Development of a Traffic Congestion Index from Spatiotemporal Big Data for Urban Transportation Research and Applications

Keywords: Traffic Congestion, Mile Hour, Spatiotemporal Big Data, Urban Transportation
Abstract Type: Paper Abstract

Abstract

Found in urban areas throughout the world, traffic congestion brings a wide range of unwanted effects including, but not limited to, economic concerns, environmental problems, and traveler complaints. Evaluating traffic congestion amounts of road networks is important for urban transportation research and applications. Traditionally, traffic congestion amounts have been measured with volume-capacity ratio, vehicle-miles traveled, observed-posted travel time ratio, etc., and they have limitations in collecting data, both broadly and thoroughly. In this research, we aim at developing a simple index that does not require massive field data collection and processing. Particularly, we propose MileHour (MH) to summarize the total amount of congestion in a region. MH is geographically scalable so that it may be applied to, for example, a road section, an intersection, or multiple roads in a city or metropolitan area. MH can be calculated with online maps that show traffic conditions such as Google Maps, Bing Maps, and TomTom Mapping Platform. In this research, Google Maps was used, sampled from six metropolitan areas (Atlanta, Chicago, Washington D.C. and Baltimore, Dallas and Fort Worth, Los Angeles, and New York), from April to September 2022. Results show significant variations of traffic congestions, both temporally and geographically. The traffic amount calculation using MH, developed in this research, may help researchers and transportation planners by providing a convenient measure that summarizes traffic congestion amounts with affordable resources.