scholarly journals Evaluating the Vulnerability of Time-Sensitive Transportation Networks: A Hub Center Interdiction Problem

2019 ◽  
Vol 11 (17) ◽  
pp. 4614 ◽  
Author(s):  
Ting L. Lei
Keyword(s):  
Linear Programming ◽  
Transit Time ◽  
Preventive Measures ◽  
Transportation Networks ◽  
Transportation Systems ◽  
Worst Case ◽  
Hub And Spoke ◽  
Research Attention ◽  
Perishable Goods ◽  
Critical Facilities

Time-sensitive transportation systems have received increasing research attention recently. Examples of time-sensitive networks include those of perishable goods, high-value commodity, and express delivery. Much research has been devoted to optimally locating key facilities such as transportation hubs to minimize transit time. However, there is a lack of research attention to the reliability and vulnerability of time-sensitive transportation networks. Such issues cannot be ignored as facilities can be lost due to reasons such as extreme weather, equipment malfunction, and even intentional attacks. This paper proposes a hub interdiction center (HIC) model for evaluating the vulnerability of time-sensitive hub-and-spoke networks under disruptions. The model identifies the set of hub facilities whose loss will lead to the greatest increase in the worst-case transit time. From a planning perspective, such hubs are critical facilities that should be protected or enhanced by preventive measures. An efficient integer linear programming (ILP) formulation of the new model is developed. Computational experiments on a widely used US air passenger dataset show that losing a small number of hub facilities can double the maximum transit time.

scholarly journals Lost in transportation: Information measures and cognitive limits in multilayer navigation

2016 ◽  
Vol 2 (2) ◽  
pp. e1500445 ◽  
Author(s):  
Riccardo Gallotti ◽  
Mason A. Porter ◽  
Marc Barthelemy
Keyword(s):  
New York ◽  
Transportation Networks ◽  
Transportation Systems ◽  
Traditional View ◽  
Multimodal Transportation ◽  
Large Cities ◽  
Amount Of Information ◽  
Information Measures ◽  
Transportation Modes ◽  
Metropolitan Networks

Cities and their transportation systems become increasingly complex and multimodal as they grow, and it is natural to wonder whether it is possible to quantitatively characterize our difficulty navigating in them and whether such navigation exceeds our cognitive limits. A transition between different search strategies for navigating in metropolitan maps has been observed for large, complex metropolitan networks. This evidence suggests the existence of a limit associated with cognitive overload and caused by a large amount of information that needs to be processed. In this light, we analyzed the world’s 15 largest metropolitan networks and estimated the information limit for determining a trip in a transportation system to be on the order of 8 bits. Similar to the “Dunbar number,” which represents a limit to the size of an individual’s friendship circle, our cognitive limit suggests that maps should not consist of more than 250 connection points to be easily readable. We also show that including connections with other transportation modes dramatically increases the information needed to navigate in multilayer transportation networks. In large cities such as New York, Paris, and Tokyo, more than 80% of the trips are above the 8-bit limit. Multimodal transportation systems in large cities have thus already exceeded human cognitive limits and, consequently, the traditional view of navigation in cities has to be revised substantially.

scholarly journals Role of Stress on Women’s Health: Causes and Prevention

2016 ◽  
Vol 3 (4) ◽  
Author(s):  
Razia Saleem ◽  
Shamsul Siddiqui
Keyword(s):  
General Condition ◽  
Preventive Measures ◽  
The Body ◽  
Poor Health ◽  
Research Attention ◽  
Stress Effects ◽  
After Hours ◽  
The Individual ◽  
Intense Research

In recent years, stress has been the focus of intense research attention. Stress is a misfit between the demands of the environment and the individual’s abilities; the imbalance may be corrected, according to the situation, either by adjusting external demands to fit the individual or by strengthening the individual’s ability to cope or both. Everyone is exposed to stress, and a great number of people have experienced the traces of stress. Women are socialized to be the caretakers of others. More women than men have both a career outside the home and continue to try to juggle traditional responsibilities after hours. It has often been shown that women are the worriers and often do not make time to manage their health and take care of themselves. Stress is on the rise for women as they struggle to find a balance between their homes and careers. The recession has caused a greater need for women to work outside of the home to support their families. Health is a general condition of the body or mind with reference to soundness and vigor; it will be reflected by good or poor health. A poor health affects our mind, as a stressed life affects our health. The struggle that women confront each days trying to achieve the standards of being a daughter, women, wife, mother, house, and/ or career keeper puts us in a vulnerable position of presenting stress effects that may affect our health. And there are some preventive measures to cope with stress such as meditation, yoga, quality time etc.

scholarly journals DOME PETROLEUM'S OIL SPILL RESEARCH AND DEVELOPMENT PROGRAM FOR THE ARCTIC

1981 ◽  
Vol 1981 (1) ◽  
pp. 173-181
Author(s):  
W. M. Pistruzak
Keyword(s):  
Research And Development ◽  
Oil Spill ◽  
Development Program ◽  
Transportation Systems ◽  
The Arctic ◽  
Oil Well ◽  
Development Programs ◽  
Worst Case ◽  
Ongoing Research ◽  
Future Production

ABSTRACT Canadian Marine Drilling (Canmar), a wholly owned subsidiary of Dome Petroleum Ltd., is conducting exploratory drilling in the Beaufort Sea with the objective of on-stream production by the mid-1980s. If a major oil well blow-out should occur, and the probability of such an occurrence is very small, (Bercha, 1977), oil would be released to the surface of the sea until a relief well could be drilled or the well sealed itself. The relief well could be drilled during the same drill season, or, in the worst case, it might not be completed until the following year. Therefore, Dome could be faced with the problem of cleaning up an oil spill during open-water, freeze-up, and winter or spring break-up conditions. To this end, Dome has developed a contingency plan, based on, and updated according to, its ongoing research and development programs to deal with an oil spill during each of the above-mentioned periods of time. To date, Dome has invested approximately $10 million in its research and development programs. This paper deals with Dome's research and development in oil spill countermeasures for its present ongoing exploration activities and its future production and transportation systems.

Multi-modal transportation recommendation with unified route representation learning

2020 ◽  
Vol 14 (3) ◽  
pp. 342-350
Author(s):  
Hao Liu ◽  
Jindong Han ◽  
Yanjie Fu ◽  
Jingbo Zhou ◽  
Xinjiang Lu ◽  
...  
Keyword(s):  
Large Scale ◽  
Transportation Networks ◽  
Representation Learning ◽  
Transportation Systems ◽  
Graph Representation ◽  
Dynamic Graph ◽  
Arbitrary Length ◽  
Task Learning ◽  
Semantic Coherence ◽  
Spatio Temporal

Multi-modal transportation recommendation aims to provide the most appropriate travel route with various transportation modes according to certain criteria. After analyzing large-scale navigation data, we find that route representations exhibit two patterns: spatio-temporal autocorrelations within transportation networks and the semantic coherence of route sequences. However, there are few studies that consider both patterns when developing multi-modal transportation systems. To this end, in this paper, we study multi-modal transportation recommendation with unified route representation learning by exploiting both spatio-temporal dependencies in transportation networks and the semantic coherence of historical routes. Specifically, we propose to unify both dynamic graph representation learning and hierarchical multi-task learning for multi-modal transportation recommendations. Along this line, we first transform the multi-modal transportation network into time-dependent multi-view transportation graphs and propose a spatiotemporal graph neural network module to capture the spatial and temporal autocorrelation. Then, we introduce a coherent-aware attentive route representation learning module to project arbitrary-length routes into fixed-length representation vectors, with explicit modeling of route coherence from historical routes. Moreover, we develop a hierarchical multi-task learning module to differentiate route representations for different transport modes, and this is guided by the final recommendation feedback as well as multiple auxiliary tasks equipped in different network layers. Extensive experimental results on two large-scale real-world datasets demonstrate the performance of the proposed system outperforms eight baselines.

scholarly journals Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation Networks

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2229 ◽  
Author(s):  
Sen Zhang ◽  
Yong Yao ◽  
Jie Hu ◽  
Yong Zhao ◽  
Shaobo Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Large Scale ◽  
Transportation Networks ◽  
Transportation Network ◽  
Transportation Systems ◽  
Neural Network Models ◽  
Computation Efficiency ◽  
Congestion Prediction

Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which is partially due to the severe lack of large-scale high-quality traffic congestion data and advanced algorithms. This paper proposes an accessible and general workflow to acquire large-scale traffic congestion data and to create traffic congestion datasets based on image analysis. With this workflow we create a dataset named Seattle Area Traffic Congestion Status (SATCS) based on traffic congestion map snapshots from a publicly available online traffic service provider Washington State Department of Transportation. We then propose a deep autoencoder-based neural network model with symmetrical layers for the encoder and the decoder to learn temporal correlations of a transportation network and predicting traffic congestion. Our experimental results on the SATCS dataset show that the proposed DCPN model can efficiently and effectively learn temporal relationships of congestion levels of the transportation network for traffic congestion forecasting. Our method outperforms two other state-of-the-art neural network models in prediction performance, generalization capability, and computation efficiency.

scholarly journals Optimization of perishable goods delivery in supply chains with random demand

2021 ◽  
Vol 258 ◽  
pp. 02017
Author(s):  
Vladimir Anisimov ◽  
Anton Shaban ◽  
Evgenii Anisimov ◽  
Tatyana Saurenko ◽  
Vladimir Yavorsky
Keyword(s):  
Linear Programming ◽  
Integer Programming ◽  
Supply Chains ◽  
Programming Problem ◽  
Integer Programming Problem ◽  
Branch And Bound Method ◽  
Random Demand ◽  
Stochastic Nature ◽  
Perishable Goods ◽  
Rational Range

The article suggests a model for defining a rational range and volume of supply of perishable goods in their supply chains functioning at the time of random demand. In the verbal formulation, the goal of the model is to determine the range and delivery volumes of perishable goods that maximize profits with restrictions on the funds available for their purchase, storage volumes and weight, as well as on lost profits. The formalized representation of the model is determined by the properties of the supplied perishable goods. If these goods are divisible, then the model is formalized as a linear programming problem. In this case, the rational assortment and volume of goods is determined by solving it, for example, using the simplex method.If the goods under consideration are piece (indivisible), they are formalized in the form of a corresponding integer programming problem. In this case, the rational assortment and volume of goods is determined by solving it, for example, based on the branch and bound method. The peculiarity of the model is that it takes into account the stochastic nature of demand for goods, their limited shelf life, as well as the possibility of storing goods and the availability of funds necessary to purchase the next batch.

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