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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.

This research aims to examine the US–China policy shift from Obama to Biden emphasizing the centrality of Taiwan question in the geostrategic competition with Beijing and its prospect if the US strategy remains unchanged.

A conceptual framework is outlined, illustrating how the US grand strategy is driven by the ideological foundation of Exceptionalism. The paper highlights the associated US policy changes that evolved from Obama to Trump and then Biden to advance Washington's strategic interests in its rivalry with China over Taiwan.

Biden's policy led to an escalating geopolitical competition with Beijing over Taiwan to maintain US supremacy. The Biden administration is more stringent than the previous administrations on the Taiwan question and there is the conviction that the USA must not back down on Taiwan because the alternative will be a retraction of US world primacy to Beijing. With Washington's persistent hegemonic strategy, the US–China confrontation over Taiwan seems inevitable.

The research highlights how the Biden administration managed a perpetuated Ukraine crisis and forged unprecedented high-level ties with Taiwan, indicating the administration's determination to exacerbate contentions with Beijing over Taiwan rather than de-escalate.

This study aims to analyze passenger service quality in Beijing West Railway Station from the perspective of passengers, to better understand the current service quality and obtain the areas of weakness for improvement.

The research investigates the passenger experience of service in Beijing West Railway Station by using a questionnaire survey. The service quality (SERVQUAL) evaluation method is used to analyze the survey data, and it divides the passenger service into 5 attributes with 20 indicators. This research uses the Likert five-level scale method to process data and calculates the SERVQUAL value and weight difference of each attribute to evaluate the passenger service. Therefore, the deficiencies have been pointed out, so the station manager can improve the passenger service accordingly.

It is indicated that among the five studied attributes, Beijing West Railway Station has the smallest service quality value in terms of timeliness, which means this part needs the largest improvement. To the five attributes, each lacks in station security check, ticketing efficiency, station identification accuracy, emergency processing of train delays and the restroom environment, respectively.

The research can provide specific suggestions for the optimization of the passenger service of Beijing West Railway Station, and provide reference information for the formulation of policies.

Fault diagnosis methods based on blind source separation (BSS) for rolling element bearings are necessary tools to prevent any unexpected accidents. In the field application, the actual signal acquisition is usually hindered by certain restrictions, such as the limited number of signal channels. The purpose of this study is to fulfill the weakness of the existed BSS method.

To deal with this problem, this paper proposes a blind source extraction (BSE) method for bearing fault diagnosis based on empirical mode decomposition (EMD) and temporal correlation. First, a single-channel undetermined BSS problem is transformed into a determined BSS problem using the EMD algorithm. Then, the desired fault signal is extracted from selected intrinsic mode functions with a multi-shift correlation method.

Experimental results prove the extracted fault signal can be easily identified through the envelope spectrum. The application of the proposed method is validated using simulated signals and rolling element bearing signals of the train axle.

This paper proposes an underdetermined BSE method based on the EMD and the temporal correlation method for rolling element bearings. A simulated signal and two bearing fault signal from the train rolling element bearings show that the proposed method can well extract the bearing fault signal. Note that the proposed method can extract the periodic fault signal for bearing fault diagnosis. Thus, it should be helpful in the diagnosis of other rotating machinery, such as gears or blades.

The influence of road surface temperature (RST) on vehicles is becoming more and more obvious. Accurate predication of RST is distinctly meaningful. At present, however, the prediction accuracy of RST is not satisfied with physical methods or statistical learning methods. To find an effective prediction method, this paper selects five representative algorithms to predict the road surface temperature separately.

Multiple linear regressions, least absolute shrinkage and selection operator, random forest and gradient boosting regression tree (GBRT) and neural network are chosen to be representative predictors.

The experimental results show that for temperature data set of this experiment, the prediction effect of GBRT in the ensemble algorithm is the best compared with the other four algorithms.

This paper compares different kinds of machine learning algorithms, observes the road surface temperature data from different angles, and finds the most suitable prediction method.

Based on complex network theory, a method for critical elements identification of China Railway High-speed 2 (CRH2) train system is introduced in this paper.

First, two methods, reliability theory and complex theory, are introduced, and the advantages and disadvantages for their application in identifying critical elements of high-speed train system are summarized. Second, a multi-layer multi-granularity network model including virtual and actual nodes is proposed, and the corresponding fusion rules for the same nodes in different layers are given.

Finally, taking CRH2 train system as an example, the critical elements are identified by using complex network theory, which provides a reference for train operation and maintenance.

A method of identifying key elements of CRH2 train system based on integrated importance indices is introduced, which is a meaningful extension of the application of complex network theory to identify key components.

The purpose of this paper is to measure the influence factors of their preferences for urban forest, marginal values of various properties and relative values of different scheme portfolios, thus arriving indirectly at the city residents’ demand for urban forest improvement.

This paper, based on the data from the 2015–2017 field survey questionnaire of city residents over the radius of Beijing’s 5th Ring Road, uses the choice experiment method (CEM) to conduct a study of its residents’ demand for urban forest.

Beijing’s city residents are generally inclined to accept a relatively low payment of urban forest while hoping to access a relatively high urban afforestation coverage with the construction of relatively many city parks, especially focusing on the specialized park management; the marginal values of biodiversity and greenery coverage are far higher than those of greenbelts in quantity and the maximum marginal value of biodiversity remains as high as RMB29.42, indicating that the city residents do not favor much the number of greenbelts over other aspects but they generally hope to achieve a higher greenery coverage and a richer biodiversity.

Generally speaking, what Beijing City needs most is not continuing the increase in the number of greenbelts, but engaging in the rational retrofit of its existing greenbelts and optimizing its urban forest structure.

This paper may provide reference for determining the city residents’ payment criteria for urban forest and will be of equally great significance to developing cities and their urban forest.

This paper aims to define the concept, composition, connotation, functional technology and development path of autonomous transportation systems (ATS) and provide theoretical basis and support for the construction and development of ATS.

The research analyzes the concept and connotation of ATS, studies the composition and structure of ATS, sorts out pillar function technology system including perception, digitization, interoperability, computing and integration in ATS hierarchically, and looks forward to the future development path of ATS from human participation and systems intelligence.

This paper puts forward the concept, composition, connotation and structure of ATS, proposes the pillar functional technology system of ATS and proposes four development stages of ATS.

The research can provide a theoretical and scientific basis for the high-quality, efficient, orderly construction and development of ATS.

Following the standard practice of using nutrition claims to denote food functionality, this study empirically explores Chinese consumers’ willingness-to-pay for functional processed meat products by using three nutrition claims (namely “increased calcium,” “containing omega-3”, and “reduced salt”) made on pork sausages. It also aims to outline the typical characteristics of Chinese consumer segments based on preferences.

A choice-based choice experiment is utilized to investigate Chinese consumers’ valuation on attributes of interest regarding functional sausage products. First-hand data was collected in the two cities of Xi'an and Beijing.

There are market potentials for domestic and/or imported functional processed meat products among Chinese consumers. Nutrition claims made on pork sausages are appealing to Chinese consumers, and therefore, monetarily rewarded by them. Being imported from a more developed country of origin could both positively and negatively impact consumers’ WTP for nutrition claims made on pork sausages. Furthermore, specific functional modification strategies should be taken into account when addressing different segments of the Chinese market. In addition, regional impacts between Xi'an and Beijing are implied in terms of consumers’ valuation for functional pork sausages.

Limitations in the current study are mainly two folds. First, the WTP estimation magnitudes are subject to a hypothetical bias by using a stated preference approach. Second, this study only focuses on pork sausages to explore consumers’ perceptions and selects three nutrition claims among many other relevant options.

Implications are provided for meat marketers and for Chinese official food policymakers, such that promoting meat products with a nutrition claim is an attractive marketing strategy for foreign food manufacturers in China, and more reformulated meat products with better nutritional compositions should be allowed in the Chinese market.

To the best of the author’s knowledge, this research is the first to fill in the literature blank on investigating the consumers’ valuation for functional meat in the emerging market of China. Because when taking Chinese consumers as a target market and evaluating their perceptions of food quality-related labeling and certifications, the existing literature is mainly limited to topics of product safety, organic/green products, and geographical origins. However, nutrition claims, as marketable credence attributes that associate closely to the main characteristics of the functional food products, have been explored to a much lesser extent among Chinese consumers.

Beijing rail transit can actively control the density of rail transit passenger flow, ensure travel facilities and provide a safe and comfortable riding atmosphere for rail transit passengers during the epidemic. The purpose of this paper is to efficiently monitor the flow of rail passengers, the first method is to regulate the flow of passengers by means of a coordinated connection between the stations of the railway line; the second method is to objectively distribute the inbound traffic quotas between stations to achieve the aim of accurate and reasonable control according to the actual number of people entering the station.

This paper analyzes the rules of rail transit passenger flow and updates the passenger flow prediction model in time according to the characteristics of passenger flow during the epidemic to solve the above-mentioned problems. Big data system analysis restores and refines the time and space distribution of the finely expected passenger flow and the train service plan of each route. Get information on the passenger travel chain from arriving, boarding, transferring, getting off and leaving, as well as the full load rate of each train.

A series of digital flow control models, based on the time and space composition of passengers on trains with congested sections, has been designed and developed to scientifically calculate the number of passengers entering the station and provide an operational basis for operating companies to accurately control flow.

This study can analyze the section where the highest full load occurs, the composition of passengers in this section and when and where passengers board the train, based on the measured train full load rate data. Then, this paper combines the full load rate control index to perform reverse deduction to calculate the inbound volume time-sharing indicators of each station and redistribute the time-sharing indicators for each station according to the actual situation of the inbound volume of each line during the epidemic. Finally, form the specified full load rate index digital time-sharing passenger flow control scheme.