# Effective Bus Travel Time Prediction System of Multiple Routes: Introducing PMLNet Based on MDARNN
## 1 Introduction  
As an effective approach for addressing urban transportation challenges, public transportation (PT) systems are assuming an increasingly vital role in the daily lives of individuals [1]. However, a series of quality of service (QoS) problems, such as long waiting time, low transfer efficiency, and high flow density, etc., influence passengers' travel choice behavior. In recent years, PT companies have realized that seamless transfers between different bus routes are one of the most important factors for the attractiveness of PT services. With the improvement of the intelligence level of PT systems, city buses can offer real-time driving data to optimize the performance of travel time prediction. The performance of bus travel time prediction, which determines the QoS of PT, is affected by many factors, such as bus running state, weather, real-time traffic flow and road features, etc. [2, 3]. Moreover, the effectiveness of the prediction model is also an important factor [4]. To meet the demand of high-level PT services, our goal is to design a highly accurate multi-route bus travel time prediction system through harnessing the influence of impact factors and selecting a reasonable model which considers the varying impacts of differentfactors. Therefore, the key components of the designed system are impact factor selection and prediction model construction [4]. The rationality of impact factors determines the accuracy of travel time prediction outputs to a great extent. Macro impact factors, such as weather, temperature, and air quality, affect all road vehicles and remain relatively stable [5; 6]. On the other hand, local impact factors, such as traffic features and passenger behavior, exhibit time-varying and random behavior, introducing uncertainty and complexity to travel time prediction [7]. Furthermore, real-time traffic flow can also influence the current status of bus travel or dwell time [3]. In the past few years, researchers have explored multiple approaches to represent real-time traffic features. Regarding the characteristics of bus operations, in our prior research, we introduced two distinct traffic factors--namely, the travel factor and the dwelling factor--to effectively characterize the prevailing real-time traffic conditions [3]. However, the literature [3] solely focused on Bus Arrival Time prediction, overlooking the aspect of traffic transfers. According to the findings in [8], waiting time, the frequency of transfers, the count of stops, and bus travel time are four pivotal factors influencing passengers' experiences. This underscores the need to refine Bus Arrival Time predictions. In particular, integrating transfer behavior and transfer point waiting time can enhance the QoS of urban PT services. Consequently, through analyzing the path model of multiple routes, we divided bus travel time into four components: Current Driving Segment Travel Time (CDSTT), Bus Stop Dwelling Time (BSDT), Stop-to-Stop Travel Time (SSTT), and Transfer Point Waiting Time (TPWT). However, a real challenge lies in capturing passengers' behaviors, which remains largely unknown due to privacy protection policies. To address this issue, this paper introduces the concept of a \"travel chain,\" building upon the ideas presented in [9; 10]. This travel chain aims to supplement the deficiency in travel data by incorporating transfer behaviors from the raw dataset. In order to select an appropriate prediction model, the recurrent neural network (RNN), which is well-suited for addressing time series problems, is considered an ideal choice for Bus Arrival Time prediction [11]. To improve the prediction accuracy, a broader set of impact factors are used. Additionally, the attention mechanism is introduced to construct prediction model, such as the macro dual-stage attention-based recurrent neural network (DA-RNN) [12].