# Effective Bus Travel Time Prediction System of Multiple Routes: Introducing PMLNet Based on MDARNN
## 3 Materials and Methods
### System Architecture
#### 3.1.1 Framework  
The real-time bus travel time prediction system is designed according to the demand of Chongqing Public Transportation Company, China. And the framework of system is shown in Figure 1. As shown in Figure 1, two types of databases, namely the bus driving database and public database, are set as inputs to the bus travel time prediction system. The cloud server collects the relevant data and subsequently provides Bus Arrival Time prediction services using the proposed PMLNet. Additionally, real-time data transmission occurs between the mobile application and the cloud server.  
The bus driving dataset is offered by the cloud control system of Chongqing Hengtong Bus Company, which encompasses historical travel data from approximately 3000 buses in Chongqing, China. The intelligent on-board systems of the buses collect 46 real-time bus cruising attributes, which are uploaded to the cloud server every 2 s. These 46 attributes are extracted and computed into 48 local factors (denoted as \(IF_{local}\)), including the information of date, position, speed, engine and mileage; these are denoted as Equation (1).  
\[IF_{local}=\{IF_{local}^{1},IF_{local}^{2},\dots,IF_{local}^{48}\} \tag{1}\]  
The public database consists of two sub-datasets: the bus route dataset and the meteorological dataset. The bus route dataset is extracted from the Amap open platform [34], which collects the information at one-meter intervals along the bus routes. Each entry in the bus route dataset consists of 8 attributes: route, origin, destination, stop status, longitude, latitude, stop name, and stop number (0 for non-stop). The meteorological dataset is sourced from Eastern Weather Net [35], a public service website operated by the Shanghai Public Meteorological Service Center. It records 8 attributes: date, day of the week, air quality index and level, maximum and minimum temperature, weather condition, and wind speed.  
To analyze the influence of environment factors, we utilize driving data from route 805 during the period of April to November 2016, as shown in Figure 2. Specifically, Figure 2a  
Figure 1: The framework of the real-time bus travel time prediction.  
focuses on temperature, which presents the distribution of average travel time across different temperature ranges within the study period. Meanwhile, Figure 2b compares average travel time between holidays and workdays over the same period to reveal the effect of holiday status. In addition, Figure 2c explores the correlation between air quality levels and average travel time. Further, Figure 2d demonstrates the differences in average travel time during peak hours (7:00-9:00, 17:00-19:00) and off-peak periods. Finally, Figure 2e shows the variation in average travel time across Monday to Sunday, reflecting the influence of weekdays. As shown in Figure 2, the average travel time is influenced by air quality, weather condition, time, and week day. Therefore, this paper extracts 7 macro impact factors from meteorological dataset and denotes them as  
\[IF_{macro}=IF^{1}_{macro},IF^{2}_{macro},\dots,IF^{7}_{macro} \tag{2}\]