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

Traffic Flow Forecast based on Vehicle Count Abstract - Real-time traffic predictions have now become a time-being need for efficient traffic management due to the exponentially increasing traffic congestion. In this paper, a more pragmatic traffic management system is introduced to address traffic congestion, especially in countries such as Sri Lanka where there is no proper traffic monitoring database. Here the real-time traffic monitoring is performed using TFmini Plus light detection and ranging (LiDAR) sensor and vehicle count for next five minutes will be predicted by feeding consecutively collected data into the LSTM neural network. More than ten separate prediction models were trained, varying both window size and the volume of input data delivered to train the models.Since the accuracy results of all prediction models were above 70%, it demonstrates that this system can produce accurate predictions even if it is trained using less input data collection. Similarly the sensor accuracy test also resulted in 89.7% accuracy. Keywords: traffic monitoring, lstm neural network traffic predictions, vehicle count, traffic flow forecast, real- time traffic monitoring, lidar sensor traffic monitoring. I. I ntroduction ehicle usage has risen significantly throughout the world in recent years. As a result, road traffic has gotten more complex and chaotic. Traffic congestion is becoming a more highlighted issue in cities throughout the world, owing to continuous urbanization and population increase. According to the Victoria Transport Policy Institute’s Congestion Costing Critique (CCC) Critical Evaluation of the “Urban Mobility Report” (UMR), which was published on 1 September 2021 [1], congestion cost is estimated to cost between $130 to $500 per capita annually, particularly in comparison to $2,000 in crash damages, $3,000 in vehicle ownership costs and $1,800, $600, $400 respectively in parking, pollution damage, and roadway costs. Similarly, the UMR estimates that by 2025, congestion cost will have risento $200 billion [1]. According to the statistical records by The National Highway Traffic Safety Administration (NHTSA) [2], in 2013, more than 2.3 million injuries and 32,719 deaths were recorded due to vehicular accidents. Among those recorded fatalities majority were aged between 4–27 [2]. According to the NHTSA report, the direct economic cost and the social impact that occurs due to vehicular accidents are accounted for $871 billion per year, with an average of 5.8 million collisions each year only on the United State’s highways [2]. NHTSA statistical records also emphasize that most of road accidents occur as a result of traffic congestion [2]. Further almost 5.5 billion hours are lost recorded due to traffic congestion, resulting in 2.9 billion gallons of fuel loss [2]. It is also estimated that automobile tailpipes produce around 31% (or 56 billion pounds) of CO2 due to vehicle traffic congestion each year [3]. The Global Traffic Scoreboard for 2021, published by data analytics company INRIX [5], provides insights into how people move throughout the world and provides more detailed information on congested traffic and commute times during the peak and off-peak hours in over 1000 urban cities across 50 countries. It also examines the per capita time spent on traffic congestion [5]. According to research [5], the most congested and crowded cities are "either older or developing cities". It also exclaimed that many of those developing cities where the significant population expansion is met, yet have inadequate infrastructures [5]. Therefore, the failure to develop adequate transportation infrastructures and roads to meet the increasing demand has led to traffic congestion. Similarly, when global traffic mitigation strategies are considered, there can be seen several efficient approaches, such as intelligent traffic management systems. However, most of these mitigation methods have been limited to developed countries. Most of the remaining countries do not even have adequate traffic sensing infrastructures to monitor the traffic flow [5]. Therefore, there is no way to have an acknowledgment or a method to analyze the existing congestion having a confirmation of whether the implemented or existing congestion mitigation projects have achieved the desired progress. Since the traffic is always random, which means the vehicular traffic on the roads varies from time to time depending on the number of vehicles on the road, it is important to have a clear dataset of the number of vehicles on the road at a considerable very short intervals and effective feedback on-road operations are essential in order to carry out effective traffic congestion management. Both vehicle detection and surveillance play an integral role in both effective traffic congestion management and intelligent traffic management systems (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 ITS success V Pavanee Weebadu Liyanage α & K. P. G. C. D. Sucharitharathna σ Author α : School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University Perth, Australia. e- mail: p.weebadul@student.curtin.edu.au Author σ : Department of Electrical and Electronic Engineering Sri Lanka Institute of Information Technology Sri Lanka. e-mail: charith.s@sliit.lk © 2023 Global Journals Global Journal of Computer Science and Technology Volume XXIII Issue II Version I 37 ( )D Year 2023