Abstract
Inter-city highway plays an important role in modern urban life and generates sensory data with spatio-temporal characteristics. Its current situation and future trends are valuable for vehicles guidance and transportation security management. As a domain routine analysis, daily detection of traffic hotspots faces challenges in efficiency and precision, because huge data deteriorates processing latency and many correlative factors cannot be fully considered. In this paper, an ensemble-learning based method for potential traffic hotspots detection is proposed. Considering time, space, meteorology, and calendar conditions, daily traffic volume is modeled on heterogeneous data, and trends predictive error can be reduced through gradient boosting regression technology. Using real-world data from one Chinese provincial highway, extensive experiments and case studies show our methods with second-level executive latency with a distinct improvement in predictive precision.
Highlights
With the boom of inter-city transportation, highway plays an important role in modern urban life, and traffic congestion issue has become one of the most serious problems worldwide
Motivation Our work originates from Highway Big Data Analysis System in Henan which is the most populated province in China
The system we built has been in production since October 2017 and is expected to improve routine business analytics for highway management through Big Data technologies
Summary
With the boom of inter-city transportation, highway plays an important role in modern urban life, and traffic congestion issue has become one of the most serious problems worldwide. As a typical routine analysis in domain against that problem, traffic trend is to predict traffic volumes at toll stations in few days, and significant for business to alleviate traffic congestion by dispersing traffic flow in highway network Operated by officers of Henan Transport Department, a billion records of heterogeneous data in recent 2 years have been imported into the system. Other types of data, such as daily meteorological condition, solar and lunar calendar, and real-time license plate recognition data, are loaded into that system
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