Optimal placement of omnidirectional sensors in a transportation network for effective emergency response and crash characterization

2014 ◽  
Vol 45 ◽  
pp. 64-82 ◽  
Author(s):  
Tejswaroop Geetla ◽  
Rajan Batta ◽  
Alan Blatt ◽  
Marie Flanigan ◽  
Kevin Majka
Keyword(s):  
Emergency Response ◽  
Transportation Network ◽  
Optimal Placement

scholarly journals Charging Station Allocation for Electric Vehicle Network Using Stochastic Modeling and Grey Wolf Optimization

2021 ◽  
Vol 13 (6) ◽  
pp. 3314
Author(s):  
Rawan Shabbar ◽  
Anemone Kasasbeh ◽  
Mohamed M. Ahmed
Keyword(s):  
Markov Chain ◽  
Electric Vehicles ◽  
Process Model ◽  
Transportation Network ◽  
Optimal Placement ◽  
Death Process ◽  
Chain Model ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Net Profit

Optimal placement of Charging stations (CSs) and infrastructure planning are one of the most critical challenges that face the Electric Vehicles (EV) industry nowadays. A variety of approaches have been proposed to address the problem of demand uncertainty versus the optimal number of CSs required to build the EV infrastructure. In this paper, a Markov-chain network model is designed to study the estimated demand on a CS by using the birth and death process model. An investigation on the desired number of electric sockets in each CS and the average number of electric vehicles in both queue and waiting times is presented. Furthermore, a CS allocation algorithm based on the Markov-chain model is proposed. Grey Wolf Optimization (GWO) algorithm is used to select the best CS locations with the objective of maximizing the net profit under both budget and routing constraints. Additionally, the model was applied to Washington D.C. transportation network. Experimental results have shown that to achieve the highest net profit, Level 2 chargers need to be installed in low demand areas of infrastructure implementation. On the other hand, Level 3 chargers attain higher net profit when the number of EVs increases in the transportation network or/and in locations with high charging demands.

Algorithm to prioritize the restoration of a multiple facility, multiple-hazard road network

2017 ◽  
Vol 231 (3) ◽  
pp. 221-231 ◽  
Author(s):  
Veeresh Varad Basavaraj ◽  
Venkateswaran Shekar ◽  
Lance Fiondella ◽  
Ashrafur Rahman ◽  
Nicholas Lownes
Keyword(s):  
Emergency Response ◽  
Road Network ◽  
Emergency Services ◽  
Response Times ◽  
Transportation Network ◽  
Assessment Process ◽  
Critical Infrastructures ◽  
Risk Assessment Process ◽  
Network Restoration ◽  
Different Types

Transportation networks are one of several critical infrastructures on which first responders rely in order to deliver emergency services. However, there is no guarantee that a transportation network will be fully operational following a regional event, such as a hurricane or earthquake. Emergency planning and response tools should explicitly integrate this possibility to ensure the completeness of the risk assessment process. This paper considers the elevated vulnerability to which a community is exposed when disruptions in a transportation network slow emergency response. An average weighted vulnerability metric is defined to favor a network restoration strategy that quickly reduces emergency response times to their nominal levels. This metric is incorporated into an algorithm to prioritize network restoration, so that individuals who might require assistance are not exposed to prolonged periods of lengthy response times. The formulation considers a number of types of emergency response facilities and different types of hazard; the hazards can require a response from more than one type of facility. The utility of the approach is illustrated through a series of examples. Our results demonstrate that the approach can offer systematic guidance to reduce emergency response times after a partial or complete outage of a transportation network.

scholarly journals Hazmats Transportation Network Design Model with Emergency Response under Complex Fuzzy Environment

2013 ◽  
Vol 2013 ◽  
pp. 1-22 ◽  
Author(s):  
Jiuping Xu ◽  
Jun Gang ◽  
Xiao Lei
Keyword(s):  
Network Design ◽  
Emergency Response ◽  
Transportation Network ◽  
Fuzzy Variable ◽  
Optimal Solution ◽  
Transportation Cost ◽  
Bilevel Optimization ◽  
Practical Case ◽  
Transportation Risk ◽  
Bee Colony

A bilevel optimization model for a hazardous materials transportation network design is presented which considers an emergency response teams location problem. On the upper level, the authority designs the transportation network to minimize total transportation risk. On the lower level, the carriers first choose their routes so that the total transportation cost is minimized. Then, the emergency response department locates their emergency service units so as to maximize the total weighted arc length covered. In contrast to prior studies, the uncertainty associated with transportation risk has been explicitly considered in the objective function of our mathematical model. Specifically, our research uses a complex fuzzy variable to model transportation risk. An improved artificial bee colony algorithm with priority-based encoding is also applied to search for the optimal solution to the bilevel model. Finally, the efficiency of the proposed model and algorithm is evaluated using a practical case and various computing attributes.

Transportation network reliability in emergency response

2015 ◽  
Vol 80 ◽  
pp. 56-73 ◽  
Author(s):  
Ali Edrissi ◽  
Mehdi Nourinejad ◽  
Matthew J. Roorda
Keyword(s):  
Emergency Response ◽  
Network Reliability ◽  
Transportation Network

Near Real-Time Flood Impact Analysis on Road Networks

2021 ◽  
Author(s):  
Martijn Kwant ◽  
Frederique de Groen ◽  
Margreet van Marle ◽  
Arjen Haag ◽  
Herman Haaksma
Keyword(s):  
Real Time ◽  
Emergency Response ◽  
Access To Health Care ◽  
Large Scale ◽  
Impact Analysis ◽  
Transportation Network ◽  
Early Warning Systems ◽  
Flood Hazards ◽  
Cascading Effects ◽  
The Impact

<p>Traditional flood risk studies often focus on direct economic impact, such as property damage or agricultural loss. However, the impact of floods is not limited to these direct damages. In fact societal costs and/or cascading effects are often much higher than the direct impact of floods. Cascading effects, such as access to healthcare and infrastructure accessibility are vital components for efficient emergency response management. This requires methodologies to quickly analyze the impact of large-scale floods on infrastructure networks.</p><p> </p><p>In this case study, the use of satellite-based flood maps are examined in combination with network criticality in the Mandalay region in central Myanmar. This region was severely affected by flooding after heavy monsoon rains in 2019. Many regions in the world are affected by this type of floods every year, resulting in large scale evacuations and limited access to health care. During these type of events, the transportation network is a crucial part for emergency response, as it is used for the delivery of goods, evacuation and deployment of emergency hospitals.</p><p> </p><p>The core of this study is a methodology to assess near real-time flood extents based on Sentinel-1 satellite imagery and the impact on network criticality. These tools were used to analyze the redundancy of the infrastructure network and quantify cascading impacts of flood hazards such as road accessibility and access to medical services. The methodology shows potential for operational use by linking with flood early warning systems (e.g. Delft-FEWS) enabling impact-based forecasting.</p>

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