Building a Real-Time Vehicle Tracking System with Google Cloud Platform

Project: Real time vehicle tracking

Introduction:

In today’s fast-paced world, real-time tracking of vehicles is becoming increasingly crucial for businesses operating in transportation, logistics, and other related industries. Leveraging the power of Google Cloud Platform (GCP), we can implement an efficient and scalable vehicle tracking system. In this blog post, we will guide you through the step-by-step process of building a real-time vehicle tracking system using Google Maps APIs, Google Cloud Pub/Sub, Google Cloud Run, and Google BigQuery. We will also explore how Google Cloud Looker can be utilized for real-time data visualization, enabling businesses to make informed decisions and optimize their operations.

Step 1: Data Ingestion with Google Maps APIs:

To begin, we need to obtain API credentials for Google Maps APIs, including the Distance Matrix API, Directions API, and Geolocation API. These credentials can be obtained from the Google Cloud Console. Once acquired, we can configure our application to make requests to the Google Maps APIs using the obtained credentials. In the case of vehicles equipped with smartphones pre-installed with Google Maps, we can pull real-time data, such as location, distance covered, and routes taken, directly from the Google Maps application.

Step 2: Setting up Google Cloud Pub/Sub:

Google Cloud Pub/Sub is a reliable messaging service that will allow us to handle the real-time data coming from the smartphones. We create a Pub/Sub topic in the Google Cloud Console, which will act as the central hub for receiving the real-time vehicle data. It is important to configure the appropriate permissions and access controls for the topic to ensure secure data communication.

Step 3: Configuring Smartphones to Publish Data:

Next, we configure the smartphones installed in the vehicles to publish the real-time data to the Pub/Sub topic. By installing the necessary libraries or SDKs, such as the Pub/Sub client libraries, in the smartphones’ applications, we enable them to publish data to the Pub/Sub topic. We implement logic within the applications to collect the required data from the Google Maps application and publish it to the Pub/Sub topic. This ensures that the data is sent to the central messaging system for further processing.

Step 4: Creating a Google Cloud Run Service:

Google Cloud Run allows us to build and deploy containerized applications effortlessly. We create a containerized application that will receive the data published to the Pub/Sub topic by the smartphones. The application is built using suitable programming languages, such as Python, and is containerized using tools like Docker. The container image is then uploaded to a container registry, such as Google Container Registry. We create a Google Cloud Run service and deploy the container image to it. The service is configured to handle incoming Pub/Sub messages and triggers the necessary processing logic.

Step 5: Processing and Storing Data in Google BigQuery:

Within the Google Cloud Run service, we implement the logic to consume the Pub/Sub messages and process the received vehicle data. This involves extracting relevant information, such as distances covered, routes taken, and timestamps, from the received data. The processed data is then stored in Google BigQuery, a powerful data warehousing and analytics platform offered by Google Cloud Platform. Google BigQuery provides scalability and flexibility for storing and analyzing large datasets, enabling us to gain valuable insights from the collected vehicle data.

Step 6: Real-Time Visualization with Google Cloud Looker:

To visualize the real-time vehicle tracking and other relevant metrics, we connect Google Cloud Looker to the data source where the processed data is stored, which in this case is Google BigQuery. By designing and creating dashboards or reports in Looker, we can create intuitive and interactive visualizations based on the extracted data. Looker’s visualization features allow businesses to gain actionable insights, track key performance indicators (KPIs), and make data-driven decisions to optimize their operations.

Conclusion:

By leveraging the capabilities of Google Cloud Platform, we have successfully built a real-time vehicle tracking system. Starting from data ingestion through Google Maps APIs, utilizing Google Cloud Pub/Sub and Google Cloud Run for processing and handling real-time data, and storing the data in Google BigQuery, we have established a robust foundation for tracking and analyzing vehicles’ movements and performance. With the added power of Google Cloud Looker for real-time data visualization, businesses can make informed decisions, enhance efficiency, and drive operational excellence.

Implementing a real-time vehicle tracking system using Google Cloud Platform empowers businesses to optimize their operations, improve customer satisfaction, and achieve a competitive edge in today’s dynamic market.

About the Author:

Emmanuel Odenyire Anyira is a Senior Data Analytics Engineer at Safaricom PLC. With extensive experience in designing and building data collection systems, processing pipelines, and reporting tools, Emmanuel has established himself as a thought leader in the field of data analytics and infrastructure management. He possesses expertise in various technologies, including Apache NiFi, Informatica PowerCenter, Tableau, and multiple programming languages. Emmanuel’s passion for automation and optimizing workflows has driven him to share his insights and expertise through writing and speaking engagements.