scholarly journals Modeling and Simulation of Cascading Failures in Transportation Systems during Hurricane Evacuations

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yuan Zhu ◽  
Kaan Ozbay ◽  
Hong Yang ◽  
Fan Zuo ◽  
Di Sha
Keyword(s):  
Traffic Signals ◽  
Transportation Systems ◽  
System Capacity ◽  
Cascading Failures ◽  
Signal Loss ◽  
Power Outage ◽  
Capacity Loss ◽  
Modeling Process ◽  
Macroscopic Simulation ◽  
The Impact

Effective and timely evacuation is critical in alleviating the impact of hurricanes. As such, evacuation models are often sought to support the preparedness of evacuations. One important task in the modeling process is to evaluate exogenous factors that cause transportation system capacity loss during evacuation. Typical factors include direct damage to the roadway network due to storm surge and cascading impacts because of other facilities failures. For example, power outage can lead to signal failure and subway suspension. This paper aims to develop a macroscopic simulation-based approach to study the capacity loss of the roadway network in evacuation due to signal loss as a consequence of power outage. In particular, to simulate the case in which traffic signals lose power, a capacity-reduction model from signalized intersections to unsignalized (all-way stop control) intersections was developed and calibrated using microscopic model created in SUMO and Synchro. We used the downtown Manhattan as a case study area and created a hypothetical power-grid network in terms of neighborhoods. Six scenarios were built to simulate power loss of different neighborhoods. The simulation results give insights on how cascading failures of power network affect roadway network and evacuation process.

scholarly journals Robust Filtering Techniques for RTK Positioning in Harsh Propagation Environments

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1250
Author(s):  
Daniel Medina ◽  
Haoqing Li ◽  
Jordi Vilà-Valls ◽  
Pau Closas
Keyword(s):  
Intelligent Transportation Systems ◽  
Robust Statistics ◽  
Real Data ◽  
Transportation Systems ◽  
Global Navigation Satellite Systems ◽  
Mixed Integer ◽  
Robust Filtering ◽  
Precise Positioning ◽  
The Impact ◽  
Filtering Techniques

Global navigation satellite systems (GNSSs) play a key role in intelligent transportation systems such as autonomous driving or unmanned systems navigation. In such applications, it is fundamental to ensure a reliable precise positioning solution able to operate in harsh propagation conditions such as urban environments and under multipath and other disturbances. Exploiting carrier phase observations allows for precise positioning solutions at the complexity cost of resolving integer phase ambiguities, a procedure that is particularly affected by non-nominal conditions. This limits the applicability of conventional filtering techniques in challenging scenarios, and new robust solutions must be accounted for. This contribution deals with real-time kinematic (RTK) positioning and the design of robust filtering solutions for the associated mixed integer- and real-valued estimation problem. Families of Kalman filter (KF) approaches based on robust statistics and variational inference are explored, such as the generalized M-based KF or the variational-based KF, aiming to mitigate the impact of outliers or non-nominal measurement behaviors. The performance assessment under harsh propagation conditions is realized using a simulated scenario and real data from a measurement campaign. The proposed robust filtering solutions are shown to offer excellent resilience against outlying observations, with the variational-based KF showcasing the overall best performance in terms of Gaussian efficiency and robustness.

scholarly journals Virus Transmission Risk in Urban Rail Systems: Microscopic Simulation-Based Analysis of Spatio-Temporal Characteristics

2021 ◽  
pp. 036119812110101
Author(s):  
Jiali Zhou ◽  
Haris N. Koutsopoulos
Keyword(s):  
Public Transportation ◽  
Virus Transmission ◽  
Flow Characteristics ◽  
Transportation Systems ◽  
Transmission Risk ◽  
Microscopic Simulation ◽  
Spatial And Temporal Patterns ◽  
Subway System ◽  
Public Transportation Systems ◽  
The Impact

The transmission risk of airborne diseases in public transportation systems is a concern. This paper proposes a modified Wells-Riley model for risk analysis in public transportation systems to capture the passenger flow characteristics, including spatial and temporal patterns, in the number of boarding and alighting passengers, and in number of infectors. The model is used to assess overall risk as a function of origin–destination flows, actual operations, and factors such as mask-wearing and ventilation. The model is integrated with a microscopic simulation model of subway operations (SimMETRO). Using actual data from a subway system, a case study explores the impact of different factors on transmission risk, including mask-wearing, ventilation rates, infectiousness levels of disease, and carrier rates. In general, mask-wearing and ventilation are effective under various demand levels, infectiousness levels, and carrier rates. Mask-wearing is more effective in mitigating risks. Impacts from operations and service frequency are also evaluated, emphasizing the importance of maintaining reliable, frequent operations in lowering transmission risks. Risk spatial patterns are also explored, highlighting locations of higher risk.

scholarly journals Assessing the Energy Resilience of Office Buildings: Development and Testing of a Simplified Metric for Real Estate Stakeholders

Buildings ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 96
Author(s):  
Paul Mathew ◽  
Lino Sanchez ◽  
Sang Hoon Lee ◽  
Travis Walter
Keyword(s):  
Regression Models ◽  
Health And Safety ◽  
Office Buildings ◽  
Extreme Weather Events ◽  
Economic Losses ◽  
Power Outage ◽  
Continuity Of Operations ◽  
Quasi Monte Carlo ◽  
Building Characteristics ◽  
The Impact

Increasing concern over higher frequency extreme weather events is driving a push towards a more resilient built environment. In recent years there has been growing interest in understanding how to evaluate, measure, and improve building energy resilience, i.e., the ability of a building to provide energy-related services in the event of a local or regional power outage. In addition to human health and safety, many stakeholders are keenly interested in the ability of a building to allow continuity of operations and minimize business disruption. Office buildings are subject to significant economic losses when building operations are disrupted due to a power outage. We propose “occupant hours lost” (OHL) as a means to measure the business productivity lost as the result of a power outage in office buildings. OHL is determined based on indoor conditions in each space for each hour during a power outage, and then aggregated spatially and temporally to determine the whole building OHL. We used quasi-Monte Carlo parametric energy simulations to demonstrate how the OHL metric varies due to different building characteristics across different climate zones and seasons. The simulation dataset was then used to develop simple regression models for assessing the impact of ten key building characteristics on OHL. The most impactful were window-to-wall ratio and window characteristics. The regression models show promise as a simple means to assess and screen for resilience using basic building characteristics, especially for non-critical facilities where it may not be viable to conduct detailed engineering analysis.

scholarly journals System capacity improvement by on demand channel allocation in Femto and macro cell networks

2018 ◽  
Vol 7 (1.9) ◽  
pp. 242
Author(s):  
S. Bharagavi ◽  
Banuprathap Pv
Keyword(s):  
Channel Allocation ◽  
System Capacity ◽  
On Demand ◽  
Macro Cell ◽  
System A ◽  
Real Cell ◽  
Capacity Improvement ◽  
The Impact ◽  
Cell Networks ◽  
Cell Base

Entire arrangement progress femtocells characterize a right likely response to the constantly increasing transmission constrain demand of adaptable areas. They could be clearly passed on without requesting a focal expecting to pass on the high information speed orchestrate through aim perfect scope. The Femtocells are low power, actuallesser and cost in real cell base district utilized in the inside condition. Regardless, the impact of the Femtocells is the introduction of the straight Macrocell structure indications impediment issue among the Femtocells also earlier Macrocellsby strategy for they can part of the similar approved rehash run. The Frequency Reuse is a centrality of sending the rehash supply distribution upon station's place to recuperate framework limit. This paper, a fit strategy to develop structure restrict through inter vent ionorganization in the current Femto Macro 2layer systems has been planned. In the planned system, a original rehash saving for 2layersthe cell organizes by systems for rehash reuse technique is utilized wherever Macro base placesallot rehash sub-groups portrayed out for the Femtocells operators on demand based one the Femtocells base places toward stop impedance.

scholarly journals HCFMUSP: Resilience in Response to the COVID-19 Pandemic

2021 ◽  
Vol 25 (spe) ◽  
Author(s):  
Marcelo Caldeira Pedroso ◽  
João Teixeira Pires ◽  
Ana Maria Malik ◽  
Antonio José Rodrigues Pereira
Keyword(s):  
Knowledge Management ◽  
Health System ◽  
Crisis Management ◽  
System Capacity ◽  
New Disease ◽  
Healthcare Operations ◽  
Teaching Case ◽  
Health System Governance ◽  
Emergency Actions ◽  
The Impact

ABSTRACT The teaching case describes a set of emergency actions taken by HCFMUSP to manage the needs brought by the COVID-19 pandemic in Brazil. The case objective considers the issues related to the impact of the pandemic mostly in healthcare operations, emphasizing how to: (a) adapt health system governance in response to a crisis (crisis management); (b) manage the health system capacity, which traditionally is not so resilient; (c) deal with a new disease (knowledge management). Thus, it should allow gathering elements for the management of future crises.

scholarly journals Multi-Agent Reinforcement Learning for Optimizing Traffic Signal Timing

2021 ◽  
Author(s):  
Areej Salaymeh ◽  
Loren Schwiebert ◽  
Stephen Remias
Keyword(s):  
Reinforcement Learning ◽  
Traffic Signals ◽  
Transportation Systems ◽  
Traffic Signal ◽  
Urban Traffic ◽  
Signal Timing ◽  
Model Free ◽  
Proposed Model ◽  
Multi Agent ◽  
Traffic Signal Timing

Designing efficient transportation systems is crucial to save time and money for drivers and for the economy as whole. One of the most important components of traffic systems are traffic signals. Currently, most traffic signal systems are configured using fixed timing plans, which are based on limited vehicle count data. Past research has introduced and designed intelligent traffic signals; however, machine learning and deep learning have only recently been used in systems that aim to optimize the timing of traffic signals in order to reduce travel time. A very promising field in Artificial Intelligence is Reinforcement Learning. Reinforcement learning (RL) is a data driven method that has shown promising results in optimizing traffic signal timing plans to reduce traffic congestion. However, model-based and centralized methods are impractical here due to the high dimensional state-action space in complex urban traffic network. In this paper, a model-free approach is used to optimize signal timing for complicated multiple four-phase signalized intersections. We propose a multi-agent deep reinforcement learning framework that aims to optimize traffic flow using data within traffic signal intersections and data coming from other intersections in a Multi-Agent Environment in what is called Multi-Agent Reinforcement Learning (MARL). The proposed model consists of state-of-art techniques such as Double Deep Q-Network and Hindsight Experience Replay (HER). This research uses HER to allow our framework to quickly learn on sparse reward settings. We tested and evaluated our proposed model via a Simulation of Urban MObility simulation (SUMO). Our results show that the proposed method is effective in reducing congestion in both peak and off-peak times.

scholarly journals An Advanced Deep Learning Approach for Multi-Object Counting in Urban Vehicular Environments

2021 ◽  
Vol 13 (12) ◽  
pp. 306
Author(s):  
Ahmed Dirir ◽  
Henry Ignatious ◽  
Hesham Elsayed ◽  
Manzoor Khan ◽  
Mohammed Adib ◽  
...  
Keyword(s):  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
Smart Cities ◽  
Weather Conditions ◽  
Research Area ◽  
Transportation Systems ◽  
Detection Accuracy ◽  
Adverse Weather ◽  
Object Counting ◽  
The Impact

Object counting is an active research area that gained more attention in the past few years. In smart cities, vehicle counting plays a crucial role in urban planning and management of the Intelligent Transportation Systems (ITS). Several approaches have been proposed in the literature to address this problem. However, the resulting detection accuracy is still not adequate. This paper proposes an efficient approach that uses deep learning concepts and correlation filters for multi-object counting and tracking. The performance of the proposed system is evaluated using a dataset consisting of 16 videos with different features to examine the impact of object density, image quality, angle of view, and speed of motion towards system accuracy. Performance evaluation exhibits promising results in normal traffic scenarios and adverse weather conditions. Moreover, the proposed approach outperforms the performance of two recent approaches from the literature.

Illustrating the Monetary Impact of Transportation Projects/Alternatives using the Values of Travel Time and Travel Time Reliability

2018 ◽  
Vol 2672 (51) ◽  
pp. 88-98 ◽  
Author(s):  
Venkata R. Duddu ◽  
Srinivas S. Pulugurtha ◽  
Praveena Penmetsa
Keyword(s):  
Travel Time ◽  
Cost Benefit Analysis ◽  
Cost Benefit ◽  
Transportation Systems ◽  
Travel Time Reliability ◽  
Benefit Analysis ◽  
Transportation Projects ◽  
Travel Time Savings ◽  
Time Savings ◽  
The Impact

State agencies, regional agencies, cities, towns, and local municipalities design and maintain transportation systems for the benefit of users by improving mobility, reducing travel time, and enhancing safety. Cost–benefit analysis based on travel time savings and the value of reliability helps these agencies in prioritizing transportation projects or when evaluating transportation alternatives. This paper illustrates the use of monetary values of travel time savings and travel time reliability, computed for the state of North Carolina, to help assess the impact of transportation projects or alternatives. The results obtained indicate that, based on the illustration of the effect and impact of various transportation projects or alternatives, both improved travel time and reliability on roads yield significant monetary benefits. However, from cost–benefit analysis, it is observed that greater benefits can be achieved through improved reliability compared with benefits from a decrease in travel time for a given section of road.

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