cool hit counter Ruiji Transit's Big Data Entry to IBMWatsonBuild Advances Successfully_Intefrankly

Ruiji Transit's Big Data Entry to IBMWatsonBuild Advances Successfully


Recently, the IBM Watson build 2018 Greater China Challenge has ended with the perfect conclusion of the first THINK session in Beijing and Shanghai. Rui Zhi Big Data has advanced to the second stage with its "Bus Network Optimization and Capacity Optimization System".

The "bus network optimization and capacity adjustment system" is based on Watson's machine learning capabilities and big data technology development, which can accurately grasp passenger flow and optimize bus deployment, which IBM said "represents the most innovative artificial intelligence concept".

For its part, IBM says: The Watson build plans submitted this year represent the highest caliber, with a carefully selected set of business plans representing the most innovative AI concepts. In addition, the start and end dates for Phase 2 are August 6 to November 6, 2018.

It is reported that there are 46 sharply innovative AI business solutions in this audition, and Ruiji Big Data successfully entered the second stage, which not only demonstrates the company's strong technical strength, but also confirms that AI can be everywhere.

Hu Shaobo, General Manager of Big Data Division, at the event

In fact, for the past few years Rexchip has been using its innovative Big Data solutions to help public transport system operations and city administrations across the country to optimize urban public transport systems, and has gained front-line experience in doing so.

Ruiji Big Data in the field of public transportation starts from exploring the global activities of public transportation operation in urban environment, based on the massive amount of public transportation industry basic data, starting from the most concerned issues in the public transportation industry, focusing on solving the problems of route optimization for public transportation system, improving the carrying capacity of public transportation system, improving the operation efficiency of public transportation system, and providing technical and decision support for public transportation system to improve traffic management, thus helping public transportation operation and management organizations to build a new normal of operation.

It is worth mentioning that in terms of model calculation, Ruiji Big Data makes full use of a large number of machine learning algorithms as well as self-researched algorithms, including gradient boosting decision tree algorithm, hierarchical clustering algorithm, K-means clustering algorithm algorithm, smoothing filtering algorithm, Bayesian algorithm, Douglas-Pook algorithm algorithm and GPS correction algorithm, etc., which ensures that the effect of data presentation, data analysis results and optimization of decision-making recommendations are optimized efficiently, practically and credibly.

In the future, RuiZhi Big Data will further target public transportation, especially the rail transit industry (including (high-speed) railways and urban rail transit (metro and urban railway)), carry out research and practice of social service oriented Big Data applications that combine industry needs and application characteristics, and help China's urban transportation establish a perfect Big Data platform.


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