## 1 Introduction  
### Research Background  
Urban road travel time prediction, as a key component of intelligent transportation systems, plays an increasingly important role in modern urban management and travel planning. With the acceleration of global urbanization, traffic congestion has become a universal challenge constraining sustainable urban development, making accurate and reliable travel time prediction particularly critical.  
Problem-oriented challenges: Urban road travel time prediction faces multiple technical challenges. First, urban road networks are significantly more complex than highway systems, containing numerous intersections, traffic signal controls, and interwoven different types of roads. Signal-controlled intersections cause urban road traffic to exhibit typical interrupted flow characteristics, leading to periodic fluctuations in travel times. Second, urban road traffic demand exhibits high dynamism and uncertainty, influenced by numerous factors such as weekday patterns, seasonal variations, and special events. Furthermore, random events such as traffic accidents, road construction, and adverse weather conditions occur frequently, further increasing the uncertainty of travel times. Traditional prediction methods show obvious limitations when addressing these complexities. Statistical models often struggle to capture nonlinear relationships and multi-variable interactions; shallow learning methods poorly handle sudden events; single models are prone to overfitting or underfitting specific traffic patterns. These challenges have generated demand for more advanced prediction methods, particularly those that can integrate the advantages of deep learning while overcoming its inherent randomness.  
Demand-driven requirements: Multiple practical needs further highlight the importance of precise travel time prediction. For traffic management authorities, accurate travel time prediction serves as the foundation for achieving intelligent signal control, dynamic route planning, and emergency resource allocation, helping to alleviate traffic congestion and improve overall road network efficiency. Traffic planners require reliable prediction tools to evaluate the potential impacts of different policy measures and infrastructure investments. From travelers' perspectives, accurate travel time prediction directly affects travel decisions and route choices. Compared to simple historical averages or static estimates, dynamic and reliable prediction information helps travelers make more reasonable plans, reducing unnecessary waiting and detours, and improving travel experience and efficiency. In commercial applications, precise travel time prediction is crucial for logistics delivery, ride-hailing dispatch, and shared mobility services. These sectors rely on high-quality travel time predictions to optimize vehicle scheduling, route planning, and resource allocation, directly impacting operational costs and service quality.  
Given the aforementioned problems and requirements, this study focuses on a key question: how to construct more accurate and stable urban road travel time prediction models. Traditional methods and single deep learning models each have limitations, while the multi-run training and ensemble learning prediction framework proposed in this study aims to combine the advantages of both while overcoming their respective shortcomings. Specifically, the multi-run training method in this study provides a systematic solution to the randomness problem in deep learning model training, while the ensemble learning framework improves prediction stability and accuracy by integrating the advantages of multiple models. This study not only advances methodological innovation in the traffic prediction field theoretically, but also provides technical support for building more intelligent and reliable urban traffic management systems in practice, ultimately contributing to alleviating urban traffic congestion, enhancing citizen travel experience, and promoting sustainable urban development.