24,948 research outputs found
Calibrating Path Choices and Train Capacities for Urban Rail Transit Simulation Models Using Smart Card and Train Movement Data
Transit network simulation models are often used for performance and
retrospective analysis of urban rail systems, taking advantage of the
availability of extensive automated fare collection (AFC) and automated vehicle
location (AVL) data. Important inputs to such models, in addition to
origin-destination flows, include passenger path choices and train capacity.
Train capacity, which has often been overlooked in the literature, is an
important input that exhibits a lot of variabilities. The paper proposes a
simulation-based optimization (SBO) framework to simultaneously calibrate path
choices and train capacity for urban rail systems using AFC and AVL data. The
calibration is formulated as an optimization problem with a black-box objective
function. Seven algorithms from four branches of SBO solving methods are
evaluated. The algorithms are evaluated using an experimental design that
includes five scenarios, representing different degrees of path choice
randomness and crowding sensitivity. Data from the Hong Kong Mass Transit
Railway (MTR) system is used as a case study. The data is used to generate
synthetic observations used as "ground truth". The results show that the
response surface methods (particularly Constrained Optimization using Response
Surfaces) have consistently good performance under all scenarios. The proposed
approach drives large-scale simulation applications for monitoring and
planning
A comprehensive literature classification of simulation optimisation methods
Simulation Optimization (SO) provides a structured approach to the system design and configuration when analytical expressions for input/output relationships are unavailable. Several excellent surveys have been written on this topic. Each survey concentrates on only few classification criteria. This paper presents a literature survey with all classification criteria on techniques for SO according to the problem of characteristics such as shape of the response surface (global as compared to local optimization), objective functions (single or multiple objectives) and parameter spaces (discrete or continuous parameters). The survey focuses specifically on the SO problem that involves single per-formance measureSimulation Optimization, classification methods, literature survey
Adaptive design of delta sigma modulators
In this thesis, a genetic algorithm based on differential evolution (DE) is used to generate delta sigma modulator (DSM) noise transfer functions (NTFs). These NTFs outperform those generated by an iterative approach described by Schreier and implemented in the delsig Matlab toolbox. Several lowpass and bandpass DSMs, as well as DSM\u27s designed specifically for and very low intermediate frequency (VLIF) receivers are designed using the algorithm developed in this thesis and compared to designs made using the delsig toolbox. The NTFs designed using the DE algorithm always have a higher dynamic range and signal to noise ratio than those designed using the delsig toolbox
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