Development of Multiregime Speed-Density Relationships by Cluster Analysis

Sun, Lu; Zhou, Jie

doi:10.3141/1934-07

Cited by 40 publications

(26 citation statements)

References 18 publications

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“…In recent years, driver heterogeneity has been considered as a key element in driver behavior and investigated in several macroscopic traffic studies [12,14,[36][37][38][39] (Sun et al 2001). In microscopic traffic models, heterogeneity in car-following models was investigated by deriving the joint distribution of model coefficients depending on an empirical basis [7][8][9][10] (Hoogendoorn et al 2006).…”

Section: Literature Reviewmentioning

confidence: 99%

Characterizing Heterogeneity in Drivers’ Merging Maneuvers Using Two-Step Cluster Analysis

Sun

2018

Journal of Advanced Transportation

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In order to investigate the heterogeneity in merging behaviors on freeways, a novel data mining tool, called two-step cluster analysis, is applied to the merging maneuvers (namely, initial speed, merging speed, and merging position). Merging maneuvers of 370 drivers collected from the NGSIM dataset are automatically and optimally segmented into four clusters (Early Merging Drivers at High Speed, Early Merging Drivers at Low Speed, Late Merging Drivers at Low Speed, and Late Merging Drivers at High Speed) by the two-step cluster analysis. Hypothesis test confirms the significant differences in merging maneuvers between different clusters. The clustered data are used to find the best corresponding fitting distributions. Seven distributions (Normal, Log-normal, Student's , Logistic, Log-Logistic, Gamma, and Weibull) are considered for each cluster and the Kolmogorov-Smirnov test statics are used to select the best fitted distributions. It is found that merging drivers may merge either early or late, under congestion or uncongested traffic condition. Further analysis of merging durations shows that Late Merging Drivers use significantly shorter time than Early Merging Drivers to finish the merging maneuver, no matter if they are at high or at low speed. Hypothesis test of accepted lead gaps and lag gaps indicate that merging drivers are more sensitive to the lag gaps under congestion. The proposed method can automatically identify the heterogeneity in merging drivers and the results obtained in this paper can be used to enhance the accuracy of the merge behavior models in microscopic simulation software.

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Section: Literature Reviewmentioning

confidence: 99%

Characterizing Heterogeneity in Drivers’ Merging Maneuvers Using Two-Step Cluster Analysis

Sun

2018

Journal of Advanced Transportation

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“…Multi-regime models usually include two or three regimes including: two-regime models such as Edie [37], the two-regime model [20], the modified Greenberg [20], the three-regime models [18,20], and multi-regime model by cluster analysis [38]. The idea of the two-or three-regime model is to use two or three different curves to model free-flow, transitional, and congested-flow phases separately [18,20] (refer to Table II).…”

Section: Multi-regime: Piecewised Curvesmentioning

confidence: 99%

Stochastic modeling of the equilibrium speed–density relationship

Wang

2011

J of Advced Transportation

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SUMMARYAs the graphical and mathematical representation of relationships among traffic flow, speed, and density, the fundamental diagram has been the foundation of traffic flow theory and transportation engineering. Underlying the fundamental diagram is the speed-density relationship which was originally documented in Greenshields' seminal work and followed by a variety of equilibrium models over the past 75 years. Observed in these efforts was their deterministic nature striving to pursue two seemingly competing goals: mathematical elegance and empirical accuracy, the former of which is attractive to mathematical modeling of traffic dynamics, and the latter is required if such modeling is meant to be realistic. As a continued effort of such a pursuit, this paper presents a stochastic speed-density model. The motivation is twofold: first, it is desirable to have a model which achieves both goals reasonably and second, the stochastic model can potentially lead to probabilistic traffic flow modeling and prediction which is typically not offered by a macroscopic approach.

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“…Greenshields et al [4] according to limited observations established the over-simplified linear model; Greenberg [5] developed a model by treating the traffic stream as a continuous fluid and the model turned out to be suitable for the traffic flow theory; Underwood thought that the exponential relationship between the negative density and speed could be used in free-flow conditions and the Northwestern Model is similar but a little more complicated than the Underwood model [6]; Newell [7] considered the non-linear effects in the dynamics of car following and obtained a special model; Wang et al [8] developed the logistic model of speed-density relationship motivated by the success of the generalized logistic curves in modelling the growth pattern phenomenon such as population dynamics, plant growth in agriculture, epidemic growth in biology, and market growth in economics. Besides, some multiple-regime models were also developed by Edie [9], Sun and Zhou [10], etc. Though multiple-regime models are more accurate than single-regime ones, they are not suggested to be used because of lack of mathematical elegance.…”

Section: Introductionmentioning

confidence: 99%