Reliable prediction of short-term passenger flow could greatly support metro authorities' decision processes, help passengers to adjust their travel schedule, or, in extreme cases, assist emergency management. The inflow and outflow of the metro station are strongly associated with the travel demand within metro networks. The purpose of this paper is to obtain such prediction. We first collect the origin-destination information from the smart-card data and explore the passenger flow patterns in a metro system. We then propose a data driven framework for short-term metro passenger flow prediction with the ability to utilize both spatial and temporal related information. The approach adopts two forecasts as basic models and then uses a probabilistic model selection method, random forest classification, to combine the two outputs to achieve a better forecast. In the experiments, we compare the proposed model with four other prediction models, i.e., autoregressive-moving-average, neural networks, support vector regression, and averaging ensemble model, as well as the basic models. The results indicate that the proposed approach outperforms the others in most cases. The origin-destination flows extracted from smart-card data can be successfully exploited to describe different metro travel patterns. And the framework proposed here, especially the probabilistic combination method, can improve the performance of short-term transportation prediction.
Ultrasound was uniquely applied to promote the extraction of cheap microbial flocculant (MBF) from waste activated sludge (WAS) of municipal wastewater treatment plants (WWTPs). Various influencing factors, including ultrasonic conditions (frequency, power density and treatment time) and WAS features (pH, concentration and source), were systematically investigated. The propitious ultrasonic conditions for MBF preparation from WAS were 20 kHz, 2.1 to 2.7 kW/L and 1 to 3 min. Natural sludge pH (about 7) was preferable to the MBF preparation. The major components of the extracted MBF contained polysaccharides, proteins and nucleic acids. The yield of the extracted MBF increased with rising sludge concentration. The wide application potential of the developed method was testified by the successful MBF extraction from the WAS samples of four full-scale municipal WWTPs with different typical processes. The ultrasonic method applied to extract MBF from WAS would not only provide a new way for WAS resource reuse, but also markedly cut down the cost of MBF preparation.
Predicting evacuation demand, including its generation and dissipation process, for urban rail transit systems under disruptions, such as line and station closure, often requires comprehensive historical data recorded under homogeneous situations. However, data under disruptions are hard to collect due to various reasons, which makes traditional methods impractical in evacuation demand prediction. To address this problem from the modeling perspective, we develop a data-efficient approach to predict evacuation demand for urban rail transit systems under disruptions. Our model-based approach mainly uses historical data obtained from the natural state, when no shocks take place. We first formulate the mathematical representation of the evacuation demand for every type of urban rail transit station. Input variables in this step are location features related to the station under the disruption, as well as an origin–destination matrix under the natural state. Then, based on these mathematical expressions, we develop a simulation system to imitate the spatio-temporal evolution of evacuation demand within the whole network under disruptions. The transport capacity drop under disruptions is used to describe the disruption situation. Several typical scenarios from the Shanghai metro network are used as examples to implement the proposed method. The results show that our method is able to predict the generation and dissipation processes of evacuation demand, as well model how severely stations will be affected by given disruptions. One general observation we draw from the results is that the most vulnerable stations under disruption, where the locations peak evacuation demand occurs, are mainly turn-back stations, closed stations, and the transfer stations near closed stations. This paper provides new insight into evacuation demand prediction under disruptions. It could be used by transport authorities to better respond to the urban rail transit system disruption.
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