Vehicle to Vehicle Interaction at Mid-Block Section Using Trajectory Data Base

With rapid urbanization, traffic has increased in recent years. Especially, in the rapid expansion of 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. 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 generated for Madinaguda Mid-Block Section. A vehicle trajectory data for Mid-Block Section using a semi-automated image processing method is generated. The trajectories detected are vehicle ID, vehicle Time, speed, latitude and longitude position, flow. From the analysis, microscopic traffic flow parameters like Time Headway, Space Headways are estimated, Lateral Clearance between the vehicles, Cluster analysis technique is used to classify the various severity levels and Support Vector Regression models are developed for Headways with Flow then obtained R Squared values are R2 = 0.92, 0.75 and R Square values for Lateral Clearance are R2 = 0.79 respectively. The RMSE values of Training Set and Validation set for Madinaguda observed to be 1.959, 1.623 respectively which means the model is showing great regression fit with the data. 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.