Abstract: In recent years,with the continuous development of transportation network as well as technology in high-speed railway,the speed and comfort of railway travel have been greatly improved,more and more people choose to travel by railway.As a result,co-travel behaviors have become even common in rail trips.The travel behavior of passengers can be influenced by their peers,and different travel groups will present different travel preferences.For example,for a travelling group with family mem-bers,the elderly and children will be taken good care of,hence group members are more inclined to pursue comfort during the trip.When a few young people who are mutual friends form a travelling group,they care more about the sense of experience and freshness.Therefore,predicting the type of a travel group will be beneficial for learning travel preference of this group,e.g.,not only help transportation,tourism and other related industries to define their products and services that travel groups interest in,but also provide support for market decision-making in the railway transportation industry.Based onthis,this paper proposes a methodology for analyzing railway passengers'travelling behavioral using ticket booking data.Firstly,based on ticket booking data,it proposes the quantitative method of co-travel times of a travelling group.Secondly,it formalizes the prediction problem by incorporating Hidden Markov Model.Lastly,the accuracy and consistency of the model are verified with real-life data and experiment results show that the accuracy of our model can even reach 96.38%,in the meanwhile,the consistency is as high as 95%.Thus,we conclude that the proposed method can effectively and accurately predict the relationship of railway travel groups.

Key words: Co-travel relation prediction, HMM, Railway co-travel group