Metro Train Operation Plan Analysis Based on Station Travel Time Reliability

The train operation plan plays an essential role in metro systems and directly affects transportation organization efficiency and passenger service level. In metro systems, passengers have paid more attention to the travel time reliability (TTR), reflecting the reliability of metro operation management. This article proposes an analysis method of train operation plan based on TTR in the station dimension. First, an automated fare collection (AFC) data-driven framework is established to calculate the station travel time reliability (STTR) and analyze the train operation plan at different periods. The framework structure consists of four steps: AFC data preprocessing, STTR calculation and assignment, clustering algorithm design based on SOM neural network, and train operation plan analysis and optimization. Second, the proposed method is applied to the Beijing metro network as a case study. Several promising results are analyzed that allow the optimization of the existing train operation plan. Our research shows that STTR is a good supplement for the existing metro operation assignment studies, which can help analyze and optimize the train operation plan effectively. This study is also applicable to other metro networks with AFC systems

Assessment Method for Dynamic Impact of Large Passenger Flow on Urban Rail Transit Network: A Case Study on Chengdu East Railway Station

Large-scale activities, holidays, and emergencies often cause a significantly large burst of passenger flow demand in some urban rail transit (URT) stations in a short time, called large passenger flow (LPF). The LPF will propagate through the entire URT network of the city. The impact of the frequent occurrence of LPF on network service levels is crucial and unpredictable. This article describes an analysis of how this LPF propagates through the entire network inspired by how radionuclide imaging is done in clinical medicine. In this study, with LPF of URT as the research object, a propagation model of LPF in URT based on AFC data, train operation data, and URT network topology data was developed, which was inspired by the concept of radionuclide imaging in clinical medicine. In the condition of obtaining the list of passenger route selection ratios, the dynamic propagation state matrix of the LPF in the network is solved. The contribution value matrix of the LPF was proposed to evaluate the impact of the LPF on the URT network. Considering the LPF in Chengdu East Railway Station, China, as an example, the propagation effect of LPF in the Chengdu Metro network was analyzed, and the effectiveness of the proposed model was confirmed

Mimicking Neuroplasticity in a Hybrid Biopolymer Transistor by Dual Modes Modulation

Neuromorphic computing systems that are capable of parallel information storage and processing with high area and energy efficiencies, offer important opportunities for future storage systems and in‐memory computing. Here, it is shown that a carbon dots/silk protein (CDs/silk) blend can be used as a light‐tunable charge trapping medium to fabricate an electro‐photoactive transistor synapse. The synaptic device can be optically operated in volatile or nonvolatile modes, ensuring concomitant short‐term and long‐term neuroplasticity. The synaptic‐like behaviors are attributed to the photogating effect induced by trapped photogenerated electrons in the hybrid CDs/silk film which is confirmed with atomic force microscopy based electrical techniques. In addition, system‐level pattern recognition capability of the synaptic device is evaluated by a single‐layer perceptron model. The remote optical operation of neuromorphic architecture provides promising building blocks to complete bioinspired photonic computing paradigms

Mimicking neuroplasticity in a hybrid biopolymer transistor by dual modes modulation

Neuromorphic computing systems that are capable of parallel information storage and processing with high area and energy efficiencies, offer important opportunities for future storage systems and in‐memory computing. Here, it is shown that a carbon dots/silk protein (CDs/silk) blend can be used as a light‐tunable charge trapping medium to fabricate an electro‐photoactive transistor synapse. The synaptic device can be optically operated in volatile or nonvolatile modes, ensuring concomitant short‐term and long‐term neuroplasticity. The synaptic‐like behaviors are attributed to the photogating effect induced by trapped photogenerated electrons in the hybrid CDs/silk film which is confirmed with atomic force microscopy based electrical techniques. In addition, system‐level pattern recognition capability of the synaptic device is evaluated by a single‐layer perceptron model. The remote optical operation of neuromorphic architecture provides promising building blocks to complete bioinspired photonic computing paradigms