| Presentation | 2023-04-13 Vehicle Traffic Density Estimation for Predicting Communication Traffic Volume by Vehicle Communication Service Yoshie Morita, Kengo Tajiri, Yoichi Matsuo, |
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| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | Vehicle communication services are new services using the mobile communication network. Since these services are expected to generate a large amount of communication traffic volume, the communication traffic volume has to be predicted to avoid congestion. Conventional methods predict communication traffic volume based on the previous information on the volume of mobile traffic data. However, information on these services in previous mobile traffic data does not exist because vehicle communication services have not yet been widely used in Japan. Therefore, it is difficult to predict the communication traffic volume of the vehicle service using conventional methods. In this paper, we propose a method for predicting the communication traffic volume of vehicle communication service from vehicle traffic density, communication traffic volume per vehicle, and service diffusion rate, without using previous traffic information. Additionally, since vehicle traffic density cannot be obtained for several roads, we propose a model to estimate vehicle traffic density based on road information (e.g., the number of lanes on the road). Since the proposed model can estimate vehicle traffic density for any road, communication traffic volume from vehicle services can be predicted. In this paper, we present the results of estimating vehicle traffic density with the proposed model. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | communication traffic prediction / vehicle traffic density estimation / Deep Neural Network(DNN) |
| Paper # | NS2023-3 |
| Date of Issue | 2023-04-06 (NS) |
| Conference Information | |
| Committee | NS |
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| Conference Date | 2023/4/13(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Nihon University, Koriyama Campus + Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | Communication traffic theory, Traffic and quality evaluation, Network performance evaluation, QoS/QoE, Reliability and robustness, Traffic and quality management, AI and machine learning, Network and system operation management, High capacity, low latency, many connections, General |
| Chair | Tetsuya Oishi(NTT) |
| Vice Chair | Takumi Miyoshi(Shibaura Insti of Tech.) |
| Secretary | Takumi Miyoshi(NTT) |
| Assistant | Kotaro Mihara(NTT) |
| Paper Information | |
| Registration To | Technical Committee on Network Systems |
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| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Vehicle Traffic Density Estimation for Predicting Communication Traffic Volume by Vehicle Communication Service |
| Sub Title (in English) | |
| Keyword(1) | communication traffic prediction |
| Keyword(2) | vehicle traffic density estimation |
| Keyword(3) | Deep Neural Network(DNN) |
| 1st Author's Name | Yoshie Morita |
| 1st Author's Affiliation | Nippon Telegraph and Telephone Corporation(NTT) |
| 2nd Author's Name | Kengo Tajiri |
| 2nd Author's Affiliation | Nippon Telegraph and Telephone Corporation(NTT) |
| 3rd Author's Name | Yoichi Matsuo |
| 3rd Author's Affiliation | Nippon Telegraph and Telephone Corporation(NTT) |
| Date | 2023-04-13 |
| Paper # | NS2023-3 |
| Volume (vol) | vol.123 |
| Number (no) | NS-2 |
| Page | pp.pp.13-18(NS), |
| #Pages | 6 |
| Date of Issue | 2023-04-06 (NS) |