Mobility

AI is driving the next evolutionary phase for travel, transport and mobility, helping connect people to people, places and experiences. As cities and districts grow more connected and instrumented with sensors, the possibilities for artificial intelligence to improve energy efficiency, liveability and mobility grows exponentially. Real-time intelligence allows for cities and districts to evolve and organically self-organise in a data driven way.

Artificial intelligence engines can ingest huge amounts of data from multiple sources including historical booking and pricing information, route information, schedule changes, web-crawled competitor pricing from other providers as well as web & mobile user interaction behaviour.

Dynamic pricing engines can leverage machine learning to propose price structures which can be adjusted on a dynamic basis, in response to market conditions in order to maximise an organisation’s top line. Move away from static, rules-based revenue management which relies heavily on human intervention and intuition.

Demand forecasting of passenger capacity can enable on-demand and more adaptive modes of capacity planning and operations. Insights from voluminous data from sensors, cameras and vehicles can be gleaned to give traffic prediction insights for commuters or information on shortest routes available.

AI can be used to optimise routing and combined with real-time forecasting ability can respond to dynamic changes in traffic, improving fleet efficiency. Forecasting can enable operational interventions to improve regularity and reliability of vehicles and reduce wait times. Energy use demand forecasting enables better optimisation of energy consumption to minimise costs and carbon footprint.

As cities and districts grow more connected and instrumented with sensors, the possibilities for artificial intelligence to improve energy efficiency, liveability and mobility grows exponentially. Real-time intelligence allows for cities and districts to evolve and organically self-organise in a data driven way.

Leverage real-time data fusion for smarter cities. Integrate multiple data sources and apply machine learning to obtain a better pulse of the district and city to design better spaces and enable public safety.

AI can be used to optimise routing. Combined with real-time forecasting ability, responding to dynamic changes on traffic becomes possible, in turn improving fleet efficiency. Forecasting can enable operational interventions to improve regularity and reliability of vehicles and reduce wait times.

People and traffic flow planning and optimisation can be achieved through geospatial analytics to improve liveability and commuter flow.

AI applications can be used to improve operational safety and security measures, including safety and security of public transport systems such as accident risk prediction.

Protect humans from cyber and physical harm by reducing accidents and crime via anomaly detection and anticipatory incident response.

Interested in exploring alternative AI use cases?

Speak to one of our team today.

From concept to execution

Get to live applications within months, a pathway to sustained ROI from AI over years.

Strategise, to Roadmap

40-60 days

Co-founding team work with your executives to identify a company wide roadmap and high impact opportunities for AI in weeks.

Prototype, to Validation

8 weeks

Our data scientists will work on your data, explore the possibilities and build prototype models. Technical and business metrics can be defined, impact estimated.

Deploy, to Engine

3 months

Leveraging our proprietary Bedrock platform, we deploy a production-grade, high throughput, real-time AI engine that can be easily integrated to power your systems with AI.

Sustain, and Learn

Over years

BasisAI will continue to manage and ensure the reliability and performance of the AI engine. Ensuring that the AI engines continue to learn to realise the true potential of AI.

Mobility

Dynamic Pricing

Forecasting

Smart Cities