Link Density Inference from Cellular Infrastructure
This work explores the problem of estimating road link densities from cellular tower signals by mobile subscribers in urban areas. The authors pose the estimation problem as a quadratic program, and present a robust framework that produces vehicle density estimates and is suitable for large-scale problems. The authors demonstrate that both simple and sophisticated models of cellular network connections can be handled robustly by the framework, without sacrificing efficiency or scalability. The authors present a numerical experiment on the I-15 corridor in San Diego based on a calibrated Aimsun microsimulation and a simulated cell network, demonstrating the framework can practically be implemented as part of an integrated corridor management system. The numerical results demonstrate that when the cell phone connection model is chosen appropriately, the estimates are consistent with those observed in a microsimulation.
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Supplemental Notes:
- This paper was sponsored by TRB committee ABJ50 Information Systems and Technology.
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Corporate Authors:
500 Fifth Street, NW
Washington, DC United States 20001 -
Authors:
- Yadlowsky, Steve
- Thai, Jérôme
- Wu, Cathy
- Pozdnukov, Alexey
- Bayen, Alexandre
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Conference:
- Transportation Research Board 94th Annual Meeting
- Location: Washington DC, United States
- Date: 2015-1-11 to 2015-1-15
- Date: 2015
Language
- English
Media Info
- Media Type: Digital/other
- Features: Figures; References;
- Pagination: 17p
- Monograph Title: TRB 94th Annual Meeting Compendium of Papers
Subject/Index Terms
- TRT Terms: Cellular telephones; Methodology; Microsimulation; Network links; Optimization; Traffic estimation
- Identifier Terms: AIMSUN (Computer model)
- Geographic Terms: San Diego (California)
- Subject Areas: Data and Information Technology; Highways; I72: Traffic and Transport Planning;
Filing Info
- Accession Number: 01555261
- Record Type: Publication
- Report/Paper Numbers: 15-5761
- Files: TRIS, TRB, ATRI
- Created Date: Feb 26 2015 10:05AM