How AI Traffic Enforcement Systems Are Changing the Urban Infrastructure


New Delhi [India], June 8: In India, the booming cities are grappling with the common problem of busy roads, overburdened junctions, and traffic management systems failing to keep pace with rising traffic volumes. Be it the metros or burgeoning cities across the country, traffic offences, be it jumping a signal, speeding, or lane indiscipline, are still adding to traffic bottlenecks and accidents. As over 30 crore vehicles are already registered in India, and metropolises have seen about 8–10% annual vehicle growth rates in ownership, traffic management is transitioning from human policing to smart digital enforcement systems.

In this emerging paradigm, technology is increasingly used by city governments for monitoring traffic behavior, issuing real-time violation detection, and carrying out automatic processing of violations and penalty payments. To this cause, one organization, Brihaspathi Technologies, has developed an integrated Traffic Enforcement Solution for a wide-scale deployment over urban road networks. This Traffic Enforcement Solution encompasses AI-enabled traffic monitoring, automated violation detection, evidence management, and integrated e-challan systems to function as a cohesive enforcement framework operating over a number of intersections and city corridors at the same time.

It is a fact that the government road safety figures indicate over 4.6 lakh road accidents happen each year in India, and over speeding itself contributes to over 65% of reported accidents. It is also a fact that enforcement officers frequently look after hundreds of intersections with a very small number of men and cannot be on constant lookout. Intelligent traffic systems are able to solve this problem, as any violation is automatically recorded digitally, and this would bring about uniformity in enforcement for a whole city.

Turning Cameras into Active Observers

Central to Brihaspathi’s Traffic Enforcement Solution is intelligent traffic monitoring. AI-enabled camera systems continually view vehicle flow across traffic lights and along traffic corridors. These cameras go beyond simply recording, processing the traffic flows dynamically, and immediately flagging anything abnormal.

It can also be used in conjunction with high-definition imaging and video analysis; the system will be capable of monitoring vehicle movement through the lanes, through signal adherence, and the behaviour of vehicles through more than one lane at the same time. This enables traffic authorities to monitor multiple intersections from one control centre, as opposed to having staff monitoring each individual intersection. In dense urban areas where an intersection may have 10,000–20,000 vehicle movements per hour, enforcement through personnel at each intersection would not be possible.

Identifying Violations Instantly

One of the crucial features of the platform is the use of Automatic Number Plate Recognition (ANPR). Cameras of high resolution take pictures of vehicle plates, which are then turned into a computer-based format (text) by use of optical character recognition. The data captured is immediately compared to registered vehicles; thus, vehicles committing offenses can be instantly detected.

In cities with millions of registered vehicles, ANPR technology represents the fundamental digital infrastructure for automated law enforcement. The technology is “always on”, detecting the identification of vehicles on all busy intersections, highways, and toll routes without the need for human assistance.

Monitoring High-Risk Intersections

In cities, the majority of traffic offences are related to signal jumping. The RLVD system developed by Brihaspathi monitors intersections for vehicles that jump stop lines when the lights are red.

Upon violation detection, cameras take images and a brief video clip, including the timestamped GPS information. This becomes part of the digital evidence recorded that generates the penalty. RLVD systems have been proven in numerous projects worldwide to decrease signal violations up to 40% over a period of time, due to awareness and persistent enforcement.

Addressing a Major Accident Factor

Speeding has remained one of the primary causes of road accidents. Brihaspathi employs a speed detection system, which is linked to a radar, to calculate the vehicle speed. The radar sensor tracks the vehicle beyond predefined speed limits and simultaneously captures and records the offense along with a photograph.

The system could be implemented on all highway stretches, arterial roads in cities, and accident black spots. In areas where the speed enforcement technology has been widely implemented, traffic departments have confirmed reduced instances of high-speed violations.

Building a Reliable Enforcement Archive

All detected infringements are recorded on the system as visual evidence and placed into a video evidence management system where images, video footage, times, and infringement data are held in an orderly manner and can easily be accessed when needed.

Through digitalizing the evidence management system, the traffic department is breaking away from scattered filing rooms and manual evidence handling. This would create a transparent enforcement process where violation data are accessible via authorized devices.

 AI Traffic

Understanding Traffic Behaviour

In addition to logging instances of violation, Brihaspathi’s platform features AI-powered violation analytics, which processes huge amounts of traffic data in order to recognize behavioral patterns. By analyzing patterns at individual intersections, enforcement agencies are able to predict intersections prone to frequent signal violations, the time period within which most speed offenses are committed, or persistent points of traffic congestion.

All this is useful for the city planners and enforcement personnel to formulate strategies for optimizing signal timing, designing road networks, and directing the enforcement efforts more effectively.

Automated e-Challan Generation and Smart Notification Systems

The digital e-challan containing the vehicle’s identification information, the offense type, photographic evidence, and location is generated and electronically forwarded by the platform once the violation is confirmed. The owner of the vehicle is informed about the same using authorized governmental channels.

The system is also enabled for smart notifications. The drivers can be informed through the SMS or other messaging services of violations and payment of fines. This enables paperless processing and shortens processing time for fines.

Integrated with Government Databases and Enforcement Platforms

The Traffic Enforcement Solution integrates into existing government infrastructure, including the vehicle registration database, the traffic police database, and the digital payment gateways for challan payment. Linking the enforcement process with the official database enables enforcement agencies to carry out penalties in the administrative procedures already existing.

This interoperability makes the system suitable for large-scale government deployments, where technology must work seamlessly within regulatory infrastructure.

Real-Time Traffic Analytics and Multi-Location Monitoring

Control rooms usually supervise hundreds of intersections across the entire city. Brihaspathi has developed traffic analytics dashboards that show the status (real-time data on traffic density, violation frequency, and camera health) of hundreds of intersections from a centralized location.

An operator can view traffic behavior throughout city zones from one display, reducing the response time in traffic incidents like congestion, accidents, or strange circumstances. Multi-location monitoring can be particularly useful to cities with large-scale traffic controls (hundreds of traffic lights).

Designed for Citywide Deployment and Market Impact

In many modern cases, traffic enforcement systems have been planned for citywide coverage, and this infrastructure can grow with expanding cities. Brihaspathi’s architecture enables gradual expansion from single intersections to entire urban traffic networks without interrupting current infrastructure.

Globally, the intelligent traffic management market is expected to reach above $60 billion in 2030 on account of rapid urbanization and the adoption of smart cities. India has become one of the fastest-growing markets for AI-based traffic monitoring technologies due to its increasing focus on smart mobility and digital governance.

In addition to helping record and document violations, as cities more widely implement automated enforcement, increased digital enforcement may encourage drivers to slow down, lower accident rates, and supply city engineers with data required for designing safer roads.

Technology Supporting Safer Roads

Commenting on the vision behind the platform, Chairman and Managing Director Rajasekhar Papolu said:

“With the volume of traffic increasing year after year, manual surveillance can never be enough in urban transportation networks. Our ambition is to develop intelligent systems in which technology is watching around-the-clock, accurately recording offenses and enabling operational transparency for effective citywide traffic control for law enforcement agencies.”

Shaping the Future of Urban Mobility

With the growth of Indian cities and the increasing sophistication of travel systems, intelligent traffic enforcement systems will soon become an integral part of urban infrastructure. Solutions that integrate monitoring, analysis, and automatic enforcement would help officials in optimizing road networks and also induce an atmosphere of lawfulness among drivers.

The Brihaspathi Technologies, with its Traffic Enforcement Solution, is helping pave the way for the time when each signal, each lane, each intersection will become a part of the digitally monitored traffic network, facilitating safer traffic and wiser urban movement.

Visit our website to discover more, and for the company’s vision & leadership, connect with our CMD on LinkedIn

If you object to the content of this press release, please notify us at pr.error.rectification@gmail.com. We will respond and rectify the situation within 24 hours.





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What are Data Catalogs?

A Data Catalog seems to be an accumulation of metadata especially in data planning and search tools that assists experts as well as other data consumers in locating the data they require, acts as a current asset of data available, and offers criteria to assess strength and conditioning data for potential purposes.

This succinct process made several locations regarding data catalogs—data management, looking, data inventory, and interpretation of data; they all rely on the central capacity to deliver a catalogue of metadata.

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What is the Denodo Data Catalog?

The Data Catalog seems to be a web-based self-service device that is included in the Denodo System that would allow technology and commercial consumers to query, lookup, and explore details and metadata saved in a Virtual DataPort server. Consumers could use this device to create knowledge and open the way for best choices.

If you want to explore the denodo server metadata, then use the dendo data catalog. Here I am going to explain how all these things happen.

Launching the data catalog:

The Data Catalog seems to be a web based application distributed as part of Denodo 8.0 that allows data analysts, enterprise customers, and app developers to search and browse data and metadata in a business-friendly way for personal exploration and predictive analysis.

To use this web tool, open the Denodo Platform Control Center and launch the Data Catalog. When the status changes to “Running,” click the Data Catalog link to launch the Web tool (by default, https://127.0.0.1:9090/denodo-data-catalog).

Login into the denodo platform with your login details.If you are logging for the first time, you will see a pop up window showing the synchronize metadata option.This must be run the first time you start the Data Catalog to make sure that it reflects the most recent state of the Denodo 8.0 server to which you are connected.

The VDP Synchronization should be performed as follows:

  • Click the Synchronize metadata now button.
  • On each Synchronization step, click Continue.
  • The views have now been synchronized, and you can begin exploring!

Using the metadata search:

The first example comes from the Data Catalog’s home page.

Let’s use the Business Analyst’s scenario to look at a simple use case: searching for clients by typing in client and pressing enter.

denodo data catalog

Here are the outcomes of our search. Starting with Data Catalog 8.0, this search will look for views or web services that include the query terms in the element’s metadata, such as:

  • It has a name.
  • It is described.
  • The names of the fields in which it operates.
  • The descriptions of its various fields.
  • The values of any custom properties that have been assigned to it.

denodo data catalog 1

For instance, let’s click on the view client to go to the summary of the selected view:

denodo data catalog 2

For the time being, we have conducted a search in the Virtual DataPort metadata. In the following section, we’ll look into the Data Catalog’s more advanced features!

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Exploring the features of Data Catalog:

We’ll now look at the features in the Data Catalog that allow for more in-depth interrogation of a view. This includes the following:

  • Filtering and querying results from a view
  • Results are being saved to a file.
  • Developing new fields
  • Queries saved
  • Investigating viewpoint relationships
  • Investigating data ancestry
  • Views with related fields can be queried

Exploration of Data Catalog Views:

We chose our client view from the previous section. We can now look through the contents of this view.

Summary Tab:

We could see a summary of the selected view under the Summary Tab. It will display the metadata of the selected view, such as the database name, the list of categories, the list of tags, and collaboration information provided by the user, such as Endorsement and Warnings. You can edit the view’s description by clicking the Edit button next to the Description option. If the view is deprecated, an indication will appear at the top of the summary tab.

Furthermore, the Summary tab includes buttons such as Add Tags/Categories , Collaborative effort possibilities further to create custom the view, and Connection URLs, Tableau to display different opportunities to link to the view/datasource.

denodo data catalog 3

Schema tab:

Under the Schema Tab, we can see the view’s schema, which includes the view description as well as all of the fields and types. We can add a field description by clicking the Edit button next to the column. We can also use the search option at the top of each section to look for fields, data types, and descriptions.

denodo data catalog 4

Query Tab:

The Query Tab is the following tab. Ad-hoc queries can be run against the view here (the query is created graphically).

Business Intelligence & Analytics, denodo-data-catalog-description-3, Business Intelligence & Analytics, denodo-data-catalog-description-6

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Select all of the following fields for our view and drag them into the Output columns area.

client_id

name

surname

Client_type

denodo data catalog 5

Click on the execute to get the results.

denodo data catalog 6

Editing of data catalog metadata:

In this segment, we would then look at the metadata characteristics of the Data Catalog. Users could use Denodo Data Catalog to append tags and categories to views, and also keep updating the view as well as field descriptions, with such a function.

In our instance, we will: (1) add explanations to the customer fields in addition to allowing for more particular discovery of such a view, (2) add tags and categories, and (3) implement those to our client view.

Data catalog Metadata:

The capacity to exhibit view metadata, such as the View Description and Field Descriptions, is a good feature of the Data Catalog. Then see how we can make that data more modifiable.

Editing view and field descriptions:

  • Browse to the Client View’s Summary page and click the Edit option beside Description.
  • Add the necessary descriptions to the View and then click Ok.
  • Similarly, you can add a description to fields by going to the Schema tab and clicking on the Edit button next to each one.
  • The new descriptions are now visible in the view. These descriptions are saved as metadata in the Data Catalog.

Adding of tags and categories in data catalog metadata:

Tags and Categories are useful for allowing users to search the Data Catalog more precisely. While the number of Data Sources and Views in our tutorial is small, maintaining good Categorization and Tagging habits will pay off in the long run by allowing users to navigate the Data Catalog more easily.

In order to add categories follow the below mentioned points.

  • Navigate to Administration > Configuration and Management.
  • Click the Categories option in the Administration window’s Catalog Management section.
  • Select the + Add Category option.
  • Make a category with the following information:
    • Customer’s name
    • Customer data sources
  • Make a new category with the following information:
    • CRM (Customer Relationship Management)
    • Customer is the parent of the Acme crm System.
  • Make a final category with the following information:
    • Name: Billing 
    • Description: Billing
    • Parent:customer

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Adding tags:

  • Navigate to Administration > Configuration and Management.
  • Click the Tags option in the Administration window’s Catalog Management section.
  • Click the + Add Tag icon to add a new Tag with the following information:
    • Name:JDBC 
    • Description: JDBC data sources
  • Creating another tag with the following data.
    • Name:SOAP
    • Description:SOAP data sources

We can easily modify the views for adding tags and categories as well.

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Recommendation and collaboration in data catalog:

Recommendations in data catalog:

The AI Feature Package includes Automatic recommendation of datasets in the Data Catalog to assist you in discovering new elements among your company’s data resources.

This feature displays individualized recommendations based on earlier activity in the Data Catalog, like datasets which are most utilised, lately used, suggested, and so on.

Go to the Data Catalog’s homepage to see the recommendations.

The homepage displays a collection of products provided by various topics, such as one titled Recommended to you. This dataset recommendation is really only accessible only with the AI Feature Pack.

Collaboration in data catalog:

In the collaboration there are 3 options such as endorsements, warnings and decrepation notes.

Endorsements seem to be comments made by users on a view or a webservice to express their support. A user can only endorse a perspective or web service once, which means whenever a new comment is added, the prior endorsement is removed.

Warnings have been used by customers to write and exhibit “advise against” texts on opinions and web services. A consumer could only add one warning to a view as well as web service.

Deprecations have been used to notify people that a feature has become outdated and should no longer be used. A consumer could only write one deprecation for a perspective as well as web application.

Conclusion:

In the above blog post we had clearly discussed the dendo data catalog, data catalog metadata, adding of tags and categories, recommendations, collaboration etc in a more detailed way. If you have any doubts please drop your query in the comments section to get them clarified.

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