# Highway Speed Prediction Using Gated Recurrent Unit Neural Networks
## 5 Discussion and Conclusions  
This study investigated highway traffic speed prediction using GRU, a recurrent neural network. In terms of prediction accuracy, this study compared the proposed method with alternative methods, ARIMA and LSTM. The experimental results show that the GRU outperformed other methods, which was statistically confirmed by an ANOVA test. However, the current model used only one factor (i.e., vehicle velocity). Although this study included another factor, i.e., traffic volume, the one factor (i.e., speed) model performed better. The rationale behind this is that not all cars are equipped with the DTG device. The traffic volume from the Korea Expressway Corporation includes all cars in the Gyeongbu Expressway. This discrepancy causes the one-factor model to have better prediction performance.  
It is also noteworthy that there is no statistically significant difference for the prediction performance in bus lanes among GRU, LSTM, and ARIMA. However, there are statistically significant differences in regular lanes. While the DTG data include most bus operation information, they do not include all private car information. This difference makes the speed prediction of regular lanes difficult. Moreover, this study did not attempt to investigate two-lane intersection operations due to the difficulty of getting the exact data. Besides, the currently trained model may fail to predict speed when events (e.g., car accidents) occur. The current model was not fully trained in various environments. This study would be more useful if these limitations were investigated.  
Furthermore, this study examined the computational cost of the GRU and LSTM models. The comparison between the GRU and LSTM showed statistically significant differences in the time reduction of GRU as compared to LSTM. The GRU is more scalable than the LSTM because it is designed with fewer parameters than the LSTM.  
Although road traffic managers can directly use the proposed model to predict traffic speed in the experimental area, it is reasonable to train new datasets in other areas or leverage transfer learning. Transfer learning can improve the efficiency of solving new tasks without relearning everything from scratch.  
In addition, it is worth noting that the GRU method is limited to time dependency problems. Further investigation should focus on the design of a robust algorithm for cap  
Figure 11: Scalability comparisons between gated recurrent unit (GRU) and long short-term neural network (LSTM) models.  
turing spatiotemporal traffic speed or flow. Such research will lead to increased knowledge and a better understanding of traffic movement patterns.  
Conceptualization, M.-H.J.; methodology, M.-H.J. and T.-Y.L.; software, M.-H.J. and T.-Y.L.; validation, M.-H.J. and [PERSON]-[PERSON]; formal analysis, [PERSON]-[PERSON], [PERSON]-[PERSON] and [PERSON]-[PERSON]; investigation, [PERSON]-H.J., [PERSON]-[PERSON] and [PERSON]-Y.L.; resources, [PERSON]-H.J., [PERSON]-[PERSON] and [PERSON]-Y.L.; data curation, [PERSON]-[PERSON] and T.-Y.L.; writing--original draft preparation, M.-H.J.; writing--review and editing, M.-H.J. and M.Y.; visualization, [PERSON]-H.J. and T.-Y.L.; supervision, M.Y.; project administration and funding acquisition, M.-H.J. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a grant (2020-MOIS31-014) from the Fundamental Technology Development Program for Extreme Disaster Response funded by the Ministry of the Interior and Safety (MOIS, Korea).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.