Abstract: Analyzing the impact of urban public service spaces on public travel through big data is a critical step in the route selection planning of pedestrian and bicycle transport corridors (PBTC). Existing researches mainly focus on the spatial distribution laws of urban public service spaces, while paying less attention to their inherent differences. In view of this, the research introduces the internet word-of-mouth (IWOM) big data to characterize the attractiveness of urban public service spaces through word-of-mouth scores, based on which establishes the framework for PBTC route selection planning based on IWOM big data, and carries out empirical analysis with Haidian District in Beijing as an example. In combination with such methods as minimum cumulative resistance (MCR) model and network analysis, the research finally builds a PBTC network composed of three types of PBTCs respectively for commercial complexes, leisure and entertainment, and life services. Research results show that IWOM big data has the dual attributes of spatial distribution and word-of-mouth quality, which can help planners and decision-makers identify and quantify the inherent differences of some types of public service facilities from a regional perspective. At the same time, the PBTC planning supported by IWOM big data puts more emphasis on the impact of public satisfaction on route selection process, which has a good application prospect in the field of future urban transport planning.