Network Modeling of Hurricane Evacuation Using Data-Driven Demand and Incident-Induced Capacity Loss Models

The development of a hurricane evacuation simulation model is a crucial task in emergency management and planning. Two major issues affect the reliability of an evacuation model: one is estimations of evacuation traffic based on socioeconomic characteristics, and the other is capacity change and its influence on evacuation outcome due to traffic incidents in the context of hurricanes. Both issues can impact the effectiveness of emergency planning in terms of evacuation order issuance, and evacuation route planning. The proposed research aims to investigate the demand and supply modeling in the context of hurricane evacuations. This methodology created three scenarios for the New York City (NYC) metropolitan area, including one base and two evacuation scenarios with different levels of traffic demand and capacity uncertainty. Observed volume data prior to Hurricane Sandy is collected to model the response curve of the model, and the empirical incident data under actual evacuation conditions are analyzed and modeled. Then, the modeled incidents are incorporated into the planning model modified for evacuation. Simulation results are sampled and compared with observed sensor-based travel times as well as O-D-based trip times of NYC taxi data. The results show that the introduction of incident frequency and duration models can significantly improve the performance of the evacuation model. The results of this approach imply the importance of traffic incident consideration for hurricane evacuation simulation.

A Critical Look at Coulomb Counting Approach for State of Charge Estimation in Batteries

In this paper, we consider the problem of state-of-charge estimation for rechargeable batteries. Coulomb counting is a well-known method for estimating the state of charge, and it is regarded as accurate as long as the battery capacity and the beginning state of charge are known. The Coulomb counting approach, on the other hand, is prone to inaccuracies from a variety of sources, and the magnitude of these errors has not been explored in the literature. We formally construct and quantify the state-of-charge estimate error during Coulomb counting due to four types of error sources: (1) current measurement error; (2) current integration approximation error; (3) battery capacity uncertainty; and (4) timing oscillator error/drift. It is demonstrated that the state-of-charge error produced can be either time-cumulative or state-of-charge-proportional. Time-cumulative errors accumulate over time and have the potential to render the state-of-charge estimation utterly invalid in the long term.The proportional errors of the state of charge rise with the accumulated state of charge and reach their worst value within one charge/discharge cycle. The study presents methods for reducing time-cumulative and state-of-charge-proportional mistakes through simulation analysis.

Perceived Burdensomeness and the Wish for Hastened Death in Persons With Severe and Persistent Mental Illness

Background: In several European countries, medical assistance in dying (MAID) is no longer confined to persons with a terminal prognosis but is also available to those suffering from persistent and unbearable mental illness. To date, scholarly discourse on MAID in this population has been dominated by issues such as decision-making capacity, uncertainty as to when a disease is incurable, stigmatization, isolation, and loneliness. However, the issue of perceived burdensomeness has received little attention.Objective: The study explores the possible impact of perceived burdensomeness on requests for MAID among persons with severe and persistent mental illness (SPMI).Method: Using the method of ethical argumentation, we discuss the issue of access to MAID for persons with SPMI and perceived burdensomeness.Conclusion: Perceived burdensomeness may be a contributing factor in the wish for hastened death among persons with SPMI. MAID is ethically unsupportable if SPMI causes the individual to make an unrealistic assessment of burdensomeness, indicating a lack of decision-making capacity in the context of that request. However, the possibility that some individuals with SPMI may perceive burdensomeness does not mean that they should be routinely excluded from MAID. For SPMI patients with intact decision-making capacity who feel their life is not worth living, perceived burdensomeness as a component of this intolerable suffering is not a sufficient reason to deny access to MAID.

A Simultaneous Optimization Model for Airport Network Slot Allocation under Uncertain Capacity

Serious congestion and delay problems exist in most of the busiest airports worldwide because of imbalance between scarce airport slot resources and increasing traffic demand. Various factors, especially weather conditions, exacerbate the demand–capacity imbalance. This paper presents a robust model for simultaneous slot allocation on an airport network in multiple calendar days, considering airport capacity uncertainty. The idea of robust optimization is conducive to sustainable and stable decision-making. Robustness is represented through reducing the potential scheduling conflicts in the worst case. Then the model links the strategic decisions and pre-tactical decisions in air traffic management (ATM) through the tradeoff between strategic discrepancy cost and operational congestion cost. Under the support of the Cplex solver, numerical analyses are taken to validate the characteristics and effectiveness of the proposed model. The results show that the proposed model effectively eliminates the existing and potential scheduling conflicts, and makes effective tradeoffs between airline preference and potential airport congestion risk.