A tool to model both current rail and future high-speed rail (HSR) corridors has been presented in this work. The model is designed as an addition to the existing TSAM (Transportation System Analysis Model) capabilities of modeling commercial airline and automobile demand. TSAM is a nationwide county to county multimodal demand forecasting tool based on the classical four step process. A variation of the Box-Cox logit model is proposed to best capture the characteristic behavior of rail demand in US. The utility equation uses travel time and travel cost as the decision variables for each model. Additionally, a mode specific geographic constant is applied to the rail mode to model the North-East Corridor (NEC). NEC is of peculiar interest in modeling, as it accounts for most of the rail ridership. The coefficients are computed using Genetic Algorithms. A one county to one station assignment is employed for the station choice model. Modifications are made to the station choice model to replicate choices affected by the ease of access via driving and mass transit. The functions for time and cost inputs for the rail system were developed from the AMTRAK website. These changes and calibration coefficients are incorporated in TSAM. The TSAM model is executed for the present and future years and the predictions are discussed. Sensitivity analysis for cost and speed of the predicted HSR is shown. The model shows the market shift for different modes with the introduction of HSR. Limited data presents the most critical hindrance in improving the model further. The current validation process incorporates essential assumptions and approximations for transfer rates, short trip percentages, and access and egress distances. The challenges for the model posed by limited data are discussed in the model. / Master of Science

Warning systems with the ability to predict floods days in advance can benefit tens of millions of people. Because of these potential impacts there have been efforts to improve prediction systems such as the United States’ Advanced Hydrologic Prediction Service and European-developed Global Flood Awareness System. However, these projects are currently limited to relatively coarse resolutions. This thesis presents a method for downscaling and routing global runoff forecasts generated by the European Centre for Medium-Range Weather Forecasts using the Routing Application for Parallel computatIon of Discharge program that make possible orders of magnitude increases in the density of the resolution of stream forecasts. The processing method involves using the Amazon Web Services to distribute execution in a cloud-computing environment to make it possible to solve for large watersheds with high-density stream networks. Using the Amazon Web Services, the number of streams that can be used in the downscaling process in a twelve-hour period is approximated to be close to five million. In addition, an application for visualizing large high-density stream networks has been created using the Tethys Platform of water resources modeling developed as part of the CI-WATER NSF grant. The web application is tested with the HUC-2 Region 12 watershed network with over 67,000 reaches and is able to display analyzed results to the user for each reach.

Denna avhandling presenterar ett tillvägagångssätt för att förutspå förflyttning inom folkmassor med hjälp av LSTM-neurala nätverk. Specifikt analyseras inflytandet som olika frekvenser av tidsserier har på både prognosen för folkmassorna och designen i arkitekturen inom transport och handel. Arkitekturen påverkas även då frekvensändringar provocerar fram en ökning eller minskning i datamängd och arkitekturen därför bör anpassas. Tidigare forskning inom prognoser relaterade till folkmassor har huvudsakligen fokuserat på att förutspå folkmassans nästa förflyttning snarare än att definiera mängden människor på en specifik plats under ett specifikt tidsspann. Dessa studier har använt olika tekniker som till exempel Random Forest eller Feed Forward neurala nätverk för att ta reda på inflytandet som de olika frekvenserna har över prognosens resultat. Denna avhandling tillämpar istället LSTM-neurala nätverk för analysering av detta inflytande och använder specifika fältrelaterade tekniker för att hitta de bästa parametrarna för att förutspå framtida välstånd i folkmassor. Resultatet visar att frekvensordningen i en tidsserie tydligt påverkar resultatet av prognoserna inom transport och handel, och att detta inflytande är positivt när frekvensordningen av tidsserierna kan fånga upp frekvensens form i prognosen. Därför, med frekvensordningen i åtanke, visar resultaten i prognoserna för de analyserade platserna en förbättring på 40% för SMAPE och 50% för RMSE jämfört med inhemska tillvägagångssätt och andra tekniker. Utöver detta visar de även att det finns ett samband mellan frekvensordningen och komponenterna i arkitekturerna. / This thesis presents an approach to predict crowd movement in defined placesusing LSTM neural networks. Specifically, it analyses the influence that different frequencies of time series have in both the crowd forecast and the design of the architecture in the domains of transportation and retail. The architecture is also affected because changes in the frequency provokes an increment or decrement in the quantity of data and, therefore, the architecture should be adapted. Previous research in the field of crowd prediction has been mainly focused on anticipating the next movement of the crowd rather than defining the amount of people during a specific range of time in a particular place. These studies have used different techniques such as Random Forest or Feed-Forward neural networks in order to find out the influence that the different frequencies have in the results of the forecast. However, this thesis applies LSTM neural networks for analysing this influence and uses specific field-related techniques in order to find the best parameters for forecasting future crowd movement. The results show that the order of the frequency of a time series clearly affects the outcomes of the predictions in the field of transportation and retail, being this influence positive when the order of the frequency of time series is able to catch the shape of the frequency of the forecast. Therefore, taking into account the order of the frequency, the results of the forecast for the analyzed places show an improvement of 40% for SMAPE and 50% for RMSE compared to the Naive approach and other techniques. Furthermore, they point out that there is a relation between the order of the frequency and the components of the architectures.

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Rogue waves are unexpected, individual ocean surface waves that are disproportionately large compared to the background sea state. They present considerable risk to mariners and offshore structures when encountered in large seas. Rogue waves have gone from seafarer’s folktales to an actively researched and debated phenomenon. In this work an easily derived spectral parameter, as an indicator of rogue wave risk, is presented, and further evidence for the generation mechanism responsible for these abnormal waves is provided. With the additional goal of providing a practical rogue wave forecast, the ability of a standard wave model to predict the rogue wave probability is assessed. Current forecasts, like those at the European Centre for Medium-Range Weather Forecasts (ECMWF), rely on the Benjamin Feir Index (BFI) as a rogue wave predictor, which reflects the nonlinear process of modulation instability as the generation mechanism for rogue waves. However, this analysis finds BFI has little predictive power in the real ocean. From the analysis of long term sea surface elevation records in nearshore areas and hourly bulk statistics from open ocean and coastal buoys in the Northeast Pacific, crest-trough correlation shows the highest correlation with rogue wave probability. These results provide evidence in support of a probabilistic prediction of rogue waves based on random linear superposition and should replace forecasts based on modulation instability. Crest-trough correlation was then forecast by a regional WAVEWATCH III ® wave model with moderate accuracy compared with the high performance of forecasting significant wave height. Results from a case study of a large fall storm October 21-22, 2021, are presented to show that the regional wave model produces accurate forecasts of significant wave height at high seas and presents a potential rogue wave probability forecast. / Graduate

The city of Stockholm has a goal of reducing traffic work within the municipality by the year 2030. To achieve this, a change in the travel pattern will be required for Stockholm County in its entirety. The population has increased in all municipalities in Stockholm County and that increase is expected to continue. In recent years, traffic work has decreased per capita but increased overall. This indicates that countermeasures are required to reduce the amount of traffic work. This study aims to investigate whether a reduction in the capacity of the Central Bridge can lead to a reduction in traffic.According to studies globally, a reduction in capacity leads to reduced traffic work rather than the system breaking down. Important lessons in avoiding system breakdown are that the road network is robust and that there are alternative means of transport. In this study, the Swedish Transport Administration's base forecast for the year 2040 has been used together with a compilation of similar projects globally and lessons learned from them. According to the base forecast, the traffic work is estimated to increase until 2040. If the Central Bridge has a reduced capacity, it leads to a one percentage point reduction in traffic work in the municipality of Stockholm per reduced lane. This further leads to better air quality in the inner city as traffic work moves out into the rest of the municipality and county.The study has used Sampers, the demand model that handles the Swedish Transport Administration's basic forecasts, to analyse various aspects of the transport system. Despite its usefulness, the model has some limitations such as the separation of the public transport road network and the car road network, where congestion is only considered for car traffic. This shortcoming means that the effects of a change in congestion do not affect public transport journey times. The model is good at identifying the impact of a change in the road network on vehicle traffic but has more difficulty in identifying transfers between transport modes. Nevertheless, the model gives some indication of a shift to more sustainable modes of transport such as public transport, walking and cycling. It also gives an indication of more local car travel as transport costs increase for trips to and from Stockholm city centre for motorists. Over both the Customs section and the Saltsjö-Mälar section, the share of public transport increases.