Comparisons of mandatory and discretionary lane changing behavior on freeways

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scopus – master journals – JCR

ایمپکت فاکتور

۴٫۲۷۶ در سال ۲۰۲۰

شاخص H_index

۲۶ در سال ۲۰۲۱

شاخص SJR

۰٫۹۰۱ در سال ۲۰۲۰

شاخص Quartile (چارک)

Q1 در سال ۲۰۲۰

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Comparisons of mandatory and discretionary lane changing behavior on freeways

فهرست مطالب مقاله:


This  research performs comparative analyses on  drivers’ behavior during mandatory and discretionary lane changes. We  do  this by  examining the statistical properties of four lane changing decision variables that describe the gaps between the subject vehicle and the sur- rounding vehicles. Mandatory and discretionary lane changes in NGSIM’s I-80  Freeway and U.S. Highway 101 data collection sites were identified. First,  for  each variable at the same site, descriptive statistics for the two types of lane changes were compared, and hypothesis tests on  the difference between two means were conducted. Then, for  each decision vari- able at the same site, the observed cumulative distributions between the mandatory and discretionary lane changes were compared by  means of  the  Kolmogorov–Smirnov test. This  test was repeated for the fitted distributions of the same decision variable at the same site. The  results show that, for the three decision variables associated with gaps in the tar- get  lane, the means and distributions between the two types of lane changes are  not sig- nificantly different. The  only variable found to have significant differences in  means and distributions is the gap  between the subject vehicle and the preceding vehicle in the orig- inal lane. This  may be  because this variable is not an  important input in  mandatory lane change decisions. This  finding provides statistical justification for  researchers to develop models with different inputs for mandatory and discretionary lane changes in driver assist systems, in  autonomous vehicles, and in  microscopic traffic simulation tools.

بخشی از متن مقاله:
  1. Introduction

Lane  changing is one  of the basic activities in freeway driving. Drivers change lanes so as to, among other reasons, gain speed or move into the correct lane in anticipation of the next turning movement downstream (Balal  et al., 2016, 2014; Pan et al., 2016; Zheng, 2014). A lane change that is not  executed in a safe manner may result in a rear end,  side  swipe, or angled crash (Romo et al., 2014). With the advent of connected and autonomous vehicles, a good  understanding of drivers’ lane changing behavior and the ability to  model it under different conditions has  critical impacts on  the safety and capacity of autonomous driving on  highways.

A lane change may be classified, depending on the driver’s motivation, as mandatory or discretionary. A Mandatory Lane

Change (MLC) usually occurs when the subject driver is trying to move his/her vehicle from its existing lane into the target

lane in  anticipation of the next left  or  right-turn, or  lane closure immediately downstream. A Discretionary Lane  Change (DLC) usually occurs when a driver desires a faster speed, greater following distance, further line  of sight, better ride  quality, etc. in the target lane (Balal et al., 2016, 2014; Pan et al., 2016; Zheng, 2014). Because of the different motives, the risk-taking behavior of a driver when executing MLCs and DLCs are  believed to  be  different (Pan  et al., 2016).

The objective of this research is to perform statistical comparisons of lane changing behavior between MLCs and DLCs. We used four  gap  values that describe the distances between the subject vehicle and the surrounding vehicles to represent the subject driver’s risk-taking behavior. This  research used NGSIM  data collected at two sites: I-80  Freeway at Emeryville (Cambridge, 2005a) and U.S. Highway 101  in  Los Angeles (Cambridge, 2005b), both in  California. The  objective is accom- plished by the following four  tasks that were applied to  data extracted from each site:


(i)  Examine the descriptive statistics for each lane changing decision variable for MLCs and DLCs, comparatively; (ii)  Conduct hypothesis tests on  the difference between the means of MLCs and DLCs, for each decision variable;

(iii)  Apply  the Kolmogorov–Smirnov test (Ang  and Tang,  ۲۰۰۷), for  each decision variable, to  test the difference in  the observed cumulative probability distributions between MLCs and DLCs;

(iv)  For each variable, fit  the probability distributions to the MLC and DLC data respectively,  and use  the Kolmogorov– Smirnov test to  test the difference between the fitted probability distributions.


This article is organized as follows. After this introduction, issues related to the modeling of MLCs and DLCs are  reviewed. The  decision variables are  defined. This  is  followed by  a  description of  the data. The  next section, which is  the most important part of this paper, presents and discusses the results of statistical tests. This  paper concludes by  highlighting the findings, limitations, and contributions of this research.

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