# Multiscale Backcast Convolution Neural Network for Traffic Flow Prediction in The Frequency Domain
## 5 Conclusions and Future Research  
With the goal of exploiting the strong periodicity of traffic flow data, this paper proposes a MBCNN to predict traffic flow. We implement multiscale convolution with fewer parameters through different dilation sizes of cavity convolution, and we achieve efficient use of feature maps via two layers of multiscale convolution. At the same time, we enhance features within the hidden layers of multiscale convolutional extraction via co-optimization of the feedback branch and prediction branch; these two branches use the same set of feature maps in the frequency domain to achieve efficient reconstruction and prediction. We validate the effectiveness of this architecture through ablation experiments. Comparative experiments with the latest results from other models show that our model achieves the best performance indicators for the traffic road occupancy dataset. Notably, the worst ablated variant of our model, nevertheless, outperforms most baseline models, further emphasizing the effectiveness of extracting features from traffic flow data in the frequency domain.  
The traffic flow prediction problem, as an essential part of the intelligent transportation systems (ITS), is still facing a lot of challenges. Based on neural network and supervised learning, this study proposes a new solution for traffic flow prediction. In addition, with the rapid development of other methods in the field of artificial intelligence, many advanced algorithms have the potential to solve the traffic flow prediction problem and be used for building ITS, such as heuristics optimization algorithm. For example, a learning-based evolutionary many-objective algorithm (RVEMA/OL) with better generalization ability [35], or a Mixed-integer Linear Programming (MILP) model used for finding the best ambulance dispatching strategy [36]. Beyond that, the Adaptive Polyploid Memetic Algorithm (APMA) [37] proposed to solve the problem of scheduling cross-docking terminal (CDT) trucks. [PERSON] et al. proposed a novel multi-objective optimization model for the vehicle routing problem [38], which aimed to minimize the total cost associated with traversing the edges of the network and the total cost associated with visiting the nodes of the network. [PERSON] et al. developed a mixed-integer linear programming mathematical model to minimize the summation of waiting costs, handling costs, and late departure costs of the vessels that are to be served at a marine container terminal [39]. The above methods provide different ideas for ITS construction from varying perspectives, which can be referred as the direction of future research.  
Conceptualization, S.W.; Methodology, S.W.; Software, S.W.; Validation, E.F.; Investigation, E.F.; Data curation, S.W.; Writing--original draft, S.W.; Project administration, S.T.; Funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by [National Key R&D Program for Young Scientists] grant number [2021 YFB175700] And [Science and Technology Program Project of Beijing Education Committee] grant number [KM202111417003].  
The authors declare no conflict of interest.