, BIM has been mainly used as a visualization tool for concise representation of design and construction data, and it has not been integrated and utilized to assess geotechnical risks associated with underground infrastructures. This paper proposes a BIM-based approach for automatic numerical modelling and geotechnical analysis. Structural information (e.g. geometries) of different project BIMs are grouped, exported, and saved, which provides a unified interface for automatic information extraction using computer codes (e.g. Python). Subsequently, subsurface ground models generated from sparse data are integrated with extracted basic geometric properties for automatic geotechnical model set-up and finite element analysis. The performance of the proposed framework is illustrated using a real deep excavation project. It is revealed that BIM, as a data repository, enables timely and accurate information exchange between structural and geotechnical models in an automatic manner, which emphasizes the need of a BIM-based approach for assessing and managing geotechnical risks associated with deep excavations for underground transportation infrastructures, especially in the urban context.","PeriodicalId":191628,"journal":{"name":"Intelligent Transportation Infrastructure","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125167287","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

Intelligent Monitoring of Vibration and Structural-borne Noise induced by Rail Transit 轨道交通振动与结构噪声的智能监测

Intelligent Transportation Infrastructure Pub Date : 2023-07-27 DOI: 10.1093/iti/liad013

Qingjie Liu, Lu Xu, Q. Feng

{"title":"Intelligent Monitoring of Vibration and Structural-borne Noise induced by Rail Transit","authors":"Qingjie Liu, Lu Xu, Q. Feng","doi":"10.1093/iti/liad013","DOIUrl":"https://doi.org/10.1093/iti/liad013","url":null,"abstract":"\u0000 The monitoring of urban rail transit vibration and structural-borne noise can well solve the problem of small amount of data and large discreteness in traditional evaluation tests. In this study, a monitoring system is utilized to collect and preprocess vibration and noise signals. By employing cellular network and cloud-based services, real-time acquisition and analysis of vibration and noise signals are achieved. In this paper, it is proposed to normalize the vibration data obtained after edge computing. After treatment, the gray correlation analysis method was used the correlation between each vibration data component and vibration data classification. Combining frequency domain analysis of vibration data, the data components with high correlation are used as inputs to an improved K-nearest neighbors (KNN) model. Additionally, the correlation of each data component is introduced into the distance calculation formula. The improved KNN model shows improvements in recall rate, precision rate, F-measure, and accuracy compared to the original KNN model, with increases of 0.76%, 2.76%, 1.81%, and 1.61% respectively. Through practical measurements, it is found that different vehicles cause significant variations in vibration, with differences of up to 11 dB in tunnel wall vibration. The differences in tunnel wall vibration caused by the same vehicle at different passenger loads do not exceed 5 dB. Combining practical application cases, the rail transit environmental noise monitoring system established in this study demonstrates its applicability in monitoring vibration and noise-sensitive areas, as well as analyzing the effectiveness of vibration reduction and noise control measures.","PeriodicalId":191628,"journal":{"name":"Intelligent Transportation Infrastructure","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133280593","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

Overview of shared-bike repositioning optimization with artificial intelligence 基于人工智能的共享单车重新定位优化综述

Intelligent Transportation Infrastructure Pub Date : 2023-07-12 DOI: 10.1093/iti/liad008

Wenwen Tu, Feng Xiao

{"title":"Overview of shared-bike repositioning optimization with artificial intelligence","authors":"Wenwen Tu, Feng Xiao","doi":"10.1093/iti/liad008","DOIUrl":"https://doi.org/10.1093/iti/liad008","url":null,"abstract":"\u0000 Rapid developments in Artificial Intelligence (AI) present unprecedented opportunities to enhance the operational and management performance of shared bikes. Heuristic algorithms, Supervised Algorithms, Unsupervised Algorithms, and Reinforcement Learning (RL) in AI technologies enable the consideration of more possibilities in the Bike Repositioning Problem (BRP), including addressing challenges such as large-scale bike sharing, real-time dynamic repositioning, and dynamic policy interaction with the environment. This paper provides an overview of research on bike-sharing repositioning utilizing AI techniques. The applications of Heuristic Search methods and Machine Learning (ML) including RL for docked and dock-less shared bikes, are summarized based on dynamic and static environments, respectively. We provide a comprehensive analysis of the advanced development in AI-based BRP and review the application of AI technologies in obtaining scientifically repositioning strategies that effectively balance supply and demand conflicts. Moreover, this study delves into the constraints and potential advancements of AI methods for shared bike reallocation, offering valuable recommendations for future research.","PeriodicalId":191628,"journal":{"name":"Intelligent Transportation Infrastructure","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125167494","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 fusion-based approach of deep learning and edge-cutting algorithms for identification and color recognition of traffic lights 一种基于融合的深度学习方法和尖端算法用于交通信号灯的识别和颜色识别

Intelligent Transportation Infrastructure Pub Date : 2023-06-21 DOI: 10.1093/iti/liad007

Yunqian Xu

引用次数: 1