Incorporating LSTM Auto-Encoders in Optimizations to Solve ParkingOfficer Patrolling Problem

Publication Year: 2020 Publication Type : JournalArticle


The smart parking system is one of the most important problems in smart cities. Recently, an increasing number of sensors installed in parking spaces provide big spatio-temporal data which be used to analyse the parking situations in the city and help parking officers monitor the parking violations. Travelling Officer Problem was customised to formulate a path-finding problem that aims to maximise the probability of catching overstayed cars before they leave. One of the challenges is to extract effective features from the big spatio-temporal data and provide a data-driven solution to replace conventional solutions such as a simple rule-based system or single optimisation methods. In this paper, we propose a seamless end-to-end learning and optimisation framework that combines Long Short-Term Memory (LSTM) Auto-Encoder neural network, clustering and path-finding methods to solve the Travelling Officer Problem. Our extensive comparison experiments on a large scale real-world dataset have shown that our proposed solution outperforms any other single step methods or optimisation methods.


    author = {Wei Shao and Siyu Tan and Sichen Zhao and Kyle Kai Qin and Xinhong Hei and Jeffrey Chan and Flora D. Salim},
    bibsource = {dblp computer science bibliography,},
    biburl = {},
    doi = {10.1145/3380966},
    journal = {ACM Trans. Spatial Algorithms and Systems},
    number = {3},
    pages = {20:1--20:21},
    timestamp = {Fri, 05 Jun 2020 01:00:00 +0200},
    title = {Incorporating LSTM Auto-Encoders in Optimizations to Solve Parking Officer Patrolling Problem},
    url = {},
    volume = {6},
    year = {2020}


Related Publications

RUP: Large Room Utilisation Prediction with carbon dioxide sensor
Type : JournalArticle
Show More
A Scalable Room Occupancy Prediction with Transferable Time Series Decomposition of CO 2 Sensor Data
Type : JournalArticle
Show More
Topical Event Detection on Twitter
Type : ConferenceProceeding
Show More

© 2021 Flora Salim - CRUISE Research Group.