Raj Bridgelall is the program director for the Upper Great Plains Transportation Institute (UGPTI) Center for Surface Mobility Applications & Real-time Simulation environments (SMARTSeSM).The timely identification and localization of roadway anomalies that pose hazards to
the traveling public is currently a critical but very expensive task. Hence,
transportation agencies are evaluating emerging alternatives that use connected
vehicles to lower the cost dramatically and to increase simultaneously both the
monitoring frequency and the network coverage. Connected vehicle methods use
conventional GPS receivers to tag the inertial data stream with geospatial position
estimates. In addition to the anticipated GPS trilateration errors, numerous other
factors reduce the accuracy of anomaly localization. However, practitioners currently
lack information about their characteristics and significance. This study developed
error models to characterize the factors in position biases so that practitioners can
estimate and remove them. The field studies revealed the typical and relative
contributions of each factor, and validated the models by demonstrating agreement of
their statistics with the anticipated norms. The results revealed a surprising potential
for tagging errors from embedded systems latencies to exceed the typical GPS errors
and become dominant at highway speeds.University Transportation CentreU.S. Department of Transportation (USDOT)Mountain Plains Consortium (MPC)Grant DTRT12-G-UTC08https://www.ugpti.org/about/staff/viewbio.php?id=7
