Travel time estimation by urgent-gentle class traffic flow model

Travel time of traffic flow is the basis of traffic guidance. To improve the estimation accuracy, a travel time estimation model based on Random Forests is proposed. 7 influence variables are viewed as candidates in this paper. Data obtained from VISSIM simulation are used to verify the model. Different from other machine learning algorithm as black boxes, Random Forests can provide interpretable results through variable importance. The result of variable importance shows that mean travel time of floating car t-f, traffic state parameter X, density of vehicle Kall, and median travel time of floating car tmenf are important variables affecting travel time of traffic flow; meanwhile other variables also have a certain influence on travel time. Compared with the BP (Back Propagation) neural network model and the quadratic polynomial regression model, the proposed Random Forests model is more accurate, and the variables contained in the model are more abundant.

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Estimation of travel time through a composite ring road by a viscoelastic traffic flow model

Mathematics and Computers in Simulation ◽

10.1016/j.matcom.2020.09.025 ◽

2021 ◽

Vol 181 ◽

pp. 501-521

Author(s):

Zejing Hu ◽

M.N. Smirnova ◽

Yongliang Zhang ◽

N.N. Smirnov ◽

Zuojin Zhu

Keyword(s):

Travel Time ◽

Traffic Flow ◽

Flow Model ◽

Traffic Flow Model ◽

Ring Road ◽

Composite Ring

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Optimal Sensor Density for Freeway Travel Time Estimation with Multi-Class Traffic Flow

ICTIS 2013 ◽

10.1061/9780784413036.122 ◽

2013 ◽

Cited By ~ 1

Author(s):

Jisheng Zhang ◽

Maojing Jin

Keyword(s):

Travel Time ◽

Traffic Flow ◽

Time Estimation ◽

Travel Time Estimation ◽

Sensor Density

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Dynamic Traffic Flow Model for Travel Time Estimation

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/2526-08 ◽

2015 ◽

Vol 2526 (1) ◽

pp. 70-78 ◽

Cited By ~ 2

Author(s):

Ting Yi ◽

Billy M. Williams

Keyword(s):

Fixed Point ◽

Travel Time ◽

Traffic Flow ◽

Measurement Errors ◽

Flow Model ◽

Dynamic Models ◽

Estimation Method ◽

Dynamic Traffic ◽

Traffic Flow Model ◽

Point Detector

Travel time, as a fundamental measurement for intelligent transportation systems, is becoming increasingly important. Because of the wide deployment of fixed-point detectors on freeways, if travel time can be accurately estimated from point detector data, the indirect estimation method is cost-effective and widely applicable. This paper presents a modified dynamic traffic flow model for accurately estimating the travel time of freeway links under transition and congestion conditions with fixed-point detector data. The modified estimation model is based on a thorough analysis of the dynamic traffic flow model. The applications and the limitations of the model are analyzed for theory, equation derivation, and modifications. Through a simulation study and real traffic data, the (modified) dynamic models are compared according to performance measurements. A comparison of the estimated results and measurement errors shows the accuracy of the modified dynamic model for estimating the travel times of freeway links under transition and congestion traffic conditions.

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