Passenger Travel Behavior Analysis Of Urban Integrated Transportation Hub Based On Cellular Big Data

With the rapid development of passenger travel models such as high speed railway,aviation and urban rail transit,integrated passenger transportation hubs combining several travel models have become more and more important in citizens' daily life.The gathering and distributing of massive passengers make the hubs become vital nodes in the urban transportation network.Studying the travel behavior of the hub passengers can help us understand the operation situations of the hubs and analyze their current or potential influence on the urban transportation,which can provide necessary information for the transportation managers and planners.As a kind of the traffic big data,cellular signaling data has been applied to analyze the citizens' travel behavior.However,there are still limitations in the previous studies.For example,the key areas and the specific groups were paid little attention by the researchers.Based on the potential that the cellular signaling data has shown to analyze the travel behavior,the dissertation chooses the cellular signaling data to study the travel behavior of the hub passengers.The dissertation is divided into four main parts including the feature analysis of the cellular signaling data,the identification of the hub passengers,the trip characteristics analysis of the hub passengers and the travel demand study of the hub passengers.The main content and the key findings of the study are summarized as follow:Firstly,the basic concepts of the cellular signaling data are introduced including the framework of the GSM system,the generation theory and the positioning theory of the data.Based on the collected data,the data fields are explained.The space and time precisions are further analyzed to understand the characteristics of the data.Moreover,the classifications and formats of the noise data are illustrated with the corresponding pre-processing methods.Besides,the study compares the advantages and disadvantages of several kinds of position data and analyzes their applicabilities in the travel behavior research.The features of the cellular signaling data as a kind of big data is also explained in the study.Secondly,the identification methods of the hub communication area and the hub passengers are proposed based on the cellular signaling data.In the identification method of the hub communication area,the study defines the temporal distance and the spatial distance to create the temporal-spatial distance model,which can be used to measure the similarity between two base stations.Based on the temporal-spatial distance model,a complete method is presented to estimate whether a base station belongs to the hub communication area.In the identification method of the hub passengers,the study classifies the hub passengers and analyzes the data characteristics of each kind of passengers.The criteria are proposed based on the data characteristics to identify all kinds of passengers from the cellular signaling data.The Hongqiao hub in Shanghai is taken as a case study.The results validate that the methods are effective.Thirdly,the study presents a method to extract the travel trajectories of the users from the cellular signaling data.The core part of the method is to identify the staying points where the users stop for the specific activities.The study introduces the theory of the method in Asakura's work and analyzes the limitations of the method.Based on Asakura's method,the minimal enclosing circle is used as the space constraint to create an improved identification method.The cellular signaling data in Shanghai is applied to testify that the improved method is effective and better than the Asakura's method.After collecting the travel trajectories,several key trip indexes are introduced to characterize the trips of the hub passengers.Moreover,the concepts of the association rules are used to analyze the travel trajectories of the hub passengers.Based on the analysis,a method is proposed to evaluate the development status of the hub development zone,which is an innovative way to evaluate the Transit Oriented Development(TOD)model.Finally,the study presents a method to analyze the travel demand of the hub passengers in the city area including the analysis of the travel demand pattern and the identification of the transportation corridor.Two indexes,the travel generation index and the travel attraction index,are introduced to display the travel demand patterns of the hub passengers in a normalization and visualization way.Besides,an improved method is proposed to identify the transportation corridors related to the transportation hub to display the specific distribution features of the travel demand.The travel demand patterns of the passengers in Hongqiao hub are studied.Two passenger corridors are identified used the proposed method.The works in the dissertation contribute to the theory of the travel behavior research of the hub passengers and the application of the cellular signaling data in the urban transportation.The case studies provide a good reference for the field application.