| Abstract |
Traffic management is a subject of improvement all over the world, may it be the emerging or developed countries. Road safety and vehicle management requires continuous improvement mechanisms to ensure the safety of public. With the widespread growth of vehicles and thus the traffic, road coercions as well as threats to life have increased, making the management of traffic as a daunting task. A mismanaged traffic administration lead to rising road accidents, overcrowding of traffic movement, merger of distinct vehicles into prohibited lanes, and numerous additional coercions that could be menacing for life. In this paper, aggregated channel feature (ACF) algorithm is applied to track the vehicles. Further, the algorithm is enhanced for the traffic flow management with the use of classifications of various groups of transportation, enabling the road management infrastructure to identify the nonvehicle versus vehicle and further recognize vehicles according to the type of vehicle, size of vehicle, and status of vehicle as emergency or non-emergency vehicle. This helps building up of congestion as well as avoiding coercion. The proposed implementation of ACF to the road traffic for smart cities has detected true positive rate of 80, 89%, has detected true positive rate of 69 and 79% having nonvehicle detected with assigned priority. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. |
