## 1 Introduction  
The health benefits of practicing a physical activity on a daily basis, and walking in particular, is widely acknowledged [1]. Soft transportation modes are also known to ease traffic flows. Thus, municipalities are increasingly promoting the use of walking or cycling to their city dwellers, for commuting, and investing in facilities that encourage these practices [2; 3; 4; 5]. However, although soft transportation modes undoubtedly have a positive global environmental effect, an increased exposure to road traffic pollutants, namely airborne pollutants, fine particles, and noise levels, amplified by the high correlation generally observed between these pollutants [6; 7; 8], is a harmful counterpart of choosing this transportation mode in urban areas. Moreover, the environmental quality at the neighborhood scale, strongly influences the choice of walking as transportation mode [9; 10; 11]. Therefore, being able to estimate the exposure associated with an urban walking trip has many potential interests, such as for informing pedestrians about the potential health benefit of their intended walk, or for optimizing the related route choice through specific algorithms [12; 13]. However, estimating noise exposure is made difficult by the high spatial and temporal sound pressure level variability, typical in urban environments [14; 15]. Moreover, recent works have revealed the complex relations between perceptual assessments (e.g., pleasantness of the sound environment) [16; 17; 18] and physical measurements [19; 20]. The importance of the temporal and spectral dimensions of sound [19; 20], the interest of explicitly introducing the contribution of different sound sources (e.g., vehicles, voices, birds, etc.) into the modeling [16; 17; 18], the influence of non-acoustical parameters [21], such as the visual scene and the openness of the space [21; 22], and even non-physical factors, such as demographic, cultural, and social factors, or context factors [23; 24; 25], advocate not relying on energetic indicators when producing sound pleasantness maps or assessing the sound pleasantness of urban walking trips. Recently proposed noise mapping alternatives, which include mobile measurements, fulfil the requirements for estimating the sound pleasantness of walking trips, as they account for all of the sound sources that encompass urban sound environments, allowing one to estimate advanced indicators [26; 27; 28]. This new context makes it possible to estimate the sound pleasantness of an urban walking trip. However, this requires an understanding of how a pedestrian globally and retrospectively assesses a sound environment that varies with time. This paper investigates these relations through a modeling framework of three steps, described in Figure 1. First, models are proposed to relate perceptual assessments of continuous and overall pleasantness of the presented sound sequences (Figure 1C). Then, models of the instantaneous and overall sound pleasantness appreciation, based on sound levels (Figure 1A,B), are proposed. Previous research in the field of psychology, psychoacoustics, and soundscape, has shown that retrospective overall judgement is not a simple average of instantaneous judgment, but is significantly influenced by the following principal temporal effects (more details can be found in [29]):  
* The recency effect, by which initial and final momentary judgments of a sequence are more remembered at the instant when the retrospective assessment is given, has been observed for sound sequences by [PERSON] [30; 31]. * The peak-end rule, which states that the global judgement of an experiment is influenced by its most intense point and its end (negative or positive perception), has been observed in [32; 33]. * The trend effect, which describes the fact that people often make predictions about the future based on trends that they have observed in the past, has been shown by [PERSON] & [PERSON], on a corpus of various 1-min length samples [29].