Spectral Wave Modeling in Very Shallow Water at Southern Coast of Caspian Sea

This study evaluates the capability of the Simulating WAves Nearshore (SWAN) wave model (version 41.01) in predicting significant wave height and spectral peak energy content for swell waves in very shallow water of surf zone during depth-induced wave breaking and dissipation. The model results were compared with field measurements at five nearshore stations. The results demonstrated that some breaker index formulations were successful for significant wave height prediction in surf zones. However, an incorrect shape of the energy spectrum and overestimated near spectral peak energy content at shallow water stations were obtained using all of the embedded depth-induced wave breaking formulations in SWAN. The dependent breaker index on relative depth (K_pd) formulation, which was successful in predicting near spectral peak energy content, resulted in an average error of 30%. Finally, this formulation was modified to enhance the model performance in reproducing the spectral peak energy content.     

参数不确定及有扰动的情况下欠驱动遥控无人潜水器鲁棒性建模、滑模控制及仿真研究（英文）

A dynamic model of a remotely operated vehicle(ROV) is developed. The hydrodynamic damping coefficients are estimated using a semi-predictive approach and computational fluid dynamic software ANSYS-CFX~(?) and WAMIT~(?). A sliding-mode controller(SMC) is then designed for the ROV model. The controller is subsequently robustified against modeling uncertainties,disturbances, and measurement errors. It is shown that when the system is subjected to bounded uncertainties, the SMC will preserve stability and tracking response. The paper ends with simulation results for a variety of conditions such as disturbances and parametric uncertainties.     

里海南部海岸在极浅水中的波谱建模研究（英文）

This study evaluates the capability of the Simulating WAves Nearshore(SWAN) wave model(version 41.01) in predicting significant wave height and spectral peak energy content for swell waves in very shallow water of surf zone during depth-induced wave breaking and dissipation. The model results were compared with field measurements at five nearshore stations. The results demonstrated that some breaker index formulations were successful for significant wave height prediction in surf zones.However, an incorrect shape of the energy spectrum and overestimated near spectral peak energy content at shallow water stations were obtained using all of the embedded depth-induced wave breaking formulations in SWAN. The dependent breaker index on relative depth(Kpd) formulation, which was successful in predicting near spectral peak energy content, resulted in an average error of 30%. Finally, this formulation was modified to enhance the model performance in reproducing the spectral peak energy content.     

Forecasting the Suez Canal traffic: a neural network analysis

Although the Suez Canal is the most important man-made waterway in the world, rivaled perhaps only by the Panama Canal, little research has been done into forecasting its traffic flows. This paper uses both univariate ARIMA (Autoregressive Integrated Moving Average) and Neural network models to forecast the maritime traffic flows in the Suez Canal which are expressed in tons. One of the important strengths of the ARIMA modelling approach is the ability to go beyond the basic univariate model by considering interventions, calendar variations, outliers, or other real aspects of typically observed time series. On the other hand, neural nets have received a great deal of attention over the past few years. They are being used in the areas of prediction and classification, areas where regression models and other related statistical techniques have traditionally been used. The models obtained in this paper provide useful insight into the behaviour of maritime traffic flows since the reopening of the Canal in 1975—following an 8-year closure during the Arab–Israeli wars (1967–1973)—till 1998. The paper also compares the performance of ARIMA models with that of neural networks on an example of a large monthly dataset.     

Forecasting the Suez Canal traffic: a neural network analysis

Although the Suez Canal is the most important man-made waterway in the world, rivaled perhaps only by the Panama Canal, little research has been done into forecasting its traffic flows. This paper uses both univariate ARIMA (Autoregressive Integrated Moving Average) and Neural network models to forecast the maritime traffic flows in the Suez Canal which are expressed in tons. One of the important strengths of the ARIMA modelling approach is the ability to go beyond the basic univariate model by considering interventions, calendar variations, outliers, or other real aspects of typically observed time series. On the other hand, neural nets have received a great deal of attention over the past few years. They are being used in the areas of prediction and classification, areas where regression models and other related statistical techniques have traditionally been used. The models obtained in this paper provide useful insight into the behaviour of maritime traffic flows since the reopening of the Canal in 1975—following an 8-year closure during the Arab-Israeli wars (1967-1973)—till 1998. The paper also compares the performance of ARIMA models with that of neural networks on an example of a large monthly dataset.     

Vulnerability evaluation of freight railway networks using a heuristic routing and scheduling optimization model

Transportation - Railway network is an integral part of the economy of many countries. Identifying critical network elements can help network executives to take appropriate preventive actions...     

Timetable optimization models and methods for minimizing passenger waiting time at public transit terminals

This paper focuses on developing mathematical optimization models for the train timetabling problem with respect to dynamic travel demand and capacity constraints. The train scheduling models presented in this paper aim to minimize passenger waiting times at public transit terminals. Linear and non-linear formulations of the problem are presented. The non-linear formulation is then improved through introducing service frequency variables. Heuristic rules are suggested and embedded in the improved non-linear formulation to reduce the computational time effort needed to find the upper bound. The effectiveness of the proposed train timetabling models is illustrated through the application to an underground urban rail line in the city of Tehran. The results demonstrate the effectiveness of the proposed demand-oriented train timetabling models, in terms of decreasing passenger waiting times. Compared to the baseline and regular timetables, total waiting time is reduced by 6.36% and 10.55% respectively, through the proposed mathematical optimization models.     

Quantitative analysis of probe data characteristics: Coverage,speed bias and congestion detection precision

Abstract

In recent years, there has been a growing desire for the use of probe vehicle technology for congestion detection and general infrastructure performance assessment. Unlike costly traditional data collection by loop detectors, wide area detection using probe-based traffic data is significantly different in terms of the nature of data collection, measurement technique, coverage, pricing, and so on. Although many researches have studied probe-based data, there remains critical questions such as data coverage and penetration over time, or the influential factors in the accuracy of probe data. This research studied probe-sourced data from INRIX, to profoundly explore some of these questions. First, to explore coverage and penetration, INRIX real-time data was illustrated temporally over the entire state of Iowa, demonstrating the growth in real-time data over a 4-year timespan. Furthermore, the availability of INRIX real-time and historical data based on type of road and time of day, were explored. Second, a comparison was made with Wavetronix smart sensors, commonly used sensors in traffic management, to explore INRIX’s speed data quality. A statistical analysis on the behavior of INRIX speed bias, identified some of the influential factors in defining the magnitude of speed bias. Finally, the accuracy and reliability of INRIX for congestion detection purposes was investigated based on the road segment characteristics and the congestion type. Overall, this work sheds light onto some of the less explored aspects of INRIX probe-based data to help traffic managers and decision makers in better understanding this source of data and any resultant analyses.