The on-street parking system is an indispensable part of civil projects, which provides travellers and shoppers with parking spaces. With the recent in-ground sensors deployed throughout the city, there is a significant problem on how to use the sensor data to manage parking violations and issue infringement notices in a short time-window efficiently. In this paper, we use a large real-world dataset with on-street parking sensor data from the local city council, and establish a formulation of the Travelling Officer Problem with a general probabilitybased 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.
@article{DBLP:journals/iotj/ShaoSGDC18,
author = {Wei Shao and
Flora D. Salim and
Tao Gu and
Ngoc-Thanh Dinh and
Jeffrey Chan},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/journals/iotj/ShaoSGDC18.bib},
doi = {10.1109/JIOT.2017.2759218},
journal = {IEEE Internet Things J.},
number = {2},
pages = {802--810},
timestamp = {Mon, 08 Jun 2020 01:00:00 +0200},
title = {Traveling Officer Problem: Managing Car Parking Violations Efficiently
Using Sensor Data},
url = {https://doi.org/10.1109/JIOT.2017.2759218},
volume = {5},
year = {2018}
}
© 2021 Flora Salim - CRUISE Research Group.