# Study on Quantitative Expression of Cycling Workload
## 2 Cycling Safety and Comfort-Formation Mechanism and Measurement Methods
### Measurement for Cycling Workload  
In this study, a subjective scale and a physiological measure were utilized to quantitatively express cycling workload. We chose these measures because the study involved human factor engineering; accordingly, the subjective scale should be adopted in order to exclude the performance factors that were irrelevant to the research subject. As per the manifestation of the pattern of cycling workload shown in Figure 1, cycling workload can be reflected in the changes in behavior and performance. By formulating a subjective scale and connecting subjective feeling with performance description, we were able to form a preliminary assessment of workload. More specifically, as a direct measurement of change in psychophysiological status, the measure of physiological signals was assumed to directly reflect and measure the cycling workload. According to the literature on workload measurement, there are three main measurement types: performance-based, subjective, and physiological measures [21]; their application and final arrangement in this research were analyzed and summarized as follows. The two approaches to measuring performance for workload are based on primary and secondary tasks, respectively, and the theoretical background is the assumption that humans have limited attention resources. For example, in driving workload, the primary tasks used include steering wheel movements, lane-keeping, and speed control [21]. Speed choice [16] and lateral distance [22] are the measures of primary tasks that are used to evaluate safety status during the cycling task. For secondary task measurements, the peripheral detection task (PDT) [16] is the measure used in e-bike cycling tasks. Secondary task measurements may disrupt the primary task, which is a major problem [21]. Cycling speed performance for traditional bicycles can be impacted by the performance of the selected bicycle. So, lateral distance is a promising measure for performance measurement. However, it was not chosen here due to the current technical limitation of our experimental conditions, which required a high degree of accuracy. Hence, these performance measures were not applied in this study. Figure 1: Cycling safety and comfort during cycling task. (2) Subjective Scale for Cycling Workload  
There are four commonly used subjective scales for workload classification: the NASA task load index (TLX), the Cooper-Harper scale, and the Subjective Workload Assessment Technique (SWAT) are mainly used to assess mental workload; while [PERSON]'s Perceived Exertion and Pain Scales (RPE) is typically used for assessing physical workload. RPE is the most popular subjective scale for assessing stationary ergometer cycling [19; 23]. However, as mentioned in the previous section, dynamic cycling involves both physical and mental workloads; if there are too many classification levels for cyclists to identify the exact level during a given cycling task, then there is potential for additional mental workload, which would affect the results. Additionally, the unidimensional rating scale is also a good choice for practical experiments because it is fast, easy, and not distracting [21]. Given the principle of practical and easy operation, by combining cognitive psychology, information processing, and cycling risks, this study classified cycling status into a three-level system. This system had the capacity to qualitatively describe cycling workload surrounding feelings of safety and comfort and the corresponding performances, which were formulated based on interviews and surveys with cyclists who just completed their cycling task. The cycling workload increased from Level 1 to Level 3 in turn, as shown in Table 1.  
Additionally, one subject matter expert--whose expertise was traffic safe engineering-- was invited to validate this subjective scale; the results of this validation are shown in Table 2.  
Based on the results of the assessment, the application was within the \"very good\" category (so could be used without revision), scoring 86-100%.