Hybrid Traffic Data Collection Roadmap: Objectives and Methods

Alexandre Bayen, Mohammad Ashkan Sharafsaleh and Anthony D. Patire

Institute of Transportation Studies, Research Reports, Working Papers, Proceedings from Institute of Transportation Studies, UC Berkeley

Abstract: Traffic data is used to estimate current traffic conditions so that travelers and agencies can make better decisions about how to use and manage the transportation network. This research explores the fusion of probe data (vehicle speed and direction) with loop data (density, speed, and count) in the context of producing overall network speed and travel time estimates. Speed and travel time estimates are useful in many circumstances, but current system control strategies (ramp metering, for example) require density data. While it is difficult to significantly increase the quantity of loop detectors on state highways, the penetration rate of probe data is continually increasing. Multiple data sources with various characteristics were fused by running probe and loop data through the Mobile Millennium highway model, generating velocity maps and travel times. The performance of data sources both individually and when fused was evaluated. It was found that the highest quality estimates are achieved by combining probe data and loop detector data.

Keywords: Engineering; Automatic data collection; Algorithms; Cost effectiveness; Data fusion; Data quality; Detection; Traffic speed; Traffic density; Traffic volume (search for similar items in EconPapers)
Date: 2013-10-01
New Economics Papers: this item is included in nep-tre and nep-ure
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Citations: View citations in EconPapers (3)

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