Trajectory analysis at mid-block section using microscopic simulation

Abstract

With rapid urbanization, traffic has increased in recent years. For a developing nation, mobility is a key concern. Especially, in rapidly expanding urban areas, vehicle accidents are of great concern. Because of this, it is necessary to pay vital attention to transportation and examine the need for greater geometric design and capacity. To assess road user interactions at a mid-block with heterogeneous traffic complexity, innovative trajectory-based data was used. In order to evaluate microscopic traffic flow parameters under various traffic flow conditions a Support Vector Machine (SVM) is used to classify severity grades based on specified indicators which are between generated at two mid-block sections. A vehicle trajectory data for two different mid-block sections using a semi-automated image processing method is generated. The trajectories detected are vehicle ID, vehicle time, speed, longitude, latitude position, flow. From the analysis, microscopic traffic flow parameter like time headway, space headway is estimated by plotting a graph using Linear Regression model. This means that SVM is the best fit model to estimate headway at mid-block section (R2 = 0.86, 0.85) studied. From this study, it is concluded that the obtained value of R2 and goodness of fit measures using trajectory data base is highly acceptable to estimate the headway at mid-block section.