Abstract
This paper presents a novel method for mining the individual travel behavior regularity of different public transport passengers through constructing travel behavior graph based model. The individual travel behavior graph is developed to represent spatial positions, time distributions, and travel routes and further forecasts the public transport passenger’s behavior choice. The proposed travel behavior graph is composed of macronodes, arcs, and transfer probability. Each macronode corresponds to a travel association map and represents a travel behavior. A travel association map also contains its own nodes. The nodes of a travel association map are created when the processed travel chain data shows significant change. Thus, each node of three layers represents a significant change of spatial travel positions, travel time, and routes, respectively. Since a travel association map represents a travel behavior, the graph can be considered a sequence of travel behaviors. Through integrating travel association map and calculating the probabilities of the arcs, it is possible to construct a unique travel behavior graph for each passenger. The data used in this study are multimode data matched by certain rules based on the data of public transport smart card transactions and network features. The case study results show that graph based method to model the individual travel behavior of public transport passengers is effective and feasible. Travel behavior graphs support customized public transport travel characteristics analysis and demand prediction.
Highlights
Public transport has been one of the main travel modes in urban areas due to its comprehensive service for travelers and big influence on urban traffic systems
Examples of different travel information drawn from travel behavior graphs include the following: (1) P1 would be a commuter with fixed round-trip characteristics; (2) the travel behavior is relatively complicated for P2 because of more dispersive travel time and routes; (3) bus is the main travel mode for P3; and (4) the travel destination by public transport is single and the travel distance is short for P4
This paper develops a novel method for modeling an individual travel behavior based on knowledge graph to mine the travel regularity of different public transport passengers
Summary
Public transport has been one of the main travel modes in urban areas due to its comprehensive service for travelers and big influence on urban traffic systems. Many researchers have attempted to analyze public transport travel characteristics and to explore extraction methods for travel behavior regularity. These previous studies mainly focused on statistical analysis based on conventional trip survey of relatively small sample sizes or depended on limited public transport data [3,4,5,6,7]. Compared to manual surveys, these studies have lower cost and higher accuracy These statistical results were applied to obtain the average features and total attributes of public transport travelers; the characteristics of individual passenger were largely ignored. Bike sharing services (i) District name (ii) Station code (iii) Station name (iv) Station latitude and longitude the personal characteristics of public transport individuals
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