OUR BIG DATA PLATFORM FOR MOBILITY HELPS CITIES UNDERSTAND THEIR ENTIRE MOBILITY ECOSYSTEM INCLUDING PUBLIC TRANSIT AND PRIVATE TRANSPORTATION USAGE.
Our ZIG Mobility as a Service platform will collect real-time data from multiple transportation sources: automotive driving, public transport, and new mobility providers including rideshare, bikeshare, scooters, Greyhound, Toll road usage, and more…
Our data-driven analytics platform helps evaluate existing transportation infrastructure in a city. What is the travel demand during different times of the day? What is the vehicle occupancy of public transport at any given time? What are the first/last mile connectivity modes used by riders to access public transport? What are the pickup and drop-off destinations of new mobility modes such as rideshares when compared to public transit bus stops?
Our deep Analytics – show ridership, number of riders in real-time, origin and destination of the trip as well as travel modes used by riders (e.g. car, bike, walk, etc.) to reach to and from agency’s bus stops, heat maps, etc.
This data will help the agency’s leadership with insights such as:
- Make improvements to their overall transit system, understand which origins and destinations are most popular, which mobility options would benefit low-connectivity neighborhoods, etc.
- Our solution includes ready-made APIs to enable a regional integration of the agency’s fares to other mobile apps in your area without extensive development.
Why is big data analytics important?
In today’s world, Big Data analytics is fueling everything we do online—in every industry.
Benefits and Advantages of Big Data Analytics
- Risk Management
- Product Development and Innovations
- Quicker and Better Decision Making Within Organizations
- Improve Customer Experience
NOW, LET’S REVIEW HOW BIG DATA ANALYTICS WORKS
- Stage 1 – Business case evaluation – The Big Data analytics life-cycle begins with a business case, which defines the reason and goal behind the analysis.
- Stage 2 – Identification of data – Here, a broad variety of data sources are identified.
- Stage 3 – Data filtering – All of the identified data from the previous stage is filtered here to remove corrupt data.
- Stage 4 – Data extraction – Data that is not compatible with the tool is extracted and then transformed into a compatible form.
- Stage 5 – Data aggregation – In this stage, data with the same fields across different datasets are integrated.
- Stage 6 – Data analysis – Data is evaluated using analytical and statistical tools to discover useful information.
- Stage 7 – Visualization of data – With our custom built tools Big Data analysts can produce graphic visualizations of the analysis.
- Stage 8 – Final analysis result – This is the last step of the Big Data analytics life-cycle, where the final results of the analysis are made available to business stakeholders who will take action.
Our deep Analytics – show ridership, number of riders in real-time, origin and destination of the trip as well as travel modes used by riders (e.g. car, bike, walk, etc.) to reach to and from the agency’s bus stops heat maps, etc.
This data will help the agency’s leadership with insights such as:
- Make improvements to their overall transit system, understand which origins and destinations are most popular, which mobility options would benefit low-connectivity neighborhoods, etc.
- Our solution includes ready-made APIs to enable a regional integration of the agency’s fares to other mobile apps in your area without extensive development.
