Having access to data from different sources creates completely new possibilities for the future of individualized public transportation. This includes for example, maintenance logs, images, videos, sensor data in addition to transit and transactional information. New mobility and information services can be created from these different data sources such as weather data and data on existing timetables.
AI tools enable novel ways of processing data, allowing the value-added use of knowledge generated from analytics reports to automate tasks in your daily operations.
Historical data, real-time information and statistical models and methods from data mining and machine learning can be used to generate insights about current and future processes. Methods for regression and classification such as neural network algorithms, Markov chains, decision trees and support vectors are examples of processes that are utilized.
In order to create accurate predictions, the mobility behavior and transportation needs of individuals need to be closely examined. Regional traffic flows and mobility usage can be better understood by further interpreting insights gained via predictive analysis. Additionally, influences such as extreme weather conditions, public holidays, technical disruptions or customer feedback can be taken into consideration in order to optimize future services.
The AI technologies and functions used by the mobility portal can be adapted to the different needs of mobility providers, operators, municipalities and user groups.
Insights generated by DATAbility's AI analyses can be used to
Transportation providers strengthen customer loyalty by, for example, increasing the frequency of buses and trains during rush hours and peak seasons. The features provided by the mobility portal are there to enrich your existing systems, making them even more intelligent. The modular web service-based infrastructure of the mobility portal also allows on-demand integration extension of additional data analytics.
