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SUMMARY:EXIMIUS: A Measurement Framework for Explicit and Implicit Urban Traffic Sensing LOCATION:CoRE B (305) DESCRIPTION;ENCODING=QUOTED-PRINTABLE:
Abstract:
Urban traffic sensing has been investig ated extensively by different real-time sensing approaches due to important applications such as navigation and emergency services. Basically, the exi sting traffic sensing approaches can be classified into two categories, i.e ., explicit and implicit sensing. In this paper, we design a measurement fr amework called EXIMIUS for a large-scale data-driven study to investigate t he strengths and weaknesses of these two sensing approaches by using two pa rticular systems for traffic sensing as concrete examples, i.e., a vehicula r system as a crowdsourcing-based explicit sensing and a cellular system as an infrastructure-based implicit sensing. In our investigation, we utilize TB-level data from two systems: (i) vehicle GPS data from 3 thousand priva te cars and 2 thousand commercial vehicles, (ii) cellular signaling data fr om 3 million cellphone users, from the Chinese city Hefei. Our study adopts a widely-used concept called crowdedness level to rigorously explore the i mpacts of various spatiotemporal contexts on real-time traffic conditions i ncluding population density, region functions, road categories, rush hours, etc. based on a wide range of context data. We quantify the strengths and weaknesses of these two sensing approaches in different scenarios, then we explore the possibility of unifying these two sensing approaches for better performance. Our results provide a few valuable insights for urban sensing based on explicit and implicit data from transportation and telecommunicat ion domains.
DTSTAMP:20240328T131611Z DTSTART;TZID=America/New_York:20190430T100000 SEQUENCE:0 TRANSP:OPAQUE END:VEVENT END:VCALENDAR