Bhavya Kukrety, Associate Director, Meinhardt Group
The definition of “smart cities” is now simple. We have effortlessly knit it around the four principles of business: convenience, customer satisfaction, secured platform, and seamless interoperability.
For the last decade, the urban sustainability framework has been strongly focusing on resource efficiency and optimization. A key success has been in the greenfield developments, where, an integrated approach and complementing eco-friendly design solutions have delivered effective results.
However, the existing cities in complex ecosystems are struggling to deliver quantifiable and visible outcomes. The cities are facing technical and cost challenges to interface with legacy or incompatible aging infrastructure. To ensure sustainable outcomes, the ability to deal with dynamic variables requires analysis of contextual real-time information, shared sector-specific information, and operational technology (OT) systems. Robust and stable communication infrastructure, and a unified platform would be pertinent to facilitate the flow of data and the co-relation of information for insights and decision making.
Urban Vehicular Traffic Management is a classic example of the use of technology and its impact on livability, environment, and businesses. In the last decade, the use of artificial intelligence and smart technology has penetrated traffic planning and multi-modal transportation systems. Integrated multi-modal traffic simulation is now being used to design optimal traffic control strategies and transport infrastructure using advanced AI methods.
With real time data for traffic prediction and navigation, frequency, and duration of trips—the carbon footprint can be reduced
This real-time information and analysis has helped in optimizing traffic signal control strategies, selecting locations, and quantity of parking spaces. The automated use of smart illumination of road markers and signages and profiling of pedestrians has enabled safer pedestrian crossing for the elderly and mobility-impaired users. A connected city becomes more livable as neighborhoods become complete and amenities are more accessible, improving the quality of its citizens.
Vehicle-to-vehicle and vehicle-to-infrastructure communication are amongst the future autonomous vehicles, shared mobility devices, MRT/LRT, and electric buses/EV infrastructure. Market engineering research and development have seen a huge opportunity for IoT industry, EV industry, renewables, and smart connected appliances.
Real time information and analysis may reduce the need for surveys in the future to assess the traffic pattern in a day and in designing new infrastructure for the future planning. With real time data for traffic prediction and navigation, frequency, and duration of trips—the carbon footprint can be reduced. People and businesses are incentivized to plan, organize, and manage the efficient movement persons and cargo.
Mass transportation is a huge market, and has many benefits, that can contribute to real estate and economic opportunities, jobs, reduced air pollution, and less congestion. It is assumed that every $1 invested in public transportation has a potential to generate $4 in economic returns.
The mass transportation industry has applied advanced deep learning, a form of artificial intelligence, to build data-based models and determine the optimized performance of the transit facility and the transit option.
AI models have been well-embedded into the scheduling optimization, behavior mapping, system efficiency and automatic suggestions for peak-hour demand management, system upgrade or crisis management. These can be well-knit around the constraints include budget, fleet size, vehicle type, number of drivers, union rules regarding driver breaks and working conditions; and other factors.
Ultimately, the aim of smart city framework is to strike a balance between inter mobility and a flexible transportation system enabling people and goods to move around more easily by combining alternative modes of transport, to the last mile.