Guan Huang

While the relationship between local housing prices and the urban form and distribution of urban functional zones in a single city is well-discussed, the conclusion is usually sensitive to a particular city context, and cross-city comparative study is limited. This study attempts to examine the influences of urban form and urban functional zone distribution on housing prices within and between cities after controlling the city-wide socio-economic and demographic differences. Based on multiple open-source big data, such as points-of-interest (POI) and historical housing transaction data, the hierarchical linear model is utilized to compare the housing market of 10 extra-large cities in China. Results indicate that the urban form and the urban functional zone distribution significantly influence housing prices after the socio-economic and demographic differences are controlled. For inter-city comparison, an urban form with high compactness, low centrality, low polycentricity, high density, and low dissimilarity in housing development is related to lower city-level housing prices. For intra-city, proximity to work centers, high-quality hospitals, and schools shows positive associations to housing prices.

For bike sharing systems, demand prediction is crucial to ensure the timely re-balancing of available bikes according to predicted demand. Existing methods for bike sharing demand prediction are mostly based on its own historical demand variation, essentially regarding it as a closed system and neglecting the interaction between different transportation modes. This is particularly important for bike sharing because it is often used to complement travel through other modes (e.g., public transit). Despite some recent progress, no existing method is capable of leveraging spatiotemporal information from multiple modes and explicitly considers the distribution discrepancy between them, which can easily lead to negative transfer. To address these challenges, this study proposes a domain-adversarial multi-relational graph neural network (DA-MRGNN) for bike sharing demand prediction with multimodal historical data as input. A temporal adversarial adaptation network is introduced to extract shareable features from demand patterns of different modes. To capture correlations between spatial units across modes, we adapt a multi-relational graph neural network (MRGNN) considering both cross-mode similarity and difference. In addition, an explainable GNN technique is developed to understand how our proposed model makes predictions. Extensive experiments are conducted using real-world bike sharing, subway and ride-hailing data from New York City. The results demonstrate the superior performance of our proposed approach compared to existing methods and the effectiveness of different model components.