  A comprehensive data analysis system is implemented for the extraction of
information and comparison of North American public transport systems. The
system is based on network representations of the transport systems and makes
use of a span of metrics and algorithms from the established properties in
graph theory to complicated domain specific measurements. Due to nature of big
data systems and the requirement of scalability, many heuristic optimizations
and approximations have been considered in the system. Integration with other
sources of data specially population density maps is also executed in the
system. Formal evaluations are done on subcomponents of the system to make sure
the approximations have reasonable precision. Results on comparison of four
cities, San Francisco, Boston, Toronto and Los Angeles, approves that the big
data approach to comparison of public transit systems can successfully reveal
the underlying similarities and differences.
