  With the rapid development of smart cities, smart buildings are generating a
massive amount of building sensing data by the equipped sensors. Indeed,
building sensing data provides a promising way to enrich a series of
data-demanding and cost-expensive urban mobile applications. In this paper, we
study how to reuse building sensing data to predict traffic volume on nearby
roads. Nevertheless, it is non-trivial to achieve accurate prediction on such
cross-domain data with two major challenges. First, relationships between
building sensing data and traffic data are not unknown as prior, and the
spatio-temporal complexities impose more difficulties to uncover the underlying
reasons behind the above relationships. Second, it is even more daunting to
accurately predict traffic volume with dynamic building-traffic correlations,
which are cross-domain, non-linear, and time-varying. To address the above
challenges, we design and implement BuildSenSys, a first-of-its-kind system for
nearby traffic volume prediction by reusing building sensing data. First, we
conduct a comprehensive building-traffic analysis based on multi-source
datasets, disclosing how and why building sensing data is correlated with
nearby traffic volume. Second, we propose a novel recurrent neural network for
traffic volume prediction based on cross-domain learning with two attention
mechanisms. Specifically, a cross-domain attention mechanism captures the
building-traffic correlations and adaptively extracts the most relevant
building sensing data at each predicting step. Then, a temporal attention
mechanism is employed to model the temporal dependencies of data across
historical time intervals. The extensive experimental studies demonstrate that
BuildSenSys outperforms all baseline methods with up to 65.3% accuracy
improvement (e.g., 2.2% MAPE) in predicting nearby traffic volume.
