Classification of driving workload affected by highway alignment conditions based on classification and regression tree algorithm.
Objective: Guaranteeing a safe and comfortable driving workload can contribute to reducing traffic injuries. In order to provide safe and comfortable threshold values, this study attempted to classify driving workload from the aspects of human factors mainly affected by highway geometric conditions...
| Publicado en: | Traffic Injury Prevention Vol. 19; no. 2; pp. 214 - 219 |
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| Autores principales: | , |
| Formato: | research Journal Article |
| Publicado: |
Taylor & Francis Ltd
2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=127587357&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 127587357 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 15389588 RGF jtl: Traffic Injury Prevention issn: 15389588 maglogo: Y pubinfo: dt: 2018 vid: 19 iid: 2 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 127587357 127587357 NLM28771038 127587357 10.1080/15389588.2017.1353084 NLM28771038 127587357 ppf: 214 ppct: 5 formats: tig: atl: Classification of driving workload affected by highway alignment conditions based on classification and regression tree algorithm. aug: au: Hu, Jiangbi Wang, Ronghua affil: College of Architecture and Civil Engineering, Beijing University of Technology, Chaoyang District, Beijing, P. R. China sug: subj: Environment Workload Classification Automobile Driving Statistics and Numerical Data Algorithms Adult Motor Vehicles Statistics and Numerical Data Regression Middle Age Male Heart Rate Physiology Female Decision Trees Human Validation Studies Comparative Studies Evaluation Research Multicenter Studies Ferrans and Powers Quality of Life Index Scales Adult: 19-44 years Middle Aged: 45-64 years Male Female ab: Objective: Guaranteeing a safe and comfortable driving workload can contribute to reducing traffic injuries. In order to provide safe and comfortable threshold values, this study attempted to classify driving workload from the aspects of human factors mainly affected by highway geometric conditions and to determine the thresholds of different workload classifications. This article stated a hypothesis that the values of driver workload change within a certain range.Methods: Driving workload scales were stated based on a comprehensive literature review. Through comparative analysis of different psychophysiological measures, heart rate variability (HRV) was chosen as the representative measure for quantifying driving workload by field experiments. Seventy-two participants (36 car drivers and 36 large truck drivers) and 6 highways with different geometric designs were selected to conduct field experiments. A wearable wireless dynamic multiparameter physiological detector (KF-2) was employed to detect physiological data that were simultaneously correlated to the speed changes recorded by a Global Positioning System (GPS) (testing time, driving speeds, running track, and distance). Through performing statistical analyses, including the distribution of HRV during the flat, straight segments and P-P plots of modified HRV, a driving workload calculation model was proposed. Integrating driving workload scales with values, the threshold of each scale of driving workload was determined by classification and regression tree (CART) algorithms.Results: The driving workload calculation model was suitable for driving speeds in the range of 40 to 120 km/h. The experimental data of 72 participants revealed that driving workload had a significant effect on modified HRV, revealing a change in driving speed. When the driving speed was between 100 and 120 km/h, drivers showed an apparent increase in the corresponding modified HRV. The threshold value of the normal driving workload K was between -0.0011 and 0.056 for a car driver and between -0.00086 and 0.067 for a truck driver.Conclusion: Heart rate variability was a direct and effective index for measuring driving workload despite being affected by multiple highway alignment elements. The driving workload model and the thresholds of driving workload classifications can be used to evaluate the quality of highway geometric design. A higher quality of highway geometric design could keep driving workload within a safer and more comfortable range. This study provided insight into reducing traffic injuries from the perspective of disciplinary integration of highway engineering and human factor engineering. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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