Data Analytics to Support a Smart Fleet Management Strategy

车队管理 运输工程 燃料效率 智能交通系统 计算机科学 公共交通 分析 大数据 能源消耗 驱动因素 工程类 汽车工程 数据科学 数据挖掘 电气工程 中国 法学 政治学
作者
Laura Pozueco,Nishu Gupta,Xabiel G. Pañeda,Víctor Corcoba Magaña,David Melendi,Roberto García,Abel Rionda
出处
期刊:IEEE Intelligent Transportation Systems Magazine [Institute of Electrical and Electronics Engineers]
卷期号:15 (2): 115-127 被引量:2
标识
DOI:10.1109/mits.2022.3208316
摘要

The correct application of efficient and safe driving techniques plays an important role for professional drivers. Monitoring and analyzing driving data can promote changes in the sector in terms of the better use of vehicles, reduction in energy consumption, and improved on-road safety. However, the results in driving performance can vary considerably among different fleets that have received the same training in efficient and safe driving. The aim of this article is to perform an in-depth analysis of the driving performance of professional drivers during their working day, taking into account the influence of fleet management decisions. For this, we have selected four urban public transport companies with clear differences in terms of the employees scheduled and rostered drivers to bus lines. The driving behavior of 745 drivers has been evaluated over a period of 10 months, considering performance in terms of efficient and safe driving through the use of driving patterns. A total of 6,517,983.995 km of real-time driving data retrieved from vehicles every 1.5 s has been analyzed. The results show significant differences in the evolution and acquisition of new driving habits. In addition, significant observations from this article provide valuable information for fleet managers and allow them take advantage of data provided by the adoption of intelligent transportation systems.

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