  Edge intelligence, a new paradigm to accelerate artificial intelligence (AI)
applications by leveraging computing resources on the network edge, can be used
to improve intelligent transportation systems (ITS). However, due to physical
limitations and energy-supply constraints, the computing powers of edge
equipment are usually limited. High altitude platform station (HAPS) computing
can be considered as a promising extension of edge computing. HAPS is deployed
in the stratosphere to provide wide coverage and strong computational
capabilities. It is suitable to coordinate terrestrial resources and store the
fundamental data associated with ITS-based applications. In this work, three
computing layers,i.e., vehicles, terrestrial network edges, and HAPS, are
integrated to build a computation framework for ITS, where the HAPS data
library stores the fundamental data needed for the applications. In addition,
the caching technique is introduced for network edges to store some of the
fundamental data from the HAPS so that large propagation delays can be reduced.
We aim to minimize the delay of the system by optimizing computation offloading
and caching decisions as well as bandwidth and computing resource allocations.
The simulation results highlight the benefits of HAPS computing for mitigating
delays and the significance of caching at network edges.
