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The Data Platform for Intelligent Transportation

The Florida Department of Transportation (FDOT) is developing a connected vehicle data exchange platform (DEP) to help analyse real-time road conditions and communicate travel information to drivers. FDOT tapped Southwest Research Institute (SwRI) to develop a cloud-based vehicle-to-everything (V2X) DEP as part of the department’s Connected and Automated Vehicles (CAV) Initiative. The US$8 million project is expected to take four years.

The V2X DEP is intended to encompass the FDOT’s operational, development and planned CAV project corridors and networks. The system plans to ingest data from CAV devices and intelligent transportation systems, to assist with decision-making by FDOT and stakeholders in the auto industry, research, traffic engineering and other transportation areas.

Data has to travel along with a backhaul to a Florida DOT traffic management centre, and then from there, it’s got to travel over the public internet to reach this cloud-hosted data-processing platform. At that point, the data is going to be consolidated, duplication is going to be removed and personally, identifiable information is going to be stripped out to ensure anonymity for the travelling public.

After it’s cleaned, the data will be reconciled with the broader dataset, which includes data from other sources such as traffic cameras, proprietary cellular-based communications from various vehicle manufacturers and third-party data feeds. All of that needs to look the same so SwIR’s algorithms can work with it. This stream of enriched data will be the basis for real-time and historic analysis, leveraging a combination of machine learning and traditional algorithms.

The department needs little on-premises hardware to support this because the idea is to use the cloud to provide most of the compute and storage capacity. The reason for that is that managing the servers necessary to support this effort would be cost-prohibitive. Additionally, the system will autonomously scale its ability to process data as the volume increases. In 2021, there were about 84 million connected cars on U.S. roads, but that number is expected to pass 305 million in 2035, according to Statista.

The project is structured to have five iterative releases over four years. It’s currently in a proof-of-concept to demonstrate feasibility, and although it’s intended for statewide deployment, it will likely kick off in just a few districts. The team will also provide clean, high-volume data to researchers at Florida International University who can perform historical analysis to see patterns over time.

As reported by OpenGov Asia, To reduce speed-related casualties related to vehicles running red lights, researchers have developed technology to dynamically extend the duration of traffic lights. According to the Federal Highway Administration, traffic signals are prime locations for accidents, with more than 2 million crashes and 3,000 fatalities a year.

Technology developed by Purdue University’s Joint Transportation Research Program and the Indiana Department of Transportation (INDOT) will collect data from wireless transmitters installed in vehicles, calculate the speed and trajectory of oncoming vehicles and communicate that information to the signal, which uses embedded intelligence to adjust the time the light stays green or to change to a yellow light earlier than necessary.

Because the technology is built on the wireless transmission of data rather than sensors embedded in the roadway, the solution requires much less infrastructure investment. The technology has been initially designed for large vehicles and semi-trailers that need more stopping distance and are therefore twice as likely to run a red light.

In the past, there were only conceptual-use cases involving onboard vehicle communication technology integrating with live traffic signal control. The new technology moves this integration beyond the merely conceptual. This work provides an implemented real-world use case that addresses an important safety concern, among other applications.

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