Traffic Demand Management with AI

The use of artificial intelligence for the development of dynamic tariff and restriction scenarios, redistribution of transport demand, incentives for public transport and bicycle usage, and effective communication with drivers.

Transport demand management helps reduce congestion, emissions, and improve mobility. AI assists in developing effective strategies and adapting them to real-world conditions.

Incentives for Public Transport Usage

Support for bicycle infrastructure

Integration with ticketing systems

live demo · related simulation● LIVE

Data on On-Demand Service Usage

Ticketing and tariff data

Integration with ticketing and mapping systems

Frequently asked questions

What is the purpose of an effective communication system with drivers?

An effective communication system with drivers provides alternative routes and travel times. AI personalizes messages, improving estimated time of arrival (ETA) and reducing carbon dioxide emissions.

How should implementation begin? Should we start with a pilot project?

Implementation should begin with a pilot project in one zone or area. Collect data on mobility and traffic, set up a basic management system.

What data is required? Minimum: mobility data?

Minimum data requirements include mobility data, traffic data, event information. Additionally, ticketing data, on-demand service usage data, historical records, and bicycle infrastructure data are needed.

How much does implementation cost? Cost depends on scale?

Implementation costs depend on the scale: a management system ($50k - $200k), integration ($30k - $150k), and equipment ($20k - $100k). ROI is achieved through reduced congestion.

▶ Try it live

Everything above runs in your browser — open Hash Function Avalanche Visualizer and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open Hash Function Avalanche Visualizer simulation

Traffic Demand Management with AI – Tariffs, Scenarios, Balance