Improving Law Enforcement Daily Deployment Through Machine Learning-Informed Optimization under Uncertainty Article Swipe
Jonathan Chase
,
Duc Thien Nguyen
,
Haiyang Sun
,
Hoong Chuin Lau
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.24963/ijcai.2019/806
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.24963/ijcai.2019/806
Urban law enforcement agencies are under great pressure to respond to emergency incidents effectively while operating within restricted budgets. Minutes saved on emergency response times can save lives and catch criminals, and a responsive police force can deter crime and bring peace of mind to citizens. To efficiently minimize the response times of a law enforcement agency operating in a dense urban environment with limited manpower, we consider in this paper the problem of optimizing the spatial and temporal deployment of law enforcement agents to predefined patrol regions in a real-world scenario informed by machine learning. To this end, we develop a mixed integer linear optimization formulation (MIP) to minimize the risk of failing response time targets. Given the stochasticity of the environment in terms of incident numbers, location, timing, and duration, we use Sample Average Approximation (SAA) to find a robust deployment plan. To overcome the sparsity of real data, samples are provided by an incident generator that learns the spatio-temporal distribution and demand parameters of incidents from a real world historical dataset and generates sets of training incidents accordingly. To improve runtime performance across multiple samples, we implement a heuristic based on Iterated Local Search (ILS), as the solution is intended to create deployment plans quickly on a daily basis. Experimental results demonstrate that ILS performs well against the integer model while offering substantial gains in execution time.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.24963/ijcai.2019/806
- https://www.ijcai.org/proceedings/2019/0806.pdf
- OA Status
- gold
- Cited By
- 5
- References
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- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2966010980
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2966010980Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.24963/ijcai.2019/806Digital Object Identifier
- Title
-
Improving Law Enforcement Daily Deployment Through Machine Learning-Informed Optimization under UncertaintyWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2019Year of publication
- Publication date
-
2019-07-28Full publication date if available
- Authors
-
Jonathan Chase, Duc Thien Nguyen, Haiyang Sun, Hoong Chuin LauList of authors in order
- Landing page
-
https://doi.org/10.24963/ijcai.2019/806Publisher landing page
- PDF URL
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https://www.ijcai.org/proceedings/2019/0806.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.ijcai.org/proceedings/2019/0806.pdfDirect OA link when available
- Concepts
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Software deployment, Law enforcement, Computer science, Enforcement, Heuristic, Iterated local search, Operations research, Sample (material), Law, Computer security, Artificial intelligence, Metaheuristic, Engineering, Chemistry, Operating system, Political science, ChromatographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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5Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1, 2022: 1, 2021: 1, 2020: 2Per-year citation counts (last 5 years)
- References (count)
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14Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.basis. | 211 |
| abstract_inverted_index.create | 204 |
| abstract_inverted_index.demand | 164 |
| abstract_inverted_index.learns | 159 |
| abstract_inverted_index.linear | 104 |
| abstract_inverted_index.patrol | 86 |
| abstract_inverted_index.police | 34 |
| abstract_inverted_index.robust | 141 |
| abstract_inverted_index.within | 16 |
| abstract_inverted_index.Average | 135 |
| abstract_inverted_index.Minutes | 19 |
| abstract_inverted_index.against | 219 |
| abstract_inverted_index.dataset | 173 |
| abstract_inverted_index.develop | 100 |
| abstract_inverted_index.failing | 113 |
| abstract_inverted_index.improve | 182 |
| abstract_inverted_index.integer | 103, 221 |
| abstract_inverted_index.limited | 64 |
| abstract_inverted_index.machine | 94 |
| abstract_inverted_index.problem | 72 |
| abstract_inverted_index.quickly | 207 |
| abstract_inverted_index.regions | 87 |
| abstract_inverted_index.respond | 9 |
| abstract_inverted_index.results | 213 |
| abstract_inverted_index.runtime | 183 |
| abstract_inverted_index.samples | 151 |
| abstract_inverted_index.spatial | 76 |
| abstract_inverted_index.timing, | 129 |
| abstract_inverted_index.Iterated | 194 |
| abstract_inverted_index.agencies | 3 |
| abstract_inverted_index.budgets. | 18 |
| abstract_inverted_index.consider | 67 |
| abstract_inverted_index.incident | 126, 156 |
| abstract_inverted_index.informed | 92 |
| abstract_inverted_index.intended | 202 |
| abstract_inverted_index.minimize | 48, 109 |
| abstract_inverted_index.multiple | 186 |
| abstract_inverted_index.numbers, | 127 |
| abstract_inverted_index.offering | 224 |
| abstract_inverted_index.overcome | 145 |
| abstract_inverted_index.performs | 217 |
| abstract_inverted_index.pressure | 7 |
| abstract_inverted_index.provided | 153 |
| abstract_inverted_index.response | 23, 50, 114 |
| abstract_inverted_index.samples, | 187 |
| abstract_inverted_index.scenario | 91 |
| abstract_inverted_index.solution | 200 |
| abstract_inverted_index.sparsity | 147 |
| abstract_inverted_index.targets. | 116 |
| abstract_inverted_index.temporal | 78 |
| abstract_inverted_index.training | 178 |
| abstract_inverted_index.citizens. | 45 |
| abstract_inverted_index.duration, | 131 |
| abstract_inverted_index.emergency | 11, 22 |
| abstract_inverted_index.execution | 228 |
| abstract_inverted_index.generates | 175 |
| abstract_inverted_index.generator | 157 |
| abstract_inverted_index.heuristic | 191 |
| abstract_inverted_index.implement | 189 |
| abstract_inverted_index.incidents | 12, 167, 179 |
| abstract_inverted_index.learning. | 95 |
| abstract_inverted_index.location, | 128 |
| abstract_inverted_index.manpower, | 65 |
| abstract_inverted_index.operating | 15, 57 |
| abstract_inverted_index.criminals, | 30 |
| abstract_inverted_index.deployment | 79, 142, 205 |
| abstract_inverted_index.historical | 172 |
| abstract_inverted_index.optimizing | 74 |
| abstract_inverted_index.parameters | 165 |
| abstract_inverted_index.predefined | 85 |
| abstract_inverted_index.real-world | 90 |
| abstract_inverted_index.responsive | 33 |
| abstract_inverted_index.restricted | 17 |
| abstract_inverted_index.demonstrate | 214 |
| abstract_inverted_index.effectively | 13 |
| abstract_inverted_index.efficiently | 47 |
| abstract_inverted_index.enforcement | 2, 55, 82 |
| abstract_inverted_index.environment | 62, 122 |
| abstract_inverted_index.formulation | 106 |
| abstract_inverted_index.performance | 184 |
| abstract_inverted_index.substantial | 225 |
| abstract_inverted_index.Experimental | 212 |
| abstract_inverted_index.accordingly. | 180 |
| abstract_inverted_index.distribution | 162 |
| abstract_inverted_index.optimization | 105 |
| abstract_inverted_index.Approximation | 136 |
| abstract_inverted_index.stochasticity | 119 |
| abstract_inverted_index.spatio-temporal | 161 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/11 |
| sustainable_development_goals[0].score | 0.5899999737739563 |
| sustainable_development_goals[0].display_name | Sustainable cities and communities |
| citation_normalized_percentile.value | 0.7397252 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |