ACP-Based Modeling of the Parallel Vehicular Crowd Sensing System: Framework, Components and an Application Example

As an emerging paradigm for urban sensing, vehicular crowd sensing (VCS) has received increasing attention in recent years. Unlike traditional sensing paradigms, VCS leverages ubiquitous connected vehicles (CVs) and diverse onboard sensors to efficiently collect city-scale data. Despite the considerable benefits of CVs, the fast-changing traffic environment and attendant human and social factors bring significant complexity to the VCS system and make it a typical cyber-physical-social system (CPSS), followed by the challenge of robust and efficient modeling of VCS systems. To cope with the complexity of social dimensions and optimize the decision-making process in the physical VCS, this article introduces the artificial societies, computational experiments, and the parallel execution (ACP) approach to the VCS system and develops a novel framework called parallel VCS (P-VCS). Three key components empower P-VCS to balance the physical environment, cyber networks, and human and social factors, namely, an artificial system that is used to parametrically describe the physical VCS, two types of computational experiments that simulate the decision process and evaluate different strategies, and the parallel execution mechanism that is used to characterize the system operation. To demonstrate the feasibility of the framework, the authors take participant selection under traffic events as an application example. Experimental results illustrate that the P-VCS-based parallel learning strategy maintains competitive performance in all cases.

• Record URL:
  http://dx.doi.org/10.1109/TIV.2022.3221927
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/23798858
• Supplemental Notes:
  • Copyright © 2023, IEEE.
• Authors:
  • Ren, Yilong
  • Jiang, Han
  • Feng, Xiaoyuan
  • Zhao, Yanan
  • Liu, Runkun
  • Yu, Haiyang
• Publication Date: 2023-2

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 1536-1548
• Serial:
  • IEEE Transactions on Intelligent Vehicles
  • Volume: 8
  • Issue Number: 2
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 2379-8858
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7274857

Subject/Index Terms

• TRT Terms: Crowdsourcing; Data collection; In vehicle sensors; Machine learning; Mobile computing; Remote sensing; Traffic data
• Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01884268
• Record Type: Publication
• Files: TRIS
• Created Date: May 31 2023 10:58AM