Evacuating Orange County, California, (Part 2) — The Approximate Versus the Exact

James Riechel

doi:doi:10.11648/j.ijsts.20221001.11

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Evacuating Orange County, California, (Part 2) — The Approximate Versus the Exact

James Riechel

Published in International Journal of Science, Technology and Society (Volume 10, Issue 1)

Received: 18 October 2021 Accepted: 10 January 2022 Published: 15 January 2022

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Abstract

This paper completes the science on the first paper also on the Orange County, California, evacuation problem. Here, the execution time of the exact solution is correctly reported—it is not 53+ days as originally reported, but is still over three (3) hours, 900+ times slower than the approximate solution. Comparing the Load Balancing Metric of both the approximate and exact solutions, it is clear that both produce similar results, supporting the use of the approximate solution as it takes mere seconds to complete. The Orange County, California, dataset contains 1.1 to 1.2 million addresses, both residential and business. On a map, a random 100 routes in Orange County are shown, connecting addresses (incidents) to the closest of four (4) waypoints (facilities) with respect to drive time without consideration of traffic conditions. In the Appendix, a Python toolkit for ArcGIS Pro is given that computes the approximate solution. This did not appear in the first paper.

Published in	International Journal of Science, Technology and Society (Volume 10, Issue 1)
DOI	10.11648/j.ijsts.20221001.11
Page(s)	1-7
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Vehicle Evacuation Planning By Waypoints, Nuclear Threats, Approximate Versus Exact, Load Balancing, Drive-Time Network, Traffic Conditions

References

[1]	Alabdouli, K. (2017). Hypothetical Tsunami Scenarios and Evacuation Intention: A Case Study of Orange County, California. Journal of Geography & Natural Disasters, 7 (3). https://doi.org/10.4172/2167-0587.1000212
[2]	Hargitai, H., Willner, K., & Hare, T. (2019). Fundamental Frameworks in Planetary Mapping: A Review. In H. Hargitai (Ed.), Planetary Cartography and GIS (pp. 75–101). Springer International Publishing. https://doi.org/10.1007/978-3-319-62849-3_4
[3]	Li, S., Dragicevic, S., Castro, F. A., Sester, M., Winter, S., Coltekin, A., Pettit, C., Jiang, B., Haworth, J., Stein, A., & Cheng, T. (2016). Geospatial big data handling theory and methods: A review and research challenges. ISPRS Journal of Photogrammetry and Remote Sensing, 115, 119–133. https://doi.org/10.1016/j.isprsjprs.2015.10.012
[4]	Ory, C., Leboulleux, S., Salvatore, D., Le Guen, B., De Vathaire, F., Chevillard, S., & Schlumberger, M. (2021). Consequences of atmospheric contamination by radioiodine: The Chernobyl and Fukushima accidents. Endocrine, 71 (2), 298–309. https://doi.org/10.1007/s12020-020-02498-9
[5]	Riechel, J. (n.d.-a). Extending Manhattan Euclidean and Actual Driving Distances into 3D.pptx. Retrieved August 27, 2021, from https://drive.google.com/file/d/16rkw9Ysn8BWfT7CpfvlnlaSwUgBw4Y4v/view?usp=sharing&usp=embed_facebook
[6]	Riechel, J. (n.d.-b). Extremely Fast “Solution” to the Large-Scale and Very Large-Scale Vehicle Routing Problem. Google Docs. Retrieved August 27, 2021, from https://drive.google.com/file/d/12BWEcFeLOQZ0_nFAV6qtMrs9fKFoC_S4/view?usp=sharing
[7]	Riechel, J. (2020). Comparing Manhattan, Euclidean, and Actual Driving Distances.pptx. CalGIS 2020, Long Beach, CA. https://drive.google.com/file/d/1_wgjePJH6LXM6OAYHg-PJkr2o-wVr8AK/view?usp=sharing&usp=embed_facebook
[8]	Riechel, J. (2021). EVACUATING ORANGE COUNTY, CALIFORNIA, IN ABOUT ELEVEN (11) SECONDS. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIV-M-3–2021, 143–147. https://doi.org/10.5194/isprs-archives-XLIV-M-3-2021-143-2021
[9]	Riechel, J. A. (2019). A fast algorithm for computing approximate distances in the Cartesian plane. URISA GIS-Pro 2019, New Orleans, LA. https://urisa.library.esri.com/cgi-bin/koha/opac-detail.pl?biblionumber=183148&query_desc=kw%2Cwrdl%3A%20riechel
[10]	Singh, A., Yadav, A., & Rana, A. (2013). K-means with Three different Distance Metrics. International Journal of Computer Applications, 67 (10), 13–17. https://doi.org/10.5120/11430-6785
[11]	Thompson, R. R., Garfin, D. R., & Silver, R. C. (2017). Evacuation from Natural Disasters: A Systematic Review of the Literature. Risk Analysis, 37 (4), 812–839. https://doi.org/10.1111/risa.12654
[12]	Yanovskiy, M., Levi, O. N., Shaki, Y. Y., & Socol, Y. (2020). Consequences of a large-scale nuclear accident and guidelines for evacuation: A cost-effectiveness analysis. International Journal of Radiation Biology, 96 (11), 1382–1389. https://doi.org/10.1080/09553002.2020.1779962
[13]	Yoshimura, K., Saegusa, J., & Sanada, Y. (2020). Initial decrease in the ambient dose equivalent rate after the Fukushima accident and its difference from Chernobyl. Scientific Reports, 10 (1), 3859. https://doi.org/10.1038/s41598-020-60847-0
[14]	Zeager, J., & Stitz, C. (2016). College Algebra. http://dspace.calstate.edu/handle/10211.3/180387

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APA Style

James Riechel. (2022). Evacuating Orange County, California, (Part 2) — The Approximate Versus the Exact. International Journal of Science, Technology and Society, 10(1), 1-7. https://doi.org/10.11648/j.ijsts.20221001.11

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ACS Style

James Riechel. Evacuating Orange County, California, (Part 2) — The Approximate Versus the Exact. Int. J. Sci. Technol. Soc. 2022, 10(1), 1-7. doi: 10.11648/j.ijsts.20221001.11

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AMA Style

James Riechel. Evacuating Orange County, California, (Part 2) — The Approximate Versus the Exact. Int J Sci Technol Soc. 2022;10(1):1-7. doi: 10.11648/j.ijsts.20221001.11

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@article{10.11648/j.ijsts.20221001.11,
  author = {James Riechel},
  title = {Evacuating Orange County, California, (Part 2) — The Approximate Versus the Exact},
  journal = {International Journal of Science, Technology and Society},
  volume = {10},
  number = {1},
  pages = {1-7},
  doi = {10.11648/j.ijsts.20221001.11},
  url = {https://doi.org/10.11648/j.ijsts.20221001.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsts.20221001.11},
  abstract = {This paper completes the science on the first paper also on the Orange County, California, evacuation problem. Here, the execution time of the exact solution is correctly reported—it is not 53+ days as originally reported, but is still over three (3) hours, 900+ times slower than the approximate solution. Comparing the Load Balancing Metric of both the approximate and exact solutions, it is clear that both produce similar results, supporting the use of the approximate solution as it takes mere seconds to complete. The Orange County, California, dataset contains 1.1 to 1.2 million addresses, both residential and business. On a map, a random 100 routes in Orange County are shown, connecting addresses (incidents) to the closest of four (4) waypoints (facilities) with respect to drive time without consideration of traffic conditions. In the Appendix, a Python toolkit for ArcGIS Pro is given that computes the approximate solution. This did not appear in the first paper.},
 year = {2022}
}

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TY  - JOUR
T1  - Evacuating Orange County, California, (Part 2) — The Approximate Versus the Exact
AU  - James Riechel
Y1  - 2022/01/15
PY  - 2022
N1  - https://doi.org/10.11648/j.ijsts.20221001.11
DO  - 10.11648/j.ijsts.20221001.11
T2  - International Journal of Science, Technology and Society
JF  - International Journal of Science, Technology and Society
JO  - International Journal of Science, Technology and Society
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PB  - Science Publishing Group
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UR  - https://doi.org/10.11648/j.ijsts.20221001.11
AB  - This paper completes the science on the first paper also on the Orange County, California, evacuation problem. Here, the execution time of the exact solution is correctly reported—it is not 53+ days as originally reported, but is still over three (3) hours, 900+ times slower than the approximate solution. Comparing the Load Balancing Metric of both the approximate and exact solutions, it is clear that both produce similar results, supporting the use of the approximate solution as it takes mere seconds to complete. The Orange County, California, dataset contains 1.1 to 1.2 million addresses, both residential and business. On a map, a random 100 routes in Orange County are shown, connecting addresses (incidents) to the closest of four (4) waypoints (facilities) with respect to drive time without consideration of traffic conditions. In the Appendix, a Python toolkit for ArcGIS Pro is given that computes the approximate solution. This did not appear in the first paper.
VL  - 10
IS  - 1
ER  -

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Author Information

James Riechel

Center for Information Systems & Technology (CISAT), Claremont Graduate University (CGU), Claremont, USA

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APA Style

James Riechel. (2022). Evacuating Orange County, California, (Part 2) — The Approximate Versus the Exact. International Journal of Science, Technology and Society, 10(1), 1-7. https://doi.org/10.11648/j.ijsts.20221001.11

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ACS Style

James Riechel. Evacuating Orange County, California, (Part 2) — The Approximate Versus the Exact. Int. J. Sci. Technol. Soc. 2022, 10(1), 1-7. doi: 10.11648/j.ijsts.20221001.11

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AMA Style

James Riechel. Evacuating Orange County, California, (Part 2) — The Approximate Versus the Exact. Int J Sci Technol Soc. 2022;10(1):1-7. doi: 10.11648/j.ijsts.20221001.11

Copy | Download

@article{10.11648/j.ijsts.20221001.11,
  author = {James Riechel},
  title = {Evacuating Orange County, California, (Part 2) — The Approximate Versus the Exact},
  journal = {International Journal of Science, Technology and Society},
  volume = {10},
  number = {1},
  pages = {1-7},
  doi = {10.11648/j.ijsts.20221001.11},
  url = {https://doi.org/10.11648/j.ijsts.20221001.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsts.20221001.11},
  abstract = {This paper completes the science on the first paper also on the Orange County, California, evacuation problem. Here, the execution time of the exact solution is correctly reported—it is not 53+ days as originally reported, but is still over three (3) hours, 900+ times slower than the approximate solution. Comparing the Load Balancing Metric of both the approximate and exact solutions, it is clear that both produce similar results, supporting the use of the approximate solution as it takes mere seconds to complete. The Orange County, California, dataset contains 1.1 to 1.2 million addresses, both residential and business. On a map, a random 100 routes in Orange County are shown, connecting addresses (incidents) to the closest of four (4) waypoints (facilities) with respect to drive time without consideration of traffic conditions. In the Appendix, a Python toolkit for ArcGIS Pro is given that computes the approximate solution. This did not appear in the first paper.},
 year = {2022}
}

Copy | Download

TY  - JOUR
T1  - Evacuating Orange County, California, (Part 2) — The Approximate Versus the Exact
AU  - James Riechel
Y1  - 2022/01/15
PY  - 2022
N1  - https://doi.org/10.11648/j.ijsts.20221001.11
DO  - 10.11648/j.ijsts.20221001.11
T2  - International Journal of Science, Technology and Society
JF  - International Journal of Science, Technology and Society
JO  - International Journal of Science, Technology and Society
SP  - 1
EP  - 7
PB  - Science Publishing Group
SN  - 2330-7420
UR  - https://doi.org/10.11648/j.ijsts.20221001.11
AB  - This paper completes the science on the first paper also on the Orange County, California, evacuation problem. Here, the execution time of the exact solution is correctly reported—it is not 53+ days as originally reported, but is still over three (3) hours, 900+ times slower than the approximate solution. Comparing the Load Balancing Metric of both the approximate and exact solutions, it is clear that both produce similar results, supporting the use of the approximate solution as it takes mere seconds to complete. The Orange County, California, dataset contains 1.1 to 1.2 million addresses, both residential and business. On a map, a random 100 routes in Orange County are shown, connecting addresses (incidents) to the closest of four (4) waypoints (facilities) with respect to drive time without consideration of traffic conditions. In the Appendix, a Python toolkit for ArcGIS Pro is given that computes the approximate solution. This did not appear in the first paper.
VL  - 10
IS  - 1
ER  -

Copy | Download