How Neural Networks Help Cities Design Public Transport Routes and Regulate Traffic

Traffic Growth and Route Complexity: The Challenge AI Solves

Modern cities face a growing volume of vehicles and passengers, making manual public transport route planning and scheduling increasingly inefficient. Traditional methods often struggle with rapidly changing situations: traffic, congestion, seasonal demand fluctuations, and irregular peak loads. Artificial intelligence comes to the rescue – it is capable of analyzing data, forecasting traffic, load, identifying bottlenecks, and suggesting optimal routes and schedules. This allows cities to make transport more efficient, faster, and convenient for people.

What AI Can Do – From Data to Routes and Schedules

AI systems for urban transport infrastructure rely on large datasets: GPS tracks of buses, traffic sensor data, passenger flow data, weather conditions, city events, etc. Based on this, models can:

Forecast route load – predict how many people will use transport at different times and days. This helps adjust schedules in advance.

Optimize routes and intervals – find faster, more logical, and in-demand routes, minimizing dead mileage and waiting times.

Automatically adjust schedules during changes – respond to congestion, accidents, demand changes, events, and rearrange routes "on the fly".

Reduce costs and increase comfort – buses and trains move with greater occupancy, fewer empty trips, increased efficiency and reliability.

All this leads to transport becoming predictable, convenient, and sustainable – even in a complex, dynamic urban environment.

What Does the Technological "Foundation" for AI and Urban Logistics Look Like?

To effectively assist in planning, a modern data and technology infrastructure is required. This usually includes:

Data collection from various sources: GPS trackers, roadside sensors, fare payment systems, mobile app data.

Storage and pre-processing: cleaning, aggregation, normalization – to ensure data is suitable for analysis.

Training ML/neural network models: time series (demand forecasting), route graphs (optimal path finding), computer vision (transport monitoring), multimodal systems.

Visualization tools and dashboards for city operators: easy to read, plan, and adjust.

Feedback and adaptation: real-time data + learning from new scenarios, so the model "learns" from mistakes.

As a result, the city receives a tool that not only reacts to the current situation but can also predict and plan taking into account many factors.

Real-World Cases of AI Implementation in Transport Planning

Case #1: City Brain (Alibaba Cloud) – Large-Scale Neural Network Traffic Management System in Hangzhou

→ City Brain analyzes data from traffic lights, cameras, and road sensors in real time, optimizing traffic light phases and vehicle routes. After implementation in Hangzhou, the average speed on controlled sections increased by approximately 15%, and emergency service response time was noticeably reduced – significantly increasing the throughput of streets and reducing congestion.

Case #2: Via Transportation – AI Platform that Helps Cities Design Public Transport Routes

→ The Via Intelligence platform uses traffic data, passenger flow, and demand to automatically build and optimize routes. It helps cities and municipalities forecast load, choose optimal directions, and create flexible transport networks – especially in areas where there were previously no sustainable routes.

Such systems allow public transport to be planned not "according to a template", but adapted to the real needs of residents – based on data and forecasts, not hypotheses.

Why is Now the Time for AI in Transport?

The growth of urban population, traffic growth, the need for ecology and sustainability, and the expectations of residents – all this turns traditional transport planning into a task that simply cannot be solved effectively without AI. AI provides a triple benefit:

For the city – reduced congestion, reduced emissions, and optimized logistics;

For operators – resource savings, fewer empty trips, a more stable schedule;

For passengers – comfort, predictability, fewer waiting times and delays. If you are considering implementing an AI solution for transport – it is reasonable to start with a pilot: collect route data, demand, load, and test models. To assess the budget, you can
→ use the EasyByte neural network development cost calculator.
And if you need to design a turnkey solution – it is most convenient to
→ schedule a free consultation with EasyByte experts.

📌FAQ: Frequently Asked Questions about AI for Routes and Traffic

Question: How reliable are the forecasts that AI provides for routes?

Answer: Accuracy depends on the quality and volume of the initial data. If there is historical traffic, passenger flow, and route data – modern models show high stability and resilience to changes.

Question: Does the city need to change its infrastructure to implement AI?

Answer: Not necessarily. Many systems work with existing data: sensors, GPS tracks, mobile app data. Sometimes it is useful to install additional sensors, but often it is enough of what already exists.

Question: How large is the budget for launching such a solution?

Answer: It all depends on the scale. For a pilot – a small one is enough: collect data and launch an MVP. A more extensive system will require investments, but savings on logistics and traffic often pay off.

Question: How quickly can the effects of AI implementation be seen?

Answer: Depending on the tasks – from a few months. When adaptive traffic management and route optimization are set up, the first improvements (reduction in delays, increased schedule accuracy) may appear within 3-6 months.

Question: Is AI suitable for medium and small cities?

Answer: Yes. Even in cities with modest scales and infrastructure, with the availability of basic data, AI can have a significant effect: optimize routes, reduce costs, and improve comfort for residents.

Telegram X / Twitter