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Predictive Analysis for Passenger Movement and Demand

Kogenta Predictor.

Revolutionise the way you manage, plan and reward your city’s transport systems with our cutting-edge machine learning predictive solution.  

Designed exclusively for transport and local authorities, Kogenta Predictor empowers you to anticipate transport system usage across various geographies with unparalleled accuracy. Our advanced machine learning algorithms analyse historical and real-time data, delivering predictive insights that enable proactive planning and optimisation of public transport services. Whether you’re looking to enhance efficiency, reduce congestion, or improve commuter satisfaction, Kogenta Predictor is your gateway to a smarter, more connected urban mobility landscape. Experience seamless integration, user-friendly interfaces, and the power to shape the future of urban transport with confidence. 

Predicting mobility and public transport usage and demand is crucial for efficient transportation planning and service provision. Kogenta has developed the Kogenta Predictor product specifically for this purpose. Already in use by numerous transport directorates, Kogenta Predictor allows you to accurately forecast how many people will use public transportation systems, when they will use them and how frequently they will do so. 

Machine Learning Drives Accuracy

Sophisticated Statistical Models Provide Unprecidented Accuracy.

Leveraging advanced machine learning models, Kogenta Predictor analyses complex datasets with a wide range of variables to make accurate predictions. These advanced predictive analytics and machine learning algorithms can uncover patterns and trends not immediately apparent, offering more accurate and dynamic demand forecasts. 

Kogenta Predictor uses a multitude of machine learning algorithms determined by the specific problem to be solved. The algorithms are trained on large datasets resulting in highly accurate predictions. 

With Kogenta Predictor, you no longer need expensive and high maintenance APC counters on every vehicle. Sophisticated statistical modelling enables a true picture of customer usage and predictive demand. Instead, Kogenta Predictor estimates passenger numbers in real time by analysing a range of input data that has been fed into the model. Some of these data sources include: 

Smart access to public transport.
    • APC: For each door in a vehicle with APC-equipment installed the counter returns the number of boarding and alighting passengers on the specific departure. 
    • Production Data: Overview of all departures on the specific stations and departure. Includes information on vehicleID, planned and actual arrival and departures for all vehicles – both vehicles with APC and vehicles without APC equipment. 
    • Weather: Using the API for yr.no to extract different weather information like temperature, precipitation, air pressure, humidity and wind. 
    • Calendar Information: The calendar information attributes key date information to specific dates. 
    • Traffic Data: This includes some variables extracted from the production data and other calculations to describe disruptions in the service. 
    • Area Characteristics: To describe the area the stations/stops are situated in we extract key information from SSB. 
    • And More.

The resulting insights and reports can be analysed on the platform itself or the enriched data can be made available externally via API for a wide range of external uses. 

Predictions you can Rely upon like Never Before

Kogenta Predictor Key Features.

Kogenta Predictor provides the following key features: 

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Data Integration

Making use of the proven Kogenta ETL (KETL) data engine, Kogenta Predictor integrates with and processes data from diverse sources, including historical ridership data, ticket sales, real-time passenger counts, demographic information and land use data for a more comprehensive demand analysis.

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Real Time Data Processing

Kogenta Predictor works on data in real-time to handle changing conditions and unexpected events. This allows operators to make immediate adjustments to service levels if necessary.
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Significantly Less APC Hardware

Kogenta Predictor means only a small portion of the network needs to install APC counters. This results in a large saving on capex and ongoing maintenance fees. 

Granular Temporal and Spatial Analysis

Kogenta Predictor’s detailed analysis capabilities include breaking down demand predictions by time of day, day of the week, and specific routes or stations. This granularity helps in precise planning and resource allocation. 

Scalability

The Kogenta Predictor platform is designed to be highly scalable to easily accommodate future growth in data volume and complexity, as well as expansions of the transport network. 

Customisable Reporting and Visualisation

The Kogenta Predictor platform supports customisable dashboards and reporting tools that provide clear visualisations of demand forecasts, trends, and anomalies, enabling quick interpretation and decision-making. 

Feedback Loop and Continuous Learning

Incorporating a feedback mechanism that allows the tool to learn from past predictions and outcomes, continuously improving its accuracy over time. 

The Kogenta Predictor features collectively enable a public transport or mobility operator to not only predict passenger demand accurately but also to respond effectively to changing conditions, enhance planning, and improve service quality for all users.  

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Kogenta’s products cater to global geographies, boasting extensive data sets and indices for key markets such as the USA, UK, France, Germany, Italy, Spain, Ireland, Nordics, Australia, Canada, among many others.