Ning Zhang

Wool is a natural biopolymer with excellent biocompatibility. This dense structure makes it difficult to recycle the millions of wool textiles discarded annually. This study reports, for the first time, the regeneration of keratin using thiourea dioxide. Disulfide bonds were cleavage from the result of Raman test. FTIR, XRD and ¹³C-NMR tests determined that regenerated wool keratin retained protein macromolecule backbone, though the crystallinity degree and α-helix structure content decreased slightly. SDS-PAGE results demonstrated that the process contributed to an increase of low molecular weight keratin. All the results in this study indicate that thiourea dioxide is an effective solvent for the regeneration of wool keratin. The study offers a novel approach for the regeneration and recycle of other natural protein-based material.

With reliable and accurate predictions of short-term passenger flow, metro agencies can assign proper trains and crews, optimize schedules, and operate efficiently. This paper presents a hybrid model, which is intended for forecasting short-term passenger flow. The forecasting model aggregates SVR-based sub-models developed with subsets extracted by combining bootstrap sampling with dimensions subsampling. The sub-models consider not only the data from the reconstructed chaos attractor but also the periodic historical data. Passenger flow data collected from four different metro stations in Nanjing are used for model implementation and performance evaluation. Results confirm that the proposed EICSVR model was able to significantly improve predictive performance and generalization. The excellent accuracy and stability obtained in the empirical study indicate that the proposed model has good development potential for forecasting short-term passenger flow.

With the construction of the urban rail transit (URT) network, the explosion of passenger volume is more rapid than the increased capacity of the newly built infrastructure, which results in serious passenger flow congestion (PLC). Understanding the propagation process of PLC is the key to formulate sustainable policies for reducing congestion and optimizing management. This study proposes a susceptible-infected-recovered (SIR) model based on the theories of epidemiological dynamics and complex network to analyze the PLC propagation. We simulate the PLC propagation under various situations, and analyze the sensitivity of PLC propagation to model parameters. Finally, the control strategies of restricting PLC propagation are introduced from two aspects, namely, supply control and demand control. The results indicate that both of the two control strategies contribute to relieving congestion pressure. The propagating scope of PLC is more sensitive when taking mild supply control, whereas, the demand control strategy shows some advantages in flexibly implementing and dealing with serious congestion. These results are of important guidance for URT agencies to understand the mechanism of PLC propagation and formulate appropriate congestion control strategies.

Reliable and accurate estimates of metro demand can provide metro authorities with insightful information for the planning of route alignment and station locations. Many existing studies focus on metro demand from daily or annual ridership profiles, but only a few concern the variation in hourly ridership. In this paper, a geographically and temporally weighted regression (GTWR) model was used to examine the spatial and temporal variation in the relationship between hourly ridership and factors related to the built environment and topological structure. Taking Nanjing, China as a case study, an empirical study was conducted with automatic fare collection (AFC) data in three weeks. With an analysis of variance (ANOVA), it was found that the GTWR model produced the best fit for hourly ridership data compared with traditional regression models. Four built-environment factors, namely residence, commerce, scenery, and parking, and two topological-structure factors, namely degree centrality and closeness centrality, were proven to be significantly related to station-level ridership. The spatial distribution pattern and temporal nonstationarity of these six variables were further analyzed. The result of this study confirmed that the GTWR model can provide more realistic and useful information by capturing spatiotemporal heterogeneity.

Grasping the trip path of passengers accurately is directly related to the efficiency of subway operation management and reasonable allocation of revenue from different line operators. In the existing research on subway passenger trip path identification based on phone data, the identification criteria of trip sites in the matching algorithm are not comprehensive, which leads to inaccurate origin-destination identification in partial matching results. In addition, the validity of the path identification method decreases, owing to the modification of the K shortest path method. In view of these problems, this paper divides the subway base station database into a passageway database and a network database and identifies trip sites, in turn, according to the relationship between the time and location information of signaling data and location information of the two databases. Compared with the existing methods, the proposed method has higher identification accuracy and a simpler identification process.

Forecasting for short-term ridership is the foundation of metro operation and management. A prediction model is necessary to seize the weekly periodicity and nonlinearity characteristics of short-term ridership in real-time. First, this research captures the inherent periodicity of ridership via seasonal autoregressive integrated moving average model (SARIMA) and proposes a support vector machine overall online model (SVMOOL) which insets the weekly periodic characteristics and trains the updated data day by day. Then, this research captures the nonlinear characteristics of the ridership via successive ridership value inputs and proposes a support vector machine partial online model (SVMPOL) which insets the nonlinear characteristics and trains the updated data of the predicted day by time interval (such as 5-min). Afterwards, to avoid the drawbacks and to take advantages of the strengths of the two individual online models, this research takes the average predicted values of two models as the final predicted values, which are called support vector machine combined online model (SVMCOL). Finally, this research uses the 5-min ridership at Zhujianglu and Sanshanjie Stations of Nanjing Metro to compare the SVMCOL model with three well-known prediction models including SARIMA, back-propagation neural network (BPNN), and SVM models. The resultant performance comparisons suggest that SARIMA is superior for the stable weekday ridership to other models. Yet the SVMCOL model is the best performer for the unstable weekend ridership and holiday ridership. It shows that for metro operation manager that gear toward timely response to real-world unstable and abnormal situations, the SVMCOL may be a better tool than the three well-known models.

Due to different functions and land use types, there are significant differences in ridership patterns among different urban rail transit stations. Considering the characteristics of different ridership and coping with the uncertainty, periodical and stochastic natures of short-term passenger flow, and this paper proposes a novel hybrid methodology that combines the autoregressive integrated moving average (ARIMA) model and wavelet decomposition, which has strong strengths in signal processing, to short-term ridership forecasting. The seasonal ARIMA is used to represent the relatively stable and regular ridership patterns while the wavelet decomposition is used to capture the stochastic or sometimes drastic changing characteristics of ridership patterns. The inclusion of wavelet decomposition and reconstruction provides the hybrid model with a unique strength in capturing sudden change in ridership patterns associated with certain rail stations. The case study is carried out by analyzing real ridership data of Metro Line 1 in Nanjing, China. The experimental results indicate that the hybrid method is superior to the individual ARIMA model for all ridership patterns, but particularly advantageous in predicting ridership at stations often associated with sudden pattern changes due to special events.

Because there is a conflict between promoting the level of service and saving operation resources in passenger flow congestion management for urban rail transit (URT), the congestion duration forecast has become an important decision-support element for management optimization. This paper presents a hazard-based duration model for the purpose of accurate duration forecasting considering loss minimization for decision making. The congestion duration was modeled with a hazard-based approach and revised by a strategy imitating the Bayesian minimum risk rule. Six hundred twenty-seven congestion instances during 470 days from Metro Line 2 of Nanjing, China, were divided into a training data set and a testing data set and were used for model implementation and performance evaluation. The model estimation results indicate that the log-logistic distribution produces the best fit for congestion during weekdays according to the Akaike information criterion, and the accuracy of the duration model is high according to the mean absolute percentage error. The model was also confirmed to be stable over time in the two data sets. In addition, the contrast of losses for the median forecast and the revisionary forecast based on numerical examples in the data set shows that the forecast revising strategy realizes a considerable relative decrease in the loss caused by the randomness of duration. The results of this study are useful for URT congestion management and provide a demonstration of a revising method to reduce economic loss resulting from forecast deviation.