How to Implement Vehicle Traffic Analysis in OMNeT++

To implement Vehicle Traffic Analysis in OMNeT++ has encompasses to emulate the vehicular networks, capturing data relevant to vehicle movement, communication, and network performance, and measuring this information to familiarize the traffic patterns, network efficiency, and potential bottlenecks. This is especially helpful for applications such Intelligent Transportation Systems (ITS) in which vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications are critical. The following are the structured approach to implement Vehicle Traffic Analysis in OMNeT++:

Step-by-Step Implementation:

1. Set up Your OMNeT++ Environment

• Make sure that OMNeT++ is installed along with the INET framework.
• For vehicular networks, we will also need Veins (Vehicles in Network Simulation), that incorporates SUMO (Simulation of Urban MObility) with OMNeT++ for realistic vehicle mobility modelling.

2. Integrate SUMO with OMNeT++

• Veins deliver the integration of SUMO and OMNeT++ that enables to replicate realistic vehicle movements.
• Install SUMO and make sure it is configured to operate with Veins.

3. Define the Network Topology and Mobility Model

• Generate a NED file that describe the network topology and this involves road-side units (RSUs), vehicles, and communication links among them.
• State the mobility model using SUMO to emulate vehicle movement within a predefined map.

Example NED file:

network VehicleTrafficNetwork

{

submodules:

rsu1: RSU;

rsu2: RSU;

vehicle1: Car;

vehicle2: Car;

vehicle3: Car;

connections allowunconnected:

rsu1.ethg++ <–> EthLink <–> rsu2.ethg++;

vehicle1.ethg++;

vehicle2.ethg++;

vehicle3.ethg++;

}

Example SUMO configuration:

<configuration>

<input>

<net-file value=”network.net.xml”/>

<route-files value=”routes.rou.xml”/>

</input>

<time>

<begin value=”0″/>

<end value=”3600″/>

</time>

</configuration>

4. Implement Vehicle Communication Modules

• Generate or setup the communication modules on the vehicles to replicate the V2V and V2I communication and these modules should generate, transmit, and receive messages as vehicles move via the network.

Example communication module in C++:

class VehicleApp : public cSimpleModule {

protected:

virtual void initialize() override {

scheduleAt(simTime() + uniform(1, 5), new cMessage(“sendMsg”));

}

virtual void handleMessage(cMessage *msg) override {

if (msg->isSelfMessage()) {

sendEmergencyMessage();

scheduleAt(simTime() + uniform(1, 5), msg);

} else {

handleReceivedMessage(msg);

}

}

void sendEmergencyMessage() {

EmergencyMessage *emMsg = new EmergencyMessage(“Emergency”);

emMsg->setSenderId(getParentModule()->getId());

send(emMsg, “out”);

}

void handleReceivedMessage(cMessage *msg) {

// Handle the received message

delete msg;

}

};

Define_Module(VehicleApp);

Example .ini configuration:

**.vehicle*.app[0].typename = “VehicleApp”

**.rsu*.app[0].typename = “RSUApp”

5. Capture and Analyse Traffic Data

• Execute mechanisms to capture the information relevant to vehicle movement, communication delays, packet delivery, and other relevant metrics and this contain logging data to files or using OMNeT++’s built-in recording tools.

Example metrics to capture:

• Vehicle Speed and Position: Capture the speed and position of each vehicle over time.
• Message Delivery Delay: Evaluate the time taken for messages to reach their destination.
• Packet Loss: Record the number of packets lost during communication.
• Network Throughput: Assess the data transmission rate among the vehicles and RSUs.

Example data logging in C++:

void VehicleApp::handleReceivedMessage(cMessage *msg) {

if (EmergencyMessage *emMsg = dynamic_cast<EmergencyMessage*>(msg)) {

recordScalar(“MessageDelay”, simTime() – emMsg->getCreationTime());

delete emMsg;

} else {

delete msg;

}

}

Example .ini file configuration for recording metrics:

**.vehicle*.app[0].recordScalar = true

**.vehicle*.mobility.typename = “TraCIMobility”

6. Run the Simulation

• Implement the simulation that permits vehicles to move based on the SUMO-defined mobility model and interact using the OMNeT++ modules.
• During the simulation, OMNeT++ will capture the defined the parametric in which we can measure post-simulation.

7. Analyse Results

• Use OMNeT++’s built-in analysis tools or export the information to external tools such as Python or MATLAB for further analysis.
• Visualize traffic patterns, communication delays, and network performance metrics to detect the bottlenecks, inefficiencies, or areas for improvement.

Example Python script for data visualization:

import pandas as pd

import matplotlib.pyplot as plt

data = pd.read_csv(‘results/scalars.csv’)

plt.plot(data[‘time’], data[‘MessageDelay’])

plt.xlabel(‘Time (s)’)

plt.ylabel(‘Message Delay (s)’)

plt.title(‘Emergency Message Delivery Delay’)

plt.show()

8. Refine and Optimize

• Based on the analysis, improve the network design, vehicle communication protocols, or mobility model to enhance the performance.
• Consider experimenting with numerous traffic densities, communication strategies, or network topologies to optimize system efficiency.

Example OMNeT++ Configuration:

network = VehicleTrafficNetwork

sim-time-limit = 3600s

**.vehicle*.mobility.typename = “TraCIMobility”

**.vehicle*.mobility.x = uniform(0, 1000)

**.vehicle*.mobility.y = uniform(0, 1000)

**.vehicle*.app[0].typename = “VehicleApp”

**.rsu*.app[0].typename = “RSUApp”

**.vehicle*.app[0].recordScalar = true

Additional Considerations:

• Scalability: Make certain the simulation can manage large numbers of vehicles and RSUs that specifically in urban or highway scenarios.
• Real-Time Analysis: Consider to execute the real-time monitoring and control mechanisms if the application needs dynamic adaptation based on traffic conditions.
• Interoperability: If network consists to the multiple communication standards like DSRC, 5G that make sure the simulation accurately models the communication among numerous technologies.

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