Impact of Roadway Capacity and Travel Demand Variation on Freeway Travel Speed Distributions

Nonrecurring congestion creates significant delay on freeways in urban areas, lending importance to the study of facility reliability. In locations where traffic detectors record and archive data, approximate probability distributions for travel speed can be determined from historical data; however, the coverage of detectors is not always complete, and many regions have not deployed such infrastructure. This paper describes procedures for estimating such distributions in the absence of this data, considering both supply-side factors (reductions in capacity due to events such as incidents or poor weather) and demand-side factors (such as daily variation in travel activity). Using data from the Dallas metropolitan area, the supply-side analysis identifies probability distributions that fit observed speed data, and develops regression models for estimating their parameters. For cases in which data is available on demand variations, a demand-side procedure is presented for refining the analysis to account for this source of uncertainty.