A Dual-Flow Attentive Network with Feature Crossing for Chained Trip
Purpose Inference
Abstract
Trip purpose is essential information supporting many downstream tasks
in intelligent transportation systems, such as travel behaviour
comprehension, location-based service, and urban planning. The
observation of trip purpose is a necessary aspect of travel surveys, as
it is difficult to obtain clear annotated purposes by another approach.
However, the limitations of sampling volume, survey budget, and survey
frequency make it difficult to rely solely on travel surveys in the era
of big data. There has long been a demand for methods to accurately
infer trip purpose. An accurate, generalizable, and robust inference
method for trip purpose can be the solid first step towards wide and
diverse applications. Existing studies have made significant efforts to
reveal features correlated with the trip purpose and leverage chaining
patterns between trips. However, geographic contextual information has
not often been considered. The spatial correlations and chaining
patterns hidden in travelled zones are worth further exploration.
Additionally, complex activity-zone interactions have not been
considered in previous models. In terms of the trip chain level, the
generation of a trip could not be only correlated to its directly
associated zones but also the zones before or after it, and vice versa.
Here, we propose a framework-Dual-Flow Attentive
Network with Feature Crossing (DACross), specifically for
chained trip purpose inference. We form trip chains based on a new
modelling perspective that treats trip activities and travelled
geographic zones as two chains with interactions. Correspondingly, we
propose DACross, which consists of two parallel attentive branches and a
co-attentive feature crossing module, for fully learning intra- and
inter-chain dependencies. We conducted extensive experiments on four
large-scale real-world datasets to evaluate not only the performance of
DACross but also the generalizability of the proposed framework among
different cities and various scenarios. Experimental results prove the
overall superiority of the proposed DACross.