Route optimization
Machine learning models that plan routes and assign vehicles — an example of how our expertise reaches beyond the image, toward process optimization.
Business value
_You're one step away
Lilli Trans Sp. z o.o., a taxi operator handling a monthly volume of nearly a million kilometers, reported a need to optimize its route-planning process. The pace of operations and the number of constraints — such as passenger count, dynamic time windows and maximum driver working time — made the decisions taken by dispatchers increasingly inefficient. The goal of the project was to develop an AI algorithm that would reduce the number of kilometers driven while still delivering passengers to their destinations smoothly.
_You take the step
The AI-based algorithm we built analyzes ride demand and operational conditions — such as passenger count and time windows — in real time. Based on this data it generates route-optimization recommendations for dispatchers, who make the final decisions. The algorithm accounts for the maximum driver working time, ensuring regulatory compliance. Our solution was also integrated with the client's internal system, and the data gathered while the algorithm runs is archived, enabling historical analysis and continuous model improvement.
_You've taken the step
By deploying the Edge AI algorithm, Lilli Trans Sp. z o.o. reduced the number of kilometers driven, generating savings of several thousand euros per month. The system increased operational efficiency and relieved dispatchers in the planning process, letting them focus on supervising ride execution. Our solution contributed to more efficient fleet utilization, strengthening the company's position in the transport market.
Shorter routes
Better fleet utilization
_Route map
Optimal plan
_Demand forecast
Predictive model
_Fleet assignment
Real-time optimization
