# Effective Bus Travel Time Prediction System of Multiple Routes: Introducing PMLNet Based on MDARNN
## 5 Conclusions  
In this paper, we introduced a cutting-edge bus travel time prediction system-based PMLNet, which leverages a partition and combination framework to accurately predict multi-route bus travel times. PMLNet uniquely integrates macro and local impact factors through the MDARN model, and employs four real-time traffic flow factors to refine the predicted results, which significantly enhances prediction accuracy. The reproducibility of the PMLNet method in other contexts is feasible given the availability of essential  
Figure 14: The client page of the system. (a) Start location page. (b) Destination page.  
databases and the generalizability of the proposed framework. The core components of our system, including the partitioning of travel time into distinct components and the use of both LSTM and MDARNN models, are designed to be adaptable to various urban transportation scenarios. Our experimental results showcase PMLNet's superior performance, achieving remarkable precision in bus travel time predictions across multiple routes. This advancement not only bridges the gap in existing prediction methodologies but also offers reference options for intelligent transportation systems, potentially improving urban mobility and passenger experience.  
There are still some shortcomings in our work that need to be further addressed, such as the model's dependency on high-quality, comprehensive datasets and its performance under varying traffic conditions. Future research will focus on optimizing PMLNet's architecture for broader applicability, including the integration of multimodal transportation data, to further refine prediction accuracies. Additionally, exploring adaptive algorithms capable of real-time adjustments to sudden traffic changes presents a promising avenue for enhancing the robustness and reliability of our prediction system.  
Conceptualization, Q.H., L.Z. and J.L.; methodology, L.Z., G.H. and Y.C.; software, Y.C. and G.H.; validation, Q.H., L.Z. and Y.C.; formal analysis, Y.C. and G.H.; investigation, Y.C. and G.H.; resources, Q.H. and L.Z.; data curation, Y.C.; writing--original draft preparation, Y.C.; writing--review and editing, Q.H., L.Z. and J.L.; visualization, Y.C. and G.H.; supervision, Q.H. and L.Z.; project administration, L.Z.; funding acquisition, Q.H., L.Z. and J.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Science and Technology Innovation Key R&D Program of Chongqing No. CSTRB2022 TIAD-STX0001.  
Not applicable.  
The bus driving dataset was provided by our partner company Chongqing Hengtong Bus Company and they have not given permission for the researchers to share their data. Data requests can be made via [EMAIL_ADDRESS]. The bus route dataset was extracted from the Amap open platform, and data will be made available through [[https://lbs.amap.com/](https://lbs.amap.com/)]([https://lbs.amap.com/](https://lbs.amap.com/)). The meteorological dataset was sourced from Eastern Weather Net, and data will be made available through [[https://tianqi.eastday.com/](https://tianqi.eastday.com/)]([https://tianqi.eastday.com/](https://tianqi.eastday.com/)).  
This research is supported by Science and Technology Innovation Key R&D Program of Chongqing No. CSTRB2022 TIAD-STX0001.  
The authors declare no conflict of interest.