PAS: Prediction-Based Actuation System for City-Scale Ridesharing Vehicular Mobile Crowdsensing

Xinlei Chen, Susu Xu, Jun Han, Haohao Fu, Xidong Pi, Carlee Joe-Wong, Yong Li, Lin Zhang, Hae Young Noh, Pei Zhang

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

Vehicular mobile crowdsensing (MCS) enables many smart city applications. Ridesharing vehicle fleets provide promising solutions to MCS due to the advantages of low cost, easy maintenance, high mobility, and long operational time. However, as nondedicated mobile sensing platforms, the first priorities of these vehicles are delivering passengers, which may lead to poor sensing coverage quality. Therefore, to help MCS derive good (large and balanced) sensing coverage quality, an actuation system is required to dispatch vehicles with a limited amount of monetary budget. This article presents PAS, a prediction-based actuation system for city-wide ridesharing vehicular MCS to achieve optimal sensing coverage quality with a limited budget. In PAS, two prediction models forecast probabilities of potential near-future vehicle routes and ride requests across the city. Based on prediction results, a prediction-based actuation planning algorithm is proposed to decide which vehicles to actuate and the corresponding routes. Experiments on city-scale deployments and physical feature-based simulations show that our PAS achieves up to 40% more improvement in sensing coverage quality and up to 20% higher ride request matching rate than baselines. In addition, to achieve a similar level of sensing coverage quality as the baseline, our PAS only needs 10% budget.

Original languageEnglish
Article number8964368
Pages (from-to)3719-3734
Number of pages16
JournalIEEE Internet of Things Journal
Volume7
Issue number5
DOIs
Publication statusPublished - 2020 May

Bibliographical note

Funding Information:
Manuscript received July 15, 2019; revised November 4, 2019; accepted January 9, 2020. Date of publication January 21, 2020; date of current version May 12, 2020. This work was supported in part by NSF under Grant CNS1149611, in part by DARPA under Grant D11AP00265, in part by Google, and in part by CMKL. (Xinlei Chen and Susu Xu contributed equally to this work.) (Corresponding author: Xinlei Chen.) Xinlei Chen, Carlee Joe-Wong, and Pei Zhang are with the Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213 USA (e-mail: xinlei.chen@sv.cmu.edu). Susu Xu and Xidong Pi are with the Department of Civil Engineering, Carnegie Mellon University, Pittsburgh, PA 15213 USA. Jun Han is with the Department of Computer Science, National University of Singapore, Singapore. Haohao Fu is with the Department of L&S, University of California at Berkeley, Berkeley, CA 94720 USA. Yong Li is with the Department of Electronic Engineering, Tsinghua University, Beijing 100084, China. Lin Zhang is with Tsinghua–Berkeley Shenzhen Institute, Berkeley, CA, USA. Hae Young Noh is with the Department of Civil Engineering, Stanford University, Stanford, CA 94305 USA. Digital Object Identifier 10.1109/JIOT.2020.2968375

Publisher Copyright:
© 2014 IEEE.

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications

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