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dc.contributor.authorMcCormack, Ed
dc.contributor.authorWang, Yinhai
dc.contributor.authorAraghi, Bahar Namaki
dc.contributor.authorMalinovskiy, Yegor
dc.contributor.authorCorey, Jonathan
dc.contributor.authorCheng, Tianxing
dc.date.accessioned2019-04-09T16:34:24Z
dc.date.available2019-04-09T16:34:24Z
dc.date.issued2014
dc.identifier.urihttp://hdl.handle.net/1773/43545
dc.description.abstractProviding accurate and reliable travel time information to roadway users is a critical part of Advanced Traffic Management Systems (ATMS) and Advanced Travelers Information Systems (ATIS). Access to travel time information can significantly influence the decision making on both the supply side (i.e. efficient management of network capacity, saving travel time, reducing congestion etc.) and the demand side (i.e. mode choice, route choice etc.) of transportation. In this context, the need for accurate and reliable travel time information sources is becoming increasingly apparent. Identifying the sensors best suited to providing travel time data for a given corridor is an important step in the process of providing travel time data. Currently, there are very few studies available that evaluate the effectiveness of various travel time data collection technologies side-by-side, thus it is often unclear which approach should be used for a given application. Therefore, a comprehensive overview of existing technologies as well as a side-by-side evaluation will provide more insight into selecting the appropriate technology for a given application. This evaluation is intended to provide decision support for transportation agencies selecting travel time systems based on the accuracy, reliability and cost of each system. Ultimately, each system in the analysis has different strengths and weaknesses that should be considered in addition to their accuracy and sample rates. Some systems can provide additional data; others trade accuracy and coverage for cost or portability. Ultimately, engineers will need to weigh their requirements for accuracy and sample rates against the other engineering constraints imposed on their system. For example, the BlueTOAD units installed on SR 522 and I-90 are solar powered and use cellular data networks, reducing infrastructure and deployment costs. The BlipTrack units have higher sampling rates and marginal accuracy superiority in exchange for power requirements. The Inrix data does not require any DOT infrastructure and has wide availability. ALPR units have high accuracy and a comparatively high installation cost. The Sensys system has perhaps the most complicated set of tradeoffs. Sensys magnetometers can be used as replacements for loop detectors in intersection operations, making the marginal costs of adding Sensys re-identification lower at some intersections than others.en_US
dc.description.sponsorshipPacific Northwest Transportation Consortiumen_US
dc.language.isoen_USen_US
dc.subjectTransportation Safetyen_US
dc.subjectTransportation Dataen_US
dc.subjectTravel Time Estimationen_US
dc.subjectError Matrixen_US
dc.subjectReliabilityen_US
dc.subjectTraffic Volumeen_US
dc.subjectTraffic Speeden_US
dc.titleError Assessment for Emerging Traffic Data Collection Devicesen_US
dc.typeTechnical Reporten_US


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