Smart Mobility in Vilnius
Safe & More Efficient Public Transport / Case
Urban centers are battling with various transportation challenges, including traffic congestion and road safety concerns. Traditional traffic management systems lack the real-time analytical capabilities needed to address these issues effectively.
Need for innovation
Urban mobility is facing major bottlenecks, with increasing populations putting more strain on existing infrastructure. Public transport, although more sustainable, often suffers from inefficiencies that reduce its appeal compared to private transportation. AAI Labs, in collaboration with the public transport organizer JUDU and Vilnius City municipality, is leading the effort to optimize public transport in Vilnius. By introducing real-time data analysis through 5G and AI, this project addresses these limitations, making public transport not only efficient but also safer for passengers and road users.
Scope and objectives
The project aims to improve urban mobility by implementing real-time traffic violation detection through stationary and mobile camera networks, identifying issues like bus lane misuse and illegal driving behaviors. It also focuses on forecasting passenger volumes on public transport routes, allowing for better resource allocation and reducing delays. Additionally, the project includes monitoring road conditions, such as identifying potholes and hazards using onboard sensors and AI-based image recognition models. Furthermore, by analyzing telematics data from public buses, the system predicts potential vehicle breakdowns or maintenance needs.
Key features and technologies
AI-Based data analytics
The project utilized AI models to analyze the data generated by sensors, cameras, and GPS systems embedded in public transport vehicles and infrastructure. By continuously learning from this data, the AI models can predict traffic flow, detect patterns in road usage, and identify potential violations in real time. For instance, AI models trained on traffic camera footage are used to automatically detect bus lane violations.
5G connectivity
The fast and reliable communication capabilities of 5G enable real-time transmission of large data sets, such as video footage from buses and street cameras. This ensures immediate detection of traffic violations and live updates on road conditions and passenger flow. Unlike previous 4G systems, the low latency and high bandwidth of 5G make it possible to process massive data streams without delay.
Traffic violation detection
AI-powered image recognition systems use video feeds from both stationary and bus-mounted cameras to detect traffic violations. These systems can identify illegal parking in bus lanes, unauthorized vehicles in restricted areas, and other traffic offenses, allowing authorities to take immediate action.
Passenger flow and telematics analysis
One of the most critical features of the project is predicting passenger flow. By analyzing historical data along with real-time information, the system optimizes bus schedules to reduce idle time and avoid overcrowding. This predictive analysis is crucial during peak hours when public transport is in high demand.
Results
The implementation of 5G and AI in Vilnius' public transport leads to measurable efficiency improvements. Average waiting times for buses are expected to eventually be reduced by up to 20%, utilizing real-time route optimization. Predictive analysis also reduces the number of underutilized buses, cutting fuel costs and emissions by optimizing bus schedules to match passenger demand more accurately.
After the full deployment of the traffic violation detection system, we expect to see a 15% decrease in bus lane violations. This improvement will directly contribute to smoother traffic flow and increased safety for both passengers and other road users. The predictive maintenance system will also reduce breakdown incidents by 10%, as buses receive timely repairs based on AI-driven diagnostics.
Passenger satisfaction will be improved due to more accurate arrival predictions and less overcrowding. The ability of the system to predict when and where buses are needed most will lead to a smoother, more reliable public transport experience, encouraging more citizens to opt for public transportation over private vehicles.
Application in other industries
In logistics and supply chain management, these technologies can optimize fleet management and route planning by processing real-time data on traffic and demand, leading to more efficient deliveries and reduced operational costs. The manufacturing sector can benefit from predictive maintenance, where AI models monitor industrial equipment to prevent costly breakdowns, aligning with Industry 4.0 goals for smarter production environments. In the energy and utilities sector, predictive analytics can optimize grid performance, balance energy loads, and schedule maintenance, ensuring reliable service and reducing outages.
Additionally, healthcare can leverage these technologies for faster emergency response times and better resource allocation by utilizing real-time data from 5G-enabled ambulances and predictive models for service demand. Lastly, the retail sector can apply AI and real-time data to optimize inventory management and improve customer experiences, enhancing both operational efficiency and satisfaction.
Partners
JUDU, Vilnius City Municipality
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