新的空间分析和混合启发式方法增强了基于动态称重数据的卡车货运吨位估算能力

IF 7.9 1区 工程技术 Q1 ENGINEERING, CIVIL
Dan Liu;Ziyuan Pu;Yinhai Wang;Tom Van Woensel;Evangelos I. Kaisar
{"title":"新的空间分析和混合启发式方法增强了基于动态称重数据的卡车货运吨位估算能力","authors":"Dan Liu;Ziyuan Pu;Yinhai Wang;Tom Van Woensel;Evangelos I. Kaisar","doi":"10.1109/TITS.2024.3453268","DOIUrl":null,"url":null,"abstract":"This paper presents a novel and practical methodology for freight tonnage estimation by leveraging two complementary datasets: Telemetric Traffic Monitoring Sites (TTMS) data and Weigh-In-Motion (WIM) systems. To estimate freight tonnage statewide and potentially nationwide with limited truck weigh-in-motion stations, we have proposed a multi-objective location-allocation model that associated TTMSs with WIM stations based on similar attributes. Additionally, we have developed a fuzzy k-prototype clustering-based non-dominated sorting genetic algorithm - simulated annealing algorithm (FKC-NSGASA) to solve the multi-objective location-allocation problem, enabling accurate estimation of truck volumes. To address the over-counting problem, we introduced a truck volume elimination method. Finally, we have aggregated annual truck tonnage using the truck volume data and the average tonnage of WIM stations. The proposed methodologies are validated using WIM data from 2012 and 2017 in Florida. The results demonstrate that our approach achieves higher estimation accuracy, showcasing its potential for accurately estimating statewide freight tonnage. Furthermore, the developed estimation framework and algorithm offer an effective and computationally efficient method for statewide freight traffic evaluation.","PeriodicalId":13416,"journal":{"name":"IEEE Transactions on Intelligent Transportation Systems","volume":"25 12","pages":"19581-19591"},"PeriodicalIF":7.9000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"New Spatial Analysis and Hybrid Heuristics Enhance Truck Freight Tonnage Estimation Based on Weigh-in-Motion Data\",\"authors\":\"Dan Liu;Ziyuan Pu;Yinhai Wang;Tom Van Woensel;Evangelos I. Kaisar\",\"doi\":\"10.1109/TITS.2024.3453268\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a novel and practical methodology for freight tonnage estimation by leveraging two complementary datasets: Telemetric Traffic Monitoring Sites (TTMS) data and Weigh-In-Motion (WIM) systems. To estimate freight tonnage statewide and potentially nationwide with limited truck weigh-in-motion stations, we have proposed a multi-objective location-allocation model that associated TTMSs with WIM stations based on similar attributes. Additionally, we have developed a fuzzy k-prototype clustering-based non-dominated sorting genetic algorithm - simulated annealing algorithm (FKC-NSGASA) to solve the multi-objective location-allocation problem, enabling accurate estimation of truck volumes. To address the over-counting problem, we introduced a truck volume elimination method. Finally, we have aggregated annual truck tonnage using the truck volume data and the average tonnage of WIM stations. The proposed methodologies are validated using WIM data from 2012 and 2017 in Florida. The results demonstrate that our approach achieves higher estimation accuracy, showcasing its potential for accurately estimating statewide freight tonnage. Furthermore, the developed estimation framework and algorithm offer an effective and computationally efficient method for statewide freight traffic evaluation.\",\"PeriodicalId\":13416,\"journal\":{\"name\":\"IEEE Transactions on Intelligent Transportation Systems\",\"volume\":\"25 12\",\"pages\":\"19581-19591\"},\"PeriodicalIF\":7.9000,\"publicationDate\":\"2024-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Intelligent Transportation Systems\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10741214/\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, CIVIL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Intelligent Transportation Systems","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10741214/","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
引用次数: 0

摘要

本文介绍了一种利用两个互补数据集进行货运吨位估算的新型实用方法:遥测交通监测站(TTMS)数据和移动称重(WIM)系统。为了利用有限的卡车动态称重站估算全州乃至全国的货运吨位,我们提出了一个多目标位置分配模型,根据相似属性将遥测交通监测站与动态称重站联系起来。此外,我们还开发了一种基于模糊 k 原型聚类的非支配排序遗传算法 - 模拟退火算法(FKC-NSGASA)来解决多目标位置分配问题,从而实现对卡车数量的精确估算。为解决过量计算问题,我们引入了卡车数量剔除法。最后,我们利用卡车运量数据和 WIM 站点的平均吨位汇总了年度卡车吨位。我们使用佛罗里达州 2012 年和 2017 年的 WIM 数据对所提出的方法进行了验证。结果表明,我们的方法实现了更高的估算精度,展示了其准确估算全州货运吨位的潜力。此外,所开发的估算框架和算法为全州货运交通评估提供了一种有效且计算效率高的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
New Spatial Analysis and Hybrid Heuristics Enhance Truck Freight Tonnage Estimation Based on Weigh-in-Motion Data
This paper presents a novel and practical methodology for freight tonnage estimation by leveraging two complementary datasets: Telemetric Traffic Monitoring Sites (TTMS) data and Weigh-In-Motion (WIM) systems. To estimate freight tonnage statewide and potentially nationwide with limited truck weigh-in-motion stations, we have proposed a multi-objective location-allocation model that associated TTMSs with WIM stations based on similar attributes. Additionally, we have developed a fuzzy k-prototype clustering-based non-dominated sorting genetic algorithm - simulated annealing algorithm (FKC-NSGASA) to solve the multi-objective location-allocation problem, enabling accurate estimation of truck volumes. To address the over-counting problem, we introduced a truck volume elimination method. Finally, we have aggregated annual truck tonnage using the truck volume data and the average tonnage of WIM stations. The proposed methodologies are validated using WIM data from 2012 and 2017 in Florida. The results demonstrate that our approach achieves higher estimation accuracy, showcasing its potential for accurately estimating statewide freight tonnage. Furthermore, the developed estimation framework and algorithm offer an effective and computationally efficient method for statewide freight traffic evaluation.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
IEEE Transactions on Intelligent Transportation Systems
IEEE Transactions on Intelligent Transportation Systems 工程技术-工程:电子与电气
CiteScore
14.80
自引率
12.90%
发文量
1872
审稿时长
7.5 months
期刊介绍: The theoretical, experimental and operational aspects of electrical and electronics engineering and information technologies as applied to Intelligent Transportation Systems (ITS). Intelligent Transportation Systems are defined as those systems utilizing synergistic technologies and systems engineering concepts to develop and improve transportation systems of all kinds. The scope of this interdisciplinary activity includes the promotion, consolidation and coordination of ITS technical activities among IEEE entities, and providing a focus for cooperative activities, both internally and externally.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信