The MOVE-model was proposed by European research consortium MOVE (Mesopic Optimization of Visual Efficiency) as a recommended model for mesopic luminance of road lighting applications. The model was derived from a series of experiments related to visual task performance such as achromatic contrast threshold, reaction time, and recognition threshold. In spite of the similar forms of the X-model and MOVE-model, the major differences of the two models are the upper luminance limits of the mesopic range, which is considered too low for the X-model and too high for the MOVE-model. Hence, a modified MOVE-model was later proposed with an appropriate upper luminance limit [4]. A CIE (International Commission on Illumination) recommended mesopic photometric system, CIE TC 1-58 model, also has been successfully used in mesopic photometry for evaluating LED outdoor lighting. Although the models could adequately predict the luminance for monochromatic lights, the effects of chromatic lights on visual performance were not yet taken into account. By applying mesopic luminous efficiency functions, there are significant differences in the luminous efficiency scale between LED light sources with different correlated color temperatures [5]. The visual performance for night-time driving conditions was investigated by carrying out three visual tasks, achromatic threshold, reaction time, and perception of details included when driving a car. With decreasing luminance level from 1 to 0.01 cd/m\({}^{2}\), the visual performance of achromatic threshold and reaction time dropped obviously, and also presented the Purkinje shift. On the basis of their measurements to visual performance, the spectral sensitivity function could be well described with a mesopic model [6]. The X-model was compared to a color model, including inputs from both the L-M color-opponent mechanism and the S-cones to characterize spectral sensitivity with reaction time. The study showed that the second model mentioned above was a better fit for the visual data, whereas the X-model did not account for the reaction time of a stimulus [7]. [PERSON] suggested a new modified MOVE-model with a suitable upper luminance limit, which was 5 cd/m\({}^{2}\) to meet the street and road lighting conditions in reality for different weather conditions. The paper tested the X-model, MOVE-model, and modified MOVE-model by use of visual data acquired from European universities. As a result, the modified MOVE-model described the data best with over half of the situations [8]. [PERSON] found that both background luminance and spectra power distribution will affect mesopic luminous efficiency. In other words, the optimal luminous efficiency can be achieved by varying the spectrum distribution of LED sources under a specific background [9]. [PERSON] recommended the coefficient \(B\) derived for efficiently calculating the mesopic equivalent illumination (\(E_{mes}\)) from photopic illumination (\(E_{p}\)) for the light sources with different correlated color temperatures (CCT) under mesopic conditions as shown in Equation (3). The findings suggested that the mesopic illumination modified by coefficient \(B\) was close to meeting the human visual perception. Under the same luminance condition, the coefficient \(B\) increased with the light source possessing higher CCT, which was beneficial to road lighting [10]. \[E_{mes}=B\cdot E_{p} \tag{3}\]  
Two methods inclusive of the Adaptation and Source SPD (spectral power distribution) were recently proposed for simply measuring the mesopic luminance and tested with real road surface spectral reflectance in [PERSON]'s study. The main difference between these two methods was that the Source SPD method considered the S/P-ratio (scotopic/photopic ratio) for both test points and the adaptation field with light source's S/P-ratio. In contrast, the Adaptation SPD method only approximately estimated the S/P-ratio of test points with the S/P-ratio of the adaptation field.