Dubai Ferry rides in Dubai Canal are popular among commuters and tourists alike Image Credit: Supplied

Dubai: The Roads and Transport Authority (RTA) in Dubai has started the implementation of a seasonal schedule for marine transport using Big Data to analyse traffic and ridership to set timetables.

This initiative covers all marine transport - Dubai Ferry, abras, water taxis, and water buses. Their operating times are altered according to the nature of each season and the movement of Dubai’s residents throughout the year. The updating of operational schedules is undertaken in coordination with the Department of Planning and Business Development at the Public Transport Agency.

What is Big Data?
The collection and analyses of large and complex streams of data - which are too vast for normal processing software - is known as Big Data. A example would be the government tracking the vast public health data to form policies.

Nabil Yousef Al Ali, Director of Marine Transport, Public Transport Agency, RTA, said: “The seasonal operational network of marine transport services is marked by enterprise agility, accuracy and compatibility with the nature of the operating seasons according to well-rehearsed plans set by RTA in this field based on international best practices.”

He added: “RTA’s shift to the summer mode is leveraged by Big Data that encapsulates all information relevant to marine transport services. This includes ridership, revenue, and operational rates that enrich service improvement studies and significantly contribute to improving network efficiency.”

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What is evaluated?

The director explained that leveraging Big Data has enabled greater flexibility in devising and implementing the seasonal operational network initiative for marine transport. It operates based on a methodical approach that employs predictive analysis for scrutinising network data, foreseeing the effects of changes, and its flexibility in operating times, journey delay times, and the ridership and the ratios of works and revenues, and the ridership of marine transport.

The scope of the project’s study included an internal algorithm for analysing and treating Big Data from multiple sources. It also entailed mapping out a flexible operational plan for a marine transit network that can also be used in analysing future data of this sector, he added.

“When developing the initiative, it was taken into account that it does not affect the quality of services provided to customers and that it contributes to improving the occupancy rate of marine transit means, while reducing operational costs at the same time,” said Al Ali.