## 1 Introduction
The GyTAR routing protocol has exhibited superior network performance compared to GSR. With the rapid proliferation of vehicles in recent years, traffic monitoring systems have become an indispensable component of ITS. These systems can monitor sensor-equipped traffic vehicles for identification, speed, and traffic congestion in real-time, offering various applications. The smart traffic monitoring system (STMS) [12] is a traffic surveillance framework designed for monitoring traffic congestion and managing traffic lights. It operates as a fog node, gathering real-time data from geographically dispersed sensors and transmitting them to the cloud for storage and processing. Additionally, it can be adapted to detect traffic incidents that require immediate assistance amidst congested traffic. However, the STMS faces challenges due to the need to transmit the collected data to a base station, which can lead to bandwidth constraints, substantially impacting latency-sensitive applications. The infrastructure-free traffic information system (IFTIS) [13] offers a distributed mechanism for vehicles to collaborate in collecting traffic information on a road segment without relying on roadside units (RSUs). In this approach, the group leader of each cell in a road segment gathers traffic data and forwards them to the next junction using a greedy strategy. This technique offers substantial cost advantages as it eliminates the need for fixed infrastructure such as base stations. Moreover, it benefits from improved connectivity with an increase in the number of vehicles. Recent research in vehicular networks has attempted to utilize artificial intelligence to address network latency and energy efficiency issues. In [14], they proposed an algorithm to improve QoS in vehicular networks by jointly scheduling deep neural network (DNN) inference tasks at the microarchitectural and network levels. This algorithm is a technique for making two-level scheduling decisions that utilizes deep reinforcement learning to respond to dynamic environments. The technique aims to minimize the total weighted sum of response time and energy consumption for all jobs under the constraints of response time, energy consumption, and storage capacity. In [15], an energy-efficiency secure offloading(EESO) technique based on asynchronous advantage actor-critic (A3C) was proposed in non-orthogonal multiple access (NOMA) offloading scenarios using physical layer security (PLS) techniques. This technique applies asynchronous deep reinforcement learning for highly dynamic automotive edge computing to reduce the energy consumed by the system and protect confidential information from eavesdropping. The centralized routing scheme with mobility prediction (CRS-MP), proposed in [16], introduces a centralized routing protocol for end-to-end unicast communication. By leveraging artificial neural networks (ANNs) to learn vehicle mobility patterns, the protocol offers a routing technique to improve packet delivery probability and minimize delay. Notably, the scheme does not necessitate continuous monitoring of vehicle mobility. Instead, it dynamically selects the routing path based on the probability of vehicle mobility, either by the software-defined networking (SDN) controller or the RSU/base station (BS). Our study proposes multiple-junction-based traffic-aware routing (MJTAR), demonstrating lower latency and higher packet delivery success rates than existing traffic-aware geographic routing methods such as GyTAR [11]. MJTAR incorporates two fundamental mechanisms. First, it introduces an enhanced infrastructure-free traffic information system (E-IFTIS) mechanism to identify vehicle traffic from multiple junctions. E-IFTIS extends the capabilities of existing IFTIS [13] to collect vehicle traffic data for a road segment from up to two junctions. Second, MJTAR offers an optimal multiple junction selection scheme (OMSS) mechanism to determine the optimal packet path based on multiple junctions.