Smart City Gnosys
Smart city article details
| Title | A Learning-Based Vehicle-Trajectory Generation Method For Vehicular Networking |
|---|---|
| ID_Doc | 2293 |
| Authors | Zhao L.; Liu Y.; Al-Dubai A.; Tan Z.; Min G.; Xu L. |
| Year | 2019 |
| Published | Proceedings - 21st IEEE International Conference on High Performance Computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on Data Science and Systems, HPCC/SmartCity/DSS 2019 |
| DOI | http://dx.doi.org/10.1109/HPCC/SmartCity/DSS.2019.00082 |
| Abstract | With the rapid development of mobile applications, networking technologies have been constantly evolved to offer a more convenient way of sharing information and online-communication anytime and anywhere. Vehicular networks have the potential to become one of the important carriers of future mobile networks. The performance of current vehicular networks has been widely evaluated through simulation experiments due to the high cost and impracticality of other experimental approaches. The most paramount factors of vehicle networks are the authenticity of simulative evaluation, where the mobility of the vehicles is the first significant feature (i.e., the nodes of the vehicular network) that must be properly considered. However, generating the corresponding real mobility datasets has always been a big challenge although it is vital to the simulations of vehicular networks. Therefore, in this paper, we propose a learning-based generation method that can be used to build the vehicle-trajectory data for variety of vehicle densities. Firstly, with analyzing the road bayonet data, we obtain the hidden pattern between road traffic and time. Secondly, we deploy Vissim (a well-known traffic simulator) to generate the experimental data by considering the urban functional areas for the origins of vehicles. The generated experimental data are learned by Extreme Learning Machine (ELM), and the weight matrix of the parameters is obtained, which presents the impact of the experimental parameters on the simulation results. We prove the effectiveness of our method by comparing the generated vehicle-trajectory datasets with the vehicle density predicted by the weight matrix and the realistic traffic flow model. © 2019 IEEE. |
| Author Keywords | dataset generation; vehicle mobility; vehicular networks; Vissim |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
58682  | 0.869 | Kong X.; Bi J.; Chen Q.; Shen G.; Chin T.; Pau G. | Traffic Trajectory Generation Via Conditional Generative Adversarial Networks For Transportation Metaverse | Applied Soft Computing, 160 (2024) |
| 48824 | 0.864 | Hachemane H.A.; Ben-Othman J. | Simulating Vehicular Mobility: A Comprehensive Framework Using Matlab Simevents | 2023 International Wireless Communications and Mobile Computing, IWCMC 2023 (2023) |
| 60254 | 0.857 | Wang M.; Cui J.; Wong Y.W.; Chang Y.; Wu L.; Jin J. | Urban Vehicle Trajectory Generation Based On Generative Adversarial Imitation Learning | IEEE Transactions on Vehicular Technology, 73, 12 (2024) |
| 37374 | 0.855 | Celes C.; Boukerche A.; Loureiro A.A.F. | Mobility Trace Analysis For Intelligent Vehicular Networks | ACM Computing Surveys, 54, 3 (2022) |
| 33428 | 0.851 | Papadakis A.; Mamatas L.; Petridou S. | Investigating The Latency Dynamics In Vehicular Platooning Networks | 2024 IEEE International Mediterranean Conference on Communications and Networking, MeditCom 2024 (2024) |
| 48735 | 0.85 | Wijesinghe A.; Ambegoda T.D.; Perera A.S.; Sivakumar T. | Siamese Networks For Rf-Based Vehicle Trajectory Prediction | 2023 IEEE 17th International Conference on Industrial and Information Systems, ICIIS 2023 - Proceedings (2023) |