Big Data & Smart City

Transportation behavior is changing rapidly with an unprecedented amount of data being generated at high speed and precision from sensors, mobile phones, smart cards, devices, apps, and social media feeds, most of which produce real-time data streams. This wealth of data, coupled with a boom of machine learning capabilities and cheap computing power is revolutionizing the way we manage our transportation network and plan for future mobility & city challenges.

At Surface Mobility, we understand the power of data in transportation, from building predictive models, to optimizing operations and understanding travel behavior. We understand transport & city authorities need for timely answers and actionable insights to their most pressing issues.

Our Big Data team is made up of: Smart City / Smart Mobility Consultants, Business & Data Analysts, Data Scientists, and Data Engineers, who will work hand in hand with our technology partners, to cover business, transportation, and IT aspects simultaneously.

Selected Projects

Enterprise Big Data Platform

The Enterprise Big Data Platform aims to enable the ingestion of complex unstructured data-sets, coming from disparate sources in high volume, velocity, and variety, into a data lake, to enable the fusion and processing of this data for timely decision making and transportation planning.

The big data platform shall enable the storing and processing of complex datasets such as:
– Geo-location data
– Sensor data
– Image / video feeds from CCTV & drones
– Social media feeds
– Real-time transactions and app usage

Once all data has been ingested in the data lake in its raw form, data scientists will be able to apply advanced machine learning/artificial intelligence algorithms and build predictive models to enable RTA to enhance its planning & operations capabilities, and enhance data sharing among RTA’s agencies which makes data more valuable and information-telling.

Below are some of the use cases developed for RTA:

– Pro-active congestion detection & prediction
– Multi-modal real-time trip information
– Short-term & long-term demand prediction for all modes of transport (TDM)
– Route optimization
– Automated anomaly detection
– Traveler behavior analysis and trip types
– Impact analysis and scenario simulation
– Mobility-as-a-service planning
– Feasibility studies of new products & services
– Measuring & reducing environmental impact
– Predictive Maintenance
– Population density and movement
– Face recognition, video/audio analytics, pattern recognition
– Sentimental analysis based on social media
– Moment marketing & customer loyalty programs
– Dynamic pricing and revenue generation
– 3rd party data usage / data monetization

Enterprise Big Data Platform

The Enterprise Big Data Platform aims to enable the ingestion of complex unstructured data-sets, coming from disparate sources in high volume, velocity, and variety, into a data lake, to enable the fusion and processing of this data for timely decision making and transportation planning.

The big data platform shall enable the storing and processing of complex datasets such as:
– Geo-location data
– Sensor data
– Image / video feeds from CCTV & drones
– Social media feeds
– Real-time transactions and app usage

Once all data has been ingested in the data lake in its raw form, data scientists will be able to apply advanced machine learning/artificial intelligence algorithms and build predictive models to enable RTA to enhance its planning & operations capabilities, and enhance data sharing among RTA’s agencies which makes data more valuable and information-telling.

Below are some of the use cases developed for RTA:

– Pro-active congestion detection & prediction
– Multi-modal real-time trip information
– Short-term & long-term demand prediction for all modes of transport (TDM)
– Route optimization
– Automated anomaly detection
– Traveler behavior analysis and trip types
– Impact analysis and scenario simulation
– Mobility-as-a-service planning
– Feasibility studies of new products & services
– Measuring & reducing environmental impact
– Predictive Maintenance
– Population density and movement
– Face recognition, video/audio analytics, pattern recognition
– Sentimental analysis based on social media
– Moment marketing & customer loyalty programs
– Dynamic pricing and revenue generation
– 3rd party data usage / data monetization

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