EXIMIUS: A measurement framework for explicit and implicit urban traffic sensing

Zhou Qin, Zhihan Fang, Yunhuai Liu, Chang Tan, Wei Chang, Desheng Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Scopus citations

Abstract

Urban traffic sensing has been investigated extensively by different real-time sensing approaches due to important applications such as navigation and emergency services. Basically, the existing traffic sensing approaches can be classified into two categories, i.e., explicit and implicit sensing. In this paper, we design a measurement framework called EXIMIUS for a large-scale data-driven study to investigate the strengths and weaknesses of these two sensing approaches by using two particular systems for traffic sensing as concrete examples, i.e., a vehicular 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 private cars and 2 thousand commercial vehicles, (ii) cellular signaling data from 3 million cellphone users, from the Chi¬ nese city Hefei. Our study adopts a widely-used concept called crowdedness level to rigorously explore the impacts of various spatiotemporal contexts on real-time traffic con¬ ditions including 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 in¬ sights for urban sensing based on explicit and implicit data from transportation and telecommunication domains.

Original languageEnglish (US)
Title of host publicationSenSys 2018 - Proceedings of the 16th Conference on Embedded Networked Sensor Systems
PublisherAssociation for Computing Machinery, Inc
Pages1-14
Number of pages14
ISBN (Electronic)9781450359528
DOIs
StatePublished - Nov 4 2018
Event16th ACM Conference on Embedded Networked Sensor Systems, SENSYS 2018 - Shenzhen, China
Duration: Nov 4 2018Nov 7 2018

Publication series

NameSenSys 2018 - Proceedings of the 16th Conference on Embedded Networked Sensor Systems

Conference

Conference16th ACM Conference on Embedded Networked Sensor Systems, SENSYS 2018
CountryChina
CityShenzhen
Period11/4/1811/7/18

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Electrical and Electronic Engineering
  • Computer Networks and Communications

Keywords

  • Measurement
  • Network
  • Telecommunication
  • Transportation

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