## 1 Introduction
We develop an Arduino-based smart bus system that can be tagged on public buses and can be queried by patients through representation state transfer application program interfaces (APIs) to provide them with the position of the buses through web app or SMS relative to their origin and destination stop. The back end of the proposed system is based on message queue telemetry transport (MQTT), which is lightweight, data efficient and scalable, unlike the traditionally used hypertext transfer protocol (HTTP). To ensure the reliability of the smart transport system, our solution makes use of the real time location of the buses to compute the approximated time for the bus to reach a particular destination. By saving a bus's location data on the server together with corresponding timestamps, the system is able to estimate the arrival time of the bus to a particular bus stop. Alternatively, the arrival time can also be approximated using services like Google maps [20]. The Internet of Things (IoT) enables advanced services by interconnecting physical and virtual things based on existing and evolving inter-operable information and communication technologies. The IoT gives immediate access to information about physical objects and leads to innovative services with high efficiency and productivity [21]. Building on the power of IoT, we demonstrate its application in the transportation system by developing an IoT-based smart bus system. Generally, a smart bus system consist of four basic components, namely smart bus depots, smart bus stops, smart buses and interactive citizen interfaces (web portal based and smart phone app based). Each of these components are connected through the Internet. The smart bus depots, smart bus stops and smart buses consist of a number of heterogeneous wireless and embedded sensors. These sensor networks are connected to the city internet backbone through Wi-Fi hotspots in the bus stops, depots and inside buses. These intelligent autonomous devices attached to or embedded into the system senses the user requirements and interacts with them, shares information with other devices and takes decisions without any human intervention. Buses are widely used public transportation in many cities today. Normal buses can be converted into smart buses with the incorporation of intelligent sensors and IoT devices. Our contributions in this paper are three fold:  
* Using a deep learning approach, we predict patient traffic flow. * We combine the deep learning patient load prediction and hospital assignment using the predicted patient load as criterion to perform the intelligent hospital assignment. * We develop an IoT-based smart bus system. We explore scalable and efficient techniques that allow the system to handle increased requests for data. The remainder of this paper is organized as follows. In Section 2, a review of the relevant literature and machine learning structures is presented. In Section 3, we model our system architecture and training model. In Section 4, we propose a deep learning-based patient load prediction method. In Section 6, we present our proposed IoT-based smart bus system that allows the estimation of the time for the smart bus to arrive at a bus stop, or the time for it to reach a destination such that transferred patients do not experience unnecessary delays due to transport congestion. Finally, Section 7 concludes the paper.