Passenger Forecasts for Local and Long-Distance Traffic

Passenger analyses are not uncommon: Many transport operators already have access to various passenger data and can evaluate them according to various aspects, e.g., regarding boarding, alighting, or unusual traffic. Often, however, this is only done in retrospect. Using Artificial Intelligence, however, learning forecasts and predictions can also be made with a high degree of reliability.

Take a look at the passenger analysis demo

Project Overview

Initial Situation

Local and long-distance transport operators collect and analyse passenger data. However, the evaluation often only results in an analysis of the past.

Vision

Predictions and forecasts of passenger volume by applying Artificial Intelligence to historical passenger data and the associated gain of knowledge as well as the targeted derivation of actions.

Solution

Arvato Systems applies machine learning methods such as autoregression analysis, parameter optimization and model aggregation to passenger data. The resulting passenger forecasts as well as trends and progressions are clearly visualized in various ways (e.g. with thermal images, trend dashboards, etc.).

Advantages

Forward-looking forecasts

Bottleneck avoidance through demand-oriented vehicle deployment planning

Targeted derivation of actions

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In our case study (German language) on optimized vehicle deployment planning at Leipziger Verkehrsbetriebe, you will gain insight into ÖPNV (public transport) digital.

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Your Contacts for Public Sector

Ekkehard Mittelstaedt

Expert for Digitization in the Public Sector

Reinhard Zenker

Expert for the Public Sector

Leipziger Verkehrsbetriebe

Intelligent insights into the number of passengers on public transport systems

Project Overview

Advantages

Want to Learn More about Mass Data Analysis and Visualization with Microsoft Power Bi?

Your Contacts for Public Sector