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An approach for safer navigation under severe hurricane damage

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Abstract

The US are increasingly prone to sustaining severe damage from climate change as hurricanes are predicted to become more severe. During and after hurricanes, navigation around flooded areas is a primary objective for both first responders and hurricane victims. Printed or cached maps, because of their static nature, are not ideal for conveying network availability that will be constantly in flux post-disaster. And online services may not be available or may provide unreliable information. We propose a relatively cost-effective decentralized sensing and navigation system for regular grid networks that allows effective and efficient navigation around flooded areas. Using the proposed algorithm, about 59% of the cars with reachable destination were successful in reaching their destinations for the worst case scenario, that is, a dynamically spreading flood. On average, the path length taken by successful cars is 33% longer than the shortest path without flooding. The results of the experiments show that the proposed algorithm has a low computational complexity which makes it a good fit for real-time safe path finding in regular grid networks and it has the potential for extension to other types of road networks.

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Notes

  1. Using network analysis protocols like in ArcGIS means having an extra application on users phone which a) is costly and b) prevents our algorithm to be used by a wider range of people. Users need a light weight, simple and to the point app.

  2. In real scenarios, GPS usually has a 5-m one-sigma error in positioning [19], which mainly affects the calculated angles. This is the less an issue the further the car and its destination are from each other. However, we would have a high error in calculating the angles in close distances, which could be mitigated by alerting the user to the inaccuracy and relying on their visual assistance.

  3. The car operating on geographic coordinates simply computes Euclidean distance on the map in our simulation. However, for the simple decision we have to make and given the assumptions we make, we could as well use Manhattan distance.

  4. If sensor nodes have additional sensors, such as accelerometers, we could also detect other hazards, e.g., the lamppost felled by a storm or earthquake. One could also add an interface, e.g., a button or special signal, that allows a first responder to mark the position as REGN, i.e., impassable.

  5. An empty set is represented by the bit-vector 0. A singleton set is represented by a bit-vector v with the properties: \(v\, >\, 0\) and \(v \wedge (v - 1) = 0,\) where \(\wedge \) is here the bit-wise and operator.

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Acknowledgements

We are grateful to the reviewers of this article for helpful comments and suggestions.

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Authors and Affiliations

  1. University of Oregon, Eugene, OR, 97403-1251, USA

    Mohammad Eshghi & Hedda R. Schmidtke

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  1. Mohammad Eshghi
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  2. Hedda R. Schmidtke
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Corresponding author

Correspondence to Hedda R. Schmidtke.

Appendix

Appendix

This section illustrates the results of the experiments for the other three groups of flood type. It should be mentioned that, as shown in Table 4, because the flood happens after creating the cars, there are flooded cars for static flood type as well. Therefore, these cars are not flooded due to the proposed algorithm, and we can say that, in these cases, the performance of the algorithm should be evaluated without counting the number of flooded cars. Hence, as expected, 100% of cars with reachable destination could successfully reach their destinations. In addition, our algorithm cannot identify all the destinations that are on an island. As shown in Table 4, our algorithm could just identify some of the destinations on islands with certainty.

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Eshghi, M., Schmidtke, H.R. An approach for safer navigation under severe hurricane damage. J Reliable Intell Environ 4, 161–185 (2018). https://doi.org/10.1007/s40860-018-0066-1

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