# An Intelligent Time-Series Model for Forecasting Bus Passengers Based on Smartcard Data
## 1 Introduction  
Public transportation is considered to be an effective solution to traffic congestion and environmental pollution. The Federal Transit Administration (FTA) also believes that public transportation is an effective way to reduce traffic congestion, air pollution, energy consumption, and private vehicle use [1]. The use rate of buses accounted for 46% of all public transportation use in 2016 by people aged over 15 years according to the Taiwan Ministry of Transportation survey [2]. Taiwan's EasyCard Company promoted the smartcard system in 2002 based on the idea of \"one card in hand, unimpeded travel\". It was the first card to be issued for Taipei mass rapid transit and was then expanded to the Taiwan railway, Taiwan high-speed railway, and various other types of public transportation. Smartcards can collect information about vehicle routes, schedules, and real-time driving conditions through the automatic fare collection (AFC) system for vehicle monitoring, which can greatly improve public transportation efficiency and safety. The AFC system, when referring to the transportation system [3], is also called the smartcard system. The smartcard system is regarded as a dynamic and real-time data source for the public transportation system. Ithas attracted a significant amount of attention from researchers, and many studies have used smartcard data [3; 4]. Although the smartcard system helps passengers greatly reduce their transaction time and shortens the time taken to board/exit buses, it also helps the bus industry collect large amounts of data to improve their service quality. Despite the utilization of the bus system, serious traffic congestion still occurs. Traffic flow describes the number of vehicles passing through a specific road section within a predetermined time interval [5]. It is different from traffic congestion, which is caused by excessive travel demand by people, resulting in abnormal traffic flow. There have been many studies on passenger flow predictions. In addition, smartcard systems can increase the convenience of users and help bus operators formulate practical route planning and reform timetables and related policies; however, this all depends on the use of smartcard system data to accurately forecast bus passenger flow. Accurately forecasting passenger flow can help cities implement transportation policies, strengthen local construction, reduce excessive energy consumption and carbon emissions, and improve urban ecosystems to achieve sustainable development. The accessibility of the urban bus system is greater than for other modes of public transport, as this system utilizes the road network; however, passenger demands are affected by a number of factors such as crowding and different weather conditions. [PERSON] et al. [6] confirmed prediction models would be better if the weather conditions were considered. The number of bus rides varies depending on the time of day, but there are still expected peak periods. For example, there will be many passengers during peak hours on weekdays and working days and at times when leisure activities are taking place during the holidays. We must consider passengers' needs, but external factors are also important. In the past ten years, many successful traffic flow forecast methods have been proposed, especially deep learning methods. [PERSON] et al. [7] proposed a dynamic radial basis function neural network to predict short-term passenger flow through the Beijing subway. [PERSON] et al. [8] proposed a fusion convolution long-term short-term memory network to forecast short-term passenger demand for ride services. [PERSON] et al. [9] used a combined seasonal autoregressive integrated moving average with a support vector regression model to forecast the demand for the aviation industry. Deep learning methods have led to great progress in transportation research, but there have been few studies on forecasting buses' passenger flow compared with other types of public transportation.