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Expanding the research on traditional history of economic ideology into the research on the history of economics composed of three elements – history of ideology, history of policies and events – is a new idea for researching the history of socialist political economy with Chinese characteristics. The start of the history of socialist political economy with Chinese characteristics is consistent with that of the Sinicization of Marxist political economy and can be dated from at least 1917.

The key point of the research on the history of ideologies of the socialist political economy with Chinese characteristics is to treat the relationship between theory and people properly, i.e. we should not neglect the effect brought out by the economists on theory construction while we attach importance to the theoretical contribution of the leaders and leading group of the Communist Party of China (CPC).

For the research on the history of economic policies of socialist political economy with Chinese characteristics, on the one hand, we should clarify the relationship among ideologies, strategies and policies; on the other hand, we should not evade the summarization of lessons from history.

Besides presenting the development route of socialist political economy with Chinese characteristics under competition, the research on the events in the history of socialist political economy with Chinese characteristics should also help develop the socialist political economy with Chinese characteristics.

The purpose of this paper is to provide a guiding framework for studying the travel patterns of PT users. The combination of public transit (PT) users’ travel data and user profiling (UP) technology to draw a portrait of PT users can effectively understand users’ travel patterns, which is important to help optimize the scheduling of PT operations and planning of the network.

To achieve the purpose, the paper presents a three-level classification method to construct the labeling framework. A station area attribute mining method based on the term frequency-inverse document frequency weighting algorithm is proposed to determine the point of interest attributes of user travel stations, and the spatial correlation patterns of user travel stations are calculated by Moran’s Index. User travel feature labels are extracted from travel data containing Beijing PT data for one consecutive week.

In this paper, a universal PT user labeling system is obtained and some related methods are conducted including four categories of user-preferred travel area patterns mining and a station area attribute mining method. In the application of the Beijing case, a precise exploration of the spatiotemporal characteristics of PT users is conducted, resulting in the final Beijing PTUP system.

This paper combines UP technology with big data analysis techniques to study the travel patterns of PT users. A user profile label framework is constructed, and data visualization, statistical analysis and K-means clustering are applied to extract specific labels instructed by this system framework. Through these analytical processes, the user labeling system is improved, and its applicability is validated through the analysis of a Beijing PT case.