The Vehicular Ad-Hoc Networks (VANETs) represents an evolving technology designed to support the rapid development of advanced vehicular applications. The increasing growth of novel applications and services within vehicular environments has gained significant interest from the research community.^{1,2,3,4,5,6,7} VANETs are categorized by their widespread, infrastructure-less and lack of centralized control.^8,9 These networks facilitate radio communication between moving vehicles using Dedicated Short Range Communication (DSRC) technology.¹⁰ Wireless Mesh Networks (WMNs), are multi-hop communication networks with self-organizing nodes that operate without a centralized coordinator.¹¹ Vehicular Mesh Network (VMesh) is a hybrid networking paradigm that incorporates the features of both VANETs and WMNs. It enables dynamic networking among vehicles, where nodes (vehicles) can be added, updated, or removed.

However, the VMesh environment causes several obstacles due to its intrinsic properties, such as dynamic vehicle mobility, frequent topology changes, lack of centralized management, non-uniform node density and neighborhood configurations, use of broadcasting and geocasting communication, congested and unreliable wireless channels, hidden terminal issues, limited resource capacity, and risk of security attacks.

The motivation of this article are as follows: Modern intelligent transportation systems, which advance real-time transmission between moving vehicles, however the network topology changes, connectivity interruptions and packet loss are facing major issues in vehicular milieu. Existing methodologies often struggle due to rapid and unpredictable changes in the environment which leads to packet loss, congestion and longer delays. To overcome these issues this article estimates the reliability of links, adapt to changing network conditions and maintain reliable data transmission. The main contributions of the article are as follows:

The rest of this article is structured as follows. The related works and comparison between the existing and proposed work are discussed in Section ‘Related Works’ and Section ‘A Proficient Heuristic-Based Routing Algorithm for VMeshs Using Random Forest Link Stability Prediction’ describes the proposed Proficient Routing Algorithm using Heuristic Functions and Random Forest (PRA-HF-RF) for VMesh milieu. Section ‘Simulation and Results’ gives the simulation results and analysis of experimental results based on the metrics. Section ‘Conclusion’ concludes this article .

A few efficient routing process have been proposed to tackle challenges such as gridlock, latency, packet loss, and protocol overhead in the VMesh environment.

Gupta et al.¹² discuss the routing issues in WMNs as an optimization issue for both stable and active network conditions. According to the authors although numerous adaptive routing process have been developed, they normally lack a solid theoretical basis for evaluating overall network proficiency. Tang et al.¹³ introduces a bandwidth allocation method which intended at enhancing routing performance. However, Gupta et al.¹² argue that while these methods focus on achieving proficient bandwidth usage, they often supervise the traffic load on transmitting nodes and, as a result, fail to account for the actual demand on network resources. To address this gap, Gupta introduced a strategy that treats the routing challenge as an optimization problem under both static and dynamic conditions. Their main goal was to improve the balance between data flow and its required resources, while also ensuring that the network remains fair and can manage scheduling effectively. Mamatha et al.¹⁴ employed the SHA-1 algorithm to accomplish information exchange between road side unit (RSU) and vehicles and the support vector machine (SVM) algorithm for road condition analysis and pothole detection. The authors suggested in¹⁵ an ensemble stacking-based machine learning (ML) model with a booster model and combining several ML models. In order to increase network traffics precision and effectiveness. Although dynamic VANETs have advanced significantly as a result of these studies significant computational complexity was also added especially in ML-based approaches. This complexity may restrict these techniques real-time adaptability and scalability in environments with limited resources and rapid change which could impede their practical implementation and the full realization of their theoretical advantages in VANETs.

Prior studies have explored various congestion-control and routing-optimization techniques for vehicular networks. Some ML driven approaches improve accuracy but introduce significant computational overhead, making real-time deployment difficult. Routing schemes based solely on mobility or signal measurements frequently overlook queue stability and load distribution. Furthermore, traditional congestion mitigation mechanisms generally react after performance degradation occurs. Unlike those methods, PRA-HF-RF prevents congestion proactively by combining predictive analytics and heuristic evaluation, ensuring a more consistent forwarding path.

The proposed algorithm is designed to proficiently identify the most suitable relay node and Base Station (BS) in a VMesh network. As defined in Equation 1, the Proficient Routing Algorithm Heuristic Fitness (PRA-HF) assesses each proficient relay node based on three key parameters:

The PRA-HF function is expressed as:

where,

Deploying and evaluating a VMesh network in real traffic conditions is difficult, expensive, and time-consuming. To overcome these limitations, simulation environments are used because they provide a controlled, repeatable, and cost-efficient platform for assessing routing performance before real-world deployment.

In this work, the proposed PRA-HF-RF routing scheme was implemented and tested using the Traffic and Network Simulation Environment (TraNS) framework, which links mobility, mapping, and network simulation components. The experimental setup are as follows:

The Random Forest model is trained offline using mobility traces generated from the SUMO-NS-3 milieu. Each link sample has a features such as RSSI, relative speed, inter-vehicle distance, node density, queue availability, packet delivery ratio, and prior link duration. Each link instance is classified as stable or unstable according to whether it remained active past the specified stability threshold. After training, the model produces a set of stability values that NS-3 loads at the start of the simulation. During routing, each node checks the real-time details of its neighbors and finds the LS that matches them (between 0 and 1). This score is then used along with the PRA-HF metric to pick the most reliable relay vehicle. The training data contains several thousand link samples collected under different vehicle speeds, traffic levels, and communication conditions. The data is split into training, validation, and testing sets in a 70/15/15 ratio. The important parameters such as number of trees, the depth of each tree, and the number of input fields are adjusted through k-fold cross-checks to improve prediction accuracy. All routing protocols are tested under the same simulation conditions to allow a fair comparison and to confirm the performance gains of the proposed PRA-HF-RF.

This article shows that PRA-HF-RF offers a reliable and highly proficient routing mechanism for dynamic and high-density VMesh environments. By integrating a heuristic function with link stability prediction, the proposed PRA-HF-RF consistently identifies reliable relay paths, lessens redundant retransmissions, and reduces route interruptions. This article proves the strength of the PRA-HF-RF routing mechanism through the performance metrics.