Characterizing Car Trips Through Information Theory Metrics-Reference-Cited by-同舟云学术

Characterizing Car Trips Through Information Theory Metrics

Published:2019 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 22nd International ACM Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems - MSWIM '19
language:
Short-container-title:

Author:

Campolina André¹,Boukerche Azzedine¹,Loureiro Antonio A. F.²

Affiliation:

1. University of Ottawa, Ottawa, ON, Canada

2. Universidade Federal de Minas Gerais, Belo Horizonte, Brazil

Publisher

ACM Press

Reference26 articles.

1. Mimonah Al Qathrady and Ahmed Helmy. 2018. Toward Advanced Indoor Mobility Models Through Location-Centric Analysis: Spatio-Temporal Density Dynamics. In Proceedings of the 21st ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM '18). ACM, New York, NY, USA, 347--350.

2. Andre L.L. Aquino, Tamer S.G. Cavalcante, Eliana S. Almeida, Alejandro C. Frery, and Osvaldo A. Rosso. 2015. Characterization of vehicle behavior with information theory . The European Physical Journal B , Vol. 88, 10 (2015), 257.

3. André B. Campolina, Azzedine Boukerche, Max do V. Machado, and Antonio A. F. Loureiro. 2018. Fuel Efficient Routes Using Vehicular Sensor Data. In Proceedings of the 16th ACM International Symposium on Mobility Management and Wireless Access (MobiWac'18). ACM, New York, NY, USA, 29--36.

4. Thomas M. Cover and Joy A. Thomas. 2005. Elements of Information Theory. John Wiley & Sons, Inc., Hoboken, NJ, USA. 1--12 pages.

5. Fabrí cio R. de Souza, Augusto C.S.A. Domingues, Pedro O.S. Vaz de Melo, and Antonio A F Loureiro. 2018. MOCHA: A Tool for Mobility Characterization. In Proceedings of the 21st ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems - MSWIM '18. ACM Press, New York, New York, USA, 281--288.

Cited by 8 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Novel Multimodal Vehicle Path Prediction Method Based on Temporal Convolutional Networks;IEEE Transactions on Intelligent Transportation Systems;2022-12

2. Siamese Temporal Convolutional Networks for Driver Identification Using Driver Steering Behavior Analysis;IEEE Transactions on Intelligent Transportation Systems;2022-10

3. DriverRep: Driver identification through driving behavior embeddings;Journal of Parallel and Distributed Computing;2022-04

4. Predicting Traffic Characteristics of Real Road Scenarios in Jordan and Gulf Region;Proceedings of the 17th ACM Symposium on QoS and Security for Wireless and Mobile Networks;2021-11-22

5. Toward Driver Intention Prediction for Intelligent Vehicles: A Deep Learning Approach;2021 IEEE 46th Conference on Local Computer Networks (LCN);2021-10-04