Adaptive neuro-fuzzy interface system for gap acceptance behavior of right-turning vehicles at partially controlled T-intersections

Abstract

Gap acceptance theory is broadly used for evaluating unsignalized intersections in developed countries. Intersections with no specific priority to any movement, known as uncontrolled intersections, are common in India. Limited priority is observed at a few intersections, where priorities are perceived by drivers based on geometry, traffic volume, and speed on the approaches of intersection. Analyzing such intersections is complex because the overall traffic behavior is the result of drivers, vehicles, and traffic flow characteristics. Fuzzy theory has been widely used to analyze similar situations. This paper describes the application of adaptive neuro-fuzzy interface system (ANFIS) to the modeling of gap acceptance behavior of right-turning vehicles at limited priority T-intersections (in India, vehicles are driven on the left side of a road). Field data are collected using video cameras at four T-intersections having limited priority. The data extracted include gap/lag, subject vehicle type, conflicting vehicle type, and driver's decision (accepted/rejected). ANFIS models are developed by using 80 % of the extracted data (total data observations for major road rightturning vehicles are 722 and 1,066 for minor road rightturning vehicles) and remaining are used for model validation. Four different combinations of input variables are considered for major and minor road right turnings separately. Correct prediction by ANFIS models ranges from 75.17 % to 82.16 % for major road right turning and 87.20 % to 88.62 % for minor road right turning. The models developed in this paper can be used in the dynamic estimation of gap acceptance in traffic simulation models.

Multi Vehicle-Type Right Turning Gap-Acceptance and Capacity Analysis at Uncontrolled Urban Intersections

“…However, these studies are focused on modeling driver's behaviour. Recently, works are also reported on modeling gap acceptance behaviour of right turning vehicles at partially controlled three-legged intersections using adaptive neuro-fuzzy interface system and found that the predictions for major right turning ranged from 75% to 82% whereas for minor right turning, it is 87% to 89% (Sangole and Patil, 2014).…”

Predicting Real‐Time Crash Risk for Urban Expressways in China

“…However, the fuzzy logic system is more complex; thus, it is difficult for the human brain to understand the causality existing in such system [25]. According to recent literature [26], an adaptive neuro-fuzzy inference system (ANFIS) is a combination of neural network and fuzzy logic approaches; hence, it inherently has the advantages of both, such as having a good…”

“…To obtain the more accurate real-time crash risk prediction model, we used the main factors influencing the crash risk as the input variables (as shown in Table 1) and the crash risk as the output variable to train ANFIS of real-time crash risk in this study. All the input variables were fuzzy variables, which should be described and measured using linguistic rather than precise numerical values [26]. In this study, each input variable was divided into the following linguistic variables: negative big (NB), negative small (NS), zero (ZO), positive small (PS), and positive big (PB).…”

Modeling crossing behavior of drivers at unsignalized intersections with consideration of risk perception

“…Thus, describing the risk perception of drivers using one particular formula is difficult. According to recent literature [8], an adaptive neuro-fuzzy inference system (ANFIS) is a combination of neural network and fuzzy logic approaches; hence, it inherently has the advantages of both, such as having a good learning mechanism and reasoning capability. Accordingly, we adopted ANFIS to model drivers' risk perception at unsignalized intersections in China in this study.…”