Comparisons of mandatory and discretionary lane changing behavior on freeways
|نوع نگارش مقاله||
scopus – master journals – JCR
۴٫۲۷۶ در سال ۲۰۲۰
۲۶ در سال ۲۰۲۱
۰٫۹۰۱ در سال ۲۰۲۰
|شاخص Quartile (چارک)||
Q1 در سال ۲۰۲۰
خرید محصول توسط کلیه کارت های شتاب امکان پذیر است و بلافاصله پس از خرید، لینک دانلود محصول در اختیار شما قرار خواهد گرفت و هر گونه فروش در سایت های دیگر قابل پیگیری خواهد بود.
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:|
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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