Dubai Roads uses artificial intelligence to map bus routes

  • The authority seeks to employ technologies to improve the experience of mass transit riders.

    From the source

  • Ahmed Mahboob: “The use of artificial intelligence techniques represented by machine learning algorithms in planning the bus route.”


The Roads and Transport Authority in Dubai has announced the start of experimenting with a model for employing artificial intelligence techniques - represented by machine learning algorithms (MachineLearning) to plan bus routes in terms of the degree of use throughout the day, as part of the authority’s endeavor to employ technologies to save time and effort and improve the customer experience in Mass transportation.

Ahmed Mahboob, Director of the Smart Services Executive Department at the Corporate Technical Support Services Sector at the Authority, said that the experiment aims to re-plan the paths on the artificial intelligence road and machine learning algorithms for all 150 lanes within Dubai serving 2,158 buses, and that these technologies have been implemented as an experimental phase on 10 routes through the data of using the Nol card, as part of its endeavor to determine bus stops with heavy traffic throughout the day, and stops used only during peak hours, in addition to knowing the stops with rare demand.

He added that based on analyzing this data using machine learning and artificial intelligence algorithms and building an integrated system, the concerned departments can make decisions related to not stopping in certain situations, or suggest fast paths bypassing these situations while at the same time being able to take into account the needs of passengers, which contributes effectively. In improving this service is vital.

He pointed out that the pilot phase, which was implemented based on the data of the Nol card, resulted in a 30-day reduction in the wasted time for bus traffic by 13.3% through the routes subject to the test, explaining that the annual average bus traffic is estimated at 153 million kilometers, which means that these The technologies will contribute to reducing the amount of fuel consumed and thus reducing carbon emissions, as well as saving time and effort on the part of trip planners.

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