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AI for Safe Transportation Use Case - Smart Cities

Transportation is a critical sector to any country given it acts as the vessel for all other activities including individuals, enterprises, governmental and commercial services. Key drainers of transportation systems are accidents and critical incidents which either affect safety or caused by unsafe driving patterns. For example, a traffic accident on a high way could result in chain of accidents which happened quite frequently over past years. Unresponsible driving attitude resulted in severe accidents and sudden utilities incidents like broken pipeline can cause even more damage if not concealed in timely fashion. The mobilization of action force teams to handle such incidents is also key attribute in mitigating the associated risks.

The aim of this use case is utilizing AI methods and techniques can function computer vision, statistical analysis and optimization to anticipate, spot and act on such incidents prospectively including suggesting best ways to handle. On current direction adopting intelligent transportation systems nation-wide, such solution can leverage collected data from cameras, sensors and official information systems to provide capabilities to involved governmental and community sectors to act faster and more accurate. Such enhanced performance on a key sector like transportation works as cornerstone for the digital cities by enabling better planning and implementation of related services. Using Spatial AI as part of the solution can overcome the difficulties associated with transportation networks being operating on very large-scale geographies.

Who can benifit of such solution: Vehicle Drivers – Transportation Operators – Traffic Authorities – Gov Sectors – Gov Planners – Gov Executives.

The use case solution has been structured using our project implementation method into 18 main activities as listed in this repo. The objective of adopting such real-life implementation si to help whomever working on developing solutions for this use case to organize effrot toward a working solution covering all aspects:

01 - Statement of Work: The SoW provides clear definition of the problem being solved and the associated challenges and pain points coming from the business domain context. It provided the scope and specifications of the solution being delivered.

02 - Project Management: Planning, executing, and controlling the project in terms of scope, budged, time, quality, resources, and team assignment. Running project delivery in Lean Agile approach for lower risk and incremental delivered value.

03 - Domain Understanding: Getting deeper into the context of the relevant business domains to understand strategies, policies, drivers, challenges, pain points and desired outcomes that represent value to the business, organization, community and/or individuals.

04 - Solution Framing: Analyzing the challenges, pain points and at the same time the data in a business context to come out with solution elements especially the core machine learning models so an AI solution can deliver the desired outcome.

05 - MLOps Design: As a first step for the integrated pipeline for delivering ML/AI solutions, the three layers of data, ML model and code pipelines are defined and designed to address the framed AI solution with needed CI/CD setup.

06 - Sourcing Data: Identifying and implementing needed procedures and components to ingest required and supportive data/metadata from different sources using appropriate patterns for batch, real-time and streaming ingestion.

07 - Modeling Platform: One key asset in an ML/AI project is the computing resources needed during developing the core AI models which typically requires high performance computing infrastructure as well as suitable technology components for manipulating data and applying processing and algorithms.

08 - Data Preparation: Implementing and conducting different activities on the modeling to get the data loaded, quality-checked, transformed, reasoned, and explored and provisioned as preparation for the core modeling activities.

09 - Model Planning: Start point of the core modeling by researching and exploring the potential models applicable to the given elements of the framed solution typically starts with features engineering and moves one to applying different ML/AI techniques to shortlist sounding ones.

10 - Model Development: For potential models shortlisted on the model planning activity, models are taken to full model building in a setup emulating desired operational setup and workload to produce evaluation metrics and diagnostics against the actual operational workload.

11 - Model Evaluation: The metrics and diagnostics as well as different applied parameters are compared so that one final model can be selected as the core for the AI solution taking into consideration the model efficiency as well as performance in the context of the required business outcomes.

12 - Applications Development: Designing and implementing the application needed to make the core AI model operational including end user applications, integration with data sources, Microservices, data platforms, reports, dashboards, APIs among others.

13 - Production Platform: Engineering, architecting, acquiring, and implementing the computing infrastructure needed for deploying and operating the full solution including data, ML/AI model and applications in an integrated way within the organization ICT landscape of other systems.

14 - Operationalization: Planning a go-live with all solution components within DevOps setup including data, ML/AI model, application code and production platform ensuring all functional and non-functional requirements are to be met.

15 - MLOps Rollout: Based on the MLOPs design as well as the operationalization plan, actual components of MLOps are deployed in production with the three elements of data, ML model and application code so that CI/CD can commence.

16 - Solution Delivery: Defining and applying procedures to ensure the delivered AI solution components are solid and aligned with the customer organization’s ICT operations so that the solution continuously deliver value as intended.

17 - Managed Services: Setting up teaming with the customer organization to outsource all or part of the operations, support, and sustainability of the AI solution to the project team on different levels of support.

18 - Project Closing: Conducting final checks and validation of the different components of the delivered AI solution and handing over to the customer organization’s IT operations team.