Digvijay S. Pawar

Temporal and Spatial Gap Acceptance for Minor Road at Uncontrolled Intersections in India

Capacity analysis of unsignalized intersections is done primarily with gap acceptance principles. The vehicles on lower-priority approaches maneuver when a suitable gap is available in higher-priority conflicting streams. Although temporal gaps are widely used, some researchers advocate the use of spatial gaps. The focus of this study was on analyzing temporal and spatial gaps at four-legged, partially controlled intersections in India. Unlike in developed countries, unsignalized intersections in India are not controlled with stop and yield signs with explicit priorities. The priorities are mainly set by the situations drivers perceive. Field data were collected at three four-legged intersections with video cameras. Temporal and spatial critical gaps were estimated with Raffs, logit, lag, Ashworths, and maximum likelihood methods. The values of temporal critical gap by different methods were found to vary between 3.0 and 3.9 s. The spatial critical gap values varied from 29 to 36 m. These values were smaller than the similar values reported in developed countries, indicating aggressiveness in Indian drivers. The insights from this study can be used for the capacity analysis of unsignalized intersections in India.

Classification of Gaps at Uncontrolled Intersections and Midblock Crossings Using Support Vector Machines

Gap acceptance predictions provide very important inputs for performance evaluation and safety analysis of uncontrolled intersections and pedestrian midblock crossings. The focus of this paper is on the application of support vector machines (SVMs) in understanding and classifying gaps at these facilities. The SVMs are supervised learning techniques originating from statistical learning theory and are widely used for classification and regression. In this paper, the feasibility of the SVM in analyzing gap acceptance is examined by comparing its results with existing statistical methods. To accomplish that objective, SVM and binary logit models (BLMs) were developed and compared by using data collected at three types of uncontrolled intersections. SVM performance was found to be comparable with that of the BLM in all cases and better in a few. Also, the categorical statistics and skill scores used for validating gap acceptance data revealed that the SVM performed reasonably well. Thus, the SVM technique can be used to classify and predict accepted and rejected gap values according to speed and distance of oncoming vehicles. This technique can be used in advance safety warning systems for vehicles and pedestrians waiting to cross major stream vehicles.

Analyzing Gap Acceptance Behavior at Unsignalized Intersections Using Support Vector Machines, Decision Tree and Random Forests

Abstract: Drivers gap acceptance behaviour highly influences the performance and safety of unsignalized intersections. At unsignalized intersections, crossing drivers have to accept or reject the available gap. The gap acceptance decision is influenced by different dynamics such as traffic, geometric, environmental and human factors. The decision to cross the major road largely depends on the speed, distance and the heaviness of the potentially conflicting vehicle on the major road. The drivers gap acceptance data was collected at 4-legged unsignalized intersections. The information such as the type of crossing vehicle, conflicting vehicle, speed and the spatial gap was extracted from the video data. This paper deals with the application of three widely used non-parametric data mining techniques, namely, Support Vector Machines (SVMs), Random Forests (RF) and Decision Trees (DT) to predict the gap acceptance behaviour of the driver. While SVMs are insensitive to class imbalance, decision tree generated by CART algorithm provides critical insights into decision making process employed by the driver. Random forests and decision tree implicitly establish the relative importance of different factors affecting the drivers decision. Further, skill scores used to validate the models revealed that SVM and DT models performed almost similarly whereas, RF model outperformed SVM and DT.

Microscopic analysis of traffic behavior at unsignalized intersections in developing world

Unsignalized intersections in India are uncontrolled, and are characterized by chaotic traffic situation and have become accident hot spots. In this study, we have collected traffic data at three uncontrolled intersections, one each is from city center (Type I), suburb (Type II), and outskirt (Type III). Traffic parameters, such as traffic composition, speed variations, lane distribution, trajectories, conflict points, and pedestrian movements are analyzed. All the vehicle classes prefer inner lane, except auto-rickshaws. The speed on inner lane is higher than outer lane vehicles as latter are affected by the roadside friction. It is also found that the minor approach vehicles have to slow down or stop many times. Trajectories of two-wheelers are found to be much flatter than the trajectories in the standard conflict point diagram. This study has great potential in assessing the performance and safety of unsignalized intersections in developing world.

Minor-Street Vehicle Dilemma While Maneuvering at Unsignalized Intersections

AbstractThe drivers on a minor approach at an unsignalized intersection intending to maneuver are usually at risk because of the difficulty in judging if available gaps are safe or not. Any misjudg...