By Vivek Mehra, Co-founder & CTO, Onlygood.ai
As urban dwellers keep increasing, cities are experiencing increasing congestion, pollution, and inefficiencies in transportation systems. The imperative for sustainable urban mobility has never been greater, and artificial intelligence (AI) is at the forefront of revolutionising the way people move around urban areas. Through the use of AI, cities can streamline transportation networks, lower carbon emissions, and improve overall mobility experiences.
Traffic Management for Lower Emissions
One of the main contributors to urban carbon emissions is traffic congestion. Cars idling on congested roads are a major source of greenhouse gas emissions. AI-based traffic management systems deal with this by streamlining traffic and minimising congestion. With the help of machine learning algorithms and real-time analysis of data, AI can dynamically change traffic light sequences, optimise public transport priorities, and route cars to less congested streets.
Smart traffic management systems, as deployed in cities such as Singapore and Los Angeles, employ AI to analyse traffic patterns and foretell congestion areas ahead of time. Such systems drastically reduce travel time and lower fuel usage, resulting in fewer emissions.
Public Transportation Optimisation
Public transportation networks are a key part of sustainable urban mobility. AI improves the reliability and efficiency of public transit through passenger demand analysis, prediction of peak travel hours, and route optimisation. Predictive analytics powered by AI enable transit authorities to better manage resources, minimising redundant fuel consumption and emissions.
AI is also applied in managing fleets of electric buses and trains to optimise battery usage and charging schedules. This reduces energy waste and prolongs the life of electric transit vehicles, further enhancing sustainability objectives.
Shared Mobility and Micro-Mobility
The emergence of shared mobility solutions, including car-sharing, ride-hailing, and micro-mobility like e-scooters and bike-sharing, is transforming urban transportation. AI facilitates these services in various ways, including demand prediction, dynamic pricing, and optimised fleet deployment.
For example, algorithms powered by AI assist in ride-sharing networks in minimising vacant trips by routing passengers traveling in the same direction. This brings down the aggregate number of automobiles on the streets, lowering pollution and city jam. Likewise, AI-based micro-mobility solutions optimise location of e-bikes and e-scooters, enhancing ease of access, and promoting environmental- friendly commuting.
Carbon Emissions Monitoring
AI plays a critical role in effectively monitoring and lowering carbon emissions from urban mobility systems. Cities can track emissions in real-time from different transport sources by combining AI with IoT sensors and GPS data. AI models process this information to detect emission hotspots and recommend policy interventions, including congestion pricing or low-emission zones.
AI platforms also allow individuals to monitor their carbon footprint related to transportation. Certain sustainability tech platforms apply AI to review commuting behaviors, propose more environmentally friendly options, and offer rewards for using low-emission modes of travel. Businesses can use AI-informed carbon accounting platforms to quantify and compensate for their transportation-linked emissions, aligning with corporate sustainability objectives.
Autonomous Vehicles and AI-Driven Electrification
AI is leading the way to cleaner and more efficient urban transport through the creation of autonomous and electric vehicles (EVs). Autonomous vehicles (AVs) use AI-based navigation systems to plan routes, minimise idle time, and enhance fuel efficiency. These vehicles are anticipated to decrease urban congestion by facilitating smooth traffic flow and eliminating human driving inefficiencies.
AI also has a central role to play in EV uptake by optimising charging infrastructure. Intelligent AI-powered charging stations forecast charging demand, allocate energy in an efficient manner, and connect with renewable energy sources. AI-based battery management systems further improve the sustainability of EVs by prolonging battery life and maximising energy efficiency.
Urban Mobility Planning and Policy Making
Urban planners and policymakers utilise AI-powered simulations and digital twins to simulate future transportation futures. AI, by examining various variables like population growth, infrastructure projects, and climate policies, designs sustainable urban mobility systems balancing efficiency and sustainability.
AI insights can be used by governments to enact environmentally friendly transportation policies, like widening bike lanes, encouraging electric public transportation, or instituting congestion pricing policies. AI additionally assists in determining the effectiveness of such policies in real time, which supports adaptive decision-making.
AI is transforming sustainable urban mobility by making transportation systems more efficient, less congested, and less carbon-intensive. From smart traffic management and optimised public transport to AI-enabled emissions monitoring and integrating electric vehicles, the contribution of AI in urban mobility cannot be overemphasised. As cities increasingly adopt AI-based solutions, they become closer to realising cleaner, greener, and sustainable transport ecosystems, effectively enhancing the quality of city life and supporting international climate objectives.