Global Journal of Computer Science and Technology, D: Neural & Artificial Intelligence, Volume 23 Issue 2

a) Traffic Detection Methods Vehicle detection and surveillance play an integral role in both effective traffic congestion management and ITMS. Since ITMS plays a critical role in national traffic management systems (TMS), the quality of provided data and the geographical arrangement of traffic sensors are also important factors for ITMS success [6]. Similarly, both the vehicle detection and surveillance functions are always subject to constantly being improved in order to increase vehicle detection and monitoring, counting their direction headway, and speed, and categorizing vehicles. Further the real-time traffic information such as the number of vehicles and various sorts of road users and the vehicle types are useful to enhance the performances of the traffic management system. Vehicle detection technologies that are widely used can beclassified into three groups: intrusive, non - intrusive, and off- roadway sensors [16]. The inductive loops, magneticdetectors, piezoelectric sensors, weight - in-motion sensors and pneumatic road tubes are considered invasive sensors according to the above classification. These are usually embedded in the road surface after saw-cutting the surface or adding roadway holes. The detection methods such as vision- based systems such as image processing traffic monitoring systems, infrared sensors, microwave radar and ultrasonic detectors are categorized as non-intrusive sensors which can be installed atop roadway or roadside surfaces or mounted overhead. Remote sensing by airplane or satellite, as well asprobe vehicles equipped with GPS receivers, are examples of off - roadway sensors that do not require installation on highways [16]. More detailed descriptions of these technologies are available in [17], [18]. Consequently, these sensors are not suitable for large-scale integration or temporary installation. They are exclusively stationed in strategic areas and operate independently of one another. Video Image Processor is a very common traffic monitoring method since now Image processing has become a tendency and the most prominent traffic monitoring system in the world [6]. Video Image Processor (VIP) systems normally consist ofa camera, a processor-based workstation for analyzing the images, and software for understanding the images and transforming them into data. This can also be operated in multiple lanes. Image processing systems provide live images of real-time traffic status, which covers multiple detection zones. So that it offers broad area detection [19]. In addition to that, wide-area detection can also perform by gathering information generated from cameras located at different locations. Vehicular detection of the image processing systems is performed with the assistance of the contours drawn in the snapshots taken in constant time intervals thereby the vehicular presence is identified. It offers occupancy, classification, and count of vehicles, as usual in most other sensors. Moreover, in the literature, several disadvantages of image processing have also been discussed. Being sensitive to weather conditions, vehicle shadows, and dust on the camera lens is notable. Lane closure requirements for installation and maintenance, specific camera mounting height requirements for better vehicle presence detection and speed measurement, higher installation, and maintenance costs are also significant drawbacks of this camera-based traffic monitoring system [19]. In spite of the weaknesses like costly equipment for thetransfer of real-time video-image data, separating algorithms required for day and night traffic detections, possibilities of discrepancies appearing during traffic data transition, and performance prone to obscurants and heavy atmospheric conditions cannot be ignored [19]. Besides image processing, there are a variety of technologies for traffic monitoring that use various types of electronic sensors. Light Detection and Ranging (LiDAR) technology is a novel technology in which research and investigations have been performed in recent years [24], [25]. LiDAR is a remotesensing method that uses light in the form of a pulsed laser tomeasure ranges of variable distances. The point cloud of LiDAR data is made up of thousands of points in X, Y, and Z coordinates. A point cloud depicts the environment in three dimensions. However, point cloud data is huge and contains duplicate information [25]. Downsampling, noise reduction, ground removal, object grouping, distant irrelevant object rejection, and ultimately vehicle recognition utilizing point cloud data are all part of this architecture. The authors of [26] have proposed a technique for removing backdrops and detecting lanes from a point cloud based on roadside LiDAR data. Several articles and researchers have proposed many approaches for vehicle detection using LiDAR technology [26]. The authors propose an L-shape fitting model for automobile identification. It makes use of the fact that a vehicle's top view in LiDAR dataresembles an L shape. A segment-based technique for recognizing automobiles using mobile LiDAR has also been suggested [25], [26]. Geometric characteristics such as size, form, and height are retrieved for categorization in this method. The distance traveled by the segments and the direction of movement is utilized to locate moving autos [25].The strengths and weaknesses of each traffic monitoring technique are compared in Table 1 below. © 2023 Global Journals Global Journal of Computer Science and Technology Volume XXIII Issue II Version I 40 ( ) Year 2023 D Traffic Flow Forecast based on Vehicle Count