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Keywords: BioModel, inhibition modeling, model parameters calibration, active set optimization procedure, gadient-based optimization
Published in DKUM: 23.08.2024; Views: 77; Downloads: 7
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Keywords: urban traffic, microsimulation, calibration, neural networks, rundabouts, validation
Published in DKUM: 15.04.2024; Views: 239; Downloads: 6
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Abstract: Walking, as a mode of transport, is becoming widespread, in a world, where urban conglomerates are broadening and becoming denser. Modern lifestyle trends on a side, and eco-friendly policies on the other, push people into walking habits, increasing the need for a suitable, attractive, accessible, connected and safe walking infrastructure. To reach such a result, it is necessary to understand, what are the needs of the users of this infrastructure, taking into consideration the behavioral specificities and the safety needs of pedestrians. In this process pedestrian microsimulation models, surrogate safety techniques, and technologies able to measure specific traits of pedestrian dynamics play a central role. The firsts allow to reproduce repeatedly in a virtual environment a specific infrastructure and to study the response of pedestrians. Nevertheless, to be accurate and efficient, they need to go through long and tedious calibration and validation processes, that are often seen as an important limitation by technicians. Surrogate safety techniques are methods, that are based on the concept, that it is possible to predict the safety level of a location, using near accidents. The main advantage of such techniques is that they are proactive. Till this moment, these techniques have been mainly applied to on-field measurements and are primarily centered on motorized road users. Less interest has been shown for vulnerable road users, especially for pedestrians, who have been less extensively studied. Finally, an element that could highly affect pedestrian safety is their reaction time. Nevertheless, its measurement has long been a big issue. Eye-tracking technology could be one of the solutions, allowing to analyze the directions and objects fixated by pedestrians. These listed issues are also the topics that are addressed by this research work. Focusing on the study of the action of pedestrians while crossing the road on an unsignalized crosswalk set on a roundabout entry leg, the dissertation thesis aims at studying the crossing time, reaction time and surrogate safety aspects typical of pedestrians at the recalled location. The main purpose of the research work is to develop a methodology to calibrate pedestrian Social Force Model at a selected location, using a specifically formulated neural network as a tool to fine-tune model's behavioral parameters. Eight parameters have been chosen to be fine-tuned, five of those are related to pedestrian behavior and three of them are related to car-following behavior. After the selection of input parameters, a feedforward network has been formulated. Its application in the framework of the whole calibration process has brought to considerably positive results, finding a combination of input parameters that improved the performance of the microsimulation model of 37 % in comparison to the default one. The outputs of the calibrated model have been used to calculate three measures of surrogate safety, and also in this case results demonstrated an improvement in the calculation of surrogate safety measures when using the calibrated outcomes in comparison to their calculation on the “default” model outputs. Finally, reaction time measurement and prediction have been addressed by the thesis, in order to be able to describe pedestrian crossing action in its completeness. Quantitative eye-tracking outputs have been the starting point for the calculation of pedestrian reaction time at different locations, and they allowed to create a database of behavioral, geometric, regulatory and flow characteristics, which was the foundation for the formulation of a new prediction model for pedestrian reaction time. The prediction model, which consists of a cascade-correlation neural network, gave a good response to the learning and generalization steps, turning a 74 % correlation between the measured reaction time values and the predicted ones, and being able to follow the variability of these values.
Keywords: pedestrian, microsimulation model, calibration, neural network, surrogate safety indicators, reaction time.
Published in DKUM: 03.10.2022; Views: 873; Downloads: 98
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Abstract: The reliability of a horizontal-to-vertical spectral ratio (HVSR) curve depends on the results obtained by a verified seismological system. Seismic microzonation provides the basis for a site-specific risk analysis and it can be evaluated using the microtremor HVSR method, where the data are recorded using modern seismological systems. Changes in the transfer function of seismological systems affect the HVSR curve and, consequently, also its interpretation, if these changes are not detected and taken into consideration while performing the microtremor spectral calculations. The reliability of the seismic microzonation performed by such a procedure becomes questionable. An algorithm is developed with a two references system, where the influence of the transfer function on the HVSR curve by the tested system can be evaluated without any a-priori knowledge regarding the transfer functions of any of the systems. This approach is applied to a Lennartz Le-3D/5s seismometer and to a TROMINO seismological system, where two Streckeisen STS2 seismometers are used as the reference systems.
Keywords: seismic microzonation, horizontal-to-vertical spectral ratio method, ambient vibrations, microtremor, seismic system transfer function, reliability and calibration of seismic systems
Published in DKUM: 11.06.2018; Views: 1139; Downloads: 60
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Abstract: Microsimulation models are frequently used in traffic analysis. Various optimization methods are used in calibration, and the one method that has shown success is neural networks. This paper shows the responses of neural networks during calibration of a microsimulation traffic model. We analyzed two calibration methods by applying neural networks and comparing their neural network learning (according to their achieved correlation and the mean error of prediction) and their generalization ability (comparison of generalization results was analyzed in two steps). The best correlation between the microsimulation results and neural network prediction was 88.3%, achieved for the traveling time prediction, on which the first calibration method is based.
Keywords: microsimulation traffic models, calibration, response of neural networks, traveling time, queue parameters
Published in DKUM: 02.08.2017; Views: 1331; Downloads: 383
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