Calculating method for journey time on basis of soft set

Abstract

The invention discloses a calculating method for the journey time on the basis of a soft set. The calculating method comprises the steps that a traffic condition data sheet of a certain road section is built, wherein the sheet mainly comprises factors of speed, flow, environment weather, emergency and time; the mutual relation among the factors is obtained, and a fuzzy approximation space (U, R) is built, wherein U represents a set constituted by all the factors influencing the journey time, and R represents the fuzzy relation among the factors in U; the fuzzy rough set ([Q with a bar below]Ii, [Q with a bar above]Ii) of the fuzzy approximation space (U, R) represents the traffic condition of the road section, the threshold value set, occurring to the road condition, of the road section represents a certain type of traffic state of the road section through the fuzzy set Ii on the set U, and then the fuzzy rough set ([Q with the bar below]Ii, [Q with the bar above]Ii) is obtained to build a soft fuzzy rough set; the relevancy of ([Q with the bar below](t), [Q with the bar above](t)) and ([q with a bar below](t), [q with a bar above](t)) is obtained through a matching function, and then the traffic state of the road section is judged according to the magnitude of the relevancy; and the journey time is mapped according to the traffic state of a road.

Description

technical field [0001] The invention relates to a method for estimating travel time, in particular to a method for calculating travel time based on soft sets. Background technique [0002] The travel time on urban road sections mainly depends on road technical conditions and road traffic flow conditions. The existing road section traffic cost prediction methods are mainly based on historical and real-time floating car data and traffic coil flow data, using traffic flow mechanism models and Can the statistical model predict the time for vehicles to pass through the road section, but the complexity of the road network makes it difficult to predict the travel time of the entire road network section on multiple scales, and there is still a lack of complete and feasible methods. First, the complexity of the road traffic network determines that the travel time of the road segment is related to many influencing factors of the target road segment, and there is a serious nonlinear re...

AI Technical Summary

Problems solved by technology

Second, there are a large number of block roads in the road network where people and vehicles mix as the main traffic mode and lack traffic status information, and it is difficult to obtain a road segment travel time that is consistent with the road network state only by using the road itself parameters as the basic parameters for predicting the travel time of the road segment

Third, under the constraints of traffic lights, the periodic start and stop of vehicles at the intersection makes the change of the traffic state at the intersection more complicated. At the same time, the length of the vehicle queuing has a great influence on the passing time of the traffic intersection and is difficult to measure, which brings difficulties to the prediction of the passing time of the traffic intersection. greater difficulty

[0003] At this stage, there are many models used to predict travel time, but the experimental results show that the fitting results are not very good, there are some errors with the actual situation, and many of them give exact results for the prediction of travel time, but often road traffic There are many situations where it is difficult to accurately predict travel times

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