Traffic congestion identification and analysis of urban roadnetworkbased on granular computing

• This paper proposes a generalized model based on the granular computing to recognize and analyze the traffic congestion of urban road network. Using the method of quotient space to reduce the attributes associatingwith traffic congestion, the identification of traffic congestion evaluation system is established including 3 first class indexes and second class indexes of 11. The weight of evaluation indexes are sorted by value in descending order, which are calculated based on rough set theory. In order to improve the efficiency of traffic congestion identification, the appropriate granular is determined by the model parameter μ. When μis larger, the identification is more effective and the run time of model is longer conversely. Experiments show when the value of μ is between 0.8 and 0.98, the effect of traffic congestion identification is comprehensive optimal.

Ämnesord

TEKNIK OCH TEKNOLOGIER  -- Samhällsbyggnadsteknik -- Transportteknik och logistik (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Civil Engineering -- Transport Systems and Logistics (hsv//eng)

Nyckelord

Dynamic traffic data

Fuzzyquotient space

Granular computing

Traffic congestion

Computation theory

Hydraulic structures

Hydraulics

Motor transportation

Rough set theory

Shore protection

Urban growth

Congestion evaluation

Evaluation index

Generalized models

Model parameters

Quotient space

Urban road networks

Publikations- och innehållstyp

ref (ämneskategori)

kon (ämneskategori)