  Social networks provide a new perspective for enterprises to better
understand their customers and have attracted substantial attention in
industry. However, inferring high quality customer social networks is a great
challenge while there are no explicit customer relations in many traditional
OLTP environments. In this paper, we study this issue in the field of passenger
transport and introduce a new member to the family of social networks, which is
named Co-Travel Networks, consisting of passengers connected by their co-travel
behaviors. We propose a novel method to infer high quality co-travel networks
of civil aviation passengers from their co-booking behaviors derived from the
PNRs (Passenger Naming Records). In our method, to accurately evaluate the
strength of ties, we present a measure of Co-Journey Times to count the
co-travel times of complete journeys between passengers. We infer a high
quality co-travel network based on a large encrypted PNR dataset and conduct a
series of network analyses on it. The experimental results show the
effectiveness of our inferring method, as well as some special characteristics
of co-travel networks, such as the sparsity and high aggregation, compared with
other kinds of social networks. It can be expected that such co-travel networks
will greatly help the industry to better understand their passengers so as to
improve their services. More importantly, we contribute a special kind of
social networks with high strength of ties generated from very close and high
cost travel behaviors, for further scientific researches on human travel
behaviors, group travel patterns, high-end travel market evolution, etc., from
the perspective of social networks.
