research-article

A Cluster-based Framework for Predicting Large Scale Road-Network Constrained Trajectories

Authors:

Roniel S. de Sousa,

Azzedine Boukerche,

Antonio A. F. LoureiroAuthors Info & Claims

PE-WASUN '20: Proceedings of the 17th ACM Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, & Ubiquitous Networks

Pages 1 - 8

https://doi.org/10.1145/3416011.3424751

Published: 16 November 2020 Publication History

Abstract

The increasing availability of vehicle trajectory and road network datasets is crucial for the development of novel trajectory data mining-based applications. For instance, we can design more efficient routing protocols by applying vehicle trajectory prediction. In this paper, we propose a new cluster-based framework to predict road-network constrained trajectories. The framework, designed to perform long-term predictions, combines several steps that use historical trajectory datasets to train prediction models. Experimental results show the framework's effectiveness and efficiency to predict trajectories with different characteristics in a new real-world, large-scale scenario. Besides that, the framework outperformed some other solutions found in the literature in terms of prediction accuracy and computational overhead.mmm;

References

[1]

Hatem Abou-zeid, Hossam S. Hassanein, and Ramy Atawia. 2014. Towards Mobility-Aware Predictive Radio Access: Modeling; Simulation; and Evaluation in LTE Networks. In Proceedings of the 17th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (Montreal, QC, Canada) (MSWiM '14). Association for Computing Machinery, New York, NY, USA, 109--116.

Digital Library

[2]

Noura Aljeri and Azzedine Boukerche. 2018. Mobility and Handoff Management in Connected Vehicular Networks. In Proceedings of the 16th ACM International Symposium on Mobility Management and Wireless Access (Montreal, QC, Canada) (MobiWac'18). Association for Computing Machinery, New York, NY, USA, 82--88.

Digital Library

[3]

Geoff Boeing. 2017. OSMnx: New methods for acquiring, constructing, analyzing, and visualizing complex street networks. Computers, Environment and Urban Systems, Vol. 65 (2017), 126--139.

[4]

Ali Bohlooli and Kamal Jamshidi. 2012. A GPS-free method for vehicle future movement directions prediction using SOM for VANET. Applied Intelligence, Vol. 36, 3 (01 Apr 2012), 685--697.

Digital Library

[5]

Felipe D. Cunha, Davidysson A. Alvarenga, Guilherme Maia, Aline C. Viana, Raquel A.F. Mini, and Antonio A.F. Loureiro. 2016. Exploring Interactions in Vehicular Networks. In Proceedings of the 14th ACM International Symposium on Mobility Management and Wireless Access (Malta, Malta) (MobiWac '16). Association for Computing Machinery, New York, NY, USA, 131--138.

Digital Library

[6]

Roniel S. de Sousa, Azzedine Boukerche, and Antonio A.F. Loureiro. 2020 a. A distributed and low-overhead traffic congestion control protocol for vehicular ad hoc networks. Computer Communications, Vol. 159 (2020), 258 -- 270.

[7]

R. S. de Sousa, A. Boukerche, and A. A. F. Loureiro. 2019. DisTraC: A Distributed and Low-Overhead Protocol for Traffic Congestion Control Using Vehicular Networks. In 2019 IEEE Symposium on Computers and Communications (ISCC). IEEE Computer Society, Washington, DC, USA, 1--6.

[8]

R. S. de Sousa, A. Boukerche, and A. A. F. Loureiro. 2020 b. A Map Matching Based Framework to Reconstruct Vehicular Trajectories from GPS Datasets. In 2020 IEEE International Conference on Communications (ICC). IEEE Computer Society, Washington, DC, USA, 1--6.

[9]

R. S. de Sousa, A. Boukerche, and A. A. F. Loureiro. 2020 c. Vehicle Trajectory Similarity: Models, Methods, and Applications. ACM Comput. Surv. (June 2020), 33.

Digital Library

[10]

R. S. de Sousa, F. S. da Costa, A. C. B. Soares, L. F. M. Vieira, and A. A. F. Loureiro. 2018. Geo-SDVN: A Geocast Protocol for Software Defined Vehicular Networks. In 2018 IEEE International Conference on Communications (ICC). IEEE Computer Society, Washington, DC, USA, 1--6.

[11]

Allan Mariano de Souza and Leandro Aparecido Villas. 2016. A Fully-Distributed Traffic Management System to Improve the Overall Traffic Efficiency. In Proceedings of the 19th ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (Malta, Malta) (MSWiM '16). Association for Computing Machinery, New York, NY, USA, 19--26.

Digital Library

[12]

Augusto C. S. A. Domingues, Henrique de Souza Santana, Fabr'icio A. Silva, Pedro O. S. Vaz de Melo, and Antonio A. F. Loureiro. 2019. Are We Still Friends? Evaluating Tie Persistence in Mobility Traces. In Proceedings of the 17th ACM International Symposium on Mobility Management and Wireless Access (Miami Beach, FL, USA) (MobiWac '19). Association for Computing Machinery, New York, NY, USA, 1--8.

Digital Library

[13]

Martin Ester, Hans-Peter Kriegel, Jörg Sander, and Xiaowei Xu. 1996. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (Portland, Oregon) (KDD'96). AAAI Press, 226--231.

Digital Library

[14]

Jon Froehlich and John Krumm. 2008. Route Prediction from Trip Observations. In Society of Automotive Engineers (SAE) 2008 World Congress, April 2008.

[15]

Richard J. Hathaway, James C. Bezdek, and Jacalyn M. Huband. 2006. Scalable visual assessment of cluster tendency for large data sets. Pattern Recognition, Vol. 39, 7 (2006), 1315 -- 1324.

Digital Library

[16]

T. C. Havens and J. C. Bezdek. 2012. An Efficient Formulation of the Improved Visual Assessment of Cluster Tendency (iVAT) Algorithm. IEEE Transactions on Knowledge and Data Engineering, Vol. 24, 5 (2012), 813--822.

Digital Library

[17]

B. Kim, C. M. Kang, J. Kim, S. H. Lee, C. C. Chung, and J. W. Choi. 2017. Probabilistic vehicle trajectory prediction over occupancy grid map via recurrent neural network. In 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC). 399--404.

[18]

D. Kumar, J. C. Bezdek, M. Palaniswami, S. Rajasegarar, C. Leckie, and T. C. Havens. 2016. A Hybrid Approach to Clustering in Big Data. IEEE Transactions on Cybernetics, Vol. 46, 10 (2016), 2372--2385.

[19]

Yuexin Ma, Xinge Zhu, Sibo Zhang, Ruigang Yang, Wenping Wang, and Dinesh Manocha. 2019. TrafficPredict: Trajectory Prediction for Heterogeneous Traffic-Agents. Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33 (07 2019), 6120--6127.

Digital Library

[20]

K. Okamoto, K. Berntorp, and S. Di Cairano. 2017. Similarity-based vehicle-motion prediction. In 2017 American Control Conference (ACC). 303--308.

[21]

OpenStreetMap contributors. 2017. Planet dump retrieved from https://planet.osm.org. https://www.openstreetmap.org.

[22]

Punit Rathore, Dheeraj Kumar, Sutharshan Rajasegarar, Marimuthu Palaniswami, and James C. Bezdek. 2018. A Scalable Framework for Trajectory Prediction. CoRR, Vol. abs/1806.03582 (2018), 14.arxiv: 1806.03582

[23]

M. Schreier, V. Willert, and J. Adamy. 2014. Bayesian, maneuver-based, long-term trajectory prediction and criticality assessment for driver assistance systems. In 17th International IEEE Conference on Intelligent Transportation Systems (ITSC). 334--341.

[24]

Fabr'icio A. Silva, Clayson Celes, Azzedine Boukerche, Linnyer B. Ruiz, and Antonio A.F. Loureiro. 2015. Filling the Gaps of Vehicular Mobility Traces (MSWiM '15). Association for Computing Machinery, New York, NY, USA, 47--54.

Digital Library

[25]

V. S. Tiwari, S. Chaturvedi, and A. Arya. 2013. Route prediction using trip observations and map matching. In 2013 3rd IEEE International Advance Computing Conference (IACC). 583--587.

[26]

J. Won, S. Kim, J. Baek, and J. Lee. 2009. Trajectory clustering in road network environment. In 2009 IEEE Symposium on Computational Intelligence and Data Mining. IEEE Computer Society, Nashville, TN, USA, 299--305.

[27]

G. Xie, H. Gao, L. Qian, B. Huang, K. Li, and J. Wang. 2018. Vehicle Trajectory Prediction by Integrating Physics- and Maneuver-Based Approaches Using Interactive Multiple Models. IEEE Transactions on Industrial Electronics, Vol. 65, 7 (July 2018), 5999--6008.

Cited By

de Sousa RBoukerche ALoureiro A(2022)Vehicle-Assisted Data Delivery Based on Trajectory PredictionGLOBECOM 2022 - 2022 IEEE Global Communications Conference10.1109/GLOBECOM48099.2022.10001329(6295-6300)Online publication date: 4-Dec-2022
https://doi.org/10.1109/GLOBECOM48099.2022.10001329
de Sousa RBoukerche ALoureiro A(2022)On the prediction of large-scale road-network constrained trajectoriesComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2021.108337206:COnline publication date: 7-Apr-2022
https://dl.acm.org/doi/10.1016/j.comnet.2021.108337

Index Terms

A Cluster-based Framework for Predicting Large Scale Road-Network Constrained Trajectories
1. Information systems
  1. Information systems applications
    1. Data mining
    2. Spatial-temporal systems

Recommendations

On the prediction of large-scale road-network constrained trajectories
Abstract
Trajectory data mining-based applications benefit from the increasing availability of vehicle trajectory and road network datasets. For instance, the application of trajectory prediction makes it possible to design more efficient ...
Efficient in-memory indexing of network-constrained trajectories
SIGSPACIAL '16: Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

With the decreasing cost and growing size of main memory, it is increasingly relevant to utilize main-memory indexing for efficient query processing. We propose SPNET, which we believe is the first in-memory index for network-constrained trajectory ...
Continuous k nearest neighbor queries over large multi-attribute trajectories: a systematic approach

We study multi-attribute trajectories by combining standard trajectories (i.e., a sequence of timestamped locations) and descriptive attributes. A new form of continuous k nearest neighbor queries is proposed by integrating attributes into the ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

PE-WASUN '20: Proceedings of the 17th ACM Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, & Ubiquitous Networks

November 2020

107 pages

ISBN:9781450381185

DOI:10.1145/3416011

General Chair:
Luis J. de la Cruz Llopis
Universitat Politècnica de Catalunya, Spain
,
Program Chairs:
Mónica Aguilar Igartua
Universitat Politècnica de Catalunya, Spain
,
Carolina Tripp Barba
Universidad Autonoma de Sinaloa, Mexico

Copyright © 2020 ACM.

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 ACM 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]

Sponsors

SIGSIM: ACM Special Interest Group on Simulation and Modeling

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 November 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

MSWiM '20

Sponsor:

SIGSIM

MSWiM '20: 23rd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems

November 16 - 20, 2020

Alicante, Spain

Acceptance Rates

Overall Acceptance Rate 70 of 240 submissions, 29%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
84
Total Downloads

Downloads (Last 12 months)7
Downloads (Last 6 weeks)0

Reflects downloads up to 07 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

de Sousa RBoukerche ALoureiro A(2022)Vehicle-Assisted Data Delivery Based on Trajectory PredictionGLOBECOM 2022 - 2022 IEEE Global Communications Conference10.1109/GLOBECOM48099.2022.10001329(6295-6300)Online publication date: 4-Dec-2022
https://doi.org/10.1109/GLOBECOM48099.2022.10001329
de Sousa RBoukerche ALoureiro A(2022)On the prediction of large-scale road-network constrained trajectoriesComputer Networks: The International Journal of Computer and Telecommunications Networking10.1016/j.comnet.2021.108337206:COnline publication date: 7-Apr-2022
https://dl.acm.org/doi/10.1016/j.comnet.2021.108337

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.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten