5 Solar Energy Breakthroughs with Machine Learning
Intro: Machine learning in the solar energy industry The high availability of data in the energy sector makes it a...
The Smart Cities of today are powered by advanced technologies that are constantly reshaping urban areas. AI and IoT are becoming increasingly integral to how the world operates. Cloud-based services, the Internet of Things, analytics platforms, and many AI tools are changing the way citizens interact with and move within their environment.
These modern technologies, as outlined by Blue Orange Digital, a top-ranked AI consulting and development agency in NYC, enable applications ranging from waste management to food supply optimization and healthcare digitization. In the process, they are disrupting entire industries and creating new business opportunities and applications.
Among all urban responsibilities, transportation management poses an interesting problem, even for the most advanced AI tools and technologies. City traffic is a highly dynamic environment, where thousands of participants using different transportation modalities interact in complex manners. On top of that, decisions need to be taken in real-time, in order to ensure the safety and well being of all traffic participants. Activity planning in such an environment is an extremely challenging task. Luckily, AI-powered Smart City technologies are already making great progress in tackling some of the most pressing transportation management issues.
Below is a list of the most common traffic management solutions that IoT and AI technologies are powering.
Data is power, and this holds true especially for city planners: it has become mandatory that their decisions are backed by data. Information about how different city areas are used by the citizens (mobility data) can provide crucial insights into transportation needs. It offers them an accurate overview of how different city pathways are being used and thus increases the chances for more accurate, citizen-friendly planning.
Crowdsourced data is nowadays ubiquitous and originates in a variety of devices. Our smartphones, tablets, laptops and even cars are all constantly emitting geolocation data. A variety of applications are capturing this data and using it to power consumer-facing services. At the same time, analytics frameworks make it straightforward to extract insights from such heterogeneous data sources. By sharing this data with city administration and city planners, it is possible to capitalize on this rich mobility data in order to improve the planning process.
Think about the most popular bike pathways in your city or the most populated pedestrian areas. Planning without knowledge of how these areas are used would be equivalent to climbing Mount Everest blindfolded, in the dark. Visualization and analytics are definitely needed to bring light to the process and to make sure that all planning decisions are powered by citizen-generated data.
The benefits of crowdsourced mobility data can translate into improved walkability and reduced commute times. For bike riders, this translates to optimized routes and greener pathways, while for the car drivers it means less time spent in city centers, waiting for traffic lights and pedestrians. Mobility data makes it a win-win-win for all traffic participants.
Ensuring public road safety is a crucial responsibility of transportation management systems. The complex environment created by vehicles and pedestrians needs to be kept under close surveillance, in order to ensure the safety of all traffic participants.
Luckily, technology is available that makes it possible to automate such surveillance tasks and delegate them to software and algorithms. Computer vision and video analytics can be implemented both on roadside cameras, but also on cars. Algorithms can perform computation on the edge and can detect situational and behavioral abnormalities at the moment when they happen. From the automated reading of license plates to detecting walking patterns, a variety of applications become possible thanks to computer vision. When implemented as part of traffic management systems, they can minimize the high risks associated with careless driving and ensure the safety of public pedestrian areas.
Delegating and automated tasks to software have the potential to create a much safer environment for all traffic participants. Computer vision and video analytics are the leading technologies for efforts in this direction.
Understanding traffic is a task that needs to be done in real-time, in order to be able to optimize the traffic flow, both within and outside of urban areas. This involves the identification and communication of accidents, congestion, and temporary roadside obstacles, among other traffic events.
Sensor technologies and advanced wireless communication protocols make it possible for all kinds of vehicles to communicate direction, speed, and travel times. There is no limit to the amount of information that they can communicate, given the increased customizability of IoT devices. Not only can they be attached to any moving object, but they also make it possible to collect and communicate contextual information from the environment.
Sensor-collected data makes it possible to run real-time analytics, that power immediate traffic management decisions. Such an example application is that of adaptive traffic signals, which are not simply programmed, but take into account live traffic information.
The benefits of sensor-based solutions can be translated into active traffic management measures. They enable short-term prediction and control and can lead to reduced congestion and increased traffic fluidity. By helping traffic management institutions cut down on emissions, noise, and travel times, IoT-based sensor technologies play a crucial role in any modern transportation management system.
City planners and engineers are now working in increasingly complex environments and need to solve increasingly complex problems. AI and IoT are helping them tackle these problems. Traffic and transportation management poses a modern challenge that would be tricky to tackle without the help of software and algorithms. Additionally, traffic management plays a crucial role in any Smart City since it can easily impact the well functioning of all other city functions.
Luckily, modern technologies make it possible to leverage citizen-generated mobility data in order to tackle such complex tasks. With the increased availability of analytics frameworks, cloud services, and data collection devices, it becomes possible to find modern solutions and integrate real-time data as part of traffic management decisions.
When data is used for decision making and for gaining a better understanding of city travel dynamics, the quality of the management applications also increases. This ensures that traffic control strategies and future infrastructure development projects will accurately match the citizens’ needs. AI and IoT are becoming the new technological norm and that’s a future we are eagerly looking forward to.
Josh Miramant is the CEO and founder of Blue Orange Digital, a data science and machine learning agency with offices in New York City and Washington DC. Miramant is a popular speaker, futurist, and a strategic business & technology advisor to enterprise companies and startups. He is a serial entrepreneur and software engineer that has built and scaled 3 startups. He helps organizations optimize and automate their businesses, implement data-driven analytic techniques, and understand the implications of new technologies such as artificial intelligence, big data, and the Internet of Things.
Featured on IBM ThinkLeaders, Dell Technologies, and NYC’s Top 10 AI Development and Custom Software Development Agencies as reviewed on Clutch and YahooFinance for his contributions to NLP, AI, and Machine Learning. Specializing in predictive maintenance, unified data lakes, supply chain/grid/marketing/sales optimization, anomaly detection, recommendation systems, among other ML solutions for a multitude of industries.
Visit BlueOrange.digital for more information and Case Studies.