research-article

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Hard to Park?: Estimating Parking Difficulty at Scale

Authors:

Ivan Kuznetsov,

Huai-Jen Liang,

Andrew Tomkins,

Iveel Tsogsuren,

Yi WangAuthors Info & Claims

KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Pages 2296 - 2304

https://doi.org/10.1145/3292500.3330767

Published: 25 July 2019 Publication History

Abstract

In this paper we consider the problem of estimating the difficulty of parking at a particular time and place; this problem is a critical sub-component for any system providing parking assistance to users. We describe an approach to this problem that is currently in production in Google Maps, providing inferences in cities across the world. We present a wide range of features intended to capture different aspects of parking difficulty and study their effectiveness both alone and in combination. We also evaluate various model architectures for the prediction problem. Finally, we present challenges faced in estimating parking difficulty in different regions of the world, and the approaches we have taken to address them.

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Cited By

Yang GTu MLi ZHang JLiu TLiu RDing YYang YZhang DSerra ESpezzano F(2024)Behavior-Aware Hypergraph Convolutional Network for Illegal Parking Prediction with Multi-Source Contextual InformationProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679563(2827-2836)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679563
Qiao FLin S(2024)Predicting Electrical Vehicle Charging Patterns at Public Charging Stations2024 IEEE 7th International Conference on Big Data and Artificial Intelligence (BDAI)10.1109/BDAI62182.2024.10692786(329-334)Online publication date: 5-Jul-2024
https://doi.org/10.1109/BDAI62182.2024.10692786
Cao RChoudhury FWinter SWang D(2024)Artificial intelligence for parking forecasting: an extensive survey of machine learning techniquesTransportmetrica A: Transport Science10.1080/23249935.2024.2409229(1-39)Online publication date: 16-Oct-2024
https://doi.org/10.1080/23249935.2024.2409229
Show More Cited By

Index Terms

Hard to Park?: Estimating Parking Difficulty at Scale
1. Information systems
  1. Information systems applications
    1. Spatial-temporal systems
      1. Geographic information systems
      2. Location based services

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cover image ACM Conferences

KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

July 2019

3305 pages

ISBN:9781450362016

DOI:10.1145/3292500

General Chairs:
Ankur Teredesai
KenSci
,
Vipin Kumar
University of Minnesota
,
Program Chairs:
Ying Li
EV Analysis Corporation
,
Rómer Rosales
LinkedIn
,
Evimaria Terzi
Boston University
,
George Karypis
University of Minnesota

Copyright © 2019 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 25 July 2019

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KDD '19

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KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

August 4 - 8, 2019

AK, Anchorage, USA

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KDD '19 Paper Acceptance Rate 110 of 1,200 submissions, 9%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Yang GTu MLi ZHang JLiu TLiu RDing YYang YZhang DSerra ESpezzano F(2024)Behavior-Aware Hypergraph Convolutional Network for Illegal Parking Prediction with Multi-Source Contextual InformationProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679563(2827-2836)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679563
Qiao FLin S(2024)Predicting Electrical Vehicle Charging Patterns at Public Charging Stations2024 IEEE 7th International Conference on Big Data and Artificial Intelligence (BDAI)10.1109/BDAI62182.2024.10692786(329-334)Online publication date: 5-Jul-2024
https://doi.org/10.1109/BDAI62182.2024.10692786
Cao RChoudhury FWinter SWang D(2024)Artificial intelligence for parking forecasting: an extensive survey of machine learning techniquesTransportmetrica A: Transport Science10.1080/23249935.2024.2409229(1-39)Online publication date: 16-Oct-2024
https://doi.org/10.1080/23249935.2024.2409229
Zhang KCui YLiu QShu HPeng L(2024)Spread of parking difficulty in urban environments: A parking network perspectiveIET Intelligent Transport Systems10.1049/itr2.12525Online publication date: 11-Jun-2024
https://doi.org/10.1049/itr2.12525
Rahmani SBaghbani ABouguila NPatterson Z(2023)Graph Neural Networks for Intelligent Transportation Systems: A SurveyIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.325775924:8(8846-8885)Online publication date: Aug-2023
https://doi.org/10.1109/TITS.2023.3257759
Anand DSingh AAlsubhi KGoyal NAbdrabou AVidyarthi ARodrigues J(2023)A Smart Cloud and IoVT-Based Kernel Adaptive Filtering Framework for Parking PredictionIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.320435224:3(2737-2745)Online publication date: Mar-2023
https://doi.org/10.1109/TITS.2022.3204352
Gomari SDomakuntla RKnoth CAntoniou C(2023)Development of a Data-Driven On-Street Parking Information System Using Enhanced Parking FeaturesIEEE Open Journal of Intelligent Transportation Systems10.1109/OJITS.2023.32358984(30-47)Online publication date: 2023
https://doi.org/10.1109/OJITS.2023.3235898
Matiunina DSautter NLoder ABogenberger K(2023)Predicting Parking Occupancy with Deep Learning on Noisy Empirical Data2023 8th International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)10.1109/MT-ITS56129.2023.10241370(1-6)Online publication date: 14-Jun-2023
https://doi.org/10.1109/MT-ITS56129.2023.10241370
Ковито М(2022)Обнаружении неисправностей механического оборудования с использованием методов интеллектуального анализа данныхИнформатика. Экономика. Управление - Informatics. Economics. Management10.47813/2782-5280-2022-1-2-0121-01331:2(0121-0133)Online publication date: 28-Oct-2022
https://doi.org/10.47813/2782-5280-2022-1-2-0121-0133
Bassetti EBerti ABisante AMagnante APanizzi E(2022)Exploiting User Behavior to Predict Parking Availability through Machine LearningSmart Cities10.3390/smartcities50400645:4(1243-1266)Online publication date: 25-Sep-2022
https://doi.org/10.3390/smartcities5040064
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