# Traffic Monitoring System Based on Deep Learning and Seismometer Data
## 1 Introduction  
Many countries invest heavily in traffic monitoring systems [1], which collect and analyze traffic data to derive statistical information, such as the numbers of vehicles on the road and their temporal patterns. Governments use these statistics to forecast transportation needs, improve transportation safety, and schedule pavement maintenance work. Identifying the size of vehicles is a key task that helps to predict noise levels and road damage. The characteristic mix of vehicle types that use a roadway can determine the geometric design of the road based on the Traffic Monitoring Guide report published by the Federal Highway Administration in the United States [2]. Vehicle classification systems make use of many recent advances in sensing and machine learning technologies [3]. Although newer systems perform vehicle classification with higher accuracy, they differ in their characteristics and requirements, such as the types of sensors used, parameter settings, operating environment, and cost. Many traffic monitoring systems rely on vision-based vehicle classification techniques, usually based on cameras, that deliver high classification accuracy ranged between 90%-99% [4], coveringlarge areas compared with emerging alternatives. Although camera-based systems have high classification accuracy, their performance can be affected by weather and lighting conditions as well as other factors. For instance, vehicles can be missed when they are obscured by large vehicles. Furthermore, the system requires huge investments in infrastructures to perform a complete coverage of the road network. Another important problem is the privacy concerns of vehicle occupants, as many people do not feel comfortable being exposed to cameras. An inductive loop detector based on magnetic characteristics of vehicles is one of the most commonly used traffic monitoring systems for vehicle detection and classification [5]. The loop detector system is based on a coil of wire placed under the roadway to capture the change in the magnetic profile signal's characteristics, such as amplitude, phase, and frequency, when a vehicle passes over it [6]. Several studies on the loop detector technique have shown its high accuracy (99% accuracy) for large vehicle classification, such as cars, trucks, and vans [7; 8; 9; 10], it was also proven that loop detectors have no dependency on the vehicle speed [11]. Although the loop detector system is the most widely adopted in-roadway-based vehicle classification technique, it might not be the most suitable system for easy and low-cost implementation, as it requires coil installation under the roadway surface. Various privacy-preserving solutions have been proposed, using different kinds of sensors in, over or at the side of roadways [4]. A combination of infrared and ultrasonic sensors (up to 99% accuracy) [12] or magnetic sensors used in roadways and on the side of roadways with accuracy up to 96.4% in the case of using multiple sensor networks [13; 14; 15]. In addition to previous methods, new methods for traffic congestion monitoring in urban areas were proposed based on GPS, social media data, and network data collected directly from vehicles [16; 17; 18; 19; 20]. These methods have contributed to evolving intelligent transport systems (ITSs) and proved clear information of traffic flow and traffic destiny for urban areas. However, most proposed methods have not achieved a classification accuracy comparable to inductive loops and camera-based systems; moreover, they may require special installations, such as loop detectors in the road [21]. Various vibration-based vehicle classification systems have been developed to avoid these shortcomings. Vehicles produce vibrations from two main sources, the engine system and the interaction between the tires and the road [22; 23; 24]. These signals depend strongly on the size of the vehicle.