Analysis and Classification of Volcanic Eruptions

Among natural disasters, volcanic eruptions are some of the most dangerous. The severity level of the most extreme volcanic eruption for which data is available can be categorized as Catastrophe Type II according to the scale introduced by Wirasinghe, Caldera, Durage, and Ruwanpura, (2013). However, an unusually large eruption of a “super volcano” can even cause a partial or full extinction. Aftermaths of a major eruption, such as climate effects, tsunami, and famine, severely impacts populations. Potential severity levels of volcanic eruptions are studied. A multidimensional scale for volcanic eruptions is investigated. Intensity, fatalities, affected population, impacted region, cost of damage, and GDP per capita, are some factors that can be considered to determine the severity level. Relationships among the factors are also considered. An analysis is conducted to identify the specific factors to be considered in the multidimensional scale. The extreme values of known historical eruptions of each volcano are studied in terms of fatalities. However, the study does not consider any secondary effect caused by the volcanic eruptions. The extreme values of fatalities from eruptions of 136 volcanoes are shown to be distributed as a 3 parameter Weibull (α = 0.33925, μ = 1, σ = 109.04) distribution

NDM-528: AN APPROACH TO CLASSIFICATION OF NATURAL DISASTERS BY SEVERITY

Existing scales for natural disasters describe severity in terms of intensity. Intensity scales are not highly correlated with impact factors such as fatalities, injuries, homelessness, affected population, and cost of damage. The descriptive words for disasters are also not sufficient to clearly comprehend the real magnitude of severity as there is no consistent method to distinguish one terminology from another. Further, data collection standards vary among countries and, therefore, comparisons across space and time are difficult to make. Several discrepancies between various sources of information complicate the interpretation of trends in disaster data. Furthermore, comparing different events and obtaining a sense of scale are problematic due to the deficiencies that reduce the quality of the data set, and disaster managers may face inconsistencies in identifying the magnitude of a disaster, responding to the event properly, and allocating resources for mitigation measures. There is no scale currently that is supported with data that can rate the severity of any natural disaster. This ongoing study attempts to develop a multidimensional scale. It also proposes a unified way of describing disasters by focusing on clear definitions, analyzing extreme events, and developing a set of criteria to make comparisons and rank natural disasters based on their impact, to help governments and relief agencies respond when disaster strikes. An initial severity scale based on fatalities is used to compare and rate disasters such as earthquake, tsunami, volcano and tornado. This concept can be applied to any type of disaster including windstorms, snowstorms, and wildfires

Route layout analysis for express buses

For a fixed bus route, the path is the fundamental parameter that determines the passenger catchment area, which, via the dispatching policy, determines the timetable and fleet size. However, the path cannot be chosen independent of the other parameters; they have a symbiotic relationship, even though the planning time frames are different ranging from ‘years’ for the path of the route to ‘days’ for the crew schedule. The express route planning problem deals with the optimum selection of the sequence of passenger generators to be served by the planned route. An analytical model is developed to enable minimization of operating cost and costs of passenger access, waiting and travel times. Applications of this model have provided an insight to the relative importance of different cost components. The analytical model first selects an initial trial path for the purpose of comparison and then the optimum path is sought by considering a process of swapping, adding and removing generators or extending the route. Routes based on both grid and non-grid road networks are considered. In particular, non-grid road network based routes have a clear optimum. The cost of access is shown to be the influential parameter with respect to route selection. The insights gained from such analysis are highlighted

Optimal time point configuration of a bus route - A Markovian approach

For a scheduled bus route adopting the holding control strategy, determining the optimal number and location of time points is considered a long-standing but elusive problem. In this paper, we take a new approach to the problem by developing a Markov Chain model to accurately capture the stochastic nature of a bus as it moves along a route in mixed traffic. Transition matrices are created using theoretical distributions of travel time calibrated with stop-to-stop travel time and dwell time data. The approach captures analytically the bus behavior while still allowing the model to be informed by the unique characteristics of the route, including travel time between stops and passenger demand. This stochastic process model mimics the physical phenomenon of Brownian motion, and it is found that the compounding nature of randomness leads to greater unreliability as the route progresses. Theoretical analysis of routes allows us to demonstrate where problem points may exist on the route and can point to locations where reliability improvements may be more effective. We develop a cost function to capture the values of time of passengers including waiting time due to early and late buses, and lost time at time points. We include operating cost capturing the increased cost of travel time caused by added control, and the improved overtime costs resulting from more consistent service. Using data from automated vehicle location (AVL) and automated passenger count (APC) systems, an operational route in Calgary, Canada is optimized using the developed model and cost function. A heuristic optimization algorithm is developed to consider high-cost stops iteratively which improves the cost function compared with existing configurations and with fewer time points

Cost-of-crowding model for light rail train and platform length

With light rail transit (LRT) and other similar rail-based commuter transit systems, train and associated station platform length provides an added dimension of flexibility not available to buses. Train and platform lengths are important factors in the planning and expansion phases of a network. Existing cost models that determine optimal headway by combining passenger and operational costs provide headways that are small and close to a logistical minimum (2–3 min); this type of standard waiting cost model is not sensitive to train and platform length. In this paper, on-board crowding is used as a cost factor and a cost-of-crowding model is developed from supporting psychological research. Two models are proposed and optimized with respect to train length to determine the optimal train and platform length for a many-to-one peak period commuter LRT system. Data from the C-Train network in Calgary, Alberta is used for numerical analysis of the model. The model demonstrated that crowding has an effect on optimal train length. The model produced feasible results when applied to a real-world scenario

Airport gate position estimation for minimum total costs--Approximate closed form solution

A method to determine the optimum number of gate positions at an airport terminal, which minimizes the sum of the cost of gates and the cost of delays to aircraft is presented. It is based on an approximate procedure to determine the total deterministic delay to aircraft caused by a limited number of gates, given information regarding the peaking of the aircraft arrival rate, and the number of peaks per day. Closed-form solutions are obtained for the cases of one peak and several identical nonoverlapping peaks, respectively. The optimum number of gates required for the Calgary International Airport, based on a common gate use policy, is reported.

Walking distance minimization for airport terminal configurations

Passenger walking distance is a major consideration in determining the configuration of an airport terminal. Given the size of a terminal in terms of the number of aircraft gates, the mean passenger walking distance is derived based on: the fraction of arriving, departing and transferring (hub and non-hub) passengers; gate spacing; spacing requirement for aircraft maneuvering; and the terminal block dimensions. Pier, satellite, and pier-satellite terminal configurations are considered. It is assumed that all aircraft parking positions are capable of handling any type of aircraft and passengers are equally distributed among all the gate positions over the life of the facility. Two groups of hub transfers are defined to accommodate different levels of hub and spoke operations. The optimum terminal geometry in terms of the number of piers or satellites and their sizes, is obtained by minimizing the mean walking distance for all the passengers. The probability distribution of the walking distance of a passenger is generated by simulation. Given an acceptable walking distance, several statistical parameters that are suitable to compare the optimum geometries for different configurations are reported. It is shown that in most cases the lower and the upper bounds of the optimum number of piers or satellites are proportional to the square root of the total number of gates in the terminal. For a wide range of passenger mixes and numbers of gates, a semi-centralized pier configuration appears to be the best terminal configuration with respect to passenger walking. Guidelines for the selection of the best terminal configuration for non-hub, moderate-hub and all-hub (wayport) terminals are presented. The application of the proposed method in a terminal expansion situation is given.

Long-term planning for ring-radial urban rail transit networks

Extensive work exists on regular rail network planning. However, few studies exist on the planning and design of ring-radial rail transit systems. With more ring transit lines being planned and built in Asia, Europe and the America\u27s, a detailed study on ring transit lines is timely. An analytical model to find the optimal number of radial lines in a city for any demand distribution is first introduced. Secondly, passenger route choice for different rail networks is analyzed, for a many-to-many Origin-Destination (OD) demand distribution, based on a total travel time cost per passenger basis. The routes considered are: (1) radial lines only; (2) ring line only or radial lines and ring line combined; or (3) direct access to a destination without using the rail system. Mathematica and Matlab are used to code the route choice model. A cost-benefit optimization model to identify the feasibility and optimality of a ring line is proposed. Unlike simulations and agent-based models, this model is shown to be easily transferable to many ring-radial transit networks. The City of Calgary is used as an example to illustrate the applicability of each model. The existing urban rail network and trip distribution are major influencing factors in judging the feasibility and optimal location of the ring line. This study shows the potential net benefit of introducing a ring line by assessing changes in passengers’ costs. The changes in passenger cost parameters, such as ride cost and access cost, are shown to greatly influence the feasibility of a ring line