Evaluating OpenStreetMap’s Performance Potential for Level of Traffic Stress Analysis

Abstract

Increasingly, metropolitan areas are prioritizing growth in the share of trips taken by bicycle to improve health outcomes, transportation affordability, and environmental performance of the transport system. Evidence is building that network quality is an important determinant in bicycle commuting and route choice. One prominent metric of facility attractiveness is bicycle level of traffic stress (LTS). In tandem, OpenStreetMap (OSM) is becoming an important source of network data for routing and for generating measures of multimodal accessibility. Although there are studies that examine the completeness of OSM tags and utilize OSM data to compute LTS on networks, none of them examine the accuracy of these analyses. The goal of this paper is to evaluate the accuracy of OSM-derived LTS predictions and offer quality assurance strategies to reduce inaccurate predictions. This study compares OSM-derived LTS predictions with ground-truthed LTS scores created by Montgomery County. It finds that OSM-derived LTS networks provide comparable results to the ground-truthed data. The OSM-derived LTS scores correctly identified 89.9% of the length of the network as either high (LTS 3 or 4) or low stress (LTS 1 or 2). However, this study demonstrates there is a higher potential for error within certain street typologies and urban contexts, and that low-stress accessibility calculations can be very sensitive to even a small number of incorrectly classified segments. Finally, practices to improve the quality of OSM-derived LTS predictions and low-stress accessibility calculations are suggested.