Transport

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ML and AI driving Transport Networks

While the much-anticipated adoption of driverless cars fills the screens of tech blogs and twitter feeds and ride-sharing companies redefine the way we get around, the application of advanced data science techniques in transport runs much deeper. The liveable cities of tomorrow are under pressure to embrace data science practices to optimise network performance, bring down traffic fatalities and identify trends and opportunities early.

The Solution

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From government transport departments, to rail and bus operators, to large-scale logistics firms, innovators are embracing machine learning, advanced statistics and AI to better serve their customers, passengers, staff and fleets. AutoStat® allows these organisations to access the most advanced AI and ML technologies within one easy-to-use platform, enabling data-driven decision making and automation previously inaccessible. From real-time passenger sentiment analysis, to using spatial statistics to predict train network demand across a city, best-practice data science solutions are finally available within in one unified platform, and without needing to code.

AutoStat® in Transport

AutoStat®’s vast range of inbuilt frameworks and modules allows users to:

1. Connect to live datafeeds for real-time data analytics and reporting, from proprietary APIs to streaming databases.
2. Identify trends and changes in passenger demand and behavior at a granular level using a combination ML and statistical frameworks
3. Distribute insights and data visualizations across the whole organisation with customizable dashboards.
4. Build analytical models to quantify the impact of changes in passenger demand and transport resources.
5. Build prediction models to identify passenger types, demographics and future behavior.
6. Analyze passenger feedback using natural language processing and sentiment analysis
7. Clean and consolidate duplicate customer records.”

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Experience AutoStat®

From data processing and visualisation, to machine learning and beyond, get up to speed with the most advanced data science techniques with real-world projects using AutoStat®.

Ensemble Machine Learning

Applying ML techniques to predict Heart Disease

Regression Models in Practice

A deep-dive into frequentist and Bayesian regression models

Mixed Effects Modelling

Modelling chick weights over time with fixed and random effects

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