1660779 - Developing Congestion Factors to Enhance Accurate Accessibility Calculation Using OSM Dataset

In urban environments, traffic congestion is a pervasive issue with profound impacts on the efficiency of transportation systems, accessibility, and quality of life. One significant consequence of congestion is reduced access to opportunities such as jobs, education, food, healthcare, and recreation. While data sources such as OpenStreetMap (OSM) facilitate access calculations at scale, their utility is often hindered by the lack of proper reflection of congestion on the road networks during different times of the day. Addressing this issue, the primary objective of this study is to develop congestion factors that convert posted speed limits in OSM (or similar data sources) into realistic operating speeds for different types of roadways within urban areas.

While accurately translating posted speed limits into operating speeds offers several benefits for traffic-related research, this study primarily focuses on the realistic representation of network data for accessibility calculations. OSM network data includes information about the posted speed (aka speed limits) of roadway segments, but using that data often overestimates accessibility calculations due to urban congestion, where operating speeds are usually lower than the posted limits in urban settings. Therefore, congestion factors are necessary to convert the OSM network’s posted speed limits into more accurate operating speeds.

In contrast to many existing studies that predict operating speeds based on traffic characteristics and roadway geometry, this study adopts a data-driven approach to derive congestion factors for different times of the day for various roadway classes (e.g., primary, secondary, tertiary, residential, etc.) and land use characteristics (population density, job density, and other opportunities). To achieve this, roadway class, posted speed limits, geometric characteristics (the number of lanes), job density, and other opportunities data will be extracted from OSM, population density data will be obtained from the census, and historical traffic operating speeds data will be collected from a third-party data source. Consequently, congestion factors will be developed for each roadway class and land use type for different times of the day. These factors will be stochastic in nature (with some distribution), capturing the variations in speed discrepancies, even within a specific roadway class and land use type.

The developed factors aim to estimate the operating speed of a given roadway segment based on the posted speed limit and local land use characteristics. This work will facilitate a realistic representation of speeds in OSM for estimating the accessibility of a city or region. We plan to leverage the Mobility Energy Productivity (MEP) tool, developed by the National Renewable Energy Laboratory (NREL), to demonstrate the impact of reflecting congestion appropriately in accessibility calculations for a representative US city. By bridging the gap between posted speed limits and actual operating speeds, this study contributes to more precise and reliable accessibility calculations, ultimately supporting urban planning, transportation efficiency, and the overall well-being of urban residents.