Article

Predestination: inferring destinations from partial trajectories

Authors:

Eric HorvitzAuthors Info & Claims

UbiComp'06: Proceedings of the 8th international conference on Ubiquitous Computing

Pages 243 - 260

https://doi.org/10.1007/11853565_15

Published: 17 September 2006 Publication History

Abstract

We describe a method called Predestination that uses a history of a driver's destinations, along with data about driving behaviors, to predict where a driver is going as a trip progresses. Driving behaviors include types of destinations, driving efficiency, and trip times. Beyond considering previously visited destinations, Predestination leverages an open-world modeling methodology that considers the likelihood of users visiting previously unobserved locations based on trends in the data and on the background properties of locations. This allows our algorithm to smoothly transition between “out of the box” with no training data to more fully trained with increasing numbers of observations. Multiple components of the analysis are fused via Bayesian inference to produce a probabilistic map of destinations. Our algorithm was trained and tested on hold-out data drawn from a database of GPS driving data gathered from 169 different subjects who drove 7,335 different trips.

References

[1]

Hu, P.S. and T.R. Reuscher, Summary of Travel Trends, 2001 National Household Travel Survey. 2004, U. S. Department of Transportation, U.S. Federal Highway Administration. p. 135.

[2]

Patterson, D.J., et al. Opportunity Knocks: A System to Provide Cognitive Assistance with Transportation Services. in UbiComp 2004: Ubiquitous Computing. 2004. Nottingham, UK: Springer.

[3]

Cheng, C., R. Jain, and E.v.d. Berg, Location Prediction Algorithms for Mobile Wireless Systems, in Wireless Internet Handbook: Technologies, Standards, and Application. 2003, CRC Press: Boca Raton, FL, USA. p. 245-263.

Digital Library

[4]

Krumm, J. and E. Horvitz, The Microsoft Multiperson Location Survey. 2005, Microsoft Research (MSR-TR-2005-103): Redmond, WA USA.

[5]

Karbassi, A. and M. Barth. Vehicle Route Prediction and Time of Arrival Estimation Techniques for Improved Transportation System Management. in Proceedings of the Intelligent Vehicles Symposium. 2003.

[6]

Marmasse, N. and C. Schmandt, A User-Centered Location Model. Personal and Ubiquitous Computing, 2002(6): p. 318-321.

Digital Library

[7]

Ashbrook, D. and T. Starner, Using GPS To Learn Significant Locations and Predict Movement Across Multiple Users. Personal and Ubiquitous Computing, 2003. 7(5): p. 275- 286.

Digital Library

[8]

Hariharan, R. and K. Toyama. Project Lachesis: Parsing and Modeling Location Histories. in Geographic Information Science: Third International Conference, GIScience 2004. 2004. Adelphi, MD, USA: Springer-Verlag GmbH.

[9]

Liao, L., D. Fox, and H. Kautz. Learning and Inferring Transportation Routines. in Proceedings of the 19th National Conference on Artificial Intelligence (AAAI 2004). 2004. San Jose, CA, USA.

Digital Library

[10]

Gogate, V., et al. Modeling Transportation Routines using Hybrid Dynamic Mixed Networks. in Uncertainty in Artificial Intelligence (UAI 2005). 2005.

[11]

http://landcover.usgs.gov/ftpdownload.asp.

[12]

http://landcover.usgs.gov/classes.asp.

[13]

Letchner, J., J. Krumm, and E. Horvitz. Trip Router with Individualized Preferences (TRIP): Incorporating Personalization into Route Planning. in Eighteenth Conference on Innovative Applications of Artificial Intelligence. 2006. Boston.

Digital Library

[14]

Krumm, J. Real Time Destination Prediction Based on Efficient Routes. in Society of Automotive Engineers (SAE) 2006 World Congress. 2006. Detroit.

[15]

https://nhts.ornl.gov/2001/Login.do.

[16]

Rish, I. An Empirical Study of the Naive Bayes Classifier. in IJCAI-01 Workshop on Empirical Methods in AI. 2001.

[17]

Angermann, M., et al. Software Representation for Heterogeneous Location Data Sources Using Probability Density Functions. in International Symposium on Location Based Services for Cellular Users (LOCELLUS 2001). 2001. Munich.

[18]

Elfes, A., Using Occupancy Grids for Mobile Robot Perception and Navigation. IEEE Computer, 1989. 22(6): p. 46-57.

Digital Library

Cited By

Fassmeyer DFassmeyer PBrefeld UKoyejo SMohamed SAgarwal ABelgrave DCho KOh A(2022)Semi-supervised generative models for multi-agent trajectoriesProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3602971(37267-37281)Online publication date: 28-Nov-2022
https://dl.acm.org/doi/10.5555/3600270.3602971
Yang JCao JLiu Y(2022)Deep Learning-Based Destination Prediction Scheme by Trajectory Prediction FrameworkSecurity and Communication Networks10.1155/2022/83858542022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/8385854
Tenzer MRasheed ZShafique KVasconcelos NRenz MSarwat M(2022)Meta-learning over time for destination prediction tasksProceedings of the 30th International Conference on Advances in Geographic Information Systems10.1145/3557915.3560980(1-10)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1145/3557915.3560980
Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

UbiComp'06: Proceedings of the 8th international conference on Ubiquitous Computing

September 2006

526 pages

ISBN:9783540396345

Editors:
Paul Dourish
Donald Bren School of Information and Computer Sciences, University of California, Irvine, Irvine, CA
,
Adrian Friday
Computing Department, Lancaster University, InfoLab 2, South Drive, Lancaster, CA, UK

Sponsors

Google Inc.
Nokia
Microsoft: Microsoft
Intel: Intel
IBM: IBM

In-Cooperation

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 17 September 2006

Check for updates

Qualifiers

Article

Acceptance Rates

UbiComp'06 Paper Acceptance Rate 30 of 204 submissions, 15%;

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

139
Total Citations
View Citations
0
Total Downloads

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

Reflects downloads up to 08 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Fassmeyer DFassmeyer PBrefeld UKoyejo SMohamed SAgarwal ABelgrave DCho KOh A(2022)Semi-supervised generative models for multi-agent trajectoriesProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3602971(37267-37281)Online publication date: 28-Nov-2022
https://dl.acm.org/doi/10.5555/3600270.3602971
Yang JCao JLiu Y(2022)Deep Learning-Based Destination Prediction Scheme by Trajectory Prediction FrameworkSecurity and Communication Networks10.1155/2022/83858542022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/8385854
Tenzer MRasheed ZShafique KVasconcelos NRenz MSarwat M(2022)Meta-learning over time for destination prediction tasksProceedings of the 30th International Conference on Advances in Geographic Information Systems10.1145/3557915.3560980(1-10)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1145/3557915.3560980
Zhao BLiu MHan JJi GLiu X(2021)Efficient Semantic Enrichment Process for Spatiotemporal TrajectoriesWireless Communications & Mobile Computing10.1155/2021/44887812021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/4488781
Niu HZhu HSun YLu XSun JZhao ZXiong HLang B(2021)Exploring the Risky Travel Area and Behavior of Car-hailing ServiceACM Transactions on Intelligent Systems and Technology10.1145/346505913:1(1-22)Online publication date: 23-Dec-2021
https://dl.acm.org/doi/10.1145/3465059
Zhang HRoth MPanta RWang HBagchi SValois FJulien CMurphy AGnawali O(2020)CrowdBind: Fairness Enhanced Late Binding Task Scheduling in Mobile CrowdsensingProceedings of the 2020 International Conference on Embedded Wireless Systems and Networks10.5555/3400306.3400314(61-72)Online publication date: 17-Feb-2020
https://dl.acm.org/doi/10.5555/3400306.3400314
Aly HKrumm JRanade GHorvitz E(2020)Computing value of spatiotemporal informationCommunications of the ACM10.1145/341038763:9(85-92)Online publication date: 21-Aug-2020
https://dl.acm.org/doi/10.1145/3410387
Han JLiu MJi GZhao BLiu RLi Y(2020)Efficient Semantic Enrichment Process for Spatiotemporal Trajectories in Geospatial EnvironmentWeb and Big Data10.1007/978-3-030-60290-1_27(342-350)Online publication date: 12-Aug-2020
https://dl.acm.org/doi/10.1007/978-3-030-60290-1_27
Gaurav SZiebart BElkind EVeloso MAgmon NTaylor M(2019)Discriminatively Learning Inverse Optimal Control Models for Predicting Human IntentionsProceedings of the 18th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3306127.3331844(1368-1376)Online publication date: 8-May-2019
https://dl.acm.org/doi/10.5555/3306127.3331844
Jin CLin ZWu M(2019)Augmented Intention Model for Next-Location Prediction from Graphical Trajectory ContextWireless Communications & Mobile Computing10.1155/2019/28601652019Online publication date: 26-Dec-2019
https://dl.acm.org/doi/10.1155/2019/2860165
Show More Cited By

View Options

View options

Figures

Tables

Media

View Table of Conten