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.