Uncertainty and Fuzzy Decisions in Earthquake Risk Evaluation of Buildings

The Northern region of Thailand has been considered as one of the seismic risk zones. However, most existing buildings in the area had been designed and constructed based on old building design codes without seismic consideration. Therefore, those buildings are required to upgrade based on earthquake building damage risk evaluation. With resource limitations, it is not feasible to retrofit all buildings in a short period. In addition, the results of the risk evaluation contain uncertain inputs and outputs. The objective of this study is to prioritize building retrofit based on fuzzy earthquake risk assessment. The risk assessment of a building was made considering the risk factors including (1) building vulnerability, (2) seismic intensity and (3) building values. Then, the total risk was calculated by integrating all the risk factors with their uncertainties using a fuzzy rule based model. An example of the retrofit prioritization is shown here considering the three fuzzy factors. The ranking is hospital, temple, school, government building, factory and house, respectively. The result helps decision makers to screen and prioritize the building retrofitting in the seismically prone area

Crash Prediction Models for Horizontal Curve Segments on Two-Lane Rural Roads in Thailand

The number of road crashes continues to rise significantly in Thailand. Curve segments on two-lane rural roads are among the most hazardous locations which lead to road crashes and tremendous economic losses; therefore, a detailed examination of its risk is required. This study aims to develop crash prediction models using Safety Performance Functions (SPFs) as a tool to identify the relationship among road alignment, road geometric and traffic conditions, and crash frequency for two-lane rural horizontal curve segments. Relevant data associated with 86,599 curve segments on two-lane rural road networks in Thailand were collected including road alignment data from a GPS vehicle tracking technology, road attribute data from rural road asset databases, and historical crash data from crash reports. Safety Performance Functions (SPFs) for horizontal curve segments were developed, using Poisson regression, negative binomial regression, and calibrated Highway Safety Manual models. The results showed that the most significant parameter affecting crash frequency is lane width, followed by curve length, traffic volume, curve radius, and types of curves (i.e., circular curves, compound curves, reverse curves, and broken-back curves). Comparing among crash prediction models developed, the calibrated Highway Safety Manual SPF outperforms the others in prediction accuracy

Constructing Transit Origin-Destination Tables from Fragmented Data

This study proposes an approach that constructs the origin-destination table (O-D table) for urban bus or light rail lines from fragmented data about the number of boarding and alighting passengers at stops (B-A data) and the analyst\u27s spot knowledge about the trip pattern for selected O-D pairs. The B-A data of transit lines in the city center are often incomplete, yet they may be the only data available to characterize the passenger travel pattern. The proposed approach constructs the O-D table by using data that contain different levels of uncertainties and incompleteness. The model is based on two basic principles, maximum uncertainty and minimum uncertainty. The former is implemented by maximizing the entropy of the O-D table to derive the least-biased values. The latter refers to the maximum consistency with the available data including language-based knowledge about some of the O-D table elements. These principles are implemented by the multiobjective optimization structure. The model is found to be robust if it can incorporate various types of available data as well as the analyst\u27s knowledge. It was tested by using O-D data and B-A data from an actual transit operation. The quality of the derived O-D table is clearly related to the availability and the quality of the data; however, it can be improved significantly with the analyst\u27s spot knowledge about the values of selected O-D pairs. This method will open the way for transit planners to quickly develop a reasonable O-D table from the available incomplete data

Constructing a Transit Origin-Destination Table Using the Uncertainty Maximization Concept

An origin–destination (O-D) table contains information essential for making decisions about the operation and management of a transit system, such as determining the schedule, train composition, and fare structure. The table needs to be updated frequently. However, the collection of O-D data is time-consuming, costly, and cumbersome. This paper proposes a method that produces an O-D table on the basis of generally available data: passenger boarding and alighting counts at individual stations and the analyst’s knowledge, either qualitative or quantitative, about the values for some station pairs; for example, the number of trips (i, j) is approximately 100 or the number of trips (i, j) is much greater than the number of trips (m, n) (where i, j, m, and n are stops). The method applies the entropy maximization principle, in which the values for the O-D pairs whose information is not available are maximally unbiased and the available information is used as a constraint in the optimization problem. The uniqueness of the proposed approach is its ability to deal with qualitative and often language-based information, which the analyst often possesses. An example from a real transit line is presented to show the usefulness of the method and also to show how the additional information about select O-D pairs affects the quality of the solution

Use of reasoning maps in evaluation of transport alternatives: inclusion of uncertainty and “I Don’t Know”: demonstration of a method

Selection of a transport alternative is usually a messy process. The traditional approaches consider the relationships as either deterministic or probabilistic, neither of which incorporates the degree of ignorance (i.e., “I don’t know” opinion). Further, different stakeholders seek to justify their preferences with reasoning that suits their agenda. This paper proposes and demonstrates a method that evaluates the validity of the reasoning process and derives the degrees of belief that stated goals are achieved. The paper demonstrates a ‘reasoning map’ method for evaluating transport alternatives, where the analysts accept and employ the notion of “I don’t know” about an issue. The reasoning map depicts the relational chains from the attributes of an action to the stated goals, and recognizes the notion of “I don’t know”. This paper uses the theory of evidence to account for ignorance; it calculates the propagation of uncertainties along the reasoning chains. The context chosen for this demonstration is the selection of a public transit mode, personal rapid transit, over Bus, in a commercial complex in Washington DC. The paper has a limited objective and is not a comprehensive evaluation of alternatives. It merely explains how to compute a numerical value for the strength of reasoning, how to deal with analyst’s notion of “I don’t know,” how to interpret the overall reliability of the reasoning process, how to measure the goal achievement of an alternative, and how to find the critical paths linking the planning options to goals. For use in planning practice, consultation of experts and affected citizens and aggregation of their views is needed to develop the reasoning maps

The Accuracy of Risk Measurement Models on Bitcoin Market during COVID-19 Pandemic

Since late 2019, during one of the largest pandemics in history, COVID-19, global economic recession has continued. Therefore, investors seek an alternative investment that generates profits during this financially risky situation. Cryptocurrency, such as Bitcoin, has become a new currency tool for speculators and investors, and it is expected to be used in future exchanges. Therefore, this paper uses a Value at Risk (VaR) model to measure the risk of investment in Bitcoin. In this paper, we showed the results of the predicted daily loss of investment by using the historical simulation VaR model, the delta-normal VaR model, and the Monte Carlo simulation VaR model with the confidence levels of 99%, 95%, and 90%. This paper displayed backtesting methods to investigate the accuracy of VaR models, which consisted of the Kupiec’s POF and the Kupiec’s TUFF statistical testing results. Finally, Christoffersen’s independence test and Christoffersen’s interval forecasts evaluation showed effectiveness in the predictions for the robustness of VaR models for each confidence level

Quantifying Road-Network Robustness toward Flood-Resilient Transportation Systems

Amidst sudden and unprecedented increases in the severity and frequency of climate-change-induced natural disasters, building critical infrastructure resilience has become a prominent policy issue globally for reducing disaster risks. Sustainable measures and procedures to strengthen preparedness, response, and recovery of infrastructures are urgently needed, but the standard for measuring such resilient elements has yet to be consensually developed. This study was undertaken with an aim to quantitatively measure transportation infrastructure robustness, a proactive dimension of resilience capacities and capabilities to withstand disasters; in this case, floods. A four-stage analytical framework was empirically implemented: (1) specifying the system and disturbance (i.e., road network and flood risks in Chiang Mai, Thailand), (2) illustrating the system response using the damaged area as a function of floodwater levels and protection measures, (3) determining recovery thresholds based on land use and system functionality, and (4) quantifying robustness through the application of edge- and node-betweenness centrality models. Various quantifiable indicators of transportation robustness can be revealed; not only flood-damaged areas commonly considered in flood-risk management and spatial planning, but also the numbers of affected traffic links, nodes, and cars are highly valuable for transportation planning in achieving sustainable flood-resilient transportation systems

Intersection Safety Assessment Using Video-Based Traffic Conflict Analysis: The Case Study of Thailand

In the road transport network, intersections are among the most critical locations leading to a risk of death and serious injury. The traditional methods to assess the safety of intersections are based on statistical analyses that require crash data. However, such data may be under-reported and omit important crash-related factors. The conventional approaches, therefore, are not easily applied to making comparisons of intersection designs under different road classifications. This study developed a risk-based approach that incorporates video-based traffic conflict analysis to investigate vehicle conflicts under mixed traffic conditions including motorcycles and cars in Thailand. The study applied such conflict data to assess the risk of intersections in terms of time-to-collision and conflict speed. Five functional classes of intersections were investigated, including local-road/local-road, local-road/collector, collector/arterial, collector/collector, and arterial/arterial intersections. The results showed that intersection classes, characteristics, and control affect the behavior of motorists and the safety of intersections. The results found that the low-order intersections with stop/no control are high risks due to the short time-to-collision of motorcycle-related conflicts. They generate frequent conflicts with low chance of injury. The high-order intersections with signal control are high risks due to high conflicting speeds of motorcycle–car conflicts. They generate few conflicts but at a high chance of injury. The study presents the applicability of video-based traffic conflict analysis for systematically estimating the crash risk of intersections. The risk-based approach can be deemed as a supplement indicator in addition to limited crash data to evaluate the safety of intersections. However, future research is needed to explore the potential of other road infrastructure under different circumstances

Intersection Safety Assessment Using Video-Based Traffic Conflict Analysis: The Case Study of Thailand

In the road transport network, intersections are among the most critical locations leading to a risk of death and serious injury. The traditional methods to assess the safety of intersections are based on statistical analyses that require crash data. However, such data may be under-reported and omit important crash-related factors. The conventional approaches, therefore, are not easily applied to making comparisons of intersection designs under different road classifications. This study developed a risk-based approach that incorporates video-based traffic conflict analysis to investigate vehicle conflicts under mixed traffic conditions including motorcycles and cars in Thailand. The study applied such conflict data to assess the risk of intersections in terms of time-to-collision and conflict speed. Five functional classes of intersections were investigated, including local-road/local-road, local-road/collector, collector/arterial, collector/collector, and arterial/arterial intersections. The results showed that intersection classes, characteristics, and control affect the behavior of motorists and the safety of intersections. The results found that the low-order intersections with stop/no control are high risks due to the short time-to-collision of motorcycle-related conflicts. They generate frequent conflicts with low chance of injury. The high-order intersections with signal control are high risks due to high conflicting speeds of motorcycle–car conflicts. They generate few conflicts but at a high chance of injury. The study presents the applicability of video-based traffic conflict analysis for systematically estimating the crash risk of intersections. The risk-based approach can be deemed as a supplement indicator in addition to limited crash data to evaluate the safety of intersections. However, future research is needed to explore the potential of other road infrastructure under different circumstances

The Accuracy of Risk Measurement Models on Bitcoin Market during COVID-19 Pandemic

Since late 2019, during one of the largest pandemics in history, COVID-19, global economic recession has continued. Therefore, investors seek an alternative investment that generates profits during this financially risky situation. Cryptocurrency, such as Bitcoin, has become a new currency tool for speculators and investors, and it is expected to be used in future exchanges. Therefore, this paper uses a Value at Risk (VaR) model to measure the risk of investment in Bitcoin. In this paper, we showed the results of the predicted daily loss of investment by using the historical simulation VaR model, the delta-normal VaR model, and the Monte Carlo simulation VaR model with the confidence levels of 99%, 95%, and 90%. This paper displayed backtesting methods to investigate the accuracy of VaR models, which consisted of the Kupiec’s POF and the Kupiec’s TUFF statistical testing results. Finally, Christoffersen’s independence test and Christoffersen’s interval forecasts evaluation showed effectiveness in the predictions for the robustness of VaR models for each confidence level