Building Up Agency-Wide Automated Image Processing Capability to Inform Safety and Mobility

Project Description

This project will develop a roadmap for a DDOT-wide approach to automated image processing that will ultimately enhance DDOT’s ability to assess traveler behavior and roadway conditions for planning, design, and operations. 

Project Need

DDOT frequently uses camera imagery (e.g., panoramic street-level photography, time-lapse cameras, closed-circuit television (CCTV) cameras) to better understand traveler behavior and existing configuration and condition of the roadway and associated infrastructure. This imagery can provide insight into:

  • Existing patterns or conditions for future planning or design
  • Real-time conditions for operational decision making
  • Impacts of a change in condition (i.e., before-and-after analyses)

To date, much of the processing of this imagery has been done manually, which often proves costly and inefficient, thereby limiting the degree to which DDOT is able to use camera imagery.

Recent advances in artificial intelligence and machine learning have the potential to speed up and improve processes for analyzing camera imagery. DDOT staff have engaged with vendors to explore some of these possibilities on a limited scale, but have recognized the need for a consistent, agency-wide approach to ensure quality of analysis, maximize utility across divisions, and minimize any duplication of effort.

Desired Outcome & Expected Benefits

This project will provide comprehensive recommendations on how DDOT can expand its use of camera footage via automated image processing to ensure all agency information needs are met. Results will directly identify actions and changes that DDOT can make to existing technology, policy, and processes to ensure quality of analysis, maximize utility across divisions, and minimize any duplication of effort. The project will benefit the District by enhancing DDOT’s ability to understand traveler behavior and roadway conditions toward better planning, design, and operational decision-making.


The primary objective of this project is to build up DDOT’s automated image processing capability. This will be achieved through a series of tasks:

  • Assessment of use cases and needs via internal stakeholder outreach
  • Market research on available tools and technologies, including use cases, implementations to date, required supportive technologies and processes, and validation and verification
  • Identification of existing gaps in supportive technologies and processes (e.g., installation and data storage)
  • Comprehensive recommendations on next steps for standing up a consolidated program that can effectively and efficiently support need for automated image processing agency wide.

Potential use cases include, but are not limited to: vehicle trajectories; parking occupancy detection; vehicle, freight, cyclist, pedestrian counts and turning movements; collision and near miss detection; pavement and sidewalk condition; lane and signal configuration; sign and pole inventory.

Project Oversight

DDOT Stakeholders

James Graham (Deactivated), GIS

Ting Ma (Unlicensed), Performance Management

Kelli Raboy (Deactivated), ITS Manager

Data Governance Group

Peer Reviewers

Meeting notes

Quarterly updates

QuarterProgress this quarterIssues Encountered
FY21 Q4

Scope developed for procurement

Project obligated and now moving forward in procurement

FY22 Q1

RFQ published on 1/11/2022

Technical Evaluation Committee (TEC)  established and submitted to OCP

Project Materials

Any relevant materials, including problem statement, scope of work, interim deliverables, reports, data can be uploaded below.

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