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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.
Xiaoqing Dai; Han Qiu; Lijun Sun. A Data-Efficient Approach for Evacuation Demand Generation and Dissipation Prediction in Urban Rail Transit System. Sustainability 2021, 13, 9692 .
AMA StyleXiaoqing Dai, Han Qiu, Lijun Sun. A Data-Efficient Approach for Evacuation Demand Generation and Dissipation Prediction in Urban Rail Transit System. Sustainability. 2021; 13 (17):9692.
Chicago/Turabian StyleXiaoqing Dai; Han Qiu; Lijun Sun. 2021. "A Data-Efficient Approach for Evacuation Demand Generation and Dissipation Prediction in Urban Rail Transit System." Sustainability 13, no. 17: 9692.
In diabetes, glucagon secretion from pancreatic α-cells is dysregulated. We examined α-cells from human donors and mice using combined electrophysiological, transcriptomic, and computational approaches. Rising glucose suppresses α-cell exocytosis by reducing P/Q-type Ca2+ channel activity, and this is disrupted in type 2 diabetes (T2D). Upon high-fat-feeding of mice, α-cells shift towards a ‘β-cell-like’ electrophysiologic profile in concert with an up-regulation of the β-cell Na+ channel isoform Scn9a and indications of impaired α-cell identity. In human α-cells we identify links between cell membrane properties and cell surface signalling receptors, mitochondrial respiratory complex assembly, and cell maturation. Cell type classification using machine learning of electrophysiology data demonstrates a heterogenous loss of ‘electrophysiologic identity’ in α-cells from donors with T2D. Indeed, a sub-set of α-cells with impaired exocytosis is defined by an enrichment in progenitor markers suggesting important links between α-cell maturation state and dysfunction in T2D. Key findings α-cell exocytosis is suppressed by glucose-dependent inhibition of P/Q-type Ca2+ currents Dysfunction of α-cells in type 2 diabetes is associated with a ‘β-cell-like’ electrophysiologic signature Patch-seq links maturation state, the mitochondrial respiratory chain, and cell surface receptor expression to α-cell function α-cell dysfunction occurs preferentially in cells enriched in endocrine lineage markers
Xiao-Qing Dai; Joan Camunas-Soler; Linford Jb Briant; Theodore dos Santos; Aliya F Spigelman; Emily M. Walker; Rafael Arrojo e Drigo; Austin Bautista; Robert C. Jones; James Lyon; Aifang Nie; Nancy Smith; Jocelyn E Manning Fox; Seung K Kim; Patrik Rorsman; Roland W Stein; Stephen R Quake; Patrick E MacDonald. Heterogenous impairment of α-cell function in type 2 diabetes is linked to cell maturation state. 2021, 1 .
AMA StyleXiao-Qing Dai, Joan Camunas-Soler, Linford Jb Briant, Theodore dos Santos, Aliya F Spigelman, Emily M. Walker, Rafael Arrojo e Drigo, Austin Bautista, Robert C. Jones, James Lyon, Aifang Nie, Nancy Smith, Jocelyn E Manning Fox, Seung K Kim, Patrik Rorsman, Roland W Stein, Stephen R Quake, Patrick E MacDonald. Heterogenous impairment of α-cell function in type 2 diabetes is linked to cell maturation state. . 2021; ():1.
Chicago/Turabian StyleXiao-Qing Dai; Joan Camunas-Soler; Linford Jb Briant; Theodore dos Santos; Aliya F Spigelman; Emily M. Walker; Rafael Arrojo e Drigo; Austin Bautista; Robert C. Jones; James Lyon; Aifang Nie; Nancy Smith; Jocelyn E Manning Fox; Seung K Kim; Patrik Rorsman; Roland W Stein; Stephen R Quake; Patrick E MacDonald. 2021. "Heterogenous impairment of α-cell function in type 2 diabetes is linked to cell maturation state." , no. : 1.
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.
Xiaoqing Dai; Lijun Sun; Yanyan Xu. Short-Term Origin-Destination Based Metro Flow Prediction with Probabilistic Model Selection Approach. Journal of Advanced Transportation 2018, 2018, 1 -15.
AMA StyleXiaoqing Dai, Lijun Sun, Yanyan Xu. Short-Term Origin-Destination Based Metro Flow Prediction with Probabilistic Model Selection Approach. Journal of Advanced Transportation. 2018; 2018 ():1-15.
Chicago/Turabian StyleXiaoqing Dai; Lijun Sun; Yanyan Xu. 2018. "Short-Term Origin-Destination Based Metro Flow Prediction with Probabilistic Model Selection Approach." Journal of Advanced Transportation 2018, no. : 1-15.