Interaction and integration of visual and noise impacts of motorways

This study aimed to achieve a better understanding of the visual and noise impacts of motorways and their integrated impact on the environmental quality via an aural-visual interaction approach, to contribute to more reliable and efficient assessments of the impacts. The study was based on perceptual experiments involving human participants using computer-visualised scenes and edited audio recordings as experimental stimuli. Factors related to road project characteristics and existing landscape characters that potentially influence the perceived visual impact of motorways were first investigated on without considering the impact from moving traffic. An online preference survey was conducted for this part of study. The results showed substantial visual impact from motorways especially in more natural landscapes and significant increase in the impact by opaque noise barriers. Map-based predictors were identified and a regression model was developed to predict and map the perceived visual impact in GIS. The second part of the study investigated the effects of traffic condition, distance to road and background landscape on the perceived visual impact of motorway traffic, and the contribution of traffic noise to the perceived visual impact. A laboratory experiment was carried out where experimental scenarios were presented to participants both with and without sound. The results showed significant visual impact from motorway traffic which was higher in the natural landscape than in the residential counterpart, increased by traffic volume and decreased by distance. Noise increased the perceived visual impact by a largely constant level despite changes in noise level and other factors. With findings on visual impact from above studies and knowledge on noise impact from current literature, the third part of this study, with a second laboratory experiment, investigated on the perceived integrated impact of visual intrusion and noise of motorways, and explored the predictability of the impact by noise exposure indices. The results showed that traffic volume expressed by noise emission level was the most influential factor, followed by distance and background landscape. A regression model using noise level at receiver position and type of background landscape as predictors was developed, explaining about a quarter of the variation in the perceived impact. Concerning the acoustical and visual effects of noise barriers found on perceived environmental quality, the fourth part of the study focused on mitigation of the integrated visual and noise impact by noise barrier. A third laboratory experiment was conducted and the results showed that noise barriers always had either beneficial or insignificant effect in mitigating integrated impact, and the effect was largely similar to that of tree belt. Generally, barriers varying in size and transparency did not differ much in their performance, but there seems to be some difference by barrier size at different distances. Lastly, using the above findings of this study, impact mappings as possible prototype of more advanced tools to assist visual and noise impact assessment were demonstrated.