Urban route travel time forecasting method based on pattern matching

Abstract

The invention discloses an urban route travel time forecasting method based on pattern matching. The method comprises the steps that firstly, historical traffic data are analyzed, and traffic patterns are dug from the historical data and include road segment chain patterns and intersection patterns, wherein the traffic data adopted in the method are floating vehicle data; secondly, a traffic management department or an expert establishes a default road segment chain traffic pattern and a default intersection traffic pattern; the departure point, the terminal point and the departure time of an input vehicle are provided, a corresponding traffic pattern is matched according to the current traffic condition and weighting is conducted to work out the travel time of all candidate routes. By the adoption of the method, the urban road segment chain traffic patterns and the urban intersection traffic patterns can be effectively and comprehensively considered, the travel time of the urban routes can be effectively predicted, the adopted method and technology are simple and practicable, operating conditions can be met easily, and the method can guide vehicles to select the travel routes reasonably, plays an important role in relieving urban traffic congestion and is easy to apply and popularize in large and medium-sized cities.

Description

technical field [0001] The invention relates to the field of intelligent transportation, in particular to a method for predicting travel time of urban routes based on pattern matching. Background technique [0002] In the field of intelligent transportation research, how to improve the level of transportation services, many countries and regions have carried out research on urban road travel time prediction, and it has become one of the hot spots of international research. Existing travel time prediction methods mainly focus on predictable events and the impact of special weather on traffic patterns. [0003] Existing urban route travel time prediction methods have low prediction accuracy. Traffic patterns are not considered in the prediction process, and the weights of various traffic events on travel time cannot be dynamically adjusted. The accuracy of vehicle travel time prediction is not high. Contents of the invention [0004] The invention proposes a method for pred...

AI Technical Summary

Problems solved by technology

[0003] Existing urban path travel time prediction methods have low prediction accuracy, do not consider traffic patterns in the prediction process, and cannot dynamically adjust the weights of various traffic events on travel time, and the accuracy of vehicle travel time prediction is not high

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