Study on Quantitative Expression of Cycling Workload

Abstract

Improper design of the geometric elements and facilities of bikeway systems could endanger cyclists’ safety and comfort, resulting in an increased risk of bicycle accidents; such accidents sometimes have severe consequences, namely casualties. The method of expression for cyclists’ safety and comfort and the question of how the correlation of these factors with bikeway characteristics—such as the design of geometry and facilities—can be quantitatively described are the key problems facing a reduction in accident risk. Cycling workload can be employed to assess cyclists’ safety and comfort. However, there has been little quantitative expression research on this topic, with no clear definition of cycling workload. The quantitative expression of cycling workload is important for developing guidance for the safe design and operational management of bikeways; this is necessary for controlling conditions that might induce overworking and discomfort among users. In this paper, the concept of cycling workload is clearly defined based on cyclists’ comfort and safety formation mechanisms. Through a literature review and a comparative analysis, it is inferred that heart rate variability (HRV) can be used as a quantitative measure and the low-frequency–high-frequency ratio (LF/HF) can be used as a physiological signal to quantify cycling workload. A subjective scale was found to effectively express cyclists’ feelings of safety and comfort, with the performance assessed according to a human factor engineering research paradigm that classified cycling status into three qualitative levels—comfortable; a little stressful; and stressful. In order to form various cycling workload states and to obtain the relationship between LF/HF data and various bikeway characteristics, we designed a field cycling experiment. This was conducted by 24 participants who wore a physiological measuring apparatus under three different bikeway characteristic scenario types including variations in cycling width, direction, and bikeway edges at four cycling speeds in the 10–25 km/h range. Statistical analysis was used to address the collected LF/HF values and the subjective scale results, and a quantitative model for assessing cycling workload was established. By adopting a classification and regression tree (CART) algorithm as a data-mining method, the classification threshold values (ΔHRV) of three cycling workload levels were obtained: 19 indicated a level between comfortable and a little stressful; and 79 indicated a level between a little stressful and stressful.