Analysis of Discretionary Lane Changing Parameters on Freeways

Balal, Esmaeil; Cheu, Ruey Long; Gyan-Sarkodie, Thompson; Miramontes, Jessica

doi:10.1260/2046-0430.3.3.277

Cited by 55 publications

(28 citation statements)

References 12 publications

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“…The clustered data are used to find the best corresponding fitting distributions using EasyFit 5.4 [48,49]. To best investigate the distributions of the merging parameters, 7 distributions (Normal, Log-normal, Student's , Logistic, Log-Logistic, Gamma, and Weibull) are considered for each cluster and the Kolmogorov-Smirnov test is used to select the best fitted distribution.…”

Section: Best Probability Distributionmentioning

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: Best Probability Distributionmentioning

confidence: 99%

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

Sun

2018

Journal of Advanced Transportation

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“…Lane-changing behavior is becoming a significant concern in the research community. Many researchers have investigated this issue by proposing new or modified models [3][4][5][6][7], by simulating lane-changing behaviors in micro-or macro-scenarios [5,[8][9][10][11][12], or by analyzing field data from traffic sensors [3,4,[13][14][15][16].…”

Section: Impact Of Lane-changing Behaviormentioning

confidence: 99%

Determining Appropriate Lane-Changing Spacing for Off-Ramp Areas of Urban Expressways

Huang

Zhang

et al. 2019

Sustainability

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Congestion has become a significant issue in recent years and has greatly affected the efficiency of urban traffic operation. Random and disorderly lane-changing behavior greatly reduces traffic capacity and safety. This paper is mainly concerned with the relationship of lane-changing spacing intervals provided by off-ramp facilities and traffic flow conditions. Through field investigations in Beijing, several typical lane-changing behaviors at off-ramp areas are analyzed. By using field traffic data and actual road geometry parameters, VISSIM-based micro-behavior simulations at off-ramp areas are implemented to obtain traffic flow conditions with different lane-changing spacing intervals and other model parameters, such as traffic volume and ratio of off-ramp vehicles. Then, the numerical relationships between traffic flow state and model parameters can be shown. The results show that with increasing traffic volume and the ratio of off-ramp vehicles, the lane-changing spacing interval required by vehicles should be increased. For the same ratio of off-ramp vehicles, if the traffic volume increases by 100 pcu/h/lane (pcu is a unit to stand for a standard passenger car), the corresponding lane-changing spacing interval should be increased by a spacing of 50–100 m to avoid increasing congestion. Based on the results of this paper, smart lane management can be implemented by optimizing lane-changing spacing intervals and lane-changing behaviors to improve traffic capacity.

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“…The lane changing models has been reviewed extensively by [15,28]. Readers may also find the authors' review reports in [1,2]. The purpose of literature review, in the context of this paper, is to select the best model as the benchmark of comparative evaluation.…”

Section: Review Of Lane Changing Modelsmentioning

confidence: 99%

“…More details of data processing are documented in [1,2]. Although the NGSIM data is not as comprehensive and high resolution as the data acquired in and inlaboratory diver simulator [17], it was collected in the real driving environment involving a relatively larger sample.…”

Section: Datamentioning

confidence: 99%

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Comparative Evaluation of Fuzzy Inference System, Support Vector Machine and Multilayer Feed-Forward Neural Network in Making Discretionary Lane Changing Decisions

Balal¹,

Cheu²

2018

NNW

Self Cite

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This paper compares Fuzzy Inference System (FIS), Support Vector Machine (SVM) and MultiLayer Feed-forward neural network (MLF) in modeling a driver's decision when making a discretionary lane changing move on a freeway. The FIS model has been developed and published in an earlier work by the authors, whereas the SVM and MLF models are newly developed in this research. The FIS, SVM and MLF models use the same four inputs: the gap between the subject vehicle and the leading vehicle in the original lane, the gap between the subject vehicle and the leading vehicle in the destination lane, the gap between the subject vehicle and the trailing vehicle in the destination lane, and the distance between the preceding and trailing vehicles in the destination lane. The models give a binary decision of "no, stay in the same lane" or "yes, move to the destination lane now". These models were trained and then tested with the Next Generation SIMulation (NGSIM) vehicle trajectory data. The results have shown that the FIS has the highest accuracies in making correct lane changing decisions. It recommends "yes, move to the destination lane now" with 82.2 % accuracy, and "no, stay in the same lane" with 99.5 % accuracy. The SVM model also outperformed the traditional gap acceptance model which was used as the benchmark. However, the MLF model was not as accurate as the gap acceptance model.

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Analysis of Discretionary Lane Changing Parameters on Freeways

Cited by 55 publications

References 12 publications

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

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

Determining Appropriate Lane-Changing Spacing for Off-Ramp Areas of Urban Expressways

Comparative Evaluation of Fuzzy Inference System, Support Vector Machine and Multilayer Feed-Forward Neural Network in Making Discretionary Lane Changing Decisions

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