## 1 Introduction  
Vehicular Ad-hoc Networks (VANETs) are an important class of ubiquitous computing, which operate as a key technology for enabling the VANET applications [1, 2, 3, 4]. VANETs have recently provided their users with the means for safety management and data management, where the control methods are designed to work under any circumstances based on network dynamics [5]. On the one hand, the use of centralized [5] and distributed algorithms makes traffic management more complex one regarding the increasing adoption of vehicles in VANETs. On the other hand, the increase in traffic affects urban transport indirectly, and it increases the delay in transportation, fuel consumption, and emission values [6]. Even in real-time with precise traffic flow predictions, the traffic management systems in VANETs often play a vital role in predicting the traffic flow. The Intelligent TransportSystem (ITS) offers reliable traffic management services in such scenarios, enabling the user to coordinate the network in an optimal and safer way, to understand the environment. ITS information and communication technologies further enhance the management of traffic and mobility [5]. In order to ensure efficient and secured transmission, ITS strongly supports communication within VANETs between road units (vehicles) and roadside units (RSU). Several other challenges, such as limited scalability, flexibility, poor connectivity, and inadequate intelligence, lead to delays in time and congestion in communication channels, which significantly affect the performance of VANETs. Various investigations are conducted in the preexisting literature [7; 8; 9] to solve such complexities in traffic management. These methods are used to solve the traffic congestion in VANETs, including difficulties in scalability, performance, and management. Several optimization tasks are undertaken in order to ensure the regulation of traffic flow dynamics in an urban scenario [10]. Congestion regulation is limited in existing methods in terms of accuracy and timely traffic predictions. The congestion effects in VANETs are treated as a classification problem and various solutions are offered, but the majority of systems are not fully usable [11; 12]. The ITS systems support traffic predictions by analysing the network parameters to mitigate these limitations [13]. The predictions involve route planning and reprogramming to reduce the rate of congestion [14; 15]. Furthermore, the stochastic and non-linear traffic characteristics [16] and traffic-flow are challenging to predict [17]. VANET methods traditionally use linear and machine traffic-flow prediction models based on network density, which fail to interpret the non-linear uncertainty [18; 19; 20]. Moreover, researchers are attempting to further apply this proposed method to practical, artificial-intelligence-based approaches based on optimization, metaheuristics, machine learning, and deep learning [21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44]  
With exponential traffic growth and high computational resources, traffic management analysis becomes complex [45]. Therefore, a high-end intelligent system is needed for flexible and smoother transmission in vehicles with various constraints, such as uncertainty, exponential growth, and high computational resources. A high-end system uses deep learning models to manage network abstraction and resource optimisation. Researchers are developing various security-based protocols [46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58] and machine learning techniques [59; 60; 61; 62] for different applications for wireless communication and data prediction. With this motive, we introduce the Bat Algorithm (BA) [63] as a vital factor and provide inputs that enable the Deep Neural Network (DNN) routing algorithm to make optimal decisions. In this respect, this paper presents the following contributions:  
1.