tutorial

Wait time prediction: how to avoid waiting in lines?

Authors:

Ye Zhang,

Le T. Nguyen,

Joy ZhangAuthors Info & Claims

UbiComp '13 Adjunct: Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication

Pages 481 - 490

https://doi.org/10.1145/2494091.2495980

Published: 08 September 2013 Publication History

Get Access

Abstract

One of the challenges of organizations providing services to the public is the effective resource allocation. Many service providers such as hospitals, city halls or department of motor vehicles suffer from a service demand, which is unevenly distributed over the day. In this work, we evaluate techniques for predicting the service demand. We use the wait time dataset collected from the websites of California Department of Motor Vehicles (DMV). We extract patterns of the service demand in form of wait time during each hour of a day and each day of a week. This information is used to train multiple machine learning models in order to predict the future wait time at DMV offices.

References

[1]

Crumish, M. The Best and Worst Times to Visit the DMV. http://www.dmv.org/articles/the-best-and-worst-times-to-visit-the-dmv/, 2012.

Google Scholar

[2]

DMV. California DMV Statistics. http://apps.dmv.ca.gov/about/profile/ca_dmv_stats.pdf, 2012.

Google Scholar

[3]

DMV. California DMV Wait Times. http://apps.dmv.ca.gov/fo/offices/toc_fo.htm, 2013.

Google Scholar

[4]

Foundation, P. A. M. Wait Times at the Palo Alto Medical Foundation Urgent Care Centers. http://www.pamf.org/urgentcare/waittime.cfm, 2013.

Google Scholar

[5]

Hospital, E. C. About El Camino Hospital's ER Wait Times. http://www.elcaminohospital.org/Programs_and_Services/Emergency_Services/About_El_Camino_Hospitals_ER_Wait_Times, 2013.

Google Scholar

[6]

Kimoto, T., Asakawa, K., Yoda, M., and Takeoka, M. Stock market prediction system with modular neural networks. In Neural Networks, 1990., 1990 IJCNN International Joint Conference on (1990), 1--6 vol.1.

Crossref

Google Scholar

[7]

Nguyen, L., Cheng, H.-T., Wu, P., Buthpitiya, S., and Zhang, Y. Pnlum: System for prediction of next location for users with mobility. In Nokia Mobile Data Challenge 2012 Workshop. p. Dedicated challenge, vol. 2 (2012).

Google Scholar

[8]

Shu, Y., Jin, Z., Zhang, L., Wang, L., and Yang, O. W. Traffic prediction using farima models. In Communications, 1999. ICC'99. 1999 IEEE International Conference on, vol. 2, IEEE (1999), 891--895.

Google Scholar

Cited By

View all

Abdalla MLu HPinzaru BRudzicz FJaakkimainen L(2022)Predicting the target specialty of referral notes to estimate per-specialty wait times with machine learningPLOS ONE10.1371/journal.pone.026796417:5(e0267964)Online publication date: 12-May-2022
https://doi.org/10.1371/journal.pone.0267964
Aaqib SIndumathi CWedpathak GBabu G(2021)A neural network based machine learning model in digital health care for wait-time predictionMaterials Today: Proceedings10.1016/j.matpr.2021.01.195Online publication date: Feb-2021
https://doi.org/10.1016/j.matpr.2021.01.195
Crowley CGuitron SSon JPianykh O(2020)Modeling workflows: Identifying the most predictive features in healthcare operational processesPLOS ONE10.1371/journal.pone.023381015:6(e0233810)Online publication date: 11-Jun-2020
https://doi.org/10.1371/journal.pone.0233810
Show More Cited By

Index Terms

Wait time prediction: how to avoid waiting in lines?
1. Computing methodologies
  1. Machine learning

Recommendations

Accurate Emergency Department Wait Time Prediction

This paper proposes the Q-Lasso method for wait time prediction, which combines statistical learning with fluid model estimators. In historical data from four remarkably different hospitals, Q-Lasso predicts the emergency department ED wait time for low-...
Iterated time series prediction with multiple support vector regression models

Support vector regression (SVR) model has been widely applied to time series prediction. In general iterative methods, the multi-step-ahead prediction is based on the iteration of the exact one-step prediction. Even if the one-step prediction model is ...
A Bayesian Multiple Models Combination Method for Time Series Prediction

In this paper we present the Bayesian Combined Predictor (BCP), a probabilistically motivated predictor for time series prediction. BCP utilizes local predictors of several types (e.g., linear predictors, artificial neural network predictors, polynomial ...

Comments

Information & Contributors

Information

Published In

UbiComp '13 Adjunct: Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication

September 2013

1608 pages

ISBN:9781450322157

DOI:10.1145/2494091

General Chairs:
Friedemann Mattern
ETH Zurich, CH
,
Silvia Santini
TU Darmstadt, DE
,
Program Chairs:
John F. Canny
UC Berkeley, US
,
Marc Langheinrich
Università della Svizzera italiana, CH
,
Jun Rekimoto
University of Tokyo, JP

Permission to make digital or hard copies of all or part 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 components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

In-Cooperation

SIGSPATIAL: ACM Special Interest Group on Spatial Information

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 September 2013

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tag

time series prediction

Qualifiers

Tutorial

Conference

UbiComp '13

Sponsor:

SIGMOBILE
University of Florida
SIGCHI

UbiComp '13: The 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 8 - 12, 2013

Zurich, Switzerland

Acceptance Rates

UbiComp '13 Adjunct Paper Acceptance Rate 254 of 399 submissions, 64%;

Overall Acceptance Rate 764 of 2,912 submissions, 26%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

11
Total Citations
View Citations
303
Total Downloads

Downloads (Last 12 months)16
Downloads (Last 6 weeks)0

Reflects downloads up to 02 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Abdalla MLu HPinzaru BRudzicz FJaakkimainen L(2022)Predicting the target specialty of referral notes to estimate per-specialty wait times with machine learningPLOS ONE10.1371/journal.pone.026796417:5(e0267964)Online publication date: 12-May-2022
https://doi.org/10.1371/journal.pone.0267964
Aaqib SIndumathi CWedpathak GBabu G(2021)A neural network based machine learning model in digital health care for wait-time predictionMaterials Today: Proceedings10.1016/j.matpr.2021.01.195Online publication date: Feb-2021
https://doi.org/10.1016/j.matpr.2021.01.195
Crowley CGuitron SSon JPianykh O(2020)Modeling workflows: Identifying the most predictive features in healthcare operational processesPLOS ONE10.1371/journal.pone.023381015:6(e0233810)Online publication date: 11-Jun-2020
https://doi.org/10.1371/journal.pone.0233810
Zhu C(2020)How long will the Service Time in a Ride-Hailing Service?Proceedings of the 2020 3rd International Conference on Machine Learning and Machine Intelligence10.1145/3426826.3426828(6-9)Online publication date: 18-Sep-2020
https://dl.acm.org/doi/10.1145/3426826.3426828
Choudhury AShanmugavadivelu SVelpuri B(2019)Real Time Speed Trend Analysis and Hours of Service Forecasting Using LSTM Network2019 IEEE 5th International Conference for Convergence in Technology (I2CT)10.1109/I2CT45611.2019.9033624(1-6)Online publication date: Mar-2019
https://doi.org/10.1109/I2CT45611.2019.9033624
Sun FMao FGuo SYao YMalaviya PBhatt KGupta AJain MAchan KSuo XKamat N(2019)Recovery-oriented Big Data Computing for Exactly Once Message Processing2019 IEEE International Conference on Big Data (Big Data)10.1109/BigData47090.2019.9006585(2923-2930)Online publication date: Dec-2019
https://doi.org/10.1109/BigData47090.2019.9006585
Rahaman MRen YHamilton MSalim F(2018)Wait Time Prediction for Airport Taxis Using Weighted Nearest Neighbor RegressionIEEE Access10.1109/ACCESS.2018.28825806(74660-74672)Online publication date: 2018
https://doi.org/10.1109/ACCESS.2018.2882580
Tshabalala DHans R(2016)A secured model for changing a vehicle's ownership2016 International Conference on Advances in Computing and Communication Engineering (ICACCE)10.1109/ICACCE.2016.8073753(232-236)Online publication date: Nov-2016
https://doi.org/10.1109/ICACCE.2016.8073753
Pianykh ORosenthal D(2015)Can We Predict Patient Wait Time?Journal of the American College of Radiology10.1016/j.jacr.2015.04.01012:10(1058-1066)Online publication date: Oct-2015
https://doi.org/10.1016/j.jacr.2015.04.010
David KKusber RLau SSigg SZiebart BBrush AFriday AKientz JScott JSong J(2014)3rd workshop on recent advances in behavior prediction and pro-active pervasive computingProceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication10.1145/2638728.2641675(415-420)Online publication date: 13-Sep-2014
https://dl.acm.org/doi/10.1145/2638728.2641675
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Cited By

Index Terms

Recommendations

Accurate Emergency Department Wait Time Prediction

Iterated time series prediction with multiple support vector regression models

A Bayesian Multiple Models Combination Method for Time Series Prediction