Consultant -Vehicle Insight Generation

Project Tasks:

• Virtual Machine Onboarding, Component Selection, and Setup
  
  

This task involves provisioning and configuring the virtual machine (VM) that will serve as the environment for the project. It includes:

• Onboarding: Setting up the required accounts, permissions, and access to the cloud infrastructure or virtual environment.
• Component Selection: Choosing the necessary software, libraries, frameworks, and tools needed for the project (e.g., operating system, programming languages, database, and telemetry tools).
• Setup: Installing and configuring the components to prepare the environment for development, such as network configurations, security settings, and dependencies.
• Connect to Telemetry Data Stream and Repository
• Telemetry Data Stream: Refers to real-time or near-real-time data from sensors or systems monitoring vehicle movements and traffic situations.
• Repository: A storage system where the telemetry data is collected, stored, and made accessible. This step involves establishing a connection to the live data feed or database where the vehicle trajectory data is stored or streamed from, ensuring proper access and data retrieval.
  
  

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• Create Data Ingestion, Processing, and Summary Process Outlines
  
  

This task involves designing the workflow for how data will be handled:

• Data Ingestion: Defining how the raw telemetry data is collected from the source (e.g., sensors, repositories) and input into the system.
• Processing: Determining how the raw data will be cleaned, transformed, or filtered to derive useful insights.
• Summary Process: Creating methods to aggregate the data into roadway insights.
• Develop Roadway Insights from Vehicle Trajectory Data Source
  
  

The task involves developing algorithms or methods that analyze trajectory data to estimate the traffic situations in a given area using spatial and temporal analysis of vehicle paths.

• Store Roadway Insights in Database and Publish in Application Programming Interface (API)
• The roadway insights are stored in a database for further analysis or retrieval.
• Publishing via API: Developing an API that makes the vehicle density data accessible to other systems or applications. This allows users or services to retrieve this data programmatically (e.g., for real-time monitoring, dashboards).
• Demonstrate End-to-End Live Data Ingestion and Summary Using Developed API
• End-to-End Data Flow: Demonstrating the full process of data ingestion (from the telemetry source), real-time data processing storage in a database, and accessing the results via the developed API.
• Live Demo: Running the system with live data to show that it can handle real-time data streams, process and summarize the data, and provide the information through the API for external consumption or visualization.
  
  

Technical Specification:

This project aims to create a comprehensive reference framework that leverages vehicle telemetry and third-party data sources to detect and assess traffic events. The project's core objective is to design and implement a functional, end-to-end system that can ingest, process, and analyze roadway data from select geographic locations. By summarizing this data, the system will support vehicle and public agency responses to traffic events and road condition changes, ultimately facilitating more informed countermeasures and traffic management strategies.

System Architecture and Algorithm Development

The project will focus on developing an architecture and detection algorithms within a virtual machine environment. This framework will be built using existing vehicle trajectory data and will integrate external data sources, enhancing the precision of traffic event detection and analysis. The detection algorithms will provide insights into traffic performance through key metrics.

Roadway Measures from Location Telemetry

One of the project's primary challenges is creating accurate road performance measures from vehicle telemetry. To achieve this, the system will utilize location data to assess vehicle paths on specific road segments. By capturing location updates, algorithms will be able to estimate a variety

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of roadway measures.

Integration of External Data Sources for Enhanced Estimates

The use of external data sources from third-party traffic data providers will significantly improve the accuracy of roadway measures. For instance, integrating third-party traffic data from crowdsourced applications or local transportation authorities can provide real-time insights that enhance the telemetry-based estimates. These external data sources can also contribute to the detection of events and provide a broader and more accurate understanding of traffic dynamics in a given area.

Leveraging Connected Work Zone Data

A key element of this project is the incorporation of Connected Work Zone Data (using the WZDx protocol) to improve the system's understanding of roadway construction and traffic events. WZDx provides a standardized format for sharing data about current and future work zones. By integrating this data source into the system, the algorithms can detect the presence of planned construction and use this information to refine traffic event detection.

The integration of WZDx also enables the system to provide a more comprehensive picture of traffic conditions by offering a reference for known or planned construction work. This helps in distinguishing between normal traffic slowdowns due to high demand and those caused by roadwork. As a result, vehicles and public agencies can issue more accurate traffic advisories based on the expected impact of construction activities.