# Impacts of Weather on Short-Term Metro Passenger Flow Forecasting Using a Deep LSTM Neural Network
## 1 Introduction
Although the experiment verified that the proposed model with such exogenous input variables was better than that without the exogenous input variable module, it did not powerfully verify whether the addition of weather variables would be helpful for improving the forecasting performance. Second, most studies have aggregated daily and monthly passenger flows with weather data to explain the daily and seasonal ridership variation. For example, [PERSON] et al. [15] examined the effects of temperature, rainfall, wind speed, and snow on daily bus ridership forecasting for Pierce County, Washington, during 2006-2008. The results showed that each weather variable had a significant effect on bus ridership for at least one season. [PERSON] et al. [16] provided a systematic and comprehensive overview to evaluate the impact of daily weather on individual daily travel activities. The results showed that rain, snow, wind, and temperature could influence the travel behavior, departure time, travel time, and routes. [PERSON] et al. [17] investigated the seasonal and regional impacts of weather conditions on individual travel mode choice in Sweden by using the weather and travel data for 13 years with the multinomial logit method. The findings showed the existence of seasonal and regional variances, indicating that the weather contributed to individual travel mode choice. The literature [15; 16; 17] reviewed above indicates that such day-level or season-level studies do not clearly distinguish daily travel pattern changes, especially during the morning and evening peak hours. The study of weather impacts on hourly passenger flow forecasting may be more meaningful than daily, monthly, or seasonal passenger flows, which can reduce the negative impacts of weather on daily commutes. In the field of methodology modeling, many successful and high-performance passenger flow forecasting models have been proposed by using different methodologies. The authors in [18] summarized them into two categories, namely parametric methods and non-parametric methods. In a data-rich environment, deep neural network-based models, especially sequence models such as recurrent neural network (RNN) [19], long short-term memory neural network (LSTM_NN) [20], and gate recurrent unit neural network (GRU_NN) [21], have been comprehensively applied to traffic flow forecasting. An RNN-based microscopic car-following model was developed to capture and accurately predict traffic oscillation in Emeryville, California [22]. The results indicated that RNN had a stronger performance in predicting the trajectories of a group of subsequent vehicles given the trajectory of the first vehicle and initial/boundary conditions for the following vehicles. A novel traffic volume forecasting model based on an LSTM_NN was proposed in [23]. The experimental results showed that the proposed LSTM_NN was more robust for traffic volume forecasting than other state-of-the-art methodologies. A deep GRU-based car-following model was proposed to better capture and describe the complicated human behaviors [24]. The data were retrieved from the southbound direction of U.S. Highway 101 in Los Angeles, California. In summary, motivated by the close relationship between passenger flow and weather conditions, and along with the high-performance of an LSTM_NN in traffic flow forecasting, we applied a deep LSTM_NN to develop a metro passenger flow forecasting model by fusing historical hourly passenger flow data and the corresponding temporal and weather data in a station-level study. The main contributions of this paper are as follows. (1) An hourly passenger flow forecasting model considering weather conditions was developed by using a deep LSTM_NN.