Data Plumbing as a Platform (PaaP) — The Key to a Smart City

Metrolink is a Data plumbing as a platform that helps organizations orchestrate their data flows independently, intuitively and in a way that amplifies value, while maximizing efficiency of human resources and reducing overhead.

A Smart City is an urban area that uses different types of electronic methods and sensors to collect data. Insights gained from that data are used to manage assets, resources and services efficiently; as a result, the data is used to improve the operations across the city.[1]

A city is a dynamic entity affected by events that require a response both in real-time and strategic decision-making based on the accumulation of data over time.

Creating a Smart City is a resource-intensive project, both in terms of the price of entry and in terms of ongoing investments. The project outcomes can only be seen over time, so such an undertaking should be viewed as a long-term investment that requires wise management, architectural planning, and adaptation.

With planning comes perfection!

A common approach to building a Smart City is to execute numerous unconnected projects, each one dealing with a separate solution. While the main advantage of this approach is ״quick wins״ and (relatively) low costs of the various projects, it creates an array of siloed solutions. These solutions create difficulties in keeping track of new needs as they arise. They also lack the integration between projects, and in the long run, provide limited results and cost more.

We live in a world where technological change is constant. The wait for a particular technology to peak can paralyze a decision-maker from starting the journey of such a complex project as building a Smart City. On the other hand, adopting a specific tech solution will inevitably lead to a reality in which this technology will become obsolete or find itself living in parallel with newer technologies providing better quality and/or cheaper solutions than its predecessor.

That said, “appetite comes with eating” — the more successful the experience with early steps to implement a Smart City, the more the city’s management will want to utilize technological capabilities that will reflect and bring great value from the infrastructure investments made. Therefore, the motivation to use new capabilities, such as newer BI systems and AI abilities, is a fact that must be taken into account in the construction of urban architecture.

At Metrolink.ai, we believe that a Smart City is a complex project that should be taken seriously from a sober and long-term perspective. We believe that the right investment in infrastructure will allow for continuous utilization of the investments made, while maintaining flexibility to accommodate the constant evolution of both capabilities and needs.

To our understanding, the correct concept for realizing a Smart City is to release constraints and create maximum flexibility by using an uncoupled mediation and coordination platform between the information sources and the production layer of knowledge — a Central Nervous System for Data enabling interconnections between data sources and destinations with the ability to adjust and enrich data in the process.

Central Nervous System for Data

Giving the power to the Analyst!

Let’s say you have that central system where all the municipal data are met together and interconnected. But the operating expenses of such a system, usually executed by development teams or outsourced companies, can be huge, especially in light of changing data and needs.

Having said that, analysts know best the needed business results and the data in hand, but usually don’t have programing skills. By giving them the power to automatically implement a logic description of the operations they want to do to data,[2] and the ability to load the result to the destination of choice,[3] we can focus the municipal development efforts, get better time to value and save money.

Can be done!

Austin, Texas, is one of the cities with the most advanced infrastructure designs for a Smart City. The project’s architects took the analysis of the Smart City initiative seriously, understanding that this is an ongoing effort, and gave an architectural answer to the need for maximum flexibility.[4]

The logical architecture beautifully expresses the flexibility required between the layer of information sources, customers, and consumers through a central infrastructure — Traffic Management Center — a Central Nervous System for Data.

Austin, TX — Urban Transformation logical architec

Case Description — The right to Imagine

A municipality is interested in dealing with the traffic congestion in the bottlenecks of the entrances and exits to the city. The municipality’s goal is to improve the quality of life and reduce air pollution during rush hours. The city has previously invested in installing live cameras that produce live video streams on the relevant traffic arteries and considered (re)using them for the mission.

The business concept of the municipality is to provide citizens with real-time data about traffic congestion and waiting times in order to regulate the load on the roads. In addition, the municipality is interested in examining a compensation program in the form of a discount in local taxes for residents who drive regulary outside of rush hours.

A possible solution for the problem is to understand traffic flow rates in real-time by calculating the rate of entry of vehicles into the congested area relative to the exit.

The implementation could be done by streaming the existing live video to an Automatic Vehicle Counting Algorithm[5], [6] or LPR components[7], [8] and count the number of vehicles at each point within the relevant routes. The subtraction of the number of cars between different check points (within a certain timespan) defines the flow rate of cars.

This pipeline’s results can be loaded, in real-time, to the urban application server for online traffic reporting and to the City database for offline analysis of the residents that meet the “municipal challenge.”[9] The data can also enable online configuration of traffic light systems while providing real-time feedback on the integrative load effects the City utilizes (“smart traffic cop”).

Time to Business!

Using Metrolink.ai, the business motivation and problem definition (traffic loads) are 90% of the solution. The analyst in charge of a project can design the logic actions on the relevant information sources into a pipeline that defines the required resolution and deploy it for testing and then production — no coding skills needed!

A developing process, with numerous iterations between the analyst and dev teams that can take month is finished and operational in hours!

Implementation of different video sources for traffic congestion reduction

A Smart City is no different from any other data-related organization. While the potential value from Data is increasing rapidly, the need for higher flexibility, better time to value and lower investment in development resources is a mandatory demand for efficiency and profit. Unlike other companies that see data plumbing as a utility, we, at metrolink.ai, see it as a focused task and our mission is to bring a groundbreaking solution that once and for all will enable organizations to make more out of their data with little effort, at the lowest cost necessary.

If you want to be a part of our mission contact us at: contact@metrolink.ai

https://metrolink.ai

[1] McLaren, Duncan; Agyeman, Julian (2015). Sharing Cities: A Case for Truly Smart and Sustainable Cities

[2] filtration, correlation, aggregation, enrichment and mapping

[3] Such as the city databases, BI systems such as Splunk or Tableau or to application servers.

[4]https://www.austintexas.gov/sites/default/files/files/Transportation/Austin_SCCFinal_Volume1_5.25.pdf

[5] https://www.youtube.com/watch?v=XHvxp0zRdEQ

[6]https://www.anacom.pt/streaming/AbelRibeiro_8congURSI.pdf?contentId=1342438&field=ATTACHED_FILE

[7] If ANPR or camera resolution and position allows

[8] Metrolink.ai allows the embedment of external enrichment abilities by an open SDK

[9] In case of LPR data

A technology leader with 30 years of experience in R&D. Ronen has vast experience in leading multi-disciplinary organizations and projects.