# Traffic Monitoring System Based on Deep Learning and Seismometer Data
## 6 Conclusions  
Machine learning proved to be an effective and low-cost technique to enable traffic monitoring in real-time based on seismic data. In this study, we evaluated three neural network systems for this purpose and demonstrated that CNN provided the best performance in terms of accuracy and speed. CNN also surpassed the others in its ability to detect overlapping signals. RNN did not perform as well as the others for traffic monitoring because its intrinsic reliance on temporal sequences conflicts with the random nature of traffic data. Although seismic data can be used for traffic monitoring, all neural networks have a shortcoming in terms of counting vehicles because they cannot identify the presence of multiple vehicles of the same class within a waveform frame. The main limitation of neural networks is the human effort expended in acquiring and compiling a suitable amount of training data. We augmented our dataset by adding random noise. Although the models can be deployed without extra training, we recommend retraining the model as much as possible to guarantee the best performance in the generalization. Neural networks that process seismic data offer compelling advantages over current approaches to traffic monitoring. The seismic record has small file sizes compared to videos and other types of monitoring data. Because the system is simple and passive, consisting of a few geophones, it can be implemented for months at a time without supervision. The recorded data can be analyzed at a low computational cost to give clear statistical information for vehicles during the implementation period. This makes the proposed system suitable for use in hard-to-access roads. Our favored method, based on CNN, is suitable for continuous records of a month or longer; CNN was able to process a month's worth of data in approximately an hour. The proposed method can be extended by investigating the feasibility of using it to estimate more types of traffic data, such as speed, direction, and if the driving follows a probable manner or not (i.e., drive while drunk). Since we identified the car type via our CNN-based approach, the estimation of the speed of the vehicles could be possible; we presently are investigating accurate speed estimation systems. It may also be possible to extend a similar approach to other types of transportation, such as vessels, bicycles, foot traffic, or airplanes. The following are available online at [[https://www.mdpi.com/article/10.3390/app11104590/s1](https://www.mdpi.com/article/10.3390/app11104590/s1)]([https://www.mdpi.com/article/10.3390/app11104590/s1](https://www.mdpi.com/article/10.3390/app11104590/s1)), Figure S1. Other factors were monitored while the training and validation process., Figure S2. Examples of the waveforms used in the training process., Table S1. The components of DNN's architecture, the output of each layer, and the parameters, Table S2. The componentsof CNN's architecture, the output of each layer, and the parameters, Table S3. The components of RNN's architecture, the output of each layer, and the parameters. All authors contributed to this study. Conceptualization, A.B.A. ; Data curation, A.B.A. ; Formal analysis, T.T. ; Funding acquisition, T.T. ; Investigation, T.T. ; Methodology, A.B.A. and T.T. ; Resources, T.T. ; Software, A.B.A. ; Validation, T.T. ; Visualization, T.T. ; Writing--original draft, A.B.A. ; Writing--review & editing, T.T. All authors have read and agreed to the published version of the manuscript. This work was funded by the Japan Society for the Promotion of Science KAKENHI (grant number JP20H01997). Not applicable. This work was funded by the Japan Society for the Promotion of Science KAKENHI (grant number JP20H01997). Not applicable. This work was funded by the Japan Society for the Promotion of Science KAKENHI (grant number JP20H01997). Not applicable. This work was funded by the Japan Society for the Promotion of Science KAKENHI (grant number JP20H01997). Not applicable.