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

Figure 2: Traffic Behaviour of Sri Lanka Despite the above-mentioned facts, compared to some other countries where the drivers’ disciplines are very high, in Sri Lanka, vehicles do not move systematically in a lane following one after the other while maintaining adequate space. Figures 1 and 2 which are presented above, illustrate the traffic behavior of Sri Lanka in comparison to other countries such as Russia, Australia, and other European countries. In the first figure, there can be seen adequate spacesbetween the vehicles and all of them are moving forward following one lane. But according to Figure 2, which illustrates the traffic behavior in Sri Lanka, vehicles move incloser to each other without following any lane. This traffic behavior in Sri Lanka is the main issue, especially behind the difficulty in implementing image processing-based traffic monitoring systems since contours cannot be properly generated with this irregular traffic behavior in Sri Lanka. And it is harder to identify vehicles, especially in real-time traffic monitoring systems. Even though color lights are used to reduce traffic congestion since they are not intelligent and programmed for a certain period and there can be frequently seen several instances where the green color is still in “on” state even when there are no vehicles on the road. These phenomena can be commonly seen which causes traffic jams by lacking the chances for the vehicles on other lane’s access to the road. Similarly, a large number of road accidents has also significantly increased dueto the traffic jams in Sri Lanka, which now has become an adverse social impact [10]. Moreover, there can be seen poor technical and digital literacy in Sri Lanka compared to other countries. Even though many traffic surveys are conducted annually, there cannot be seen any proper traffic database. Similarly, there isno any automated system to retrieve past data from that analysis to do comparisons or other estimations such as trafficpredictions [11]. III. L iterature R eview As early as the 1970s, an autoregressive integrated moving average (ARIMA) model for short-term highway traffic flow forecasting was introduced which has been recorded as the foremost approach under ITMS strategies [12]. Since then, scholars and academics from a variety of fields, including transportation engineering, electronic and mechatronic engineering, statistics, economics as well as machine learning have proposed a wide range of models for forecasting traffic flow. Similarly, a considerable number of surveys and extensive analyses have also been carried out on traffic management in recent years, concentrating on various trafficfactors. A massive project was carried out to enhance traffic management and control of Hong Kong’s Road network, which is known as one of the busiest roads in the world [13]. In this project [12], an ITMS was built beginning in 2001 andwas successfully finished in 2010. Traffic monitoring, control operations, data collection, and analysis are among the primary platforms featured in this project to manage traffic congestion. By tracking all major highways and road tunnels, this initiative has ensured effective traffic control [12]. A low-cost sensor-based network instrument for traffic monitoring was developed and tested to be used in a work zone [14]. In that project, the entire sensor network system was used to collect data from that work zone [14]. Then the data was sent for post-facto analysis and uploaded to the internet. Al-Holou et al.[15] developed a multi-dimensional model to estimate the influence of vehicles on the environment, traffic congestion, and traffic safety. This chapter includes a comprehensive literature review on similar traffic monitoring and prediction approaches and effective traffic monitoring and short-term forecasting techniques. First of all, the overview of real-time traffic monitoring and forecasting projects, research and surveys thathave been carried out worldwide and their approaches are evaluated using the above paragraphs with the research gaps identified. Then the literature analysis is further extended, covering the traffic detection and prediction methods in separate sections. © 2023 Global Journals Global Journal of Computer Science and Technology Volume XXIII Issue II Version I 39 ( )D Year 2023 Traffic Flow Forecast based on Vehicle Count