An exact method for the integrated optimization of subway lines operation strategies with asymmetric passenger demand and operating costs

Subway lines connecting different urban functional zones in large cities have direction-dependent and time-variant passenger demand, namely, asymmetry in passenger demand. Most existing studies adopt a symmetric strategy to design operations in both directions and sequentially optimize the different problems associated with operations, thereby failing to meet the asymmetry in passenger demand. This study formulates an asymmetric operation strategy as an integrated mixed-integer non-linear model to optimize the entire operational process of rolling stock from the perspective of service quality and operating costs. Based on the proposed model, an exact algorithm is proposed with speed-up techniques to quickly generate an optimal solution. To this end, the original model is decomposed into several sub-problems that can be exactly solved by using a forward dynamic programming algorithm. Based on actual data from the Beijing subway's Yizhuang line, numerical experiments are conducted to investigate the effectiveness of the asymmetric operation strategy, to identify managerial insights on the integrated optimization, and to evaluate the performance of the proposed methodology

Automatic Classification of Polysomnographic Respiration Signals

Within the project on biomedical signal analyses with artificial neural networks, recent research is focused on polysomnographic signal analyses. In polysomnographics (PSG) a large variety of physiological signals, obtained during a patients sleep, are analyzed and classified. This research focuses on the detection of obstructiveand central-apnea (absence of air flow through the nose), paradoxical respiration, normal respiration and artifacts in the noseflow , diaphragm and thorax signals. The signals are analyzed and characteristic features are extracted by different methods: FFT, correlation, filtering. The classification is implemented by the use of classical Bayes classification combined with relative modern neural network approaches. Multi-layer perceptrons (MLP) and Kohonen networks are used for probability density estimation of the features of the different classes. Multi-level classification and multi-level feature extraction were necessary. A dynamically, interactive, evolving..

Robust capacitated train rescheduling with passenger reassignment under stochastic disruptions

During railway operations unexpected events may occur, influencing normal traffic flows. This paper focuses on a train rescheduling problem in a railway system with seat-reserved mechanism during large disruptions, such as a rolling stock breakdown leading to some canceled services, where passenger reassignment strategies have also to be considered. A novel mixed-integer linear programming formulation is established with consideration of train retiming, reordering, and reservicing. Based on a time–space modeling framework, a big-M approach is adopted to formulate the track occupancy and extra train stops. The formulation aims to maximize the passenger accessibility measured by the amount of the transported passengers subject to canceled services and to minimize the weighted total train delay for all trains at their destinations. The proposed mathematical formulation also considers planning extra stops for non-canceled trains to transport the disrupted passengers, which were supposed to travel on the canceled services, to their pre-planned destinations. Other constraints deal with seat capacity limitation, track capacity, and some robustness measures under uncertainty of disruption durations. We propose different approaches to compute advanced train dispatching decisions under a dynamic and stochastic optimization environment. A series of numerical experiments based on a part of ‘‘Beijing–Shanghai’’ high-speed railway line is carried out to verify the effectiveness and efficiency of the proposed model and methods

Timetable Rescheduling with Reassignment of Rolling Stocks and Passengers under Disruptions

This paper introduces an integrated approach for the recovery of a timetable by rescheduling train services and rolling stock circulation with the reassignment of disrupted passengers who are supposed to travel on the train service cancelled due to the broken rolling stock in a railway transportation system with the seat-reserved mechanism. We propose a novel integer linear programming model to maximize the number of disrupted passengers arriving at their pre-planned destinations, and to minimize the total delay of all train services and the number of cancelled train services. The mathematical formulation deals with the reassignment of disrupted passengers to non-cancelled train services by planning extra stops and the reassignment of rolling stocks to train services with consideration of the maintenance distance, limited seat capacity and required turnaround time. Other constraints focus on the limited track capacity, extra running time of deceleration and acceleration due to extra stops, and mapping the timetable rescheduling, the assignment of rolling stock and passengers. Numerical experiments based on a part of the Beijing-Shanghai high-speed railway line are carried out to verify the effectiveness and efficiency of the proposed method

Integrated optimization of capacitated train rescheduling and passenger reassignment under disruptions

During railway operations, unexpected events may influence normal traffic flows. This paper focuses on a train rescheduling problem for handling large disruptions, such as a rolling stock breakdown leading to a cancelled train service, where passenger reassignment strategies have to be considered. A novel mixed-integer linear programming formulation is established with consideration of train retiming, reordering, rerouting, and reservicing (addition of extra stops). The proposed mathematical formulation considers planning extra stops for non-canceled trains in order to transport the disrupted passengers, which were supposed to travel on the canceled train, to their pre-planned destination stations. Other constraints deal with limited seat capacity and track capacity, and mapping train rescheduling with passenger reassignment. A bi-objective function is optimized by a weighted-sum method to maximize the number of disrupted passengers reaching their destination stations and to minimize the weighted total train delay for all non-canceled trains at their destinations. A series of numerical experiments based on a part of the Beijing-Shanghai high-speed railway line is carried out to verify the effectiveness and efficiency of the proposed model and to perform a sensitivity analysis of various performance factors. The results show that an optimal reassignment plan of disrupted passengers is important to achieve real-time efficiency of traffic and re-ticketing. The impact of passenger reassignment on train rescheduling is influenced by the weights for objectives, duration of disruption, allowed additional dwell and running times, and relationship between passenger demand and total available train capacity

Patient independent Spike-Wave Complex Detection in EEG Signals with an Artificial Neural Network

Spike-Wave Complexes in EEG signals may occur randomly in recordings of epileptic patients. Neurologists can recognize these complexes, which differ a lot from patient to patient. Automated Spike-Wave Complex detection systems have problems with these differences. Some of these systems are rule-based, others use features extracted from examples of Spike-Wave Complexes and normal EEG for detection. A detection system is proposed that uses examples of Spike-Wave Complexes as indicated by a neurologist. A neural network extracts a set of representative Spike-Wave Complexes, which are used for detection. The set of examples can be adapted to the neurologists to provide a user-adaptable detection system. Keywords--- Medical signal processing, pattern recognition, neural networks. I. Introduction The ElectroEncephaloGram (EEG) is a recording of the electrical activity of the brain. If the EEG is analyzed by frequency, four basic waves can be distinguished, alpha, beta, theta and delta wav..

Integrated Decision Support Tools for Disruption Management

During railway operations unexpected events can require railway operators and infrastructure managers to adjust their schedules. In this research we investigate the disruption management process. More specifically, we come up with an architecture and algorithmic framework which railway operators could use for decision support during disruptions. The use of this framework results in a fully feasible timetable, rolling stock plan, and crew schedule to deal with the disruption, while minimizing the number of delayed and/or (partially) cancelled trains. We demonstrate the effectiveness of our framework on a disruption case on the Dutch Railway network, which is introduced within the EU FP7 project ON-TIME.Transport & PlanningCivil Engineering and Geoscience