  We analyze the patterns of traffic jams in urban networks of five large
cities and an urban agglomeration region in China using real data based on a
recently developed jam tree model. This model focuses on the way traffic jams
spread through a network of streets, where the first street that becomes
congested represents the bottleneck of the jam. We extended the model by
integrating additional realistic jam components into the model and find that,
while the locations of traffic jams can vary significantly from day to day and
hour to hour, the daily distribution of the costs associated with these jams
follows a consistent pattern, i.e., a power law with similar exponents. This
distribution pattern appears to hold not only for a given region on different
days, but also for the same hours on different days. This daily pattern of
exponent values for traffic jams can be used as a fingerprint for urban
traffic, i.e., jam-prints. Our findings are useful for quantifying the
reliability of urban traffic system, and for improving traffic management and
control.
