# Estimation of Lane-Level Traffic Flow Using a Deep Learning Technique
## 1 Introduction  
Self-driving cars are one of the most important areas of study in the field of artificial intelligence. By combining the data from internal and external sensors, and accurately detecting the surrounding objects, driving safety can be improved. In recent years, object detection in computer vision has been developed, including detecting nearby vehicles, pedestrians, and obstacles, and measuring the distances between them, recognizing traffic signals and signs for self-driving cars, and counting the amounts of vehicles passing on a highway. Fully-Connected Network (FCN) techniques to detect and localize objects in the form of 3D boxes from range scan data have been developed.  
This paper focuses on various methods of object detection and tracking for vehicles on a highway. These were applied to estimate regional traffic flow, detect objects, and measure their position and velocity. To achieve this, a neural network is proposed to estimate surrounding traffic flow. It is combined with processes of moving object detection and can calculate the relative position and velocity of moving objects. By individually identifying three lanes in each direction (northbound and southbound), and classifying and counting the vehicles, the vehicle density of each lane can be calculated correctly. This newly proposed method can provide real-time accurate data for traffic control and analysis.  
In Taiwan, traffic jams during commuting hours are a tremendous problem, especially for such a highly populated and urbanized small island. The main purpose of this study was to provide data to the Traffic Control Center to reduce the occurrence of traffic jams by applying artificial intelligence techniques. Existing models for vehicle detection and tracking sometimes have the problem of repeat counting due to inconsistencies in object tracking. To solve this problem, our model introduced \"virtual lines\" and \"hot zones\" (detailed in Section 3.3.2) to accurately count the number of vehicles passing in each lane of the section of freeway. The benefit of adding a hot zone is that when a vehicle moves closeto the virtual line, it can be tracked more accurately, and repeat counting is avoided. A deep learning procedure using a neural network technique was designed for estimating the velocity of vehicles. By providing the data obtained from our system to the Traffic Control Center, each car, self-driving or not, can be recommended the optimal route and, thus, the traffic situation can be improved.  
This paper has three main features. First, the traffic flow counted by the Freeway Bureau, the Ministry of Transportation and Communications (MOTC), Taiwan, uses Electronic Toll Collection (ETC) sensors to count cars; however, this misses cars that do not have the ETC installed. In this study, object detection and tracking were used to count vehicles and estimate velocity, which not only solves the aforementioned problem, but also reduces costs. Second, in this study, the traffic flow of each lane was calculated individually, rather than simply in two directions. Third, our technique is able to represent different colors in each lane according to the velocity level. These features are beneficial to traffic flow monitoring and control by the Freeway Bureau, MOTC, Taiwan, especially in situations of heavy traffic, such as those seen during commuting hours and holidays.