An Approach to Model a Traffic Environment by Addressing Sparsity in Vehicle Count Data

Abstract

<div class="section abstract"><div class="htmlview paragraph">For realistic traffic modeling, real-world traffic calibration data is needed. These data include a representative road network, road users count by type, traffic lights information, infrastructure, etc. In most cases, this data is not readily available due to cost, time, and confidentiality constraints. Some open-source data are accessible and provide this information for specific geographical locations, however, it is often insufficient for realistic calibration. Moreover, the publicly available data may have errors, for example, the Open Street Maps (OSM) does not always correlate with physical roads. The scarcity, incompleteness, and inaccuracies of the data pose challenges to the realistic calibration of traffic models. Hence, in this study, we propose an approach based on spatial interpolation for addressing sparsity in vehicle count data that can augment existing data to make traffic model calibrations more accurate. This study will primarily assist in traffic modeling for Fuel Efficiency (FE) of individual Connected and Autonomous Vehicles (CAV) estimation (road safety and fleet-wide efficiency are out of the scope). We propose a process to identify typical characteristics of trips that are most critical for CAV’s FE from single-vehicle data. We then use this data along with vehicle counts to calibrate the traffic model such that the drive cycle characteristics of the Vehicle Under Test (VUT) are matched with the data collected from the real test. This calibration procedure ensures that the vehicle in the simulation environment observes speed profiles that allow realistic FE estimates. In this paper, the traffic modeling calibration is performed in Simulation of Urban MObility (SUMO) where we demonstrate the approach for the Columbus, OH metropolitan area. The available data is in the form of edge-based traffic count commonly known as Annual Average Daily Traffic (AADT), provided by the Ohio Department of Transportation (ODOT).</div></div>