A Parking Occupancy Prediction Approach Based on Spatial and Temporal Analysis

Eric Hsueh Chan Lu, Chen Hao Liao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)

Abstract

It’s very difficult to find an appropriate parking space in urban area, when drivers are near to their destinations. The literature studies showed that 30% of the traffic congestion, unnecessary fuel consumption and exhaust emissions are caused by searching for the parking spaces. With the ever-changing nature of technology, smart parking systems composed of smart devices and sensor technologies are readily available and provide various information such as locations, real-time available counts, and parking costs. However, the drivers can’t know whether there is an available parking space at the arrival time. In this paper, we propose a parking occupancy prediction approach based on spatial and temporal analysis. In this approach, we extract related features and build the parking occupancy prediction model by Naïve Bayes classifier and decision tree. The prediction model can be used to predict the level of parking occupancy rate for each street block in the next hour. To evaluate the performance of proposed approach, we carried out the experiment by the on-street parking data collected by the SFPark system in San Francisco, USA. The results show that our proposed smart parking guidance system can significantly improve the prediction accuracy for the level of parking occupancy rate.

Original languageEnglish
Title of host publicationIntelligent Information and Database Systems - 10th Asian Conference, ACIIDS 2018, Proceedings
EditorsHoang Pham, Ngoc Thanh Nguyen, Bogdan Trawinski, Duong Hung Hoang, Tzung-Pei Hong
PublisherSpringer Verlag
Pages500-509
Number of pages10
ISBN (Print)9783319754161
DOIs
Publication statusPublished - 2018 Jan 1
Event10th International scientific conferences on research and applications in the field of intelligent information and database systems, ACIIDS 2018 - Dong Hoi City, Viet Nam
Duration: 2018 Mar 192018 Mar 21

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10751 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other10th International scientific conferences on research and applications in the field of intelligent information and database systems, ACIIDS 2018
CountryViet Nam
CityDong Hoi City
Period18-03-1918-03-21

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Lu, E. H. C., & Liao, C. H. (2018). A Parking Occupancy Prediction Approach Based on Spatial and Temporal Analysis. In H. Pham, N. T. Nguyen, B. Trawinski, D. H. Hoang, & T-P. Hong (Eds.), Intelligent Information and Database Systems - 10th Asian Conference, ACIIDS 2018, Proceedings (pp. 500-509). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10751 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-75417-8_47