Traveling officer problem: managing car parking violations efficiently using sensor data

Wei Shao*, Flora D. Salim, Tao Gu, Ngoc-Thanh Dinh, Jeffrey Chan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)

Abstract

The on-street parking system is an indispensable part of civic projects, as it provides travelers and shoppers with parking spaces. With the recent in-ground sensors deployed throughout the Melbourne central business district (CBD), there is a significant problem on how to use the sensor data to manage parking violations and issue infringement notices efficiently in a short time-window. In this paper, we use a large realworld dataset with on-street parking sensor data from the local city council, and establish a formulation of the traveling officer problem with a general probability-based model. We propose two solutions using a spatio-temporal probability model for parking officers to maximize the number of infringing cars caught with limited time cost. Using real-world parking sensor data and Google Maps road network information, the experimental results show that our proposed algorithms outperform the existing patrolling routes.
Original languageEnglish
Pages (from-to)802-810
Number of pages9
JournalIEEE Internet of Things Journal
Volume5
Issue number2
DOIs
Publication statusPublished - Apr 2018
Externally publishedYes

Keywords

  • Intelligent Transportation Systems (ITSs)
  • parking sensor data
  • parking violation management
  • smart cities

Fingerprint Dive into the research topics of 'Traveling officer problem: managing car parking violations efficiently using sensor data'. Together they form a unique fingerprint.

Cite this