## 1 Introduction  
Existing transportation systems are constantly under serious pressure as the number of vehicles and the need for mobility have enormously increased in densely populated and urban areas. Accordingly, finding effective and reliable routes has become a common logistics problem that has been researched for decades. The goal of a unique vehicle-routing problem is to find cost-effective routes for a large number of vehicles [1; 2; 3]. Meanwhile, new issues have arisen, especially in traffic factors such as low data rates, energy efficiency, congestion, and the fastest route [4; 5; 6]. This study focuses on seeking the most effective and the shortest route from a source point to a destination point in urban areas. Vehicle routing is an integrated programming problem that aims to determine the optimal set of routes for a set of vehicles. To address these issues, several efficient-routing protocols have been developed. A previous study [7] evolved an efficient linear-scheduling algorithm for multiple routes, which influences time delays caused by vehicles assigned to specific routes. The Multiple Access with Salvation Army (MASA) protocol was introduced to find the best route [8]. The proposed method combines several factors into a multi-weighted graph. These methods are intended for pedestrian tourists as well. Other issues that arise due to routing concern the network lifetime and the energy consumption. For an efficient route, the ACO-based coverage-diversity framework was proposed [9; 10]. This method addresses newly discovered customer requests (vehicles). Antcolony optimization and hybrid butterfly algorithms were introduced for energy-efficient routing [11] to maximize the network lifetime. Along with this optimization, which makes the network scalable and improves performance, several swarm-intelligence-based approaches have been proposed by researchers to facilitate vehicle routing and to identify near-optimal routes in a reasonable amount of time. Most of the predominant strategies are based on ACO, particle-swarm optimization (PSO), artificial bee colony (ABC), flower-pollination cuckoo search (FPCS), and others [8; 12; 13; 14]. These approaches address the issue of efficient routing with extension but overlook routing durability. Therefore, hybrid techniques have been proposed to address the optimized-routing problem. Hybridization includes the integration of two or more meta-heuristic algorithms by mutually handling their limitations and balancing the trade-off among exploitation and exploration rates. Furthermore, hybrid algorithms integrate the best aspects of the chosen algorithms to produce the most effective algorithm. Because the ACO algorithm can initiate local searches only, the hybrid ant-colony-optimization-particle-swarm-optimization (ACO-PSO) method was introduced to increase the network lifetime. However, PSO provides global updates to ensure a reliable route [15]. A heterogeneous adaptive ant-colony-optimization algorithm was introduced to solve the traveling-salesman problem by providing an efficient route [16]. Ant beta and alpha values were adapted and mutated to address the problem of the worst-performing ant to find the best route. Two hybrid algorithms based on ACO were used to address an efficient neighborhood route [17]. To support wireless-sensor networks (WSNs), an efficient routing protocol was introduced [18]. With these premises, such an approach opens up possibilities to devise more effective and sustainable future mobility and transportation systems. Most existing approaches in the aforementioned contexts attempt to determine the best route but do not account for vehicle congestion or automatic detection, which is the motivation behind this work. They normally assume that these services are provided in a static and passive way, which explains the urgent need for a more efficient route that addresses traffic congestion and automatic vehicle detection.