Road travel time forecasting method based on random forest and clustering algorithm

Abstract

The invention discloses a road travel time forecasting method based on a random forest and a clustering algorithm. In the road travel time forecasting method, according to the time sequence rule of historical traffic data, combined with the road property, weather factors, holiday information and states of road upstream-and-downstream traffic flow, and through a hybrid forecasting model of the density-based clustering algorithm (DBSCAN) and the random forest (RF), travel time of all key road sections at some time interval is accurately forecasted. The forecasting result can be used for pre-judging a traffic state development tendency and making a control scheme for potential congestion roads, can also be used for dynamic path induction, can project best travel plans for travelers, and can assist in social intelligence traveling. According to the road travel time forecasting method, the forecasting accuracy of all trees in the random forest is increased through density clustering, and therefore the forecasted whole accuracy is increased.

Description

Technical field [0001] The invention belongs to the field of road travel time prediction, and in particular relates to a DBSCAN-RF hybrid prediction model that utilizes a density clustering algorithm to optimize random forest prediction results. Background technique [0002] Road travel time is one of the important indicators reflecting the traffic state, as the basis for road traffic congestion management and road network optimization and integration, and is also an important content in smart transportation research. Accurate travel time prediction is an important foundation of modern traffic guidance systems and advanced traveler information systems, which can provide decision support for traffic management departments and plan the best travel path for travelers. In the past, time series forecasting algorithms were often used for forecasting and analysis in this field. The algorithm is more sensitive to data missing, and only considers the time series law of historical traffic ...

AI Technical Summary

Problems solved by technology

In the past, time series forecasting algorithms were often used in forecasting and analysis in this field. This algorithm is more sensitive to missing data, and only considers the time series of historical traffic data, ignoring other relevant factors; In this case, it is far from enough to only consider the timing law

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