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An empirical analysis of the spatial variability of fuel prices in the United States
- Source :
- Transportation Research Part A: Policy and Practice, Transportation Research Part A: Policy and Practice, Elsevier, 2020, 132, pp.131-143. ⟨10.1016/j.tra.2019.10.016⟩
- Publication Year :
- 2020
- Publisher :
- HAL CCSD, 2020.
-
Abstract
- In this paper, we use a newly constructed dataset to study the geographic distribution of fuel price across the US at a very high resolution. We study the influence of socio-economic variables through different and complementary statistical methods. We highlight an optimal spatial range roughly corresponding to stationarity scale, and significant influence of variables such as median income, wage with a non-simple spatial behavior that confirms the importance of geographical particularities. On the other hand, multi-level modeling reveals a strong influence of the state in the level of price but also of some local characteristics including population density. Through the combination of such methods, we unveil the superposition of a governance process with a local socio-economical spatial process. The influence of population density on prices is furthermore consistent with a minimal theoretical model of competition between gas stations, that we introduce and solve numerically. We discuss developments and applications, including the elaboration of locally parametrized car-regulation policies.<br />17 pages; 7 figures; 5 tables
- Subjects :
- FOS: Computer and information sciences
Physics - Physics and Society
media_common.quotation_subject
0211 other engineering and technologies
Wage
FOS: Physical sciences
Transportation
Physics and Society (physics.soc-ph)
02 engineering and technology
PARIS team
Management Science and Operations Research
[INFO.INFO-DM]Computer Science [cs]/Discrete Mathematics [cs.DM]
Statistics - Applications
[QFIN.CP]Quantitative Finance [q-fin]/Computational Finance [q-fin.CP]
Competition (economics)
Superposition principle
[STAT.ML]Statistics [stat]/Machine Learning [stat.ML]
0502 economics and business
Econometrics
Range (statistics)
Applications (stat.AP)
[NLIN.NLIN-CG]Nonlinear Sciences [physics]/Cellular Automata and Lattice Gases [nlin.CG]
021108 energy
[NLIN.NLIN-AO]Nonlinear Sciences [physics]/Adaptation and Self-Organizing Systems [nlin.AO]
Civil and Structural Engineering
media_common
Mathematics
050210 logistics & transportation
Median income
ACL
05 social sciences
Multilevel model
[SHS.GEO]Humanities and Social Sciences/Geography
[INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA]
[NLIN.NLIN-CD]Nonlinear Sciences [physics]/Chaotic Dynamics [nlin.CD]
Spatial variability
[SDE.BE]Environmental Sciences/Biodiversity and Ecology
Scale (map)
Subjects
Details
- Language :
- English
- ISSN :
- 09658564
- Database :
- OpenAIRE
- Journal :
- Transportation Research Part A: Policy and Practice, Transportation Research Part A: Policy and Practice, Elsevier, 2020, 132, pp.131-143. ⟨10.1016/j.tra.2019.10.016⟩
- Accession number :
- edsair.doi.dedup.....df768db444284a6da814c5116167f212
- Full Text :
- https://doi.org/10.1016/j.tra.2019.10.016⟩