Intelligent Transportation Systems


Intelligent Transportation Systems Laboratory


Welcome to the ITS Lab web site at Portland State University. We've released our Annual Report for 2011-12. Read the report here.


Intelligent Transportation Systems Laboratory's Featured Project:

Effects of Temporal Data Aggregation on Performance Measures and other ITS Applications

Intelligent transportation systems (ITS) data are a valuable resource for traffic operations, transportation systems management, performance measurement, and transportation research. Historically, these data are time-aggregated for collection, transmission, and storage, saving only mean values of traffic parameters for each arbitrary time interval. This convention of aggregation discards valuable information that is necessary for some applications. Toward understanding whether future systems should continue the practice of aggregation, this paper investigates how temporal aggregation can affect performance measures and other data applications. The investigation uses disaggregate speed data from loop detectors on a London freeway and vehicle trajectories from video imaging on a California freeway. Aggregating measured speed data greatly reduces the spread in reported vehicle speeds, which will distort estimates of emissions, fuel consumption, and travel delay. Using aggregate data for travel time estimates from sampled speeds results in errors attributable to the constant-speed assumption, group-averaged travel times, and using the arithmetic mean speed (as opposed to the harmonic mean speed) to estimate average travel time. Arithmetic mean speeds consistently underestimate aggregate delay, though estimating a harmonic mean speed from the arithmetic mean speed and speed

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