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Railway capacity estimation problem is typically defined as estimating the maximum number of trains that can be operated in a railway section within a given time interval. However, trains with different speeds, routes, and stopping patterns in a railway network will likely compete for the limited capacity of network nodes and sections. As these trains may provide different services, it is ambiguous to simply indicate the network capacity by a scalar number of trains. To comprehensively estimate and interpret the railway capacity considering the capacity competition between heterogeneous trains, we propose a multiobjective perspective for the capacity estimation problem to enrich the capacity theory while handling the competition among trains with different routes and stopping patterns. Based on a time-space network timetable saturation model, we extend the multiobjective capacity estimation approach to the detailed timetable level by optimizing the saturated timetable under capacity estimation objectives with respect to different routes and stopping patterns. With the ε -constraint method, we can obtain the Pareto front of saturated timetables, i.e., a set of nondominated optimized timetables that no more candidate train can be additionally scheduled. The result is a more comprehensive capacity representation than a single absolute scalar number. A case study is conducted on a combined high-speed and intercity network of Zhengzhou Railway group in China. An extensive set of Pareto-optimal saturated timetables describing the effects on the capacity of the railway network is obtained. The results can help infrastructure managers select saturated timetables as the capacity utilization reference by considering the trade-off between time indexes from passengers' and operators' perspectives., Author(s): Zhengwen Liao [1,2]; Haiying Li [1]; Jianrui Miao (corresponding author) [1,2]; Lingyun Meng [1] 1. Introduction 1.1. Background The intensive capacity utilization of railway infrastructure has become the bottleneck [...]

Focusing on a heavily congested urban rail corridor, this study investigates the passenger flow control strategy optimization problem from a mesoscopic perspective to reduce platform congestion and enhance service quality. Based on a quadratic functional approximation for passenger arrival rates, an analytical formula for calculating passenger waiting time is derived based on the classic deterministic queueing theory. We formulate the problem as a continuous nonlinear programming model to minimize the total passenger waiting time within transportation capacity constraints. A Lagrangian relaxation approach effectively transforms the original complex problem into an unconstrained minimization program. The analytical solution relating to optimal flow control strategy is derived by directly solving the unconstrained program. To further provide an integrated optimization framework from both the supply and demand sides, we extend the abovementioned passenger flow control optimization model into an integrated mixed-integer nonlinear programming model to jointly optimize the passenger-flow control strategy and train frequency setting. Numerical examples are presented to demonstrate the applicability and effectiveness of the proposed models. The computational results show that the produced high-quality passenger flow control strategy significantly reduces total passenger delay., Author(s): Qian Zhu [1]; Xiaoning Zhu [1]; Pan Shang (corresponding author) [1]; Lingyun Meng [1] 1. Introduction Peak-hour traffic congestion in megacities is a very common but critical problem when [...]

In recent years, with the increase of emerging pick-up requests during service, logistics companies have been driven to integrate delivery and pick-up service in a dynamic environment. To provide a balanced and robust approach to cope with delivery requests and emerging pick-up requests, this article aims at considering and modeling a practically useful service principle as preemptive services. To our knowledge, most existing studies assume that the dynamically arriving requests are handled in a non-preemptive processing sequence; that is, once the delivery person is allocated to a task, the process is noninterruptible till it gets completed. In the preemptive service, a service suspension of the delivery process (with low service utility) is allowed to satisfy the pick-up requests (with high service utility) first. To provide a systematic assessment on the value of preemptive service for evolving urban logistics systems, a dynamic vehicle routing problem with preemptive pick-up service (VRPPS) is proposed to systematically describe the problem with potentially complex dynamic priorities among different tasks. Based on a dynamically constructed space-time network, this study formulates a multicommodity flow model that aims at optimizing the generalized service utility and the operating cost simultaneously. To provide a fast value approximation, we present a solution framework deploying the augmented Lagrangian relaxation approach with embedded dynamic programming algorithms. This framework jointly integrates the processes of updating request information and obtaining optimal routes. Finally, the validity and effectiveness of the proposed methods are evaluated on an illustrative network and a real-world last-mile delivery network operated by a logistics company., Author(s): Weiran Meng [1]; Lingyun Meng [1]; Guoshuai Han [2]; Xiaotian Zhuang [3]; Lu Carol Tong (corresponding author) [4,5]; Shengnan Wu (corresponding author) [3] 1. Introduction Over the past few [...]

In the rapidly changing multimodal transportation market, not only the obligation in providing train services but also the responsibility in helping other transportation modes to meet emergencies are instrumental for maximizing the generalized benefit of railway companies, leading to an increased utilization. This paper proposes an optimization framework, which includes multiple dispatching measures of arranging residual seats, modifying stopping plans, and inserting additional trains, to design a rescheduling operation plan on a high-speed rail corridor to meet the passenger demand of unexpected large passenger flow (ULPF), which is generated from the disruption of other transportation modes. Considering the revenues of shifting passengers and the costs of the three dispatching measures as two objectives, we formulate a linear integer programming (LIP) model by employing a time-space network to obtain an optimization rescheduling operation plan. Several experiments based on the Beijing-Shanghai high-speed railway corridor are conducted to evaluate the effectiveness and efficiency of the proposed model. The experimental results demonstrate that the proposed model can be used to obtain a reasonable rescheduling operation plan for serving the passengers from ULPF within an acceptable calculation timeframe., Author(s): Junduo Zhao [1,2]; Haiying Li [2]; Lingyun Meng (corresponding author) [1,2]; Jianrui Miao [3] 1. Introduction High-speed railway, with its stability and capacity of transporting a large volume of [...]

Objective: To investigate the role and significance of immunoassay locus LAG3 gene in Alzheimer's disease (AD). Methods: Based on the GEO (https://www. ncbi. nlm. nih. gov/geo/) in the database of AD patients, brain sequencing dataset GSE48350 and blood sequencing dataset GSE140829 were analyzed. The scores of immune cells in the brain tissue were obtained by using the CIBERSORT ratings and the expression of LAG3 was observed. Mouse hippocampal neuron cell line HT22 induced by human Aβ1-42 amyloid was used as the cell model of AD disease. The expression of LAG3 was detected by RT-PCR. GSEA analysis was used to explore pathways directly associated with LAG3 and the String site was used to build PPI networks, genes interacting with LAG3 were discovered, and the biological functions of these genes were analyzed by KEGG and GO. Results: There was a great difference between the constitute of immune cells in the brain tissue of AD patients and that of normal controls. The level of LAG3 expression in brain tissue and blood of AD patients were higher than that of normal controls, so as the cell model. Conclusion: The constitute of immune cells in AD disease brain tissue and normal person is different. The results in the brain tissue of AD patients of the higher percentage of inactive memory CD4+T cells and mononuclear cells, lower proportion of the activation of NK cells, and the more increased expression of immune examination site LAG3 may be associated with CD4 cells and the activation and regulation of immune cells. [ABSTRACT FROM AUTHOR]