A novel framework for the assessment of public-transport drivers' well-being and satisfaction based on physiological data

IF 2 4区 工程技术 Q3 TRANSPORTATION
Guy Wachtel , Yuval Hadas
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引用次数: 0

Abstract

This paper presents a novel framework for data collection and fusion, for better analysis and assessment of public transportation (PT) drivers' well-being and satisfaction using physiological data. The goal of this framework, when combined with machine learning (ML) and discrete choice models (DCMs) to predict drivers' physiological states based on fleet management data, is to improve service reliability and assess the drivers' well-being and satisfaction. A case study based on different ML models and data collected from available physiological indicators was conducted to demonstrate the framework's ability to predict such features as Heart Rate (HR) and Electrodermal Activity (EDA) based on Automatic Vehicle Location (AVL) and Automatic Fare Collection (AFC) systems. The results indicate a significant correlation between service measures (e.g., layover duration, route characteristics and complexity) and the drivers' well-being. Our framework offers practical guidance for decision-makers to enhance operational planning, leading to improved efficiency and healthier working conditions for drivers. Future research should expand the application of the framework to different areas and branches of PT, incorporate additional physiological sensors, and integrate more ML models and DCMs for extensive analysis.
基于生理数据的公共交通司机幸福感和满意度评估的新框架
本文提出了一种新的数据收集和融合框架,以便利用生理数据更好地分析和评估公共交通(PT)司机的幸福感和满意度。该框架的目标是,结合机器学习(ML)和离散选择模型(dcm),根据车队管理数据预测驾驶员的生理状态,从而提高服务可靠性,评估驾驶员的幸福感和满意度。基于不同的机器学习模型和从可用生理指标收集的数据进行了案例研究,以证明该框架能够基于自动车辆定位(AVL)和自动收费(AFC)系统预测心率(HR)和皮电活动(EDA)等特征。结果表明,服务措施(如停留时间、路线特征和复杂性)与驾驶员幸福感之间存在显著相关性。我们的框架为决策者提供了切实可行的指导,以加强运营规划,从而提高效率,为司机提供更健康的工作条件。未来的研究应将该框架的应用扩展到PT的不同领域和分支,纳入更多的生理传感器,并整合更多的ML模型和dcm进行广泛的分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
6.40
自引率
0.00%
发文量
29
审稿时长
26 days
期刊介绍: The Journal of Public Transportation, affiliated with the Center for Urban Transportation Research, is an international peer-reviewed open access journal focused on various forms of public transportation. It publishes original research from diverse academic disciplines, including engineering, economics, planning, and policy, emphasizing innovative solutions to transportation challenges. Content covers mobility services available to the general public, such as line-based services and shared fleets, offering insights beneficial to passengers, agencies, service providers, and communities.
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