Assessment of Significant Factors Affecting Frequent Lane-Changing Related to Road Safety: An Integrated Approach of the AHP–BWM Model

Abstract

Frequent lane changes cause serious traffic safety concerns for road users. The detection and categorization of significant factors affecting frequent lane changing could help to reduce frequent lane-changing risk. The main objective of this research study is to assess and prioritize the significant factors and sub-factors affecting frequent lane changing designed in a three-level hierarchical structure. As a multi-criteria decision-making methodology (MCDM), this study utilizes the analytic hierarchy process (AHP) combined with the best–worst method (BWM) to compare and quantify the specified factors. To illustrate the applicability of the proposed model, a real-life decision-making problem is considered, prioritizing the most significant factors affecting lane changing based on the driver’s responses on a designated questionnaire survey. The proposed model observed fewer pairwise comparisons (PCs) with more consistent and reliable results than the conventional AHP. For level 1 of the three-level hierarchical structure, the AHP–BWM model results show “traffic characteristics” (0.5148) as the most significant factor affecting frequent lane changing, followed by “human” (0.2134), as second-ranked factor. For level 2, “traffic volume” (0.1771) was observed as the most significant factor, followed by “speed” (0.1521). For level 3, the model results show “average speed” (0.0783) as first-rank factor, followed by the factor “rural” (0.0764), as compared to other specified factors. The proposed integrated approach could help decision-makers to focus on highlighted significant factors affecting frequent lane-changing to improve road safety.

Analysis of traffic flow due to lane changes by heavy vehicles

“…The results of this study are in line with research [3] where the behavior of changing lanes by heavy vehicles frequently or at high intensity can have a negative impact on traffic performance, one of which is the average speed of traffic flow. Research [4] also supports the results of this study, where the difference in average speed of traffic flow between vehicles changing lanes and vehicles without changing lanes decreased from 51 km/h to 35 km/h due to congestion worsening.…”

“…Previous research has also provided macroscopic evidence that congestion can occur and cause a decrease in capacity due to the high intensity of lane change rates. One study found that the frequent act of changing lanes by heavy vehicles can have a negative impact on traffic performance [3]. An increased frequency of lane changes can also increase density and reduce the average speed of traffic flow [4].…”

Optimization of frying process for maintaining nutritional quality to satisfy consumers' sensory attributes: A novel application of multi‐criteria decision‐making approach

“…Another important direction is to involve a large number of experts from several states (different local areas and ethnicity) and gender groups under different uncertain environments. Moreover, the proposed methodology can be integrated with other MCDM methods like LOPCOW-DOBI (Ecer & Pamucar, 2022), AHP-BWM (Farooq et al, 2021), BWM-LBWA-CoCoSo (Torkayesh et al, 2021) for investigating the robustness of the results.…”

Lane Choice Behavior at Toll Plaza Under Mixed Traffic Conditions Using TODIM Method: A Case Study

“…However, this limitation is also overcome by hybrid and extended TODIM methods using fuzzy numbers [ 44 – 47 ]. Farooq et al [ 48 ] evaluated different factors affecting the lane change using the MCDM approach as Analytical Hierarchical Process (AHP) and Best Worst Method (BWM). They found that traffic characteristics significantly affect lane changes (obtained highest weightage among all criteria).…”