Hongli Wang , Qing Liu , Bowen Bai , Junfang Wang , Han Xiao , Huan Liu , Jindong Liang , Zhenhong Lin , Dongquan He , Hang Yin
{"title":"通过车载诊断系统(OBD)数据探索重型卡车的运行特性","authors":"Hongli Wang , Qing Liu , Bowen Bai , Junfang Wang , Han Xiao , Huan Liu , Jindong Liang , Zhenhong Lin , Dongquan He , Hang Yin","doi":"10.1016/j.rtbm.2024.101204","DOIUrl":null,"url":null,"abstract":"<div><p>Road traffic is a significant source of carbon emissions in China, and a thorough understanding of vehicle operating characteristics is crucial for reducing these emissions. This paper presents an integrative approach to analyze the operation of heavy-duty trucks (HDTs) by fusing On-Board Diagnostic (OBD) data. The operational intricacies of HDTs, including load statuses, origin-destination (OD) trip characteristics, and the frequency of HDTs start-stop pairs, were analyzed. We used OBD data from 3792 HDTs in China over whole year of 2022 and employed novel big data mining methods for dwell detection, load identification, and OD trip information calculation. The findings reveal that for cargo trucks, dump trucks, and tractor trucks at the OD trip level, their respective empty-loaded driving rates stand at 33.01 %, 33.58 %, and 31.71 %. The average OD travel distances for these vehicle categories are 110 km, 95 km, and 195 km, while the average travel velocities are 23 km/h, 16 km/h, and 40 km/h, respectively. Additionally, the average number of start-stop pairs for these categories is 2.8, 3.9, and 4.9, and the average dwell duration is 1.2 h, 1.6 h, and 2.1 h, respectively. This study demonstrates the great potential of optimizing load management and trip planning, and provides a reference for improving the economic efficiency and reducing emissions of HDTs in China.</p></div>","PeriodicalId":47453,"journal":{"name":"Research in Transportation Business and Management","volume":"57 ","pages":"Article 101204"},"PeriodicalIF":4.1000,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Exploring heavy-duty truck operational characteristics through On-Board Diagnostics (OBD) data\",\"authors\":\"Hongli Wang , Qing Liu , Bowen Bai , Junfang Wang , Han Xiao , Huan Liu , Jindong Liang , Zhenhong Lin , Dongquan He , Hang Yin\",\"doi\":\"10.1016/j.rtbm.2024.101204\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Road traffic is a significant source of carbon emissions in China, and a thorough understanding of vehicle operating characteristics is crucial for reducing these emissions. This paper presents an integrative approach to analyze the operation of heavy-duty trucks (HDTs) by fusing On-Board Diagnostic (OBD) data. The operational intricacies of HDTs, including load statuses, origin-destination (OD) trip characteristics, and the frequency of HDTs start-stop pairs, were analyzed. We used OBD data from 3792 HDTs in China over whole year of 2022 and employed novel big data mining methods for dwell detection, load identification, and OD trip information calculation. The findings reveal that for cargo trucks, dump trucks, and tractor trucks at the OD trip level, their respective empty-loaded driving rates stand at 33.01 %, 33.58 %, and 31.71 %. The average OD travel distances for these vehicle categories are 110 km, 95 km, and 195 km, while the average travel velocities are 23 km/h, 16 km/h, and 40 km/h, respectively. Additionally, the average number of start-stop pairs for these categories is 2.8, 3.9, and 4.9, and the average dwell duration is 1.2 h, 1.6 h, and 2.1 h, respectively. This study demonstrates the great potential of optimizing load management and trip planning, and provides a reference for improving the economic efficiency and reducing emissions of HDTs in China.</p></div>\",\"PeriodicalId\":47453,\"journal\":{\"name\":\"Research in Transportation Business and Management\",\"volume\":\"57 \",\"pages\":\"Article 101204\"},\"PeriodicalIF\":4.1000,\"publicationDate\":\"2024-09-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Research in Transportation Business and Management\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2210539524001068\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BUSINESS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Research in Transportation Business and Management","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2210539524001068","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BUSINESS","Score":null,"Total":0}
Exploring heavy-duty truck operational characteristics through On-Board Diagnostics (OBD) data
Road traffic is a significant source of carbon emissions in China, and a thorough understanding of vehicle operating characteristics is crucial for reducing these emissions. This paper presents an integrative approach to analyze the operation of heavy-duty trucks (HDTs) by fusing On-Board Diagnostic (OBD) data. The operational intricacies of HDTs, including load statuses, origin-destination (OD) trip characteristics, and the frequency of HDTs start-stop pairs, were analyzed. We used OBD data from 3792 HDTs in China over whole year of 2022 and employed novel big data mining methods for dwell detection, load identification, and OD trip information calculation. The findings reveal that for cargo trucks, dump trucks, and tractor trucks at the OD trip level, their respective empty-loaded driving rates stand at 33.01 %, 33.58 %, and 31.71 %. The average OD travel distances for these vehicle categories are 110 km, 95 km, and 195 km, while the average travel velocities are 23 km/h, 16 km/h, and 40 km/h, respectively. Additionally, the average number of start-stop pairs for these categories is 2.8, 3.9, and 4.9, and the average dwell duration is 1.2 h, 1.6 h, and 2.1 h, respectively. This study demonstrates the great potential of optimizing load management and trip planning, and provides a reference for improving the economic efficiency and reducing emissions of HDTs in China.
期刊介绍:
Research in Transportation Business & Management (RTBM) will publish research on international aspects of transport management such as business strategy, communication, sustainability, finance, human resource management, law, logistics, marketing, franchising, privatisation and commercialisation. Research in Transportation Business & Management welcomes proposals for themed volumes from scholars in management, in relation to all modes of transport. Issues should be cross-disciplinary for one mode or single-disciplinary for all modes. We are keen to receive proposals that combine and integrate theories and concepts that are taken from or can be traced to origins in different disciplines or lessons learned from different modes and approaches to the topic. By facilitating the development of interdisciplinary or intermodal concepts, theories and ideas, and by synthesizing these for the journal''s audience, we seek to contribute to both scholarly advancement of knowledge and the state of managerial practice. Potential volume themes include: -Sustainability and Transportation Management- Transport Management and the Reduction of Transport''s Carbon Footprint- Marketing Transport/Branding Transportation- Benchmarking, Performance Measurement and Best Practices in Transport Operations- Franchising, Concessions and Alternate Governance Mechanisms for Transport Organisations- Logistics and the Integration of Transportation into Freight Supply Chains- Risk Management (or Asset Management or Transportation Finance or ...): Lessons from Multiple Modes- Engaging the Stakeholder in Transportation Governance- Reliability in the Freight Sector