# Impacts of Weather on Short-Term Metro Passenger Flow Forecasting Using a Deep LSTM Neural Network
## 1 Introduction  
As one of the most important issues in an urban metro system is part of smart cities [1], passenger flow analysis and forecast for public transport have been widely studied over the last two decades. Metro systems play an important role in meeting urban transport demand. Because of their high speed, efficiency, volume, and punctuality, urban metros are the first choice for many daily commutes [2]. More accurate short-term passenger flow forecasting for metro systems is important in solving a series of problems in the process of urban development, such as mitigating the adverse effects of traffic congestion, reducing vehicle pollution, better planning of public transit networks, and better land use planning along metro lines. Following an overview of the previous studies, we can conclude that the issue of short-term passenger flow forecasting is an ongoing process, primarily studied from two perspectives, namely data modeling and methodology modeling. In the field of data modeling, accurately forecasting passenger flow is fairly challenging because many factors affect the target station's passenger flow [3]. The most widely used input variables are historical passenger flow data and their corresponding spatiotemporal data. Although experimental results show that weather data [4; 5] were able to represent the passengerflow, only limited research efforts have been directed to the issue of passenger flow forecasting with the weather factor due to the difficulties in data acquisition and data fusion among weather data, spatiotemporal data, and historical passenger flow data [6]. More researchers have preferred to analyze the relationship between historical weather conditions and the corresponding historical passenger flow, especially focusing on the negative impacts of inclement weather on passenger demand [7; 8]. The problems of the impacts of weather on passenger flow forecasting in the available literature are described as follows. First, due to the scarcity of dynamic passenger flow data caused by weather factors, the addition of one weather variable, or especially multiple weather variables, would not improve the forecasting accuracy, which would even decrease [9]. Thus, some researchers have only added one weather variable for passenger flow forecasting. For example, [PERSON] et al. [9] used random forest (RF) to rank the importance of all the input variables, including the five weather variables, namely temperature, humidity, weather state, wind speed, and visibility, while only temperature was quantified as the dominating weather factor for developing a short-term forecasting of passenger demand under an on-demand ride service platform in Hangzhou. [PERSON] et al. [10] evaluated the rain effect on short-term passenger flow forecasting for the Shenzhen metro. [PERSON] et al. [11] proposed a short-term prediction of passenger flow on bus routes in Changsha by considering rainfall intensity. [PERSON] et al. [12] proposed a short-term passenger flow forecasting model of four bus lines under rainy conditions. Furthermore, to extensively explore the impacts of multiple weather variables on passenger flow forecasting, more than one weather variable was used together to build the models. For example, [PERSON] et al. [13] applied wind power, average temperature, dew point temperature, air quality index, humidity, rainfall, and cloud cover as the exogenous input variables to propose a forecasting model of station passenger flows for Shanghai rail transit. However, it did not explicitly indicate if the forecasting performance had improved or not using the seven weather variables. [PERSON] et al. [14] selected weather conditions (i.e., sunny, cloudy, etc. ), wind direction and speed, temperature, humidity, and air pressure for the prediction of passenger demand for different air routes. However, these weather variables, combined with day of the week and holiday, are all defined as exogenous input variables.