## 3 Materials and Methods
### Proposed ATM System Design and Implementation
#### 3.3.5 Machine Learning in ITM
* **Where Only with Non-LAVs--**Assessments are necessary to verify the effectiveness of proposed ATM methods. As a result, a traffic virtual environment system must be easily adaptable to various traffic situations, allowing users to compare diverse perspectives. A baseline sequence of events is developed and evaluated on the vehicular modeling in which just the fixed-cycle traffic illumination monitors the Non-LAVs [63]. * **Where LAVs and Non-Linked both types of vehicles are moving--**The mixed-traffic case, in which both LAVs and Non-LAVs move on the roadways, should be viewed as a significant challenge for the massive implementation of automated vehicles. System model control techniques are tested on the proposed approach for this situation. Figure 5 shows the results for LAVs and Non-Linked automated vehicles [64]. IoT devices will collect the live vehicle-traffic data and road conditions and store these data in the data center, implementing the proposed ATM system. In the next phase, big data techniques will apply to process the live data. The final phase will be based on ML methods to train the data. We assume that the motor vehicles are fully equipped with a wireless system module that interacts with the RSU in the proposed ATM system. (Figure 7) positioned mainly on the highway to transfer heavy traffic data with other moving automobiles. In contrast, automobiles are assumed to be adequately equipped, mainly with the entire event query recorder (EQR) used to measure an automobile's fast movement speed and traffic details. In the proposed system, the roadside units are positioned on the road at a range of approximately 1.0 km apart. All such roadside units are ready to supply fair coverage in their region and are located by the RSUs nearby. Those on the right side of the road are also used to build architecture. Almost every RSU includes a GPS unit to acquire the precise location of automobiles, a transceiver for creating communication among passing traffic, and a computing device that delivers live traffic data gathered from automobiles, such as lane-altering distance and over speed [59; 60]. **In scenario: 1** (Figure 8), vehicles move in one direction over the road and move from the freeway. In this scenario, one vehicle consumes less fuel and travel time. Figure 8: Vehicles moving on the freeway where fuel less fuel consumption, and average less travel time is generated by the travel report. In the second scenario, vehicles move backwards and forward over a multilane path (Figure 9). In scenario 3, vehicles move forward and backwards over multiple lanes; this is caused by \"Traffic Congestion,\" as described in Figure 10. The traffic congestion is resolved by the proposed intelligent traffic-management system described in Figure 11.

Figure 10: Vehicle moving on forward and backward over multiple lanes (traffic congestion). Figure 9: Vehicle moving on the freeway (multilane), connected/linked and automated vehicle, and signals in 3 categories (red, green, and yellow). ### Experimental Results and Discussions

The simulation results demonstrate that the proposed ATM system outperforms the conventional traffic-management system in packet delivery ratio, throughput, and time delay [60]. This serves best in case of a change in traffic conditions (as per scenarios 1 to 3) and latency. The graphs below accurately depict the numerous criteria and significance regarding particular system strategies for correspondence with data packets. The experimental results were performed on MATLAB simulation with 100 vehicles, a 5000 m road, and different moving directions (i.e., Channel 1: East to West; Channel 2: North to West; Channel 3: North to East; and Channel 4: East to North). In all the simulations, traffic is moving in inflow and outflow directions. The model type is the Random model. The probability for inflow traffic is 0.58%, and for outflow it is 0.49%. The simulation results were calculated for three different scenarios. Figure 12 shows the simulation result for scenario 1 only with LAVs.