Traffic congestion is a pervasive issue in urban areas worldwide, causing delays, increasing fuel consumption, and significantly contributing to air pollution. Although the pandemic led to a temporary decrease in traffic, the problem persists, with Americans losing an average of 26 hours last year to road congestion. In India, the situation is even more critical with its rapidly growing urban population. Cities like Bengaluru and Mumbai are notorious for their traffic snarls, frustrating commuters and leading to significant economic losses and increased air pollution. The transportation sector accounts for 27% of global greenhouse gas emissions, exacerbating climate change and degrading air quality. In India, the contribution of automobiles to total air pollution is reported to be between 40-80%, and for Delhi's ambient air quality, the transport sector's contribution is estimated as high as 72% (Source - https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4746948/). Addressing this crisis requires innovative solutions that go beyond traditional traffic management methods, such as continuous major infrastructure changes, promoting public transportation, or implementing congestion pricing.
To combat these growing traffic issues, Fabrik aims to utilize machine learning and real-time data collected from a wide range of infrastructure sensors and create a digital twin to first assess existing traffic conditions. Via sensors installed on roads, the dynamic, data-informed simulation will capture real-time traffic conditions, providing us with a portal to understand some of the underlying causes of existing traffic congestion. These insights generated from the digital twin will allow us to pinpoint specific areas for improvement, test possible solutions, and recommend effective traffic management strategies for city officials.
What are Digital Twins?
Digital twins are dynamic, digital replicas of physical systems—such as a city's traffic network—that use real-time data to simulate, predict, and optimize performance. These sophisticated models are powered by data collected from various sensors and devices, providing a comprehensive, up-to-date view of traffic conditions. By leveraging advanced analytics and machine learning, digital twins enable city planners and traffic managers to test different scenarios and implement data-driven strategies to improve traffic flow and reduce congestion.
Implementing Digital Twins for Traffic Management
Digital twins rely on an extensive network of sensors installed on roads, traffic lights, and vehicles. These sensors collect data on traffic volume, speed, and patterns, feeding it into the digital twin to create a real-time, accurate representation of traffic conditions. This dynamic model allows us to understand the root causes of congestion and develop targeted interventions.
Example: Two interstate highway systems in the US carry over 250,000 vehicles per day, providing a wealth of data for traffic management.
In Chattanooga, Tennessee, implementing digital twin technology to optimize traffic signals resulted in significant improvements. By analysing traffic data and adjusting signal timings in real-time, the city achieved up to a 16% reduction in energy consumption and a substantial decrease in daily commute delays.
Reducing Energy Consumption and Delays
The benefits of digital twins extend beyond traffic management. In a simulation involving 39 traffic signals, synchronously adjusted signals reduced delays by up to 50% compared to traditional time-of-day timings. These optimizations not only improve traffic flow but also reduce fuel consumption and emissions, contributing to a cleaner environment.
Read the whole story > https://www.nrel.gov/news/program/2023/digital-twin-project-green-lights-traffic-congestion-improvements.html
Addressing Sensor Costs and Maintenance
One of the main challenges in implementing digital twin technology is the cost and maintenance of sensors. Installing and maintaining a vast network of sensors can be expensive, particularly in regions with limited resources. However, many of the existing cameras and sensors used for traffic monitoring and enforcement can also be utilized for digital twin applications. This allows us to work on implementing solutions that require minimal disruptions by leveraging software optimization.
Using Connected Vehicle Data
To complement sensor data, researchers have started using connected vehicle data purchased from data companies. This approach provides a more accurate and comprehensive picture of traffic conditions, making it possible to run effective simulations even in areas with fewer sensors.
Fabrik is actively working at multiple levels with government agencies to implement digital twin technology, leveraging our extensive experience in smart city solutions. Our collaboration focuses on integrating digital twins into existing urban infrastructure to create a comprehensive and efficient traffic management system. We aim to streamline data collection and analysis, enhance real-time decision-making capabilities, and develop tailored strategies for each city's unique traffic challenges. Fabrik's expertise in deploying cutting-edge technologies ensures that the solutions we provide are not only innovative but also practical and scalable, allowing for seamless implementation across various urban environments.
TLDR
Digital twins represent a transformative approach to traffic management, offering significant benefits in terms of reduced congestion, lower emissions, and improved urban mobility. By harnessing real-time data and advanced analytics, cities can implement smarter, more efficient traffic management strategies. As India grapples with its traffic challenges, digital twins offer a promising solution to create more sustainable and liveable urban environments.