Marcus Irmer, Ina Kalder, Marco Tönnemann, René Degen, Lucas Rüggeberg, Karin Thomas, Margot Ruschitzka
{"title":"基于高分辨率点云的大容量和重型运输的增强三维交通分析","authors":"Marcus Irmer, Ina Kalder, Marco Tönnemann, René Degen, Lucas Rüggeberg, Karin Thomas, Margot Ruschitzka","doi":"10.1049/itr2.70081","DOIUrl":null,"url":null,"abstract":"<p>Large-volume and heavy-duty transports are essential for the successful and timely execution of large-scale industrial, socio-political and climate-related projects. As these transports grow in size and complexity, the planning process becomes increasingly challenging for all stakeholders involved. To overcome these challenges, detailed planning processes are required, especially for the trafficability of narrow passages along the transportation route. This paper introduces an advanced methodology for an enhanced 3D trafficability analysis with collision detection of large-volume and heavy-duty transports. By employing high-resolution, dense, colored 3D point clouds alongside detailed transport models, this approach offers a more accurate and comprehensive assessment of the feasibility of the transport. The methodology is further generalized to accommodate a wide variety of transport configurations and maneuvers, allowing for automated analysis across different scenarios. The primary contribution of this research lies in its ability to significantly improve collision detection accuracy and provide detailed visualizations, thereby optimizing the digital planning process of large-volume and heavy-duty transports. The findings demonstrate a distinct advantage of the 3D trafficability analysis over traditional 2D methods, especially in complex environments, leading to cost-effective and reliable transportation planning.</p>","PeriodicalId":50381,"journal":{"name":"IET Intelligent Transport Systems","volume":"19 1","pages":""},"PeriodicalIF":2.5000,"publicationDate":"2025-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/itr2.70081","citationCount":"0","resultStr":"{\"title\":\"Enhanced 3D Trafficability Analysis for Large-Volume and Heavy-Duty Transports Based on High-Resolution Point Clouds\",\"authors\":\"Marcus Irmer, Ina Kalder, Marco Tönnemann, René Degen, Lucas Rüggeberg, Karin Thomas, Margot Ruschitzka\",\"doi\":\"10.1049/itr2.70081\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Large-volume and heavy-duty transports are essential for the successful and timely execution of large-scale industrial, socio-political and climate-related projects. As these transports grow in size and complexity, the planning process becomes increasingly challenging for all stakeholders involved. To overcome these challenges, detailed planning processes are required, especially for the trafficability of narrow passages along the transportation route. This paper introduces an advanced methodology for an enhanced 3D trafficability analysis with collision detection of large-volume and heavy-duty transports. By employing high-resolution, dense, colored 3D point clouds alongside detailed transport models, this approach offers a more accurate and comprehensive assessment of the feasibility of the transport. The methodology is further generalized to accommodate a wide variety of transport configurations and maneuvers, allowing for automated analysis across different scenarios. The primary contribution of this research lies in its ability to significantly improve collision detection accuracy and provide detailed visualizations, thereby optimizing the digital planning process of large-volume and heavy-duty transports. 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Enhanced 3D Trafficability Analysis for Large-Volume and Heavy-Duty Transports Based on High-Resolution Point Clouds
Large-volume and heavy-duty transports are essential for the successful and timely execution of large-scale industrial, socio-political and climate-related projects. As these transports grow in size and complexity, the planning process becomes increasingly challenging for all stakeholders involved. To overcome these challenges, detailed planning processes are required, especially for the trafficability of narrow passages along the transportation route. This paper introduces an advanced methodology for an enhanced 3D trafficability analysis with collision detection of large-volume and heavy-duty transports. By employing high-resolution, dense, colored 3D point clouds alongside detailed transport models, this approach offers a more accurate and comprehensive assessment of the feasibility of the transport. The methodology is further generalized to accommodate a wide variety of transport configurations and maneuvers, allowing for automated analysis across different scenarios. The primary contribution of this research lies in its ability to significantly improve collision detection accuracy and provide detailed visualizations, thereby optimizing the digital planning process of large-volume and heavy-duty transports. The findings demonstrate a distinct advantage of the 3D trafficability analysis over traditional 2D methods, especially in complex environments, leading to cost-effective and reliable transportation planning.
期刊介绍:
IET Intelligent Transport Systems is an interdisciplinary journal devoted to research into the practical applications of ITS and infrastructures. The scope of the journal includes the following:
Sustainable traffic solutions
Deployments with enabling technologies
Pervasive monitoring
Applications; demonstrations and evaluation
Economic and behavioural analyses of ITS services and scenario
Data Integration and analytics
Information collection and processing; image processing applications in ITS
ITS aspects of electric vehicles
Autonomous vehicles; connected vehicle systems;
In-vehicle ITS, safety and vulnerable road user aspects
Mobility as a service systems
Traffic management and control
Public transport systems technologies
Fleet and public transport logistics
Emergency and incident management
Demand management and electronic payment systems
Traffic related air pollution management
Policy and institutional issues
Interoperability, standards and architectures
Funding scenarios
Enforcement
Human machine interaction
Education, training and outreach
Current Special Issue Call for papers:
Intelligent Transportation Systems in Smart Cities for Sustainable Environment - https://digital-library.theiet.org/files/IET_ITS_CFP_ITSSCSE.pdf
Sustainably Intelligent Mobility (SIM) - https://digital-library.theiet.org/files/IET_ITS_CFP_SIM.pdf
Traffic Theory and Modelling in the Era of Artificial Intelligence and Big Data (in collaboration with World Congress for Transport Research, WCTR 2019) - https://digital-library.theiet.org/files/IET_ITS_CFP_WCTR.pdf